From dd4acd6cf6e8f7e387a9857e15d0a1b20514842d Mon Sep 17 00:00:00 2001 From: mixayloff-dimaaylov <65488726+mixayloff-dimaaylov@users.noreply.github.com> Date: Sun, 23 Apr 2023 00:55:59 +0300 Subject: [PATCH] chore: Drop old installation ISO files MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit После перехода на новые версии компонентов кластера эти файлы устарели. С новой системой на базе Docker эти файлы не пригодятся. Вероятно, подход к созданию **самодостаточных** образов будет пересмотрен в будущих релизах. Были попытки использования Git LFS (столкнувшаяся с проблемой нехватки места), затем `git-annex` (который слишком сложен для обывателя), из-за чего осталось много лишних ссылок в соответствующих хранилищах. 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b/archives/linux-headers-4.15.3-2_4.15.3-2.astra25_all.deb deleted file mode 120000 index a051337..0000000 --- a/archives/linux-headers-4.15.3-2_4.15.3-2.astra25_all.deb +++ /dev/null @@ -1 +0,0 @@ -../.git/annex/objects/Q5/8k/SHA256E-s11277624--91d77f7aaafe028ac2f5faf97d09cdef35142ea89b9462a487fd214ca84ffe22.deb/SHA256E-s11277624--91d77f7aaafe028ac2f5faf97d09cdef35142ea89b9462a487fd214ca84ffe22.deb \ No newline at end of file diff --git a/archives/ngpsusbpackage.tar.gz b/archives/ngpsusbpackage.tar.gz deleted file mode 120000 index 6cd3adb..0000000 --- a/archives/ngpsusbpackage.tar.gz +++ /dev/null @@ -1 +0,0 @@ -../.git/annex/objects/jm/xz/SHA256E-s2564--9dd7c2cb9e49bdd5d7a489626cb89fa7e7f885e7158f39d8b80bad0e0fd4d0ca.tar.gz/SHA256E-s2564--9dd7c2cb9e49bdd5d7a489626cb89fa7e7f885e7158f39d8b80bad0e0fd4d0ca.tar.gz \ No newline at end of file diff --git a/archives/novatellogreader.tar.gz b/archives/novatellogreader.tar.gz deleted file mode 120000 index de21dbd..0000000 --- a/archives/novatellogreader.tar.gz +++ /dev/null @@ -1 +0,0 @@ -../.git/annex/objects/vz/gq/SHA256E-s52396045--5bb0fec469bb47664dece7d347b6390b05ae300d2f4226597fee00b6cda8ed28.tar.gz/SHA256E-s52396045--5bb0fec469bb47664dece7d347b6390b05ae300d2f4226597fee00b6cda8ed28.tar.gz \ No newline at end of file diff --git a/archives/zookeeper_3.4.9-3+deb9u2_all.deb b/archives/zookeeper_3.4.9-3+deb9u2_all.deb deleted file mode 120000 index b4518b9..0000000 --- a/archives/zookeeper_3.4.9-3+deb9u2_all.deb +++ /dev/null @@ -1 +0,0 @@ -../.git/annex/objects/p2/1Q/SHA256E-s141954--879b7395fc219b508ff714e4f7d93e6f74fcfb99f09da359c794bd91b4724997.deb/SHA256E-s141954--879b7395fc219b508ff714e4f7d93e6f74fcfb99f09da359c794bd91b4724997.deb \ No newline at end of file diff --git a/archives/zookeeperd_3.4.9-3+deb9u2_all.deb b/archives/zookeeperd_3.4.9-3+deb9u2_all.deb deleted file mode 120000 index a3e5a8f..0000000 --- a/archives/zookeeperd_3.4.9-3+deb9u2_all.deb +++ /dev/null @@ -1 +0,0 @@ -../.git/annex/objects/W0/0j/SHA256E-s44154--8f5ae097b916bf6214f624f85f02b6988eef0479c279d97ca95fc1e3c1939681.deb/SHA256E-s44154--8f5ae097b916bf6214f624f85f02b6988eef0479c279d97ca95fc1e3c1939681.deb \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml index 1ebb965..8e66b82 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -57,10 +57,6 @@ services: environment: KAFKA_HOST: 'ionosphere-iif-kafka' CH_HOST: 'ionosphere-iif-clickhouse' - volumes: - - type: bind - source: ./image_content/config/spark/opt/spark/conf/log4j.properties - target: /spark/conf/log4j.properties depends_on: - kafka - clickhouse diff --git a/image_content/config/clickhouse-proxy/usr/local/lib/systemd/system/clickhouse-proxy.service b/image_content/config/clickhouse-proxy/usr/local/lib/systemd/system/clickhouse-proxy.service deleted file mode 100644 index b774103..0000000 --- a/image_content/config/clickhouse-proxy/usr/local/lib/systemd/system/clickhouse-proxy.service +++ /dev/null @@ -1,11 +0,0 @@ -[Unit] -Description=Process to access ClickHouse data with postprocessing -After=clickhouse-server - -[Service] -Type=simple -WorkingDirectory=/opt/clickhouse-proxy -ExecStart=/opt/clickhouse-proxy/.venv/clickhouse-proxy-env/bin/python3 /opt/clickhouse-proxy/clickhouse-proxy.py - -[Install] -WantedBy=multi-user.target diff --git a/image_content/config/grafana/etc/default/grafana-server b/image_content/config/grafana/etc/default/grafana-server deleted file mode 100644 index eb77e62..0000000 --- a/image_content/config/grafana/etc/default/grafana-server +++ /dev/null @@ -1,24 +0,0 @@ -GRAFANA_USER=grafana - -GRAFANA_GROUP=grafana - -GRAFANA_HOME=/usr/share/grafana - -LOG_DIR=/var/log/grafana - -DATA_DIR=/var/lib/grafana - -MAX_OPEN_FILES=10000 - -CONF_DIR=/etc/grafana - -CONF_FILE=/etc/grafana/grafana.ini - -RESTART_ON_UPGRADE=true - -PLUGINS_DIR=/var/lib/grafana/plugins - -PROVISIONING_CFG_DIR=/etc/grafana/provisioning - -# Only used on systemd systems -PID_FILE_DIR=/var/run/grafana diff --git a/image_content/config/grafana/etc/grafana/ldap.toml b/image_content/config/grafana/etc/grafana/ldap.toml deleted file mode 100644 index 305929b..0000000 --- a/image_content/config/grafana/etc/grafana/ldap.toml +++ /dev/null @@ -1,85 +0,0 @@ -# To troubleshoot and get more log info enable ldap debug logging in grafana.ini -# [log] -# filters = ldap:debug - -[[servers]] -# Ldap server host (specify multiple hosts space separated) -host = "127.0.0.1" -# Default port is 389 or 636 if use_ssl = true -port = 389 -# Set to true if ldap server supports TLS -use_ssl = false -# Set to true if connect ldap server with STARTTLS pattern (create connection in insecure, then upgrade to secure connection with TLS) -start_tls = false -# set to true if you want to skip ssl cert validation -ssl_skip_verify = false -# set to the path to your root CA certificate or leave unset to use system defaults -# root_ca_cert = /path/to/certificate.crt - -# Search user bind dn -bind_dn = "cn=admin,dc=grafana,dc=org" -# Search user bind password -# If the password contains # or ; you have to wrap it with trippel quotes. Ex """#password;""" -bind_password = 'grafana' - -# User search filter, for example "(cn=%s)" or "(sAMAccountName=%s)" or "(uid=%s)" -search_filter = "(cn=%s)" - -# An array of base dns to search through -search_base_dns = ["dc=grafana,dc=org"] - -# In POSIX LDAP schemas, without memberOf attribute a secondary query must be made for groups. -# This is done by enabling group_search_filter below. You must also set member_of= "cn" -# in [servers.attributes] below. - -# Users with nested/recursive group membership and an LDAP server that supports LDAP_MATCHING_RULE_IN_CHAIN -# can set group_search_filter, group_search_filter_user_attribute, group_search_base_dns and member_of -# below in such a way that the user's recursive group membership is considered. -# -# Nested Groups + Active Directory (AD) Example: -# -# AD groups store the Distinguished Names (DNs) of members, so your filter must -# recursively search your groups for the authenticating user's DN. For example: -# -# group_search_filter = "(member:1.2.840.113556.1.4.1941:=%s)" -# group_search_filter_user_attribute = "distinguishedName" -# group_search_base_dns = ["ou=groups,dc=grafana,dc=org"] -# -# [servers.attributes] -# ... -# member_of = "distinguishedName" - -## Group search filter, to retrieve the groups of which the user is a member (only set if memberOf attribute is not available) -# group_search_filter = "(&(objectClass=posixGroup)(memberUid=%s))" -## Group search filter user attribute defines what user attribute gets substituted for %s in group_search_filter. -## Defaults to the value of username in [server.attributes] -## Valid options are any of your values in [servers.attributes] -## If you are using nested groups you probably want to set this and member_of in -## [servers.attributes] to "distinguishedName" -# group_search_filter_user_attribute = "distinguishedName" -## An array of the base DNs to search through for groups. Typically uses ou=groups -# group_search_base_dns = ["ou=groups,dc=grafana,dc=org"] - -# Specify names of the ldap attributes your ldap uses -[servers.attributes] -name = "givenName" -surname = "sn" -username = "cn" -member_of = "memberOf" -email = "email" - -# Map ldap groups to grafana org roles -[[servers.group_mappings]] -group_dn = "cn=admins,dc=grafana,dc=org" -org_role = "Admin" -# The Grafana organization database id, optional, if left out the default org (id 1) will be used -# org_id = 1 - -[[servers.group_mappings]] -group_dn = "cn=users,dc=grafana,dc=org" -org_role = "Editor" - -[[servers.group_mappings]] -# If you want to match all (or no ldap groups) then you can use wildcard -group_dn = "*" -org_role = "Viewer" diff --git a/image_content/config/grafana/var/lib/grafana/grafana.db b/image_content/config/grafana/var/lib/grafana/grafana.db deleted file mode 120000 index 6ee0f50..0000000 --- a/image_content/config/grafana/var/lib/grafana/grafana.db +++ /dev/null @@ -1 +0,0 @@ -../../../../../../.git/annex/objects/mw/5K/SHA256E-s1887232--b06ed6ec5d36cb30dc49fab961d48af4bf8b8f01a1a7c500f3267b94d41368ee.db/SHA256E-s1887232--b06ed6ec5d36cb30dc49fab961d48af4bf8b8f01a1a7c500f3267b94d41368ee.db \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/.gitignore b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/.gitignore deleted file mode 100644 index 00448a2..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/.gitignore +++ /dev/null @@ -1,34 +0,0 @@ -node_modules -npm-debug.log -coverage/ -.aws-config.json -awsconfig -/emails/dist -/public_gen -/tmp -vendor/phantomjs/phantomjs - -docs/AWS_S3_BUCKET -docs/GIT_BRANCH -docs/VERSION -docs/GITCOMMIT -docs/changed-files -docs/changed-files - -# locally required config files -public/css/*.min.css - -# Editor junk -*.sublime-workspace -*.swp -.idea/ -*.iml - -/data/* -/bin/* - -conf/custom.ini -fig.yml -profile.cov -grafana -.notouch diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/Gruntfile.js b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/Gruntfile.js deleted file mode 100644 index e53beae..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/Gruntfile.js +++ /dev/null @@ -1,60 +0,0 @@ -module.exports = function(grunt) { - - require('load-grunt-tasks')(grunt); - - grunt.loadNpmTasks('grunt-execute'); - grunt.loadNpmTasks('grunt-contrib-clean'); - - grunt.initConfig({ - - clean: ["dist"], - - copy: { - src_to_dist: { - cwd: 'src', - expand: true, - src: ['**/*', '!**/*.js', '!**/*.scss'], - dest: 'dist' - }, - img_to_dist: { - cwd: 'src', - expand: true, - src: ['img/*'], - dest: 'dist/src/' - }, - pluginDef: { - expand: true, - src: [ 'plugin.json', 'README.md' ], - dest: 'dist', - } - }, - - watch: { - rebuild_all: { - files: ['src/**/*', 'plugin.json'], - tasks: ['default'], - options: {spawn: false} - }, - }, - - babel: { - options: { - sourceMap: true, - presets: ["es2015"], - plugins: ['transform-es2015-modules-systemjs', "transform-es2015-for-of"], - }, - dist: { - files: [{ - cwd: 'src', - expand: true, - src: ['*.js'], - dest: 'dist', - ext:'.js' - }] - }, - }, - - }); - - grunt.registerTask('default', ['clean', 'copy:src_to_dist', 'copy:img_to_dist', 'copy:pluginDef', 'babel']); -}; diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/LICENSE b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/LICENSE deleted file mode 100644 index 4644c8c..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -The MIT License (MIT) - -Copyright (c) 2016 Grafana - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/README.md b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/README.md deleted file mode 100644 index 1778f52..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/README.md +++ /dev/null @@ -1,53 +0,0 @@ -Use the new grafana-cli tool to install piechart-panel from the commandline: - -``` -grafana-cli plugins install grafana-piechart-panel -``` - -The plugin will be installed into your grafana plugins directory; the default is /var/lib/grafana/plugins if you installed the grafana package. - -More instructions on the cli tool can be found [here](http://docs.grafana.org/v3.0/plugins/installation/). - -You need the lastest grafana build for Grafana 3.0 to enable plugin support. You can get it here : http://grafana.org/download/builds.html - -## Alternative installation method - -It is also possible to clone this repo directly into your plugins directory. - -Afterwards restart grafana-server and the plugin should be automatically detected and used. - -``` -git clone https://github.com/grafana/piechart-panel.git -sudo service grafana-server restart -``` - - -## Clone into a directory of your choice - -If the plugin is cloned to a directory that is not the default plugins directory then you need to edit your grafana.ini config file (Default location is at /etc/grafana/grafana.ini) and add this: - -```ini -[plugin.piechart] -path = /home/your/clone/dir/piechart-panel -``` - -Note that if you clone it into the grafana plugins directory you do not need to add the above config option. That is only -if you want to place the plugin in a directory outside the standard plugins directory. Be aware that grafana-server -needs read access to the directory. - -# Changelog - -## 1.1.4 -* Add support for combining small slices (https://github.com/grafana/piechart-panel/pull/43) -* Add option to show percentage in legend https://github.com/grafana/piechart-panel/pull/41 - -## 1.0.2 - -* Add piechart piece divider setting -* Remove Unused code -* Adds fontsize option for labels on graph -* Only show the displaied piechart value in legend -* Add possibility to pick stat to use for piechart - -## 1.1.4 -* Add support diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/README.md b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/README.md deleted file mode 100644 index 1778f52..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/README.md +++ /dev/null @@ -1,53 +0,0 @@ -Use the new grafana-cli tool to install piechart-panel from the commandline: - -``` -grafana-cli plugins install grafana-piechart-panel -``` - -The plugin will be installed into your grafana plugins directory; the default is /var/lib/grafana/plugins if you installed the grafana package. - -More instructions on the cli tool can be found [here](http://docs.grafana.org/v3.0/plugins/installation/). - -You need the lastest grafana build for Grafana 3.0 to enable plugin support. You can get it here : http://grafana.org/download/builds.html - -## Alternative installation method - -It is also possible to clone this repo directly into your plugins directory. - -Afterwards restart grafana-server and the plugin should be automatically detected and used. - -``` -git clone https://github.com/grafana/piechart-panel.git -sudo service grafana-server restart -``` - - -## Clone into a directory of your choice - -If the plugin is cloned to a directory that is not the default plugins directory then you need to edit your grafana.ini config file (Default location is at /etc/grafana/grafana.ini) and add this: - -```ini -[plugin.piechart] -path = /home/your/clone/dir/piechart-panel -``` - -Note that if you clone it into the grafana plugins directory you do not need to add the above config option. That is only -if you want to place the plugin in a directory outside the standard plugins directory. Be aware that grafana-server -needs read access to the directory. - -# Changelog - -## 1.1.4 -* Add support for combining small slices (https://github.com/grafana/piechart-panel/pull/43) -* Add option to show percentage in legend https://github.com/grafana/piechart-panel/pull/41 - -## 1.0.2 - -* Add piechart piece divider setting -* Remove Unused code -* Adds fontsize option for labels on graph -* Only show the displaied piechart value in legend -* Add possibility to pick stat to use for piechart - -## 1.1.4 -* Add support diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/editor.html b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/editor.html deleted file mode 100644 index 6fd689b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/editor.html +++ /dev/null @@ -1,62 +0,0 @@ -
-
-
General
-
- Type -
- -
-
-
- Unit -
-
-
-
- Value -
- -
-
-
- Divider width - -
-
- -
-
Legend
- -
- Position -
- -
-
-
- Font size -
- -
-
- - -
- -
-
Combine
-
- Threshold: - - Combines all slices that are smaller than the specified percentage (ranging from 0 to 1) i.e. a value of '0.03' will combine all slices 3% or less into one slice). -
-
- Label - - Label text for the combined slice. -
- -
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b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/legend.js deleted file mode 100644 index e26e142..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/legend.js +++ /dev/null @@ -1,189 +0,0 @@ -'use strict'; - -System.register(['angular', 'app/core/utils/kbn', 'jquery', 'jquery.flot', 'jquery.flot.time'], function (_export, _context) { - "use strict"; - - var angular, kbn, $; - return { - setters: [function (_angular) { - angular = _angular.default; - }, function (_appCoreUtilsKbn) { - kbn = _appCoreUtilsKbn.default; - }, function (_jquery) { - $ = _jquery.default; - }, function (_jqueryFlot) {}, function (_jqueryFlotTime) {}], - execute: function () { - //import _ from 'lodash'; - angular.module('grafana.directives').directive('piechartLegend', function (popoverSrv, $timeout) { - return { - link: function link(scope, elem) { - var $container = $('

'); - var firstRender = true; - var ctrl = scope.ctrl; - var panel = ctrl.panel; - var data; - var seriesList; - var i; - - ctrl.events.on('render', function () { - data = ctrl.series; - if (data) { - for (var i in data) { - data[i].color = ctrl.data[i].color; - } - render(); - } - }); - - function getSeriesIndexForElement(el) { - return el.parents('[data-series-index]').data('series-index'); - } - - function toggleSeries(e) { - var el = $(e.currentTarget); - var index = getSeriesIndexForElement(el); - var seriesInfo = seriesList[index]; - ctrl.toggleSeries(seriesInfo, e); - } - - function sortLegend(e) { - var el = $(e.currentTarget); - var stat = el.data('stat'); - - if (stat !== panel.legend.sort) { - panel.legend.sortDesc = null; - } - - // if already sort ascending, disable sorting - if (panel.legend.sortDesc === false) { - panel.legend.sort = null; - panel.legend.sortDesc = null; - render(); - return; - } - - panel.legend.sortDesc = !panel.legend.sortDesc; - panel.legend.sort = stat; - render(); - } - - function openColorSelector(e) { - // if we clicked inside poup container ignore click - if ($(e.target).parents('.popover').length) { - return; - } - - var el = $(e.currentTarget).find('.fa-minus'); - var index = getSeriesIndexForElement(el); - var series = seriesList[index]; - - $timeout(function () { - popoverSrv.show({ - element: el[0], - position: 'bottom center', - template: '', - model: { - series: series, - toggleAxis: function toggleAxis() {}, - colorSelected: function colorSelected(color) { - ctrl.changeSeriesColor(series, color); - } - } - }); - }); - } - - function render() { - if (panel.legendType === 'On graph') { - $container.empty(); - return; - } - - if (firstRender) { - elem.append($container); - $container.on('click', '.graph-legend-icon', openColorSelector); - $container.on('click', 'th', sortLegend); - firstRender = false; - } - - seriesList = data; - - $container.empty(); - - var showValues = panel.legend.values || panel.legend.percentage; - var tableLayout = (panel.legendType === 'Under graph' || panel.legendType === 'Right side') && showValues; - - $container.toggleClass('graph-legend-table', tableLayout); - - if (tableLayout) { - var header = ''; - if (panel.legend.values) { - header += 'values'; - } - if (panel.legend.percentage) { - header += 'percentage'; - } - header += ''; - $container.append($(header)); - } - - if (panel.legend.sort) { - seriesList = _.sortBy(seriesList, function (series) { - return series.stats[panel.legend.sort]; - }); - if (panel.legend.sortDesc) { - seriesList = seriesList.reverse(); - } - } - - if (panel.legend.percentage) { - var total = 0; - for (i = 0; i < seriesList.length; i++) { - total += seriesList[i].stats[ctrl.panel.valueName]; - } - } - - for (i = 0; i < seriesList.length; i++) { - var series = seriesList[i]; - - // ignore empty series - if (panel.legend.hideEmpty && series.allIsNull) { - continue; - } - // ignore series excluded via override - if (!series.legend) { - continue; - } - - var html = '
'; - html += ''; - html += ''; - html += ''; - - html += ''; - html += '' + series.label + ''; - html += ''; - - if (showValues && tableLayout) { - var value = series.formatValue(series.stats[ctrl.panel.valueName]); - if (panel.legend.values) { - html += '
' + value + '
'; - } - if (total) { - var pvalue = (value / total * 100).toFixed(2) + '%'; - html += '
' + pvalue + '
'; - } - } - - html += '
'; - $container.append($(html)); - } - } - } - }; - }); - } - }; -}); -//# sourceMappingURL=legend.js.map diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/legend.js.map b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/legend.js.map deleted file mode 100644 index 4383aa4..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/legend.js.map +++ /dev/null @@ -1 +0,0 @@ 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angular from 'angular';\n//import _ from 'lodash';\nimport kbn from 'app/core/utils/kbn';\nimport $ from 'jquery';\nimport 'jquery.flot';\nimport 'jquery.flot.time';\n\nangular.module('grafana.directives').directive('piechartLegend', function(popoverSrv, $timeout) {\n return {\n link: function(scope, elem) {\n var $container = $('
');\n var firstRender = true;\n var ctrl = scope.ctrl;\n var panel = ctrl.panel;\n var data;\n var seriesList;\n var i;\n\n ctrl.events.on('render', function() {\n data = ctrl.series;\n if (data) {\n for(var i in data) {\n data[i].color = ctrl.data[i].color;\n }\n render();\n }\n });\n\n function getSeriesIndexForElement(el) {\n return el.parents('[data-series-index]').data('series-index');\n }\n\n function toggleSeries(e) {\n var el = $(e.currentTarget);\n var index = getSeriesIndexForElement(el);\n var seriesInfo = seriesList[index];\n ctrl.toggleSeries(seriesInfo, e);\n }\n\n function sortLegend(e) {\n var el = $(e.currentTarget);\n var stat = el.data('stat');\n\n if (stat !== panel.legend.sort) { panel.legend.sortDesc = null; }\n\n // if already sort ascending, disable sorting\n if (panel.legend.sortDesc === false) {\n panel.legend.sort = null;\n panel.legend.sortDesc = null;\n render();\n return;\n }\n\n panel.legend.sortDesc = !panel.legend.sortDesc;\n panel.legend.sort = stat;\n render();\n }\n\n function openColorSelector(e) {\n // if we clicked inside poup container ignore click\n if ($(e.target).parents('.popover').length) {\n return;\n }\n\n var el = $(e.currentTarget).find('.fa-minus');\n var index = getSeriesIndexForElement(el);\n var series = seriesList[index];\n\n $timeout(function() {\n popoverSrv.show({\n element: el[0],\n position: 'bottom center',\n template: '',\n model: {\n series: series,\n toggleAxis: function() {},\n colorSelected: function(color) {\n ctrl.changeSeriesColor(series, color);\n }\n },\n });\n });\n }\n\n function render() {\n if(panel.legendType === 'On graph') {\n $container.empty();\n return;\n }\n\n if (firstRender) {\n elem.append($container);\n $container.on('click', '.graph-legend-icon', openColorSelector);\n $container.on('click', 'th', sortLegend);\n firstRender = false;\n }\n\n seriesList = data;\n\n $container.empty();\n\n var showValues = panel.legend.values || panel.legend.percentage;\n var tableLayout = (\n panel.legendType === 'Under graph' ||\n panel.legendType === 'Right side'\n ) && showValues;\n\n\n $container.toggleClass('graph-legend-table', tableLayout);\n\n if (tableLayout) {\n var header = '';\n if (panel.legend.values) {\n header += 'values';\n }\n if (panel.legend.percentage) {\n header += 'percentage';\n }\n header += '';\n $container.append($(header));\n }\n\n if (panel.legend.sort) {\n seriesList = _.sortBy(seriesList, function(series) {\n return series.stats[panel.legend.sort];\n });\n if (panel.legend.sortDesc) {\n seriesList = seriesList.reverse();\n }\n }\n\n if (panel.legend.percentage) {\n var total = 0;\n for (i = 0; i < seriesList.length; i++) {\n total += seriesList[i].stats[ctrl.panel.valueName];\n }\n }\n\n for (i = 0; i < seriesList.length; i++) {\n var series = seriesList[i];\n\n // ignore empty series\n if (panel.legend.hideEmpty && series.allIsNull) {\n continue;\n }\n // ignore series excluded via override\n if (!series.legend) {\n continue;\n }\n\n var html = '
';\n html += '';\n html += '';\n html += '';\n\n html += '';\n html += '' + series.label + '';\n html += '';\n\n if (showValues && tableLayout) {\n var value = series.formatValue(series.stats[ctrl.panel.valueName]);\n if (panel.legend.values) {\n html += '
' + value + '
';\n }\n if (total) {\n var pvalue = ((value / total) * 100).toFixed(2) + '%';\n html += '
' + pvalue +'
';\n }\n }\n\n html += '
';\n $container.append($(html));\n }\n }\n }\n };\n});\n\n\n"]} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/module.html b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/module.html deleted file mode 100644 index c8524b3..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/module.html +++ /dev/null @@ -1,9 +0,0 @@ -
-
-
-
-
-
-
- - diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/module.js b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/module.js deleted file mode 100644 index 9e4be67..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/module.js +++ /dev/null @@ -1,16 +0,0 @@ -'use strict'; - -System.register(['./piechart_ctrl'], function (_export, _context) { - "use strict"; - - var PieChartCtrl; - return { - setters: [function (_piechart_ctrl) { - PieChartCtrl = _piechart_ctrl.PieChartCtrl; - }], - execute: function () { - _export('PanelCtrl', PieChartCtrl); - } - }; -}); -//# sourceMappingURL=module.js.map diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/module.js.map b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/module.js.map deleted file mode 100644 index 2945416..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/module.js.map +++ /dev/null @@ -1 +0,0 @@ -{"version":3,"sources":["../src/module.js"],"names":["PieChartCtrl"],"mappings":";;;;;;;;AAAQA,kB,kBAAAA,Y;;;2BAGNA,Y","file":"module.js","sourcesContent":["import {PieChartCtrl} from './piechart_ctrl';\n\nexport {\n PieChartCtrl as PanelCtrl\n};\n\n"]} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/piechart_ctrl.js b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/piechart_ctrl.js deleted file mode 100644 index 98f6b12..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/piechart_ctrl.js +++ /dev/null @@ -1,246 +0,0 @@ -'use strict'; - -System.register(['app/plugins/sdk', 'lodash', 'app/core/utils/kbn', 'app/core/time_series', './rendering', './legend'], function (_export, _context) { - "use strict"; - - var MetricsPanelCtrl, _, kbn, TimeSeries, rendering, legend, _createClass, PieChartCtrl; - - function _classCallCheck(instance, Constructor) { - if (!(instance instanceof Constructor)) { - throw new TypeError("Cannot call a class as a function"); - } - } - - function _possibleConstructorReturn(self, call) { - if (!self) { - throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); - } - - return call && (typeof call === "object" || typeof call === "function") ? call : self; - } - - function _inherits(subClass, superClass) { - if (typeof superClass !== "function" && superClass !== null) { - throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); - } - - subClass.prototype = Object.create(superClass && superClass.prototype, { - constructor: { - value: subClass, - enumerable: false, - writable: true, - configurable: true - } - }); - if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; - } - - return { - setters: [function (_appPluginsSdk) { - MetricsPanelCtrl = _appPluginsSdk.MetricsPanelCtrl; - }, function (_lodash) { - _ = _lodash.default; - }, function (_appCoreUtilsKbn) { - kbn = _appCoreUtilsKbn.default; - }, function (_appCoreTime_series) { - TimeSeries = _appCoreTime_series.default; - }, function (_rendering) { - rendering = _rendering.default; - }, function (_legend) { - legend = _legend.default; - }], - execute: function () { - _createClass = function () { - function defineProperties(target, props) { - for (var i = 0; i < props.length; i++) { - var descriptor = props[i]; - descriptor.enumerable = descriptor.enumerable || false; - descriptor.configurable = true; - if ("value" in descriptor) descriptor.writable = true; - Object.defineProperty(target, descriptor.key, descriptor); - } - } - - return function (Constructor, protoProps, staticProps) { - if (protoProps) defineProperties(Constructor.prototype, protoProps); - if (staticProps) defineProperties(Constructor, staticProps); - return Constructor; - }; - }(); - - _export('PieChartCtrl', PieChartCtrl = function (_MetricsPanelCtrl) { - _inherits(PieChartCtrl, _MetricsPanelCtrl); - - function PieChartCtrl($scope, $injector, $rootScope) { - _classCallCheck(this, PieChartCtrl); - - var _this = _possibleConstructorReturn(this, (PieChartCtrl.__proto__ || Object.getPrototypeOf(PieChartCtrl)).call(this, $scope, $injector)); - - _this.$rootScope = $rootScope; - - var panelDefaults = { - pieType: 'pie', - legend: { - show: true, // disable/enable legend - values: true - }, - links: [], - datasource: null, - maxDataPoints: 3, - interval: null, - targets: [{}], - cacheTimeout: null, - nullPointMode: 'connected', - legendType: 'Under graph', - aliasColors: {}, - format: 'short', - valueName: 'current', - strokeWidth: 1, - fontSize: '80%', - combine: { - threshold: 0.0, - label: 'Others' - } - }; - - _.defaults(_this.panel, panelDefaults); - _.defaults(_this.panel.legend, panelDefaults.legend); - - _this.events.on('render', _this.onRender.bind(_this)); - _this.events.on('data-received', _this.onDataReceived.bind(_this)); - _this.events.on('data-error', _this.onDataError.bind(_this)); - _this.events.on('data-snapshot-load', _this.onDataReceived.bind(_this)); - _this.events.on('init-edit-mode', _this.onInitEditMode.bind(_this)); - return _this; - } - - _createClass(PieChartCtrl, [{ - key: 'onInitEditMode', - value: function onInitEditMode() { - this.addEditorTab('Options', 'public/plugins/grafana-piechart-panel/editor.html', 2); - this.unitFormats = kbn.getUnitFormats(); - } - }, { - key: 'setUnitFormat', - value: function setUnitFormat(subItem) { - this.panel.format = subItem.value; - this.render(); - } - }, { - key: 'onDataError', - value: function onDataError() { - this.series = []; - this.render(); - } - }, { - key: 'changeSeriesColor', - value: function changeSeriesColor(series, color) { - series.color = color; - this.panel.aliasColors[series.alias] = series.color; - this.render(); - } - }, { - key: 'onRender', - value: function onRender() { - this.data = this.parseSeries(this.series); - } - }, { - key: 'parseSeries', - value: function parseSeries(series) { - var _this2 = this; - - return _.map(this.series, function (serie, i) { - return { - label: serie.alias, - data: serie.stats[_this2.panel.valueName], - color: _this2.panel.aliasColors[serie.alias] || _this2.$rootScope.colors[i] - }; - }); - } - }, { - key: 'onDataReceived', - value: function onDataReceived(dataList) { - this.series = dataList.map(this.seriesHandler.bind(this)); - this.data = this.parseSeries(this.series); - this.render(this.data); - } - }, { - key: 'seriesHandler', - value: function seriesHandler(seriesData) { - var series = new TimeSeries({ - datapoints: seriesData.datapoints, - alias: seriesData.target - }); - - series.flotpairs = series.getFlotPairs(this.panel.nullPointMode); - return series; - } - }, { - key: 'getDecimalsForValue', - value: function getDecimalsForValue(value) { - if (_.isNumber(this.panel.decimals)) { - return { decimals: this.panel.decimals, scaledDecimals: null }; - } - - var delta = value / 2; - var dec = -Math.floor(Math.log(delta) / Math.LN10); - - var magn = Math.pow(10, -dec); - var norm = delta / magn; // norm is between 1.0 and 10.0 - var size; - - if (norm < 1.5) { - size = 1; - } else if (norm < 3) { - size = 2; - // special case for 2.5, requires an extra decimal - if (norm > 2.25) { - size = 2.5; - ++dec; - } - } else if (norm < 7.5) { - size = 5; - } else { - size = 10; - } - - size *= magn; - - // reduce starting decimals if not needed - if (Math.floor(value) === value) { - dec = 0; - } - - var result = {}; - result.decimals = Math.max(0, dec); - result.scaledDecimals = result.decimals - Math.floor(Math.log(size) / Math.LN10) + 2; - - return result; - } - }, { - key: 'formatValue', - value: function formatValue(value) { - var decimalInfo = this.getDecimalsForValue(value); - var formatFunc = kbn.valueFormats[this.panel.format]; - if (formatFunc) { - return formatFunc(value, decimalInfo.decimals, decimalInfo.scaledDecimals); - } - return value; - } - }, { - key: 'link', - value: function link(scope, elem, attrs, ctrl) { - rendering(scope, elem, attrs, ctrl); - } - }]); - - return PieChartCtrl; - }(MetricsPanelCtrl)); - - _export('PieChartCtrl', PieChartCtrl); - - PieChartCtrl.templateUrl = 'module.html'; - } - }; -}); -//# sourceMappingURL=piechart_ctrl.js.map diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/piechart_ctrl.js.map b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/piechart_ctrl.js.map deleted file mode 100644 index c03d0cd..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/piechart_ctrl.js.map +++ /dev/null @@ -1 +0,0 @@ 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{MetricsPanelCtrl} from 'app/plugins/sdk';\nimport _ from 'lodash';\nimport kbn from 'app/core/utils/kbn';\nimport TimeSeries from 'app/core/time_series';\nimport rendering from './rendering';\nimport legend from './legend';\n\nexport class PieChartCtrl extends MetricsPanelCtrl {\n\n constructor($scope, $injector, $rootScope) {\n super($scope, $injector);\n this.$rootScope = $rootScope;\n\n var panelDefaults = {\n pieType: 'pie',\n legend: {\n show: true, // disable/enable legend\n values: true\n },\n links: [],\n datasource: null,\n maxDataPoints: 3,\n interval: null,\n targets: [{}],\n cacheTimeout: null,\n nullPointMode: 'connected',\n legendType: 'Under graph',\n aliasColors: {},\n format: 'short',\n valueName: 'current',\n strokeWidth: 1,\n fontSize: '80%',\n\t combine: {\n\t threshold: 0.0,\n\t label: 'Others'\n\t }\n };\n\n _.defaults(this.panel, panelDefaults);\n _.defaults(this.panel.legend, panelDefaults.legend);\n\n this.events.on('render', this.onRender.bind(this));\n this.events.on('data-received', this.onDataReceived.bind(this));\n this.events.on('data-error', this.onDataError.bind(this));\n this.events.on('data-snapshot-load', this.onDataReceived.bind(this));\n this.events.on('init-edit-mode', this.onInitEditMode.bind(this));\n }\n\n onInitEditMode() {\n this.addEditorTab('Options', 'public/plugins/grafana-piechart-panel/editor.html', 2);\n this.unitFormats = kbn.getUnitFormats();\n }\n\n setUnitFormat(subItem) {\n this.panel.format = subItem.value;\n this.render();\n }\n\n onDataError() {\n this.series = [];\n this.render();\n }\n\n changeSeriesColor(series, color) {\n series.color = color;\n this.panel.aliasColors[series.alias] = series.color;\n this.render();\n }\n\n onRender() {\n this.data = this.parseSeries(this.series);\n }\n\n parseSeries(series) {\n return _.map(this.series, (serie, i) => {\n return {\n label: serie.alias,\n data: serie.stats[this.panel.valueName],\n color: this.panel.aliasColors[serie.alias] || this.$rootScope.colors[i]\n };\n });\n }\n\n onDataReceived(dataList) {\n this.series = dataList.map(this.seriesHandler.bind(this));\n this.data = this.parseSeries(this.series);\n this.render(this.data);\n }\n\n seriesHandler(seriesData) {\n var series = new TimeSeries({\n datapoints: seriesData.datapoints,\n alias: seriesData.target\n });\n\n series.flotpairs = series.getFlotPairs(this.panel.nullPointMode);\n return series;\n }\n\n getDecimalsForValue(value) {\n if (_.isNumber(this.panel.decimals)) {\n return { decimals: this.panel.decimals, scaledDecimals: null };\n }\n\n var delta = value / 2;\n var dec = -Math.floor(Math.log(delta) / Math.LN10);\n\n var magn = Math.pow(10, -dec);\n var norm = delta / magn; // norm is between 1.0 and 10.0\n var size;\n\n if (norm < 1.5) {\n size = 1;\n } else if (norm < 3) {\n size = 2;\n // special case for 2.5, requires an extra decimal\n if (norm > 2.25) {\n size = 2.5;\n ++dec;\n }\n } else if (norm < 7.5) {\n size = 5;\n } else {\n size = 10;\n }\n\n size *= magn;\n\n // reduce starting decimals if not needed\n if (Math.floor(value) === value) { dec = 0; }\n\n var result = {};\n result.decimals = Math.max(0, dec);\n result.scaledDecimals = result.decimals - Math.floor(Math.log(size) / Math.LN10) + 2;\n\n return result;\n }\n\n formatValue(value) {\n var decimalInfo = this.getDecimalsForValue(value);\n var formatFunc = kbn.valueFormats[this.panel.format];\n if (formatFunc) {\n return formatFunc(value, decimalInfo.decimals, decimalInfo.scaledDecimals);\n }\n return value;\n }\n\n link(scope, elem, attrs, ctrl) {\n rendering(scope, elem, attrs, ctrl);\n }\n}\n\nPieChartCtrl.templateUrl = 'module.html';\n"]} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/plugin.json b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/plugin.json deleted file mode 100644 index b3bab38..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/plugin.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "type": "panel", - "name": "Pie Chart", - "id": "grafana-piechart-panel", - - "info": { - "description": "Pie chart panel for grafana", - "author": { - "name": "Raintank Inc.", - "url": "http://raintank.io" - }, - "keywords": ["piechart", "panel"], - "logos": { - "small": "src/img/piechart_logo_small.png", - "large": "src/img/piechart_logo_large.png" - }, - "links": [ - {"name": "Project site", "url": "https://github.com/grafana/piechart-panel"}, - {"name": "Blog Post", "url": "https://blog.raintank.io/friends-dont-let-friends-abuse-pie-charts/"}, - {"name": "MIT License", "url": "https://github.com/grafana/piechart-panel/blob/master/LICENSE"} - ], - "screenshots": [ - {"name": "Donut!", "path": "src/img/piechart-donut.png"}, - {"name": "Legend on the graph", "path": "src/img/piechart-legend-on-graph.png"}, - {"name": "Legend to the right", "path": "src/img/piechart-legend-rhs.png"}, - {"name": "Legend underneath", "path": "src/img/piechart-legend-under.png"}, - {"name": "Piechart options", "path": "src/img/piechart-options.png"} - ], - "version": "1.1.4", - "updated": "2016-09-26" - }, - - "dependencies": { - "grafanaVersion": "3.x.x", - "plugins": [ ] - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/rendering.js b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/rendering.js deleted file mode 100644 index 44f144f..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/rendering.js +++ /dev/null @@ -1,141 +0,0 @@ -'use strict'; - -System.register(['lodash', 'jquery', 'jquery.flot', 'jquery.flot.pie'], function (_export, _context) { - "use strict"; - - var _, $; - - function link(scope, elem, attrs, ctrl) { - var data, panel; - elem = elem.find('.piechart-panel'); - var $tooltip = $('
'); - - ctrl.events.on('render', function () { - render(); - ctrl.renderingCompleted(); - }); - - function setElementHeight() { - try { - var height = ctrl.height || panel.height || ctrl.row.height; - if (_.isString(height)) { - height = parseInt(height.replace('px', ''), 10); - } - - height -= 5; // padding - height -= panel.title ? 24 : 9; // subtract panel title bar - - elem.css('height', height + 'px'); - - return true; - } catch (e) { - // IE throws errors sometimes - return false; - } - } - - function formatter(label, slice) { - return "
" + label + "
" + Math.round(slice.percent) + "%
"; - } - - function addPieChart() { - var width = elem.width(); - var height = elem.height(); - - var size = Math.min(width, height); - - var plotCanvas = $('
'); - var plotCss = { - top: '10px', - margin: 'auto', - position: 'relative', - height: size - 20 + 'px' - }; - - plotCanvas.css(plotCss); - - var $panelContainer = elem.parents('.panel-container'); - var backgroundColor = $panelContainer.css('background-color'); - - var options = { - legend: { - show: false - }, - series: { - pie: { - show: true, - stroke: { - color: backgroundColor, - width: parseFloat(ctrl.panel.strokeWidth).toFixed(1) - }, - label: { - show: ctrl.panel.legend.show && ctrl.panel.legendType === 'On graph', - formatter: formatter - }, - highlight: { - opacity: 0.0 - }, - combine: { - threshold: ctrl.panel.combine.threshold, - label: ctrl.panel.combine.label - } - } - }, - grid: { - hoverable: true, - clickable: false - } - }; - - if (panel.pieType === 'donut') { - options.series.pie.innerRadius = 0.5; - } - - elem.html(plotCanvas); - - $.plot(plotCanvas, ctrl.data, options); - plotCanvas.bind("plothover", function (event, pos, item) { - if (!item) { - $tooltip.detach(); - return; - } - - var body; - var percent = parseFloat(item.series.percent).toFixed(2); - var formatted = ctrl.formatValue(item.series.data[0][1]); - - body = '
'; - body += '
' + item.series.label + ': ' + formatted; - body += " (" + percent + "%)" + '
'; - body += "
"; - - $tooltip.html(body).place_tt(pos.pageX + 20, pos.pageY); - }); - } - - function render() { - if (!ctrl.data) { - return; - } - - data = ctrl.data; - panel = ctrl.panel; - - if (setElementHeight()) { - addPieChart(); - } - } - } - - _export('default', link); - - return { - setters: [function (_lodash) { - _ = _lodash.default; - }, function (_jquery) { - $ = _jquery.default; - }, function (_jqueryFlot) {}, function (_jqueryFlotPie) {}], - execute: function () {} - }; -}); -//# sourceMappingURL=rendering.js.map diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/rendering.js.map b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/rendering.js.map deleted file mode 100644 index 5580eaf..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/dist/rendering.js.map +++ /dev/null @@ -1 +0,0 @@ -{"version":3,"sources":["../src/rendering.js"],"names":["link","scope","elem","attrs","ctrl","data","panel","find","$tooltip","$","events","on","render","renderingCompleted","setElementHeight","height","row","_","isString","parseInt","replace","title","css","e","formatter","label","slice","fontSize","color","Math","round","percent","addPieChart","width","size","min","plotCanvas","plotCss","top","margin","position","$panelContainer","parents","backgroundColor","options","legend","show","series","pie","stroke","parseFloat","strokeWidth","toFixed","legendType","highlight","opacity","combine","threshold","grid","hoverable","clickable","pieType","innerRadius","html","plot","bind","event","pos","item","detach","body","formatted","formatValue","place_tt","pageX","pageY"],"mappings":";;;;;;;AAKe,WAASA,IAAT,CAAcC,KAAd,EAAqBC,IAArB,EAA2BC,KAA3B,EAAkCC,IAAlC,EAAwC;AACrD,QAAIC,IAAJ,EAAUC,KAAV;AACAJ,WAAOA,KAAKK,IAAL,CAAU,iBAAV,CAAP;AACA,QAAIC,WAAWC,EAAE,oBAAF,CAAf;;AAEAL,SAAKM,MAAL,CAAYC,EAAZ,CAAe,QAAf,EAAyB,YAAW;AAClCC;AACAR,WAAKS,kBAAL;AACD,KAHD;;AAKA,aAASC,gBAAT,GAA4B;AAC1B,UAAI;AACF,YAAIC,SAASX,KAAKW,MAAL,IAAeT,MAAMS,MAArB,IAA+BX,KAAKY,GAAL,CAASD,MAArD;AACA,YAAIE,EAAEC,QAAF,CAAWH,MAAX,CAAJ,EAAwB;AACtBA,mBAASI,SAASJ,OAAOK,OAAP,CAAe,IAAf,EAAqB,EAArB,CAAT,EAAmC,EAAnC,CAAT;AACD;;AAEDL,kBAAU,CAAV,CANE,CAMW;AACbA,kBAAUT,MAAMe,KAAN,GAAc,EAAd,GAAmB,CAA7B,CAPE,CAO8B;;AAEhCnB,aAAKoB,GAAL,CAAS,QAAT,EAAmBP,SAAS,IAA5B;;AAEA,eAAO,IAAP;AACD,OAZD,CAYE,OAAMQ,CAAN,EAAS;AAAE;AACX,eAAO,KAAP;AACD;AACF;;AAED,aAASC,SAAT,CAAmBC,KAAnB,EAA0BC,KAA1B,EAAiC;AAC/B,aAAO,2BAA2BtB,KAAKE,KAAL,CAAWqB,QAAtC,GAAiD,uCAAjD,GAA2FD,MAAME,KAAjG,GAAyG,KAAzG,GAAiHH,KAAjH,GAAyH,OAAzH,GAAmII,KAAKC,KAAL,CAAWJ,MAAMK,OAAjB,CAAnI,GAA+J,SAAtK;AACD;;AAED,aAASC,WAAT,GAAuB;AACrB,UAAIC,QAAQ/B,KAAK+B,KAAL,EAAZ;AACA,UAAIlB,SAASb,KAAKa,MAAL,EAAb;;AAEA,UAAImB,OAAOL,KAAKM,GAAL,CAASF,KAAT,EAAgBlB,MAAhB,CAAX;;AAEA,UAAIqB,aAAa3B,EAAE,aAAF,CAAjB;AACA,UAAI4B,UAAU;AACZC,aAAK,MADO;AAEZC,gBAAQ,MAFI;AAGZC,kBAAU,UAHE;AAIZzB,gBAASmB,OAAO,EAAR,GAAc;AAJV,OAAd;;AAOAE,iBAAWd,GAAX,CAAee,OAAf;;AAEA,UAAII,kBAAkBvC,KAAKwC,OAAL,CAAa,kBAAb,CAAtB;AACA,UAAIC,kBAAkBF,gBAAgBnB,GAAhB,CAAoB,kBAApB,CAAtB;;AAEA,UAAIsB,UAAU;AACZC,gBAAQ;AACNC,gBAAM;AADA,SADI;AAIZC,gBAAQ;AACNC,eAAK;AACHF,kBAAM,IADH;AAEHG,oBAAQ;AACNrB,qBAAOe,eADD;AAENV,qBAAOiB,WAAW9C,KAAKE,KAAL,CAAW6C,WAAtB,EAAmCC,OAAnC,CAA2C,CAA3C;AAFD,aAFL;AAMH3B,mBAAO;AACLqB,oBAAM1C,KAAKE,KAAL,CAAWuC,MAAX,CAAkBC,IAAlB,IAA0B1C,KAAKE,KAAL,CAAW+C,UAAX,KAA0B,UADrD;AAEL7B,yBAAWA;AAFN,aANJ;AAUH8B,uBAAW;AACTC,uBAAS;AADA,aAVR;AAaTC,qBAAS;AACPC,yBAAWrD,KAAKE,KAAL,CAAWkD,OAAX,CAAmBC,SADvB;AAEVhC,qBAAOrB,KAAKE,KAAL,CAAWkD,OAAX,CAAmB/B;AAFhB;AAbA;AADC,SAJI;AAwBZiC,cAAM;AACJC,qBAAW,IADP;AAEJC,qBAAW;AAFP;AAxBM,OAAd;;AA8BA,UAAItD,MAAMuD,OAAN,KAAkB,OAAtB,EAA+B;AAC7BjB,gBAAQG,MAAR,CAAeC,GAAf,CAAmBc,WAAnB,GAAiC,GAAjC;AACD;;AAED5D,WAAK6D,IAAL,CAAU3B,UAAV;;AAEA3B,QAAEuD,IAAF,CAAO5B,UAAP,EAAmBhC,KAAKC,IAAxB,EAA8BuC,OAA9B;AACAR,iBAAW6B,IAAX,CAAgB,WAAhB,EAA6B,UAAUC,KAAV,EAAiBC,GAAjB,EAAsBC,IAAtB,EAA4B;AACvD,YAAI,CAACA,IAAL,EAAW;AACT5D,mBAAS6D,MAAT;AACA;AACD;;AAED,YAAIC,IAAJ;AACA,YAAIvC,UAAUmB,WAAWkB,KAAKrB,MAAL,CAAYhB,OAAvB,EAAgCqB,OAAhC,CAAwC,CAAxC,CAAd;AACA,YAAImB,YAAYnE,KAAKoE,WAAL,CAAiBJ,KAAKrB,MAAL,CAAY1C,IAAZ,CAAiB,CAAjB,EAAoB,CAApB,CAAjB,CAAhB;;AAEAiE,eAAO,mEAAP;AACAA,gBAAQ,sCAAsCF,KAAKrB,MAAL,CAAYtB,KAAlD,GAA0D,IAA1D,GAAiE8C,SAAzE;AACAD,gBAAQ,OAAOvC,OAAP,GAAiB,IAAjB,GAAwB,QAAhC;AACAuC,gBAAQ,cAAR;;AAEA9D,iBAASuD,IAAT,CAAcO,IAAd,EAAoBG,QAApB,CAA6BN,IAAIO,KAAJ,GAAY,EAAzC,EAA6CP,IAAIQ,KAAjD;AACD,OAhBD;AAiBD;;AAED,aAAS/D,MAAT,GAAkB;AAChB,UAAI,CAACR,KAAKC,IAAV,EAAgB;AAAE;AAAS;;AAE3BA,aAAOD,KAAKC,IAAZ;AACAC,cAAQF,KAAKE,KAAb;;AAEA,UAAIQ,kBAAJ,EAAwB;AACtBkB;AACD;AACF;AACF;;qBArHuBhC,I;;;;AALjBiB,O;;AACAR,O","file":"rendering.js","sourcesContent":["import _ from 'lodash';\nimport $ from 'jquery';\nimport 'jquery.flot';\nimport 'jquery.flot.pie';\n\nexport default function link(scope, elem, attrs, ctrl) {\n var data, panel;\n elem = elem.find('.piechart-panel');\n var $tooltip = $('
');\n\n ctrl.events.on('render', function() {\n render();\n ctrl.renderingCompleted();\n });\n\n function setElementHeight() {\n try {\n var height = ctrl.height || panel.height || ctrl.row.height;\n if (_.isString(height)) {\n height = parseInt(height.replace('px', ''), 10);\n }\n\n height -= 5; // padding\n height -= panel.title ? 24 : 9; // subtract panel title bar\n\n elem.css('height', height + 'px');\n\n return true;\n } catch(e) { // IE throws errors sometimes\n return false;\n }\n }\n\n function formatter(label, slice) {\n return \"
\" + label + \"
\" + Math.round(slice.percent) + \"%
\";\n }\n\n function addPieChart() {\n var width = elem.width();\n var height = elem.height();\n\n var size = Math.min(width, height);\n\n var plotCanvas = $('
');\n var plotCss = {\n top: '10px',\n margin: 'auto',\n position: 'relative',\n height: (size - 20) + 'px'\n };\n\n plotCanvas.css(plotCss);\n\n var $panelContainer = elem.parents('.panel-container');\n var backgroundColor = $panelContainer.css('background-color');\n\n var options = {\n legend: {\n show: false\n },\n series: {\n pie: {\n show: true,\n stroke: {\n color: backgroundColor,\n width: parseFloat(ctrl.panel.strokeWidth).toFixed(1)\n },\n label: {\n show: ctrl.panel.legend.show && ctrl.panel.legendType === 'On graph',\n formatter: formatter\n },\n highlight: {\n opacity: 0.0\n },\n\t\t combine: {\n\t\t threshold: ctrl.panel.combine.threshold,\n\t\t\tlabel: ctrl.panel.combine.label\n\t\t }\n }\n },\n grid: {\n hoverable: true,\n clickable: false\n }\n };\n\n if (panel.pieType === 'donut') {\n options.series.pie.innerRadius = 0.5;\n }\n\n elem.html(plotCanvas);\n\n $.plot(plotCanvas, ctrl.data, options);\n plotCanvas.bind(\"plothover\", function (event, pos, item) {\n if (!item) {\n $tooltip.detach();\n return;\n }\n\n var body;\n var percent = parseFloat(item.series.percent).toFixed(2);\n var formatted = ctrl.formatValue(item.series.data[0][1]);\n\n body = '
';\n body += '
' + item.series.label + ': ' + formatted;\n body += \" (\" + percent + \"%)\" + '
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b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/package.json deleted file mode 100644 index 45b5171..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/package.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "name": "piechart-panel", - "private": true, - "version": "1.0.0", - "description": "", - "repository": { - "type": "git", - "url": "git+https://github.com/grafana/piechart-panel.git" - }, - "author": "", - "license": "ISC", - "devDependencies": { - "grunt": "~0.4.5", - "babel": "~6.5.1", - "grunt-babel": "~6.0.0", - "grunt-contrib-copy": "~0.8.2", - "grunt-contrib-watch": "^0.6.1", - "grunt-contrib-uglify": "~0.11.0", - "grunt-systemjs-builder": "^0.2.5", - "load-grunt-tasks": "~3.2.0", - "grunt-execute": "~0.2.2", - "grunt-contrib-clean": "~0.6.0" - }, - "dependencies": { - "babel-plugin-transform-es2015-modules-systemjs": "^6.5.0", - "babel-plugin-transform-es2015-for-of": "^6.5.0", - "babel-preset-es2015": "^6.5.0", - "lodash": "~4.17.19" - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/editor.html b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/editor.html deleted file mode 100644 index 6fd689b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/editor.html +++ /dev/null @@ -1,62 +0,0 @@ -

-
-
General
-
- Type -
- -
-
-
- Unit -
-
-
-
- Value -
- -
-
-
- Divider width - -
-
- -
-
Legend
- -
- Position -
- -
-
-
- Font size -
- -
-
- - -
- -
-
Combine
-
- Threshold: - - Combines all slices that are smaller than the specified percentage (ranging from 0 to 1) i.e. a value of '0.03' will combine all slices 3% or less into one slice). -
-
- Label - - Label text for the combined slice. -
- -
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'); - var firstRender = true; - var ctrl = scope.ctrl; - var panel = ctrl.panel; - var data; - var seriesList; - var i; - - ctrl.events.on('render', function() { - data = ctrl.series; - if (data) { - for(var i in data) { - data[i].color = ctrl.data[i].color; - } - render(); - } - }); - - function getSeriesIndexForElement(el) { - return el.parents('[data-series-index]').data('series-index'); - } - - function toggleSeries(e) { - var el = $(e.currentTarget); - var index = getSeriesIndexForElement(el); - var seriesInfo = seriesList[index]; - ctrl.toggleSeries(seriesInfo, e); - } - - function sortLegend(e) { - var el = $(e.currentTarget); - var stat = el.data('stat'); - - if (stat !== panel.legend.sort) { panel.legend.sortDesc = null; } - - // if already sort ascending, disable sorting - if (panel.legend.sortDesc === false) { - panel.legend.sort = null; - panel.legend.sortDesc = null; - render(); - return; - } - - panel.legend.sortDesc = !panel.legend.sortDesc; - panel.legend.sort = stat; - render(); - } - - function openColorSelector(e) { - // if we clicked inside poup container ignore click - if ($(e.target).parents('.popover').length) { - return; - } - - var el = $(e.currentTarget).find('.fa-minus'); - var index = getSeriesIndexForElement(el); - var series = seriesList[index]; - - $timeout(function() { - popoverSrv.show({ - element: el[0], - position: 'bottom center', - template: '', - model: { - series: series, - toggleAxis: function() {}, - colorSelected: function(color) { - ctrl.changeSeriesColor(series, color); - } - }, - }); - }); - } - - function render() { - if(panel.legendType === 'On graph') { - $container.empty(); - return; - } - - if (firstRender) { - elem.append($container); - $container.on('click', '.graph-legend-icon', openColorSelector); - $container.on('click', 'th', sortLegend); - firstRender = false; - } - - seriesList = data; - - $container.empty(); - - var showValues = panel.legend.values || panel.legend.percentage; - var tableLayout = ( - panel.legendType === 'Under graph' || - panel.legendType === 'Right side' - ) && showValues; - - - $container.toggleClass('graph-legend-table', tableLayout); - - if (tableLayout) { - var header = ''; - if (panel.legend.values) { - header += 'values'; - } - if (panel.legend.percentage) { - header += 'percentage'; - } - header += ''; - $container.append($(header)); - } - - if (panel.legend.sort) { - seriesList = _.sortBy(seriesList, function(series) { - return series.stats[panel.legend.sort]; - }); - if (panel.legend.sortDesc) { - seriesList = seriesList.reverse(); - } - } - - if (panel.legend.percentage) { - var total = 0; - for (i = 0; i < seriesList.length; i++) { - total += seriesList[i].stats[ctrl.panel.valueName]; - } - } - - for (i = 0; i < seriesList.length; i++) { - var series = seriesList[i]; - - // ignore empty series - if (panel.legend.hideEmpty && series.allIsNull) { - continue; - } - // ignore series excluded via override - if (!series.legend) { - continue; - } - - var html = '
'; - html += ''; - html += ''; - html += ''; - - html += ''; - html += '' + series.label + ''; - html += ''; - - if (showValues && tableLayout) { - var value = series.formatValue(series.stats[ctrl.panel.valueName]); - if (panel.legend.values) { - html += '
' + value + '
'; - } - if (total) { - var pvalue = ((value / total) * 100).toFixed(2) + '%'; - html += '
' + pvalue +'
'; - } - } - - html += '
'; - $container.append($(html)); - } - } - } - }; -}); - - diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/module.html b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/module.html deleted file mode 100644 index c8524b3..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/module.html +++ /dev/null @@ -1,9 +0,0 @@ -
-
-
-
-
-
-
- - diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/module.js b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/module.js deleted file mode 100644 index b278df7..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/module.js +++ /dev/null @@ -1,6 +0,0 @@ -import {PieChartCtrl} from './piechart_ctrl'; - -export { - PieChartCtrl as PanelCtrl -}; - diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/piechart_ctrl.js b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/piechart_ctrl.js deleted file mode 100644 index c2e28fe..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/piechart_ctrl.js +++ /dev/null @@ -1,153 +0,0 @@ -import {MetricsPanelCtrl} from 'app/plugins/sdk'; -import _ from 'lodash'; -import kbn from 'app/core/utils/kbn'; -import TimeSeries from 'app/core/time_series'; -import rendering from './rendering'; -import legend from './legend'; - -export class PieChartCtrl extends MetricsPanelCtrl { - - constructor($scope, $injector, $rootScope) { - super($scope, $injector); - this.$rootScope = $rootScope; - - var panelDefaults = { - pieType: 'pie', - legend: { - show: true, // disable/enable legend - values: true - }, - links: [], - datasource: null, - maxDataPoints: 3, - interval: null, - targets: [{}], - cacheTimeout: null, - nullPointMode: 'connected', - legendType: 'Under graph', - aliasColors: {}, - format: 'short', - valueName: 'current', - strokeWidth: 1, - fontSize: '80%', - combine: { - threshold: 0.0, - label: 'Others' - } - }; - - _.defaults(this.panel, panelDefaults); - _.defaults(this.panel.legend, panelDefaults.legend); - - this.events.on('render', this.onRender.bind(this)); - this.events.on('data-received', this.onDataReceived.bind(this)); - this.events.on('data-error', this.onDataError.bind(this)); - this.events.on('data-snapshot-load', this.onDataReceived.bind(this)); - this.events.on('init-edit-mode', this.onInitEditMode.bind(this)); - } - - onInitEditMode() { - this.addEditorTab('Options', 'public/plugins/grafana-piechart-panel/editor.html', 2); - this.unitFormats = kbn.getUnitFormats(); - } - - setUnitFormat(subItem) { - this.panel.format = subItem.value; - this.render(); - } - - onDataError() { - this.series = []; - this.render(); - } - - changeSeriesColor(series, color) { - series.color = color; - this.panel.aliasColors[series.alias] = series.color; - this.render(); - } - - onRender() { - this.data = this.parseSeries(this.series); - } - - parseSeries(series) { - return _.map(this.series, (serie, i) => { - return { - label: serie.alias, - data: serie.stats[this.panel.valueName], - color: this.panel.aliasColors[serie.alias] || this.$rootScope.colors[i] - }; - }); - } - - onDataReceived(dataList) { - this.series = dataList.map(this.seriesHandler.bind(this)); - this.data = this.parseSeries(this.series); - this.render(this.data); - } - - seriesHandler(seriesData) { - var series = new TimeSeries({ - datapoints: seriesData.datapoints, - alias: seriesData.target - }); - - series.flotpairs = series.getFlotPairs(this.panel.nullPointMode); - return series; - } - - getDecimalsForValue(value) { - if (_.isNumber(this.panel.decimals)) { - return { decimals: this.panel.decimals, scaledDecimals: null }; - } - - var delta = value / 2; - var dec = -Math.floor(Math.log(delta) / Math.LN10); - - var magn = Math.pow(10, -dec); - var norm = delta / magn; // norm is between 1.0 and 10.0 - var size; - - if (norm < 1.5) { - size = 1; - } else if (norm < 3) { - size = 2; - // special case for 2.5, requires an extra decimal - if (norm > 2.25) { - size = 2.5; - ++dec; - } - } else if (norm < 7.5) { - size = 5; - } else { - size = 10; - } - - size *= magn; - - // reduce starting decimals if not needed - if (Math.floor(value) === value) { dec = 0; } - - var result = {}; - result.decimals = Math.max(0, dec); - result.scaledDecimals = result.decimals - Math.floor(Math.log(size) / Math.LN10) + 2; - - return result; - } - - formatValue(value) { - var decimalInfo = this.getDecimalsForValue(value); - var formatFunc = kbn.valueFormats[this.panel.format]; - if (formatFunc) { - return formatFunc(value, decimalInfo.decimals, decimalInfo.scaledDecimals); - } - return value; - } - - link(scope, elem, attrs, ctrl) { - rendering(scope, elem, attrs, ctrl); - } -} - -PieChartCtrl.templateUrl = 'module.html'; diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/plugin.json b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/plugin.json deleted file mode 100644 index b3bab38..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/plugin.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "type": "panel", - "name": "Pie Chart", - "id": "grafana-piechart-panel", - - "info": { - "description": "Pie chart panel for grafana", - "author": { - "name": "Raintank Inc.", - "url": "http://raintank.io" - }, - "keywords": ["piechart", "panel"], - "logos": { - "small": "src/img/piechart_logo_small.png", - "large": "src/img/piechart_logo_large.png" - }, - "links": [ - {"name": "Project site", "url": "https://github.com/grafana/piechart-panel"}, - {"name": "Blog Post", "url": "https://blog.raintank.io/friends-dont-let-friends-abuse-pie-charts/"}, - {"name": "MIT License", "url": "https://github.com/grafana/piechart-panel/blob/master/LICENSE"} - ], - "screenshots": [ - {"name": "Donut!", "path": "src/img/piechart-donut.png"}, - {"name": "Legend on the graph", "path": "src/img/piechart-legend-on-graph.png"}, - {"name": "Legend to the right", "path": "src/img/piechart-legend-rhs.png"}, - {"name": "Legend underneath", "path": "src/img/piechart-legend-under.png"}, - {"name": "Piechart options", "path": "src/img/piechart-options.png"} - ], - "version": "1.1.4", - "updated": "2016-09-26" - }, - - "dependencies": { - "grafanaVersion": "3.x.x", - "plugins": [ ] - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/rendering.js b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/rendering.js deleted file mode 100644 index 018ccd7..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/rendering.js +++ /dev/null @@ -1,124 +0,0 @@ -import _ from 'lodash'; -import $ from 'jquery'; -import 'jquery.flot'; -import 'jquery.flot.pie'; - -export default function link(scope, elem, attrs, ctrl) { - var data, panel; - elem = elem.find('.piechart-panel'); - var $tooltip = $('
'); - - ctrl.events.on('render', function() { - render(); - ctrl.renderingCompleted(); - }); - - function setElementHeight() { - try { - var height = ctrl.height || panel.height || ctrl.row.height; - if (_.isString(height)) { - height = parseInt(height.replace('px', ''), 10); - } - - height -= 5; // padding - height -= panel.title ? 24 : 9; // subtract panel title bar - - elem.css('height', height + 'px'); - - return true; - } catch(e) { // IE throws errors sometimes - return false; - } - } - - function formatter(label, slice) { - return "
" + label + "
" + Math.round(slice.percent) + "%
"; - } - - function addPieChart() { - var width = elem.width(); - var height = elem.height(); - - var size = Math.min(width, height); - - var plotCanvas = $('
'); - var plotCss = { - top: '10px', - margin: 'auto', - position: 'relative', - height: (size - 20) + 'px' - }; - - plotCanvas.css(plotCss); - - var $panelContainer = elem.parents('.panel-container'); - var backgroundColor = $panelContainer.css('background-color'); - - var options = { - legend: { - show: false - }, - series: { - pie: { - show: true, - stroke: { - color: backgroundColor, - width: parseFloat(ctrl.panel.strokeWidth).toFixed(1) - }, - label: { - show: ctrl.panel.legend.show && ctrl.panel.legendType === 'On graph', - formatter: formatter - }, - highlight: { - opacity: 0.0 - }, - combine: { - threshold: ctrl.panel.combine.threshold, - label: ctrl.panel.combine.label - } - } - }, - grid: { - hoverable: true, - clickable: false - } - }; - - if (panel.pieType === 'donut') { - options.series.pie.innerRadius = 0.5; - } - - elem.html(plotCanvas); - - $.plot(plotCanvas, ctrl.data, options); - plotCanvas.bind("plothover", function (event, pos, item) { - if (!item) { - $tooltip.detach(); - return; - } - - var body; - var percent = parseFloat(item.series.percent).toFixed(2); - var formatted = ctrl.formatValue(item.series.data[0][1]); - - body = '
'; - body += '
' + item.series.label + ': ' + formatted; - body += " (" + percent + "%)" + '
'; - body += "
"; - - $tooltip.html(body).place_tt(pos.pageX + 20, pos.pageY); - }); - } - - function render() { - if (!ctrl.data) { return; } - - data = ctrl.data; - panel = ctrl.panel; - - if (setElementHeight()) { - addPieChart(); - } - } -} - diff --git a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/README.md b/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/README.md deleted file mode 100644 index e69de29..0000000 diff --git a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/css/panel.css b/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/css/panel.css deleted file mode 100644 index af3fa05..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/css/panel.css +++ /dev/null @@ -1,121 +0,0 @@ -.server-state { - float: right; - position: relative; - padding-left: 7px; - margin-top: 7px; -} - -.server-state-icon { - margin-top: 12px; - font-size: 14px; - padding: 0px; -} - -.server-state-online, -.server-state-success { - color: #18a718; -} - -.server-state-pending, -.server-state-connecting { - color: #797979; -} - -.server-state-error, -.server-state-offline { - color: #ff2323; -} - -.job-state { - display: inline-block; - padding: 2px 4px; - font-size: 10.998px; - font-weight: bold; - line-height: 14px; - color: #fff; - text-shadow: 0 -1px 0 rgba(0,0,0,0.25); - white-space: nowrap; - vertical-align: baseline; - background-color: #999; - border-radius: 3px; - text-transform: uppercase; -} - -.job-state-not_started, -.job-state-shutting_down { - background-color: #999; -} - -.job-state-starting, -.job-state-idle { - background-color: #338bb8; -} - -.job-state-running, -.job-state-busy { - background-color: #f89406; -} - -.job-state-error, -.job-state-dead { - background-color: #b94a48; -} - -.job-state-success { - background-color: #468847; -} - -.jobs { - overflow-y: auto; -} - -.controls { - height: 50px; -} - -.jobs table { - width: 100%; -} - -.jobs td { - line-height: 1.4; - text-align: left; - padding: 2px 4px; - white-space: nowrap; -} - -.jobs td:first-child { - width: 35px; - font-size: 13px; - text-align: center; -} - -.jobs td:last-child { - text-align: right; -} - -.jobs .no-jobs td, -.jobs .jobs-header td { - font-size: 13px; - text-align: left; -} - -.jobs-header td { - font-weight: bold; - border-top: 0; -} - -.started { - display: block; -} - -.started-date { - font-size: 12px; - color: #797979; -} - -.started-time { - font-size: 15px; - display: block; -} - diff --git a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/editor.html b/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/editor.html deleted file mode 100644 index 1181845..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/editor.html +++ /dev/null @@ -1,54 +0,0 @@ -
-
-
Настройки задач
-
- - -
-
- - -
-
- - -
-
- - -
-
- - -
- -
-
-
Настройки исполнения
-
- - -
-
- - -
-
- - -
-
- - -
-
- - -
- -
-
- - diff --git a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/filters.js b/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/filters.js deleted file mode 100644 index 8a2503c..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/filters.js +++ /dev/null @@ -1,98 +0,0 @@ -'use strict'; - -System.register(['moment', 'lodash'], function (_export, _context) { - "use strict"; - - var moment, _, filters; - - return { - setters: [function (_moment) { - moment = _moment.default; - }, function (_lodash) { - _ = _lodash.default; - }], - execute: function () { - filters = angular.module('grafana.filters'); - - - filters.filter('calendar', function () { - return function (date) { - return moment(date).calendar(); - }; - }); - - filters.filter('dateFormat', function () { - return function (date, format) { - return moment(date).format(format); - }; - }); - - filters.filter('duration', function () { - return function (ms) { - var seconds = ms / 1000; - - return [parseInt(seconds / 60 / 60), parseInt(seconds / 60 % 60), parseInt(seconds % 60)].join(":").replace(/\b(\d)\b/g, "0$1"); - }; - }); - - filters.filter('startedAt', function () { - return function (batch) { - var timestamp = _.get(batch, 'rm.startedTime'); - - if (timestamp) { - return new Date(timestamp); - } - }; - }); - - filters.filter('serverState', function () { - var states = { - 'online': 'Доступен', - 'connecting': 'Подключение', - 'offline': 'Недоступен', - 'success': 'Успешно', - 'error': 'Ошибка', - 'pending': 'Отправка запроса' - }; - - return function (input) { - return states.hasOwnProperty(input) ? states[input] : input; - }; - }); - - filters.filter('serverStateIcon', function () { - var states = { - 'online': 'fa fa-circle', - 'connecting': 'fa fa-circle', - 'offline': 'fa fa-circle', - 'success': 'fa fa-check-circle', - 'error': 'fa fa-times-circle', - 'pending': 'fa fa-spinner fa-spin' - }; - - return function (input) { - return states.hasOwnProperty(input) ? states[input] : input; - }; - }); - - filters.filter('jobState', function () { - var states = { - 'not_started': 'Не запущена', - 'starting': 'Запускается', - 'running': 'Выполняется', - 'idle': 'Бездействует', - 'busy': 'Выполняется', - 'shutting_down': 'Завершается', - 'error': 'Ошибка', - 'dead': 'Остановлена', - 'success': 'Выполнена' - }; - - return function (input) { - return states.hasOwnProperty(input) ? states[input] : input; - }; - }); - } - }; -}); -//# sourceMappingURL=filters.js.map diff --git a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/filters.js.map b/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/filters.js.map deleted file mode 100644 index 327b508..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/filters.js.map +++ /dev/null @@ -1 +0,0 @@ -{"version":3,"sources":["../src/filters.js"],"names":["moment","_","filters","angular","module","filter","date","calendar","format","ms","seconds","parseInt","join","replace","batch","timestamp","get","Date","states","input","hasOwnProperty"],"mappings":";;;;;;;;;AAAOA,kB;;AACAC,a;;;AAEHC,mB,GAAUC,QAAQC,MAAR,CAAe,iBAAf,C;;;AAEdF,oBAAQG,MAAR,CAAe,UAAf,EAA2B;AAAA,uBAAM,UAACC,IAAD;AAAA,2BAAUN,OAAOM,IAAP,EAAaC,QAAb,EAAV;AAAA,iBAAN;AAAA,aAA3B;;AAEAL,oBAAQG,MAAR,CAAe,YAAf,EAA6B;AAAA,uBAAM,UAACC,IAAD,EAAOE,MAAP;AAAA,2BAAkBR,OAAOM,IAAP,EAAaE,MAAb,CAAoBA,MAApB,CAAlB;AAAA,iBAAN;AAAA,aAA7B;;AAEAN,oBAAQG,MAAR,CAAe,UAAf,EAA2B;AAAA,uBAAM,UAACI,EAAD,EAAQ;AACrC,wBAAIC,UAAUD,KAAK,IAAnB;;AAEA,2BAAO,CACHE,SAASD,UAAU,EAAV,GAAe,EAAxB,CADG,EAEHC,SAASD,UAAU,EAAV,GAAe,EAAxB,CAFG,EAGHC,SAASD,UAAU,EAAnB,CAHG,EAKFE,IALE,CAKG,GALH,EAMFC,OANE,CAMM,WANN,EAMmB,KANnB,CAAP;AAOH,iBAV0B;AAAA,aAA3B;;AAYAX,oBAAQG,MAAR,CAAe,WAAf,EAA4B,YAAW;AACnC,uBAAO,UAACS,KAAD,EAAW;AACd,wBAAIC,YAAYd,EAAEe,GAAF,CAAMF,KAAN,EAAa,gBAAb,CAAhB;;AAEA,wBAAIC,SAAJ,EAAe;AACX,+BAAO,IAAIE,IAAJ,CAASF,SAAT,CAAP;AACH;AACJ,iBAND;AAOH,aARD;;AAUAb,oBAAQG,MAAR,CAAe,aAAf,EAA8B,YAAW;AACrC,oBAAIa,SAAS;AACT,8BAAU,UADD;AAET,kCAAc,aAFL;AAGT,+BAAW,YAHF;AAIT,+BAAW,SAJF;AAKT,6BAAS,QALA;AAMT,+BAAW;AANF,iBAAb;;AASA,uBAAO,UAACC,KAAD;AAAA,2BAAWD,OAAOE,cAAP,CAAsBD,KAAtB,IAA+BD,OAAOC,KAAP,CAA/B,GAA+CA,KAA1D;AAAA,iBAAP;AACH,aAXD;;AAaAjB,oBAAQG,MAAR,CAAe,iBAAf,EAAkC,YAAW;AACzC,oBAAIa,SAAS;AACT,8BAAU,cADD;AAET,kCAAc,cAFL;AAGT,+BAAW,cAHF;AAIT,+BAAW,oBAJF;AAKT,6BAAS,oBALA;AAMT,+BAAW;AANF,iBAAb;;AASA,uBAAO,UAACC,KAAD;AAAA,2BAAWD,OAAOE,cAAP,CAAsBD,KAAtB,IAA+BD,OAAOC,KAAP,CAA/B,GAA+CA,KAA1D;AAAA,iBAAP;AACH,aAXD;;AAaAjB,oBAAQG,MAAR,CAAe,UAAf,EAA2B,YAAW;AAClC,oBAAIa,SAAS;AACT,mCAAe,aADN;AAET,gCAAY,aAFH;AAGT,+BAAW,aAHF;AAIT,4BAAQ,cAJC;AAKT,4BAAQ,aALC;AAMT,qCAAiB,aANR;AAOT,6BAAS,QAPA;AAQT,4BAAQ,aARC;AAST,+BAAW;AATF,iBAAb;;AAYA,uBAAO,UAACC,KAAD;AAAA,2BAAWD,OAAOE,cAAP,CAAsBD,KAAtB,IAA+BD,OAAOC,KAAP,CAA/B,GAA+CA,KAA1D;AAAA,iBAAP;AACH,aAdD","file":"filters.js","sourcesContent":["import moment from 'moment'\r\nimport _ from 'lodash'\r\n\r\nlet filters = angular.module('grafana.filters');\r\n\r\nfilters.filter('calendar', () => (date) => moment(date).calendar());\r\n\r\nfilters.filter('dateFormat', () => (date, format) => moment(date).format(format));\r\n\r\nfilters.filter('duration', () => (ms) => {\r\n let seconds = ms / 1000;\r\n\r\n return [\r\n parseInt(seconds / 60 / 60),\r\n parseInt(seconds / 60 % 60),\r\n parseInt(seconds % 60)\r\n ]\r\n .join(\":\")\r\n .replace(/\\b(\\d)\\b/g, \"0$1\");\r\n})\r\n\r\nfilters.filter('startedAt', function() {\r\n return (batch) => {\r\n var timestamp = _.get(batch, 'rm.startedTime');\r\n\r\n if (timestamp) {\r\n return new Date(timestamp);\r\n }\r\n };\r\n});\r\n\r\nfilters.filter('serverState', function() {\r\n var states = {\r\n 'online': 'Доступен',\r\n 'connecting': 'Подключение',\r\n 'offline': 'Недоступен',\r\n 'success': 'Успешно',\r\n 'error': 'Ошибка',\r\n 'pending': 'Отправка запроса'\r\n };\r\n\r\n return (input) => states.hasOwnProperty(input) ? states[input] : input;\r\n});\r\n\r\nfilters.filter('serverStateIcon', function() {\r\n var states = {\r\n 'online': 'fa fa-circle',\r\n 'connecting': 'fa fa-circle',\r\n 'offline': 'fa fa-circle',\r\n 'success': 'fa fa-check-circle',\r\n 'error': 'fa fa-times-circle',\r\n 'pending': 'fa fa-spinner fa-spin'\r\n };\r\n\r\n return (input) => states.hasOwnProperty(input) ? states[input] : input;\r\n});\r\n\r\nfilters.filter('jobState', function() {\r\n var states = {\r\n 'not_started': 'Не запущена',\r\n 'starting': 'Запускается',\r\n 'running': 'Выполняется',\r\n 'idle': 'Бездействует',\r\n 'busy': 'Выполняется',\r\n 'shutting_down': 'Завершается',\r\n 'error': 'Ошибка',\r\n 'dead': 'Остановлена',\r\n 'success': 'Выполнена'\r\n };\r\n\r\n return (input) => states.hasOwnProperty(input) ? states[input] : input;\r\n});"]} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/livy_ctrl.js b/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/livy_ctrl.js deleted file mode 100644 index bff625c..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/livy_ctrl.js +++ /dev/null @@ -1,320 +0,0 @@ -'use strict'; - -System.register(['app/plugins/sdk', 'lodash'], function (_export, _context) { - "use strict"; - - var PanelCtrl, _, _typeof, _createClass, panelDefaults, LivyCtrl; - - function _classCallCheck(instance, Constructor) { - if (!(instance instanceof Constructor)) { - throw new TypeError("Cannot call a class as a function"); - } - } - - function _possibleConstructorReturn(self, call) { - if (!self) { - throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); - } - - return call && (typeof call === "object" || typeof call === "function") ? call : self; - } - - function _inherits(subClass, superClass) { - if (typeof superClass !== "function" && superClass !== null) { - throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); - } - - subClass.prototype = Object.create(superClass && superClass.prototype, { - constructor: { - value: subClass, - enumerable: false, - writable: true, - configurable: true - } - }); - if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; - } - - return { - setters: [function (_appPluginsSdk) { - PanelCtrl = _appPluginsSdk.PanelCtrl; - }, function (_lodash) { - _ = _lodash.default; - }], - execute: function () { - _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { - return typeof obj; - } : function (obj) { - return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; - }; - - _createClass = function () { - function defineProperties(target, props) { - for (var i = 0; i < props.length; i++) { - var descriptor = props[i]; - descriptor.enumerable = descriptor.enumerable || false; - descriptor.configurable = true; - if ("value" in descriptor) descriptor.writable = true; - Object.defineProperty(target, descriptor.key, descriptor); - } - } - - return function (Constructor, protoProps, staticProps) { - if (protoProps) defineProperties(Constructor.prototype, protoProps); - if (staticProps) defineProperties(Constructor, staticProps); - return Constructor; - }; - }(); - - panelDefaults = { - rmServer: 'http://mesos-host:5050', - livyServer: 'http://livy-host:8999', - submitJar: 'hdfs:///path/to/file.jar', - jobClassName: 'com.full.job.ClassName', - satTemplateVar: 'sat', - showJobList: true, - driverMemory: '1g', - driverCores: 2, - executorMemory: '2g', - executorCores: 2, - numExecutors: 3, - dynamicAllocation: false - }; - - _export('LivyCtrl', LivyCtrl = function (_PanelCtrl) { - _inherits(LivyCtrl, _PanelCtrl); - - function LivyCtrl($scope, $injector, $http) { - _classCallCheck(this, LivyCtrl); - - var _this = _possibleConstructorReturn(this, (LivyCtrl.__proto__ || Object.getPrototypeOf(LivyCtrl)).call(this, $scope, $injector)); - - _.defaults(_this.panel, panelDefaults); - - _this.$http = $http; - _this.serverState = 'connecting'; - _this.events.on('init-edit-mode', _this.onInitEditMode.bind(_this)); - - _this.livyJobs = []; - _this.frameworks = []; - _this.skipCleanupIds = []; - - _this.updateJobs(); - return _this; - } - - _createClass(LivyCtrl, [{ - key: 'onInitEditMode', - value: function onInitEditMode() { - this.addEditorTab('Options', 'public/plugins/livy-panel/editor.html', 2); - } - }, { - key: '_cleanupStorage', - value: function _cleanupStorage(activeIds) { - activeIds = _.concat(activeIds, this.skipCleanupIds); - this.skipCleanupIds = []; - - _(localStorage).keys().filter(function (k) { - return k.startsWith('livy'); - }).differenceWith(activeIds, function (a, b) { - return a == 'livy-' + b; - }).each(function (k) { - return localStorage.removeItem(k); - }); - } - }, { - key: '_makeLivyJobModel', - value: function _makeLivyJobModel(session) { - return { - id: session.id, - appId: session.appId, - sparkUiUrl: session.appInfo.sparkUiUrl, - state: session.state, - args: JSON.parse(localStorage.getItem('livy-' + session.id)) - }; - } - }, { - key: '_makeFrameworkModel', - value: function _makeFrameworkModel(framework) { - var startTime = framework.registered_time * 1000; - var endTime = framework.unregistered_time * 1000; - - var tasks = _.concat(framework.tasks, framework.completed_tasks, framework.unreachable_tasks); - var labels = _.chain(tasks).find('labels').get('labels').flatten().mapKeys('key').mapValues('value').value(); - - var taskStats = _.countBy(tasks, 'state'); - - var _getState = function _getState() { - if (framework.active) { - return framework.tasks.length > 0 ? 'running' : 'idle'; - } else if (framework.unregistered_time > 0) { - if (taskStats['TASK_FAILED'] > 0) return 'error'; - if (taskStats['TASK_KILLED'] > 0) return 'dead'; - - return 'success'; - } - - return 'not_started'; - }; - - return { - id: framework.id, - idx: framework.id.slice(-4), - name: framework.name, - startTime: startTime, - endTime: endTime, - isRunning: endTime == 0, - isComplete: endTime > 0, - totalTime: (endTime || new Date().getTime()) - startTime, - activeTasks: (taskStats['TASK_RUNNING'] || 0) + (taskStats['TASK_STARTING'] || 0), - failedTasks: taskStats['TASK_FAILED'] || 0, - killedTasks: taskStats['TASK_KILLED'] || 0, - finishedTasks: taskStats['TASK_FINISHED'] || 0, - resources: framework.resources, - state: _getState(), - args: labels - }; - } - }, { - key: 'updateJobs', - value: function updateJobs() { - var _this2 = this; - - if (this.panel.showJobList) { - var batches = this.$http.get(this.panel.livyServer + '/batches'); - var frameworks = this.$http.get(this.panel.rmServer + '/frameworks'); - - batches.then(function (response) { - var ids = _.map(response.data.sessions, 'id'); - _this2._cleanupStorage(ids); - _this2.livyJobs = _.map(response.data.sessions, _this2._makeLivyJobModel); - _this2.serverState = 'online'; - }, function (error) { - return _this2.serverState = 'offline'; - }); - - frameworks.then(function (response) { - var active = _(response.data.frameworks).orderBy('registered_time', 'desc').map(_this2._makeFrameworkModel); - var completed = _(response.data.completed_frameworks).orderBy('unregistered_time', 'desc').map(_this2._makeFrameworkModel); - _this2.frameworks = _.concat(active.value(), completed.value()); - }, function (error) { - return _this2.serverState = 'offline'; - }); - } else { - this.$http.get(this.panel.livyServer + '/ping').then(function (response) { - return _this2.serverState = 'online'; - }, function (error) { - return _this2.serverState = 'offline'; - }); - } - - this.$timeout(function () { - return _this2.updateJobs(); - }, 3000); - } - }, { - key: 'canSubmit', - value: function canSubmit() { - return _typeof(this.dashboard.time.from) === "object" && this.serverState === 'online' && this.requestState !== 'pending'; - } - }, { - key: 'submitJob', - value: function submitJob() { - var _this3 = this; - - var _escapeLabel = function _escapeLabel(v) { - return _.toString(v).replace(/,/g, '\\,').replace(/:/g, '\\:'); - }; - var _makeLabel = function _makeLabel(v, k) { - return _escapeLabel(k) + ':' + _escapeLabel(v); - }; - - var args = { - timeFrom: this.dashboard.time.from.format('x'), - timeTo: this.dashboard.time.to.format('x'), - sat: _.find(this.dashboard.templating.list, { name: this.panel.satTemplateVar }).current.value - }; - - var options = { - "file": this.panel.submitJar, - "className": this.panel.jobClassName, - "driverMemory": this.panel.driverMemory, - "driverCores": this.panel.driverCores, - "executorMemory": this.panel.executorMemory, - "executorCores": this.panel.executorCores, - "numExecutors": this.panel.numExecutors, - "conf": { - "spark.dynamicAllocation.enabled": this.panel.dynamicAllocation, - "spark.mesos.task.labels": _.map(args, _makeLabel).join(',') - }, - "args": _.values(args) - }; - - var request = this.$http.post(this.panel.livyServer + '/batches', options); - - this.serverState = 'pending'; - - request.then(function (response) { - _this3.serverState = 'success'; - - if (response.data.id) { - args.submitted = new Date().getTime(); - _this3.livyJobs.push(_this3._makeLivyJobModel(response.data)); - _this3.skipCleanupIds.push(response.data.id); - localStorage.setItem("livy-" + response.data.id, JSON.stringify(args)); - } - }, function (error) { - return _this3.serverState = 'error'; - }); - } - }, { - key: 'killBatch', - value: function killBatch(id) { - var _this4 = this; - - var request = this.$http.delete(this.panel.livyServer + '/batches/' + id); - this.serverState = 'pending'; - request.then(function (response) { - return _this4.serverState = 'success'; - }, function (error) { - return _this4.serverState = 'error'; - }); - } - }, { - key: 'killSession', - value: function killSession(id) { - var _this5 = this; - - var request = this.$http.delete(this.panel.livyServer + '/sessions/' + id); - this.serverState = 'pending'; - request.then(function (response) { - return _this5.serverState = 'success'; - }, function (error) { - return _this5.serverState = 'error'; - }); - } - }, { - key: 'killFramework', - value: function killFramework(id) { - var _this6 = this; - - var request = this.$http.post(this.panel.rmServer + '/master/teardown', "frameworkId=" + id); - this.serverState = 'pending'; - request.then(function (response) { - return _this6.serverState = 'success'; - }, function (error) { - return _this6.serverState = 'error'; - }); - } - }]); - - return LivyCtrl; - }(PanelCtrl)); - - _export('LivyCtrl', LivyCtrl); - - LivyCtrl.templateUrl = 'module.html'; - } - }; -}); -//# sourceMappingURL=livy_ctrl.js.map diff --git a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/livy_ctrl.js.map b/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/livy_ctrl.js.map deleted file mode 100644 index f75f56b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/livy_ctrl.js.map +++ /dev/null @@ -1 +0,0 @@ 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_cleanupStorage(activeIds) {\r\n activeIds = _.concat(activeIds, this.skipCleanupIds);\r\n this.skipCleanupIds = [];\r\n\r\n _(localStorage)\r\n .keys()\r\n .filter(k => k.startsWith('livy'))\r\n .differenceWith(activeIds, (a, b) => a == 'livy-' + b)\r\n .each(k => localStorage.removeItem(k));\r\n }\r\n\r\n _makeLivyJobModel(session) {\r\n return {\r\n id: session.id,\r\n appId: session.appId,\r\n sparkUiUrl: session.appInfo.sparkUiUrl,\r\n state: session.state,\r\n args: JSON.parse(localStorage.getItem('livy-' + session.id))\r\n }\r\n }\r\n\r\n _makeFrameworkModel(framework) {\r\n let startTime = framework.registered_time * 1000;\r\n let endTime = framework.unregistered_time * 1000;\r\n\r\n let tasks = _.concat(framework.tasks, framework.completed_tasks, framework.unreachable_tasks);\r\n let labels = _.chain(tasks)\r\n .find('labels').get('labels').flatten()\r\n .mapKeys('key').mapValues('value')\r\n .value();\r\n\r\n let taskStats = _.countBy(tasks, 'state');\r\n\r\n let _getState = () => {\r\n if (framework.active) {\r\n return framework.tasks.length > 0 ? 'running' : 'idle';\r\n }\r\n else if (framework.unregistered_time > 0) {\r\n if (taskStats['TASK_FAILED'] > 0) return 'error';\r\n if (taskStats['TASK_KILLED'] > 0) return 'dead';\r\n\r\n return 'success';\r\n }\r\n\r\n return 'not_started';\r\n };\r\n\r\n return {\r\n id: framework.id,\r\n idx: framework.id.slice(-4),\r\n name: framework.name,\r\n startTime: startTime,\r\n endTime: endTime,\r\n isRunning: endTime == 0,\r\n isComplete: endTime > 0,\r\n totalTime: (endTime || (new Date()).getTime()) - startTime,\r\n activeTasks: (taskStats['TASK_RUNNING'] || 0) + (taskStats['TASK_STARTING'] || 0),\r\n failedTasks: taskStats['TASK_FAILED'] || 0,\r\n killedTasks: taskStats['TASK_KILLED'] || 0,\r\n finishedTasks: taskStats['TASK_FINISHED'] || 0,\r\n resources: framework.resources,\r\n state: _getState(),\r\n args: labels,\r\n }\r\n }\r\n\r\n updateJobs() {\r\n if (this.panel.showJobList) {\r\n let batches = this.$http.get(this.panel.livyServer + '/batches');\r\n let frameworks = this.$http.get(this.panel.rmServer + '/frameworks');\r\n\r\n batches.then(\r\n response => {\r\n let ids = _.map(response.data.sessions, 'id');\r\n this._cleanupStorage(ids);\r\n this.livyJobs = _.map(response.data.sessions, this._makeLivyJobModel);\r\n this.serverState = 'online';\r\n },\r\n error => this.serverState = 'offline');\r\n\r\n frameworks.then(response => {\r\n let active = _(response.data.frameworks).orderBy('registered_time', 'desc').map(this._makeFrameworkModel);\r\n let completed = _(response.data.completed_frameworks).orderBy('unregistered_time', 'desc').map(this._makeFrameworkModel);\r\n this.frameworks = _.concat(active.value(), completed.value());\r\n }, error => this.serverState = 'offline');\r\n }\r\n else {\r\n this.$http.get(this.panel.livyServer + '/ping').then(response => this.serverState = 'online', error => this.serverState = 'offline');\r\n }\r\n\r\n this.$timeout(() => this.updateJobs(), 3000);\r\n }\r\n\r\n canSubmit() {\r\n return typeof(this.dashboard.time.from) === \"object\" && this.serverState === 'online' && this.requestState !== 'pending';\r\n }\r\n\r\n submitJob() {\r\n let _escapeLabel = v => _.toString(v).replace(/,/g, '\\\\,').replace(/:/g, '\\\\:');\r\n let _makeLabel = (v, k) => _escapeLabel(k) + ':' + _escapeLabel(v);\r\n\r\n let args = {\r\n timeFrom: this.dashboard.time.from.format('x'),\r\n timeTo: this.dashboard.time.to.format('x'),\r\n sat: _.find(this.dashboard.templating.list, {name: this.panel.satTemplateVar}).current.value,\r\n };\r\n\r\n let options = {\r\n \"file\": this.panel.submitJar,\r\n \"className\": this.panel.jobClassName,\r\n \"driverMemory\": this.panel.driverMemory,\r\n \"driverCores\": this.panel.driverCores,\r\n \"executorMemory\": this.panel.executorMemory,\r\n \"executorCores\": this.panel.executorCores,\r\n \"numExecutors\": this.panel.numExecutors,\r\n \"conf\": {\r\n \"spark.dynamicAllocation.enabled\": this.panel.dynamicAllocation,\r\n \"spark.mesos.task.labels\": _.map(args, _makeLabel).join(',')\r\n },\r\n \"args\": _.values(args)\r\n };\r\n\r\n \tlet request = this.$http.post(this.panel.livyServer + '/batches', options);\r\n\r\n this.serverState = 'pending';\r\n\r\n request.then(\r\n response => {\r\n this.serverState = 'success';\r\n\r\n if (response.data.id) {\r\n args.submitted = (new Date()).getTime();\r\n this.livyJobs.push(this._makeLivyJobModel(response.data));\r\n this.skipCleanupIds.push(response.data.id);\r\n localStorage.setItem(\"livy-\" + response.data.id, JSON.stringify(args));\r\n }\r\n },\r\n error => this.serverState = 'error');\r\n }\r\n\r\n killBatch(id) {\r\n let request = this.$http.delete(this.panel.livyServer + '/batches/' + id);\r\n this.serverState = 'pending';\r\n request.then(response => this.serverState = 'success', error => this.serverState = 'error');\r\n }\r\n\r\n killSession(id) {\r\n let request = this.$http.delete(this.panel.livyServer + '/sessions/' + id);\r\n this.serverState = 'pending';\r\n request.then(response => this.serverState = 'success', error => this.serverState = 'error');\r\n }\r\n\r\n killFramework(id) {\r\n let request = this.$http.post(this.panel.rmServer + '/master/teardown', \"frameworkId=\" + id);\r\n this.serverState = 'pending';\r\n request.then(response => this.serverState = 'success', error => this.serverState = 'error');\r\n }\r\n}\r\n\r\nLivyCtrl.templateUrl = 'module.html';\r\n"]} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/module.html b/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/module.html deleted file mode 100644 index fbabd99..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/module.html +++ /dev/null @@ -1,58 +0,0 @@ -
- - {{ctrl.serverState | serverState}} - -
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - -
Задачи расчета
Нет задач
{{job.id}}{{job.state | jobState}} - {{job.appId}} - {{job.args.sat}} {{job.args.timeFrom}} {{job.args.timeTo}} - - -
Кластер
{{job.idx}}{{job.state | jobState}} - {{job.name}} {{job.args.sat}} - {{job.finishedTasks}} - {{job.activeTasks}} - {{job.killedTasks}} - {{job.failedTasks}} - - {{job.totalTime | duration}} - ({{job.resources.cpus}} CPU / {{job.resources.mem}} MB) - ({{job.endTime | calendar}}) - - -
-
\ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/module.js b/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/module.js deleted file mode 100644 index 102eef7..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/module.js +++ /dev/null @@ -1,16 +0,0 @@ -'use strict'; - -System.register(['./livy_ctrl', './filters', './css/panel.css!'], function (_export, _context) { - "use strict"; - - var LivyCtrl; - return { - setters: [function (_livy_ctrl) { - LivyCtrl = _livy_ctrl.LivyCtrl; - }, function (_filters) {}, function (_cssPanelCss) {}], - execute: function () { - _export('PanelCtrl', LivyCtrl); - } - }; -}); -//# sourceMappingURL=module.js.map diff --git a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/module.js.map b/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/module.js.map deleted file mode 100644 index df84618..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/module.js.map +++ /dev/null @@ -1 +0,0 @@ -{"version":3,"sources":["../src/module.js"],"names":["LivyCtrl"],"mappings":";;;;;;;;AAAQA,c,cAAAA,Q;;;2BAONA,Q","file":"module.js","sourcesContent":["import {LivyCtrl} from './livy_ctrl';\r\n\r\nimport './filters'\r\n\r\nimport './css/panel.css!'\r\n\r\nexport {\r\n LivyCtrl as PanelCtrl\r\n};"]} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/plugin.json b/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/plugin.json deleted file mode 100644 index 8869fed..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/livy-panel/plugin.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "type": "panel", - "name": "Livy", - "id": "livy-panel", - "css": { - "dark": "css/dark.css", - "light": "css/light.css" - }, - "info": { - "description": "Интерфейс задач Livy", - "author": { - "name": "Алексей Саварцов", - "url": "http://infocom-s.ru" - }, - "keywords": ["spark", "livy"], - "version": "1.0.0", - "updated": "2017-03-20" - }, - - "dependencies": { - "grafanaVersion": "3.x.x", - "plugins": [ ] - } -} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/lp.tar b/image_content/config/grafana/var/lib/grafana/plugins/lp.tar deleted file mode 120000 index a89aeb8..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/lp.tar +++ /dev/null @@ -1 +0,0 @@ -../../../../../../../.git/annex/objects/XV/Fk/SHA256E-s40960--23409321cbf08f2cb810d9227f9a8bbe76db3deccc5621df0887a2c3e22b7e24.tar/SHA256E-s40960--23409321cbf08f2cb810d9227f9a8bbe76db3deccc5621df0887a2c3e22b7e24.tar \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/.gitignore b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/.gitignore deleted file mode 100644 index f7df84e..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -node_modules -npm-debug.log -yarn.lock -package-lock.json \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/CHANGELOG.md b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/CHANGELOG.md deleted file mode 100644 index f786cc6..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/CHANGELOG.md +++ /dev/null @@ -1,108 +0,0 @@ -# 1.4.2 (2018-03-18) - -## Fixes - -* support `UNION ALL` statements -* proper format for `LIMIT N,M` construction (thx to @shankerwangmiao) -* update `Show Help` section with $unescape description - - -# 1.4.1 (2018-03-12) - -## New Features - -* $unescape - unescapes variable value by removing single quotes. Used for multiple-value string variables: "SELECT $unescape($column) FROM requests WHERE $unescape($column) = 5" - -## Fixes - -* labmda-operator `->` no more breaks while reformatting query - - -# 1.4.0 (2018-03-08) - -## New Features - -Ad-hoc filters support: -* If there is an Ad-hoc variable, plugin will fetch all columns of all tables of all databases (except system database) as tags. -So in dropdown menu will be options like `database.table.column` -* If there are ENUM columns, plugin will fetch their options and use them as tag values -* Plugin will apply Ad-hoc filters to all queries on the dashboard if their settings `$database` and `$table` are the same -as Ad-hoc's `database.table` -* There are no option to apply OR operator for multiple Ad-hoc filters - see grafana/grafana#10918 -* There are no option to use IN operator for Ad-hoc filters due to Grafana limitations - -# 1.3.1 (2018-02-12) - -## Fixes - -* support array indexing int AST - - -# 1.3.0 (2018-02-07) - -## New Features -* columns autocompletion in ace-editor - -# 1.2.7 (2018-01-05) - -## Fixes - -* properly format query with reserved names -* fix #31 - - -# 1.2.6 (2017-12-13) - -## Fixes -* allow rounding with `round` option both time filters: $from and $to - - -# 1.2.5 (2017-12-05) - -## Fixes -* support template variables with different `text` and `value` values [#27](https://github.com/Vertamedia/clickhouse-grafana/issues/27) -* fix visual glitches [#29](https://github.com/Vertamedia/clickhouse-grafana/issues/29) - - -# 1.2.4 (2017-11-22) - -## Fixes -* apply proper value formatting for table format - - -# 1.2.3 (2017-11-20) - -## Fixes -* commit generated files - - -# 1.2.2 (2017-11-20) - -## Fixes -* fix error with absent `getCollapsedText` [#24](https://github.com/Vertamedia/clickhouse-grafana/issues/24) -* suppress errors while parsing AST [#24](https://github.com/Vertamedia/clickhouse-grafana/issues/24) -* show generated SQL in textarea [#24](https://github.com/Vertamedia/clickhouse-grafana/issues/24) -* do not round timestamp after converting [#25](https://github.com/Vertamedia/clickhouse-grafana/issues/25) -* increase max-height of query editor - - -# 1.2.1 (2017-11-17) - -## Fixes -* add forgotten completions -* process NULL values [#19](https://github.com/Vertamedia/clickhouse-grafana/issues/19) -* sort by key value in `$columns` macro [#16](https://github.com/Vertamedia/clickhouse-grafana/issues/16) - - -# 1.2.0 (2017-11-15) - -## New Features -* Ace editor -* ClickHouse function completion (thx to https://github.com/smi2/tabix.ui) - - -# 1.1.0 (2017-11-13) - -## New Features -* Allow `UInt32` as Timestamp column [#15](https://github.com/Vertamedia/clickhouse-grafana/issues/15) -* Add `Format as Table` format [#17](https://github.com/Vertamedia/clickhouse-grafana/issues/17) \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/Gruntfile.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/Gruntfile.js deleted file mode 100644 index 409080b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/Gruntfile.js +++ /dev/null @@ -1,102 +0,0 @@ -module.exports = function(grunt) { - require('load-grunt-tasks')(grunt); - - grunt.loadNpmTasks('grunt-contrib-clean'); - grunt.loadNpmTasks('grunt-typescript'); - grunt.loadNpmTasks('grunt-contrib-watch'); - - grunt.initConfig({ - clean: ["dist"], - - copy: { - dist_js: { - expand: true, - cwd: 'src', - src: ['**/*.ts', '**/*.js', '**/*.d.ts'], - dest: 'dist' - }, - dist_html: { - expand: true, - flatten: true, - cwd: 'src/partials', - src: ['*.html'], - dest: 'dist/partials/' - }, - dist_css: { - expand: true, - flatten: true, - cwd: 'src/css', - src: ['*.css'], - dest: 'dist/css/' - }, - dist_img: { - expand: true, - flatten: true, - cwd: 'src/img', - src: ['*.*'], - dest: 'dist/img/' - }, - dist_statics: { - expand: true, - flatten: true, - src: ['src/plugin.json', 'LICENSE', 'README.md', 'CHANGELOG.md'], - dest: 'dist/' - } - }, - - babel: { - options: { - sourceMap: true, - presets: ['es2015'] - }, - dist: { - options: { - plugins: ['transform-es2015-modules-systemjs', 'transform-es2015-for-of'] - }, - files: [{ - cwd: 'src', - expand: true, - src: ['**/*.js'], - dest: 'dist', - ext:'.js' - }] - } - }, - - typescript: { - build: { - src: ['dist/**/*.ts', '!**/*.d.ts'], - dest: 'dist', - options: { - module: 'system', - target: 'es5', - rootDir: 'dist/', - declaration: true, - emitDecoratorMetadata: true, - experimentalDecorators: true, - sourceMap: true, - noImplicitAny: false - } - } - }, - - watch: { - files: ['src/**/*.ts', 'src/**/*.js', 'src/**/*.html', 'src/**/*.css', 'src/img/*.*', 'src/plugin.json', 'README.md', 'CHANGELOG.md'], - tasks: ['default'], - options: { - debounceDelay: 250 - } - } - }); - - grunt.registerTask('default', [ - 'clean', - 'copy:dist_js', - 'typescript:build', - 'babel', - 'copy:dist_html', - 'copy:dist_css', - 'copy:dist_img', - 'copy:dist_statics' - ]); -}; \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/LICENSE b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/LICENSE deleted file mode 100644 index 5455e76..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2017 Vertamedia - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/README.md b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/README.md deleted file mode 100644 index 2837c6b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/README.md +++ /dev/null @@ -1,354 +0,0 @@ -# ClickHouse datasource for Grafana 4.6+ - -ClickHouse datasource plugin provides a support for [ClickHouse](https://clickhouse.yandex) as a backend database. - -### Quick start -Install from [grafana.net](https://grafana.net/plugins/vertamedia-clickhouse-datasource) - -OR - -Copy files to your [Grafana plugin directory](http://docs.grafana.org/plugins/installation/#grafana-plugin-directory). Restart Grafana, check datasources list at http://your.grafana.instance/datasources/new, choose ClickHouse option. - -### Features: - - * Access to CH via HTTP - * Query setup - * Raw SQL editor - * Query formatting - * Macros support - * Additional functions - * Templates - * Table view - * SingleStat view - * Ad-hoc filters - - -### Access to CH via HTTP -Page configuration is standard - -![settings](https://cloud.githubusercontent.com/assets/2902918/25473216/3ebd20fc-2b37-11e7-9743-fbbf2c5bdd3f.png) - - -There is a small feature - ClickHouse treats HTTP Basic Authentication credentials as a database user and will try to run queries using its name. - -#### [CHProxy](https://github.com/Vertamedia/chproxy) (optional) -Using of [CHProxy](https://github.com/Vertamedia/chproxy) will bring additional features: -* Easily setup `HTTPS` access to ClickHouse as shown [here](https://github.com/Vertamedia/chproxy#authorize-users-by-passwords-via-https) -to provide secure access. -* Limit concurrency and execution time for requests from `Grafana` as shown [here](https://github.com/Vertamedia/chproxy#spread-selects-from-reporting-apps-among-cluster-nodes) -to prevent `ClickHouse` overloading from `Grafana`. -* Protection against request bursts for dashboards with numerous graphs. `CHProxy` allows to queue requests and execute them sequentially. -To learn more - read about params `max_queue_size` and `max_queue_time` at [CHProxy](https://github.com/Vertamedia/chproxy) page. -* Response caching for the most frequent queries as shown [here](https://github.com/Vertamedia/chproxy#caching). -`Caching` will protect `ClickHouse` from excessive refreshes and will be optimal option for popular dashboards. ->> Hint - if you need to cache requests like `last 24h` where timestamp changes constantly then try to use `Round` option at `Raw Editor` - - -### Query setup - -Query setup interface: - -![query editor image](https://user-images.githubusercontent.com/2902918/32498037-2e9df438-c3d7-11e7-90de-73957c20cf6d.png) - -First row `FROM` contains two options: database and table. Table values depends on selected database. -Second row contains selectors for time filtering: -* Column:Date ([EventDate](https://clickhouse.yandex/reference_en.html#Date)) - is mandatory for [MergeTree](https://clickhouse.yandex/reference_en.html#MergeTree) tables -* Column:DateTime ([DateTime](https://clickhouse.yandex/reference_en.html#DateTime)) or Column:TimeStamp (UInt32). - -> Plugin will try to detect date columns automatically - -> Column:DateTime or Column:TimeStamp are required for time-based macros and functions, because all analytics is based on these values - -Button `Go to Query` is just a toggler to Raw SQL Editor - -### Raw SQL Editor - -Raw Editor allows custom SQL queries to be written: - -![raw editor image](https://user-images.githubusercontent.com/2902918/32843338-337f2efc-ca28-11e7-9bde-ec65faa3cdc9.png) - - -Raw Editor allows to type queries, get info about functions and macroses, format queries as Clickhouse do. -Under the Editor you can find a raw query (all macros and functions have already been replaced) which will be sent directly to ClickHouse. - -### Macros - -Plugin supports the following marcos: - -* $table - replaced with selected table name from Query Builder -* $timeCol - replaced with Date:Col value from Query Builder -* $dateTimeCol - replaced with Column:DateTime or Column:TimeStamp value from Query Builder -* $from - replaced with timestamp/1000 value of selected "Time Range:From" -* $to - replaced with timestamp/1000 value of selected "Time Range:To" -* $interval - replaced with selected "Group by time interval" value (as a number of seconds) -* $timeFilter - replaced with currently selected "Time Range". - Require Column:Date and Column:DateTime or Column:TimeStamp to be selected -* $timeSeries - replaced with special ClickHouse construction to convert results as time-series data. Use it as "SELECT $timeSeries...". -* $unescape - unescapes variable value by removing single quotes. Used for multiple-value string variables: "SELECT $unescape($column) FROM requests WHERE $unescape($column) = 5" -Require Column:DateTime or Column:TimeStamp to be selected - -A description of macros is available by typing their names in Raw Editor - -### Functions - -Functions are just templates of SQL queries and you can check the final query at [Raw SQL Editor mode](https://github.com/Vertamedia/clickhouse-grafana/blob/master/README.md#raw-sql-editor). -If some additional complexity is needed - just copy raw sql into Raw Editor and make according changes. Remember that macros are still available to use. - -There are some limits in function use because of poor query analysis: -* Column:Date and Column:DateTime or Column:TimeStamp must be set in Query Builder -* Query must begins from function name -* Only one function can be used per query - - - -Plugin supports the following functions: - -#### $rate(cols...) - converts query results as "change rate per interval" - -Example usage: -``` -$rate(countIf(Type = 200) AS good, countIf(Type != 200) AS bad) FROM requests -``` - -Query will be transformed into: -``` -SELECT - t, - good / runningDifference(t / 1000) AS goodRate, - bad / runningDifference(t / 1000) AS badRate -FROM -( - SELECT - (intDiv(toUInt32(EventTime), 60)) * 1000 AS t, - countIf(Type = 200) AS good, - countIf(Type != 200) AS bad - FROM requests - WHERE ((EventDate >= toDate(1482796747)) AND (EventDate <= toDate(1482853383))) AND ((EventTime >= toDateTime(1482796747)) AND (EventTime <= toDateTime(1482853383))) - GROUP BY t - ORDER BY t ASC -) -``` ---- - -#### $columns(key, value) - query values as array of [key, value], where key will be used as label - -Example usage: -``` -$columns(OSName, count(*) c) FROM requests -``` - -Query will be transformed into: -``` -SELECT - t, - groupArray((OSName, c)) AS groupArr -FROM -( - SELECT - (intDiv(toUInt32(EventTime), 60) * 60) * 1000 AS t, - OSName, - count(*) AS c - FROM requests - ANY INNER JOIN oses USING (OS) - WHERE ((EventDate >= toDate(1482796627)) AND (EventDate <= toDate(1482853383))) AND ((EventTime >= toDateTime(1482796627)) AND (EventTime <= toDateTime(1482853383))) - GROUP BY - t, - OSName - ORDER BY - t ASC, - OSName ASC -) -GROUP BY t -ORDER BY t ASC -``` - -This will help to build the next graph: - -![req_by_os image](https://cloud.githubusercontent.com/assets/2902918/21719222/2feabf30-d425-11e6-9042-9d290ef07884.png) - ---- - -#### $rateColumns(key, value) - is a combination of $columns and $rate - -Example usage: -``` -$rateColumns(OS, count(*) c) FROM requests -``` - -Query will be transformed into: -``` -SELECT - t, - arrayMap(lambda(tuple(a), (a.1, a.2 / runningDifference(t / 1000))), groupArr) -FROM -( - SELECT - t, - groupArray((OS, c)) AS groupArr - FROM - ( - SELECT - (intDiv(toUInt32(EventTime), 60) * 60) * 1000 AS t, - OS, - count(*) AS c - FROM requests - WHERE ((EventDate >= toDate(1482796867)) AND (EventDate <= toDate(1482853383))) AND ((EventTime >= toDateTime(1482796867)) AND (EventTime <= toDateTime(1482853383))) - GROUP BY - t, - OS - ORDER BY - t ASC, - OS ASC - ) - GROUP BY t - ORDER BY t ASC -) - -``` - -### Working with panels - -#### Piechart (https://grafana.com/plugins/grafana-piechart-panel) - -Remember that piechart plugin is not welcome for using in grafana - see https://grafana.com/blog/2015/12/04/friends-dont-let-friends-abuse-pie-charts - -![top5things](https://cloud.githubusercontent.com/assets/2902918/25392562/9fadb202-29e1-11e7-95ca-5b0d2921c592.png) - -To create "Top 5" diagram we will need two queries: one for 'Top 5' rows and one for 'Other' row. - -Top5: -``` -SELECT - 1, /* fake timestamp value */ - groupArray((UserName, Reqs)) -FROM -( - SELECT - UserName, - sum(Reqs) AS Reqs - FROM requests - GROUP BY UserName - ORDER BY Reqs desc - LIMIT 5 -) -``` - -Other: -``` -SELECT - 1, /* fake timestamp value */ - tuple(tuple('Other', sum(Reqs))) -FROM -( - SELECT - UserName, - sum(Reqs) AS Reqs - FROM requests - GROUP BY UserName - ORDER BY Reqs desc - LIMIT 5,10000000000000 /* select some ridiculous number after first 5 */ -) -``` - -### Table (https://grafana.com/plugins/table) - -There are no any tricks in displaying time-series data. But to display some summary we will need to fake timestamp data: - -``` -SELECT - rand() Time, /* fake timestamp value */ - UserName, - sum(Reqs) as Reqs -FROM requests -GROUP BY - UserName -ORDER BY - Reqs -``` - -Better to hide `Time` column at `Options` tab while editing panel - - -### Vertical histogram (https://grafana.com/plugins/graph) - -![vertical histogram](https://cloud.githubusercontent.com/assets/2902918/25392561/9f3777e0-29e1-11e7-8b23-2ea9ae46a029.png) - -To make vertical histogram from graph panel we will need to edit some settings: -* Display -> Draw Modes -> Bars -* Axes -> X-Axis -> Mode -> Series - -And use next query: -``` -$columns( - Size, - sum(Items) Items) -FROM some_table -``` - -// It is also possible to use query without macros - -### Ad-hoc filters - -If there is an Ad-hoc variable, plugin will fetch all columns of all tables of all databases (except system database) as tags. -So in dropdown menu will be options like `database.table.column`. If there are ENUM columns, -plugin will fetch their options and use them as tag values. - -Plugin will apply Ad-hoc filters to all queries on the dashboard if their settings `$database` and `$table` are the same -as Ad-hoc's `database.table` - -![ad-hoc](https://user-images.githubusercontent.com/2902918/37139531-ed67f222-22b6-11e8-8815-9268850f16fb.png) - -> There are no option to apply OR operator for multiple Ad-hoc filters - see grafana/grafana#10918 - -> There are no option to use IN operator for Ad-hoc filters due to Grafana limitations - -### Millisecond resolution - -If your data has timestamp with millisecond resolution, you can use Column:TimeStampMs type with UInt64 column as your time column. -Template variables like `$from`, `$to` or `$interval` will also have a unit of millisecond. - -### Time range dependent variable values - -If you wish to have your dashboard variables to depend on selected time range, you need to set such varable to refresh on time range change. -After doing this, you can use `$from` and `$to` in your queries. Notice that in this case `$from` and `$to` will always be -milliseconds. - -Example: - -``` -SELECT DISTINCT(UserName) FROM requests WHERE EventTime BETWEEN toDateTime($from/1000) AND toDateTime($to/1000) -``` - -### FAQ - -> Time series last point is not the real last point - -Plugin extrapolates last datapoint if timerange is `last N` to avoid displaying of constantly decreasing graphs -when timestamp in table is rounded to minute or bigger. -If it so then in 99% cases last datapoint will be much less than previous one, because last minute is not finished yet. -That's why plugin checks prev datapoints and tries to predict last datapoint value just as it was already written into db. - -> Why no alerts support? - -Alerts feature requires changes in `Grafana`'s backend, which can't be extended for now. `Grafana`'s maintainers are working on this feature. - - -### Contribute - -Since we developed this plugin only for internal needs we don't have some of Grafana's features: - -* Alerts (this feature requires additional changes at backend and can't be solved by js-plugin) -* Annotations -* Labels - -We know that code quality needs a tons of improvements and unit-tests. We will continue working on this. -If you have any idea for an improvement or found a bug do not hesitate to open an issue or submit a pull request. -We will appreciate any help from the community which will make working with such amazing products as ClickHouse and Grafana more convenient. - - -Plugin creation was inspired by great [grafana-sqldb-datasource](https://github.com/sraoss/grafana-sqldb-datasource) - -License -------- -MIT License, please see [LICENSE](https://github.com/Vertamedia/clickhouse-grafana/blob/master/LICENSE) for details. - diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/CHANGELOG.md b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/CHANGELOG.md deleted file mode 100644 index f786cc6..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/CHANGELOG.md +++ /dev/null @@ -1,108 +0,0 @@ -# 1.4.2 (2018-03-18) - -## Fixes - -* support `UNION ALL` statements -* proper format for `LIMIT N,M` construction (thx to @shankerwangmiao) -* update `Show Help` section with $unescape description - - -# 1.4.1 (2018-03-12) - -## New Features - -* $unescape - unescapes variable value by removing single quotes. Used for multiple-value string variables: "SELECT $unescape($column) FROM requests WHERE $unescape($column) = 5" - -## Fixes - -* labmda-operator `->` no more breaks while reformatting query - - -# 1.4.0 (2018-03-08) - -## New Features - -Ad-hoc filters support: -* If there is an Ad-hoc variable, plugin will fetch all columns of all tables of all databases (except system database) as tags. -So in dropdown menu will be options like `database.table.column` -* If there are ENUM columns, plugin will fetch their options and use them as tag values -* Plugin will apply Ad-hoc filters to all queries on the dashboard if their settings `$database` and `$table` are the same -as Ad-hoc's `database.table` -* There are no option to apply OR operator for multiple Ad-hoc filters - see grafana/grafana#10918 -* There are no option to use IN operator for Ad-hoc filters due to Grafana limitations - -# 1.3.1 (2018-02-12) - -## Fixes - -* support array indexing int AST - - -# 1.3.0 (2018-02-07) - -## New Features -* columns autocompletion in ace-editor - -# 1.2.7 (2018-01-05) - -## Fixes - -* properly format query with reserved names -* fix #31 - - -# 1.2.6 (2017-12-13) - -## Fixes -* allow rounding with `round` option both time filters: $from and $to - - -# 1.2.5 (2017-12-05) - -## Fixes -* support template variables with different `text` and `value` values [#27](https://github.com/Vertamedia/clickhouse-grafana/issues/27) -* fix visual glitches [#29](https://github.com/Vertamedia/clickhouse-grafana/issues/29) - - -# 1.2.4 (2017-11-22) - -## Fixes -* apply proper value formatting for table format - - -# 1.2.3 (2017-11-20) - -## Fixes -* commit generated files - - -# 1.2.2 (2017-11-20) - -## Fixes -* fix error with absent `getCollapsedText` [#24](https://github.com/Vertamedia/clickhouse-grafana/issues/24) -* suppress errors while parsing AST [#24](https://github.com/Vertamedia/clickhouse-grafana/issues/24) -* show generated SQL in textarea [#24](https://github.com/Vertamedia/clickhouse-grafana/issues/24) -* do not round timestamp after converting [#25](https://github.com/Vertamedia/clickhouse-grafana/issues/25) -* increase max-height of query editor - - -# 1.2.1 (2017-11-17) - -## Fixes -* add forgotten completions -* process NULL values [#19](https://github.com/Vertamedia/clickhouse-grafana/issues/19) -* sort by key value in `$columns` macro [#16](https://github.com/Vertamedia/clickhouse-grafana/issues/16) - - -# 1.2.0 (2017-11-15) - -## New Features -* Ace editor -* ClickHouse function completion (thx to https://github.com/smi2/tabix.ui) - - -# 1.1.0 (2017-11-13) - -## New Features -* Allow `UInt32` as Timestamp column [#15](https://github.com/Vertamedia/clickhouse-grafana/issues/15) -* Add `Format as Table` format [#17](https://github.com/Vertamedia/clickhouse-grafana/issues/17) \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/LICENSE b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/LICENSE deleted file mode 100644 index 5455e76..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2017 Vertamedia - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/README.md b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/README.md deleted file mode 100644 index 2837c6b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/README.md +++ /dev/null @@ -1,354 +0,0 @@ -# ClickHouse datasource for Grafana 4.6+ - -ClickHouse datasource plugin provides a support for [ClickHouse](https://clickhouse.yandex) as a backend database. - -### Quick start -Install from [grafana.net](https://grafana.net/plugins/vertamedia-clickhouse-datasource) - -OR - -Copy files to your [Grafana plugin directory](http://docs.grafana.org/plugins/installation/#grafana-plugin-directory). Restart Grafana, check datasources list at http://your.grafana.instance/datasources/new, choose ClickHouse option. - -### Features: - - * Access to CH via HTTP - * Query setup - * Raw SQL editor - * Query formatting - * Macros support - * Additional functions - * Templates - * Table view - * SingleStat view - * Ad-hoc filters - - -### Access to CH via HTTP -Page configuration is standard - -![settings](https://cloud.githubusercontent.com/assets/2902918/25473216/3ebd20fc-2b37-11e7-9743-fbbf2c5bdd3f.png) - - -There is a small feature - ClickHouse treats HTTP Basic Authentication credentials as a database user and will try to run queries using its name. - -#### [CHProxy](https://github.com/Vertamedia/chproxy) (optional) -Using of [CHProxy](https://github.com/Vertamedia/chproxy) will bring additional features: -* Easily setup `HTTPS` access to ClickHouse as shown [here](https://github.com/Vertamedia/chproxy#authorize-users-by-passwords-via-https) -to provide secure access. -* Limit concurrency and execution time for requests from `Grafana` as shown [here](https://github.com/Vertamedia/chproxy#spread-selects-from-reporting-apps-among-cluster-nodes) -to prevent `ClickHouse` overloading from `Grafana`. -* Protection against request bursts for dashboards with numerous graphs. `CHProxy` allows to queue requests and execute them sequentially. -To learn more - read about params `max_queue_size` and `max_queue_time` at [CHProxy](https://github.com/Vertamedia/chproxy) page. -* Response caching for the most frequent queries as shown [here](https://github.com/Vertamedia/chproxy#caching). -`Caching` will protect `ClickHouse` from excessive refreshes and will be optimal option for popular dashboards. ->> Hint - if you need to cache requests like `last 24h` where timestamp changes constantly then try to use `Round` option at `Raw Editor` - - -### Query setup - -Query setup interface: - -![query editor image](https://user-images.githubusercontent.com/2902918/32498037-2e9df438-c3d7-11e7-90de-73957c20cf6d.png) - -First row `FROM` contains two options: database and table. Table values depends on selected database. -Second row contains selectors for time filtering: -* Column:Date ([EventDate](https://clickhouse.yandex/reference_en.html#Date)) - is mandatory for [MergeTree](https://clickhouse.yandex/reference_en.html#MergeTree) tables -* Column:DateTime ([DateTime](https://clickhouse.yandex/reference_en.html#DateTime)) or Column:TimeStamp (UInt32). - -> Plugin will try to detect date columns automatically - -> Column:DateTime or Column:TimeStamp are required for time-based macros and functions, because all analytics is based on these values - -Button `Go to Query` is just a toggler to Raw SQL Editor - -### Raw SQL Editor - -Raw Editor allows custom SQL queries to be written: - -![raw editor image](https://user-images.githubusercontent.com/2902918/32843338-337f2efc-ca28-11e7-9bde-ec65faa3cdc9.png) - - -Raw Editor allows to type queries, get info about functions and macroses, format queries as Clickhouse do. -Under the Editor you can find a raw query (all macros and functions have already been replaced) which will be sent directly to ClickHouse. - -### Macros - -Plugin supports the following marcos: - -* $table - replaced with selected table name from Query Builder -* $timeCol - replaced with Date:Col value from Query Builder -* $dateTimeCol - replaced with Column:DateTime or Column:TimeStamp value from Query Builder -* $from - replaced with timestamp/1000 value of selected "Time Range:From" -* $to - replaced with timestamp/1000 value of selected "Time Range:To" -* $interval - replaced with selected "Group by time interval" value (as a number of seconds) -* $timeFilter - replaced with currently selected "Time Range". - Require Column:Date and Column:DateTime or Column:TimeStamp to be selected -* $timeSeries - replaced with special ClickHouse construction to convert results as time-series data. Use it as "SELECT $timeSeries...". -* $unescape - unescapes variable value by removing single quotes. Used for multiple-value string variables: "SELECT $unescape($column) FROM requests WHERE $unescape($column) = 5" -Require Column:DateTime or Column:TimeStamp to be selected - -A description of macros is available by typing their names in Raw Editor - -### Functions - -Functions are just templates of SQL queries and you can check the final query at [Raw SQL Editor mode](https://github.com/Vertamedia/clickhouse-grafana/blob/master/README.md#raw-sql-editor). -If some additional complexity is needed - just copy raw sql into Raw Editor and make according changes. Remember that macros are still available to use. - -There are some limits in function use because of poor query analysis: -* Column:Date and Column:DateTime or Column:TimeStamp must be set in Query Builder -* Query must begins from function name -* Only one function can be used per query - - - -Plugin supports the following functions: - -#### $rate(cols...) - converts query results as "change rate per interval" - -Example usage: -``` -$rate(countIf(Type = 200) AS good, countIf(Type != 200) AS bad) FROM requests -``` - -Query will be transformed into: -``` -SELECT - t, - good / runningDifference(t / 1000) AS goodRate, - bad / runningDifference(t / 1000) AS badRate -FROM -( - SELECT - (intDiv(toUInt32(EventTime), 60)) * 1000 AS t, - countIf(Type = 200) AS good, - countIf(Type != 200) AS bad - FROM requests - WHERE ((EventDate >= toDate(1482796747)) AND (EventDate <= toDate(1482853383))) AND ((EventTime >= toDateTime(1482796747)) AND (EventTime <= toDateTime(1482853383))) - GROUP BY t - ORDER BY t ASC -) -``` ---- - -#### $columns(key, value) - query values as array of [key, value], where key will be used as label - -Example usage: -``` -$columns(OSName, count(*) c) FROM requests -``` - -Query will be transformed into: -``` -SELECT - t, - groupArray((OSName, c)) AS groupArr -FROM -( - SELECT - (intDiv(toUInt32(EventTime), 60) * 60) * 1000 AS t, - OSName, - count(*) AS c - FROM requests - ANY INNER JOIN oses USING (OS) - WHERE ((EventDate >= toDate(1482796627)) AND (EventDate <= toDate(1482853383))) AND ((EventTime >= toDateTime(1482796627)) AND (EventTime <= toDateTime(1482853383))) - GROUP BY - t, - OSName - ORDER BY - t ASC, - OSName ASC -) -GROUP BY t -ORDER BY t ASC -``` - -This will help to build the next graph: - -![req_by_os image](https://cloud.githubusercontent.com/assets/2902918/21719222/2feabf30-d425-11e6-9042-9d290ef07884.png) - ---- - -#### $rateColumns(key, value) - is a combination of $columns and $rate - -Example usage: -``` -$rateColumns(OS, count(*) c) FROM requests -``` - -Query will be transformed into: -``` -SELECT - t, - arrayMap(lambda(tuple(a), (a.1, a.2 / runningDifference(t / 1000))), groupArr) -FROM -( - SELECT - t, - groupArray((OS, c)) AS groupArr - FROM - ( - SELECT - (intDiv(toUInt32(EventTime), 60) * 60) * 1000 AS t, - OS, - count(*) AS c - FROM requests - WHERE ((EventDate >= toDate(1482796867)) AND (EventDate <= toDate(1482853383))) AND ((EventTime >= toDateTime(1482796867)) AND (EventTime <= toDateTime(1482853383))) - GROUP BY - t, - OS - ORDER BY - t ASC, - OS ASC - ) - GROUP BY t - ORDER BY t ASC -) - -``` - -### Working with panels - -#### Piechart (https://grafana.com/plugins/grafana-piechart-panel) - -Remember that piechart plugin is not welcome for using in grafana - see https://grafana.com/blog/2015/12/04/friends-dont-let-friends-abuse-pie-charts - -![top5things](https://cloud.githubusercontent.com/assets/2902918/25392562/9fadb202-29e1-11e7-95ca-5b0d2921c592.png) - -To create "Top 5" diagram we will need two queries: one for 'Top 5' rows and one for 'Other' row. - -Top5: -``` -SELECT - 1, /* fake timestamp value */ - groupArray((UserName, Reqs)) -FROM -( - SELECT - UserName, - sum(Reqs) AS Reqs - FROM requests - GROUP BY UserName - ORDER BY Reqs desc - LIMIT 5 -) -``` - -Other: -``` -SELECT - 1, /* fake timestamp value */ - tuple(tuple('Other', sum(Reqs))) -FROM -( - SELECT - UserName, - sum(Reqs) AS Reqs - FROM requests - GROUP BY UserName - ORDER BY Reqs desc - LIMIT 5,10000000000000 /* select some ridiculous number after first 5 */ -) -``` - -### Table (https://grafana.com/plugins/table) - -There are no any tricks in displaying time-series data. But to display some summary we will need to fake timestamp data: - -``` -SELECT - rand() Time, /* fake timestamp value */ - UserName, - sum(Reqs) as Reqs -FROM requests -GROUP BY - UserName -ORDER BY - Reqs -``` - -Better to hide `Time` column at `Options` tab while editing panel - - -### Vertical histogram (https://grafana.com/plugins/graph) - -![vertical histogram](https://cloud.githubusercontent.com/assets/2902918/25392561/9f3777e0-29e1-11e7-8b23-2ea9ae46a029.png) - -To make vertical histogram from graph panel we will need to edit some settings: -* Display -> Draw Modes -> Bars -* Axes -> X-Axis -> Mode -> Series - -And use next query: -``` -$columns( - Size, - sum(Items) Items) -FROM some_table -``` - -// It is also possible to use query without macros - -### Ad-hoc filters - -If there is an Ad-hoc variable, plugin will fetch all columns of all tables of all databases (except system database) as tags. -So in dropdown menu will be options like `database.table.column`. If there are ENUM columns, -plugin will fetch their options and use them as tag values. - -Plugin will apply Ad-hoc filters to all queries on the dashboard if their settings `$database` and `$table` are the same -as Ad-hoc's `database.table` - -![ad-hoc](https://user-images.githubusercontent.com/2902918/37139531-ed67f222-22b6-11e8-8815-9268850f16fb.png) - -> There are no option to apply OR operator for multiple Ad-hoc filters - see grafana/grafana#10918 - -> There are no option to use IN operator for Ad-hoc filters due to Grafana limitations - -### Millisecond resolution - -If your data has timestamp with millisecond resolution, you can use Column:TimeStampMs type with UInt64 column as your time column. -Template variables like `$from`, `$to` or `$interval` will also have a unit of millisecond. - -### Time range dependent variable values - -If you wish to have your dashboard variables to depend on selected time range, you need to set such varable to refresh on time range change. -After doing this, you can use `$from` and `$to` in your queries. Notice that in this case `$from` and `$to` will always be -milliseconds. - -Example: - -``` -SELECT DISTINCT(UserName) FROM requests WHERE EventTime BETWEEN toDateTime($from/1000) AND toDateTime($to/1000) -``` - -### FAQ - -> Time series last point is not the real last point - -Plugin extrapolates last datapoint if timerange is `last N` to avoid displaying of constantly decreasing graphs -when timestamp in table is rounded to minute or bigger. -If it so then in 99% cases last datapoint will be much less than previous one, because last minute is not finished yet. -That's why plugin checks prev datapoints and tries to predict last datapoint value just as it was already written into db. - -> Why no alerts support? - -Alerts feature requires changes in `Grafana`'s backend, which can't be extended for now. `Grafana`'s maintainers are working on this feature. - - -### Contribute - -Since we developed this plugin only for internal needs we don't have some of Grafana's features: - -* Alerts (this feature requires additional changes at backend and can't be solved by js-plugin) -* Annotations -* Labels - -We know that code quality needs a tons of improvements and unit-tests. We will continue working on this. -If you have any idea for an improvement or found a bug do not hesitate to open an issue or submit a pull request. -We will appreciate any help from the community which will make working with such amazing products as ClickHouse and Grafana more convenient. - - -Plugin creation was inspired by great [grafana-sqldb-datasource](https://github.com/sraoss/grafana-sqldb-datasource) - -License -------- -MIT License, please see [LICENSE](https://github.com/Vertamedia/clickhouse-grafana/blob/master/LICENSE) for details. - diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/adhoc.d.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/adhoc.d.ts deleted file mode 100644 index 8979250..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/adhoc.d.ts +++ /dev/null @@ -1,8 +0,0 @@ -export default class AdhocCtrl { - tagKeys: any[]; - tagValues: any[]; - /** @ngInject */ - constructor(); - GetTagKeys(datasource: any): any; - GetTagValues(options: any): Promise; -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/adhoc.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/adhoc.js deleted file mode 100644 index 930f486..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/adhoc.js +++ /dev/null @@ -1,50 +0,0 @@ -System.register([], function(exports_1) { - var columnsQuery, regexEnum, AdhocCtrl; - return { - setters:[], - execute: function() { - columnsQuery = "SELECT database, table, name, type FROM system.columns where database != 'system' ORDER BY database, table"; - regexEnum = /'(?:[^']+|'')+'/gmi; - AdhocCtrl = (function () { - /** @ngInject */ - function AdhocCtrl() { - this.tagKeys = []; - this.tagValues = []; - } - AdhocCtrl.prototype.GetTagKeys = function (datasource) { - var self = this; - if (this.tagKeys.length > 0) { - return Promise.resolve(this.tagKeys); - } - return datasource.metricFindQuery(columnsQuery) - .then(function (response) { - response.forEach(function (item) { - var text = item.database + '.' + item.table + '.' + item.name; - var value = item.name; - self.tagKeys.push({ text: text, value: value }); - if (item.type.slice(0, 4) === 'Enum') { - var options = item.type.match(regexEnum); - if (options.length > 0) { - self.tagValues[text] = []; - options.forEach(function (o) { - self.tagValues[text].push({ text: o, value: o }); - }); - } - } - }); - return Promise.resolve(self.tagKeys); - }); - }; - AdhocCtrl.prototype.GetTagValues = function (options) { - if (this.tagValues.hasOwnProperty(options.key)) { - return Promise.resolve(this.tagValues[options.key]); - } - return Promise.resolve([]); - }; - return AdhocCtrl; - })(); - exports_1("default", AdhocCtrl); - } - } -}); -//# sourceMappingURL=adhoc.js.map \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/adhoc.js.map b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/adhoc.js.map deleted file mode 100644 index 54f2ac4..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/adhoc.js.map +++ /dev/null @@ -1 +0,0 @@ -{"version":3,"file":"adhoc.js","sourceRoot":"","sources":["adhoc.ts"],"names":["AdhocCtrl","AdhocCtrl.constructor","AdhocCtrl.GetTagKeys","AdhocCtrl.GetTagValues"],"mappings":";QAAM,YAAY,EACZ,SAAS;;;;YADT,YAAY,GAAG,4GAA4G,CAAC;YAC5H,SAAS,GAAG,oBAAoB,CAAC;YAEvC;gBAIIA,gBAAgBA;gBAChBA;oBACIC,IAAIA,CAACA,OAAOA,GAAGA,EAAEA,CAACA;oBAClBA,IAAIA,CAACA,SAASA,GAAGA,EAAEA,CAACA;gBACxBA,CAACA;gBAEDD,8BAAUA,GAAVA,UAAWA,UAAeA;oBACtBE,IAAIA,IAAIA,GAAGA,IAAIA,CAACA;oBAChBA,EAAEA,CAACA,CAACA,IAAIA,CAACA,OAAOA,CAACA,MAAMA,GAAGA,CAACA,CAACA,CAACA,CAACA;wBAC1BA,MAAMA,CAACA,OAAOA,CAACA,OAAOA,CAACA,IAAIA,CAACA,OAAOA,CAACA,CAACA;oBACzCA,CAACA;oBACDA,MAAMA,CAACA,UAAUA,CAACA,eAAeA,CAACA,YAAYA,CAACA;yBAC1CA,IAAIA,CAACA,UAASA,QAAQA;wBACnB,QAAQ,CAAC,OAAO,CAAC,UAAS,IAAI;4BAC3B,IAAI,IAAI,GAAG,IAAI,CAAC,QAAQ,GAAG,GAAG,GAAG,IAAI,CAAC,KAAK,GAAG,GAAG,GAAG,IAAI,CAAC,IAAI,CAAC;4BAC9D,IAAI,KAAK,GAAG,IAAI,CAAC,IAAI,CAAC;4BACtB,IAAI,CAAC,OAAO,CAAC,IAAI,CAAC,EAAC,IAAI,EAAE,IAAI,EAAE,KAAK,EAAE,KAAK,EAAC,CAAC,CAAC;4BAC9C,EAAE,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,KAAK,MAAM,CAAC,CAAC,CAAC;gCACnC,IAAI,OAAO,GAAG,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,CAAC;gCACzC,EAAE,CAAC,CAAC,OAAO,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC,CAAC;oCACrB,IAAI,CAAC,SAAS,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC;oCAC1B,OAAO,CAAC,OAAO,CAAC,UAAS,CAAC;wCACtB,IAAI,CAAC,SAAS,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,EAAC,IAAI,EAAE,CAAC,EAAE,KAAK,EAAE,CAAC,EAAC,CAAC,CAAA;oCAClD,CAAC,CAAC,CAAA;gCACN,CAAC;4BACL,CAAC;wBACJ,CAAC,CAAC,CAAC;wBACH,MAAM,CAAC,OAAO,CAAC,OAAO,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;oBACzC,CAAC,CAACA,CAACA;gBAEXA,CAACA;gBAEDF,gCAAYA,GAAZA,UAAaA,OAAOA;oBAChBG,EAAEA,CAACA,CAACA,IAAIA,CAACA,SAASA,CAACA,cAAcA,CAACA,OAAOA,CAACA,GAAGA,CAACA,CAACA,CAACA,CAACA;wBAC7CA,MAAMA,CAACA,OAAOA,CAACA,OAAOA,CAACA,IAAIA,CAACA,SAASA,CAACA,OAAOA,CAACA,GAAGA,CAACA,CAACA,CAACA;oBACxDA,CAACA;oBACDA,MAAMA,CAACA,OAAOA,CAACA,OAAOA,CAACA,EAAEA,CAACA,CAAAA;gBAC9BA,CAACA;gBACLH,gBAACA;YAADA,CAACA,AA1CD,IA0CC;YA1CD,+BA0CC,CAAA"} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/adhoc.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/adhoc.ts deleted file mode 100644 index 803578d..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/adhoc.ts +++ /dev/null @@ -1,46 +0,0 @@ -const columnsQuery = "SELECT database, table, name, type FROM system.columns where database != 'system' ORDER BY database, table"; -const regexEnum = /'(?:[^']+|'')+'/gmi; - -export default class AdhocCtrl { - tagKeys: any[]; - tagValues: any[]; - - /** @ngInject */ - constructor() { - this.tagKeys = []; - this.tagValues = []; - } - - GetTagKeys(datasource: any){ - var self = this; - if (this.tagKeys.length > 0) { - return Promise.resolve(this.tagKeys); - } - return datasource.metricFindQuery(columnsQuery) - .then(function(response){ - response.forEach(function(item){ - let text = item.database + '.' + item.table + '.' + item.name; - let value = item.name; - self.tagKeys.push({text: text, value: value}); - if (item.type.slice(0, 4) === 'Enum') { - let options = item.type.match(regexEnum); - if (options.length > 0) { - self.tagValues[text] = []; - options.forEach(function(o) { - self.tagValues[text].push({text: o, value: o}) - }) - } - } - }); - return Promise.resolve(self.tagKeys); - }); - - } - - GetTagValues(options){ - if (this.tagValues.hasOwnProperty(options.key)) { - return Promise.resolve(this.tagValues[options.key]); - } - return Promise.resolve([]) - } -} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/clickhouse-info.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/clickhouse-info.js deleted file mode 100644 index 3b2909b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/clickhouse-info.js +++ /dev/null @@ -1,1414 +0,0 @@ -"use strict"; - -System.register([], function (_export, _context) { - "use strict"; - - return { - setters: [], - execute: function () { - // jshint ignore: start - // jscs: disable - - ace.define("ace/mode/clickhouse_info", ["require", "exports", "module"], function (require, exports, module) { - "use strict"; - - var p = {}; - p.Keywords = ["SELECT", "CASE", "THEN", "DISTINCT", "INSERT", "UPDATE", "DELETE", "WHERE", "AND", "OR", "OFFSET", "HAVING", "AS", "GLOBAL", "FROM", "WHEN", "ELSE", "END", "TYPE", "LEFT", "RIGHT", "USING", "JOIN", "ON", "OUTER", "DESC", "ASC", "UNION", "CREATE", "TABLE", "PRIMARY", "KEY", "FOREIGN", "NOT", "REFERENCES", "DEFAULT", "INNER", "CROSS", "NATURAL", "DATABASE", "DROP", "GRANT", "ANY", "BETWEEN", "ATTACH", "DETACH", "CAST", "WITH", "BIT_AND", "BIT_OR", "BIT_XOR", "DESCRIBE", "OPTIMIZE", "PREWHERE", "TOTALS", "DATABASES", "PROCESSLIST", "SHOW", "LIMIT", "IF", "IF NOT EXISTS", "IF EXISTS", "GROUP", "ORDER", "BY", "IN", "FORMAT", "GROUP BY", "ORDER BY", "UNION ALL"]; - p.DataTypes = ["int", "numeric", "decimal", "date", "varchar", "char", "bigint", "float", "double", "bit", "binary", "text", "set", "timestamp", "uint8", "uint16", "uint32", "uint64", "int8", "int16", "int32", "int64", "float32", "float64", "datetime", "enum8", "enum16", "array", "tuple", "string"]; - p.Constants = ["true", "false", "NULL"]; - p.Funcs = ["IPv4NumToString", "IPv4NumToStringClassC", "IPv4StringToNum", "IPv6NumToString", "IPv6StringToNum", "MD5", "OSHierarchy", "OSIn", "OSToRoot", "SEHierarchy", "SEIn", "SEToRoot", "SHA1", "SHA224", "SHA256", "URLHash", "URLHierarchy", "URLPathHierarchy", "abs", "alphaTokens", "appendTrailingCharIfAbsent", "argMax", "argMin", "argMinIf", "arrayAll", "arrayCount", "arrayElement", "arrayEnumerate", "arrayEnumerateUniq", "arrayExists", "arrayFilter", "arrayFirst", "arrayJoin", "arrayMap", "arraySum", "avg", "avgIf", "bitAnd", "bitNot", "bitOr", "bitShiftLeft", "bitShiftRight", "bitXor", "bitmaskToArray", "bitmaskToList", "ceil", "cityHash64", "coalesce", "concat", "corr", "corrIf", "count", "countEqual", "countIf", "countIf", "covarPop", "covarPopIf", "covarSamp", "covarSampIf", "cutFragment", "cutQueryString", "cutQueryStringAndFragment", "cutToFirstSignificantSubdomain", "cutURLParameter", "cutWWW", "dictGetDate", "dictGetDateTime", "dictGetFloat32", "dictGetFloat64", "dictGetHierarchy", "dictGetInt16", "dictGetInt32", "dictGetInt64", "dictGetInt8", "dictGetString", "dictGetUInt16", "dictGetUInt32", "dictGetUInt64", "dictGetUInt8", "dictHas", "dictIsIn", "divide", "domainWithoutWWW", "empty", "extract", "extractAll", "extractURLParameter", "extractURLParameterNames", "extractURLParameters", "first", "firstSignificantSubdomain", "floor", "groupArray", "groupUniqArray", "halfMD5", "has", "hex", "ifnull", "indexOf", "intDiv", "intDivOrZero", "intHash32", "intHash64", "isnull", "last", "lcase", "length", "lengthUTF8", "like", "lower", "lowerUTF8", "match", "max", "median", "medianIf", "mid", "min", "minus", "modulo", "multiply", "negate", "notEmpty", "notLike", "now", "now", "nvl", "plus", "position", "positionUTF8", "quantile", "quantileDeterministic", "quantileExact", "quantileExactWeighted", "quantileTDigest", "quantileTiming", "quantileTimingWeighted", "quantiles", "quantilesIf", "quantilesTimingArrayIf", "quantilesTimingIf", "queryString", "queryStringAndFragment", "rand", "rand64", "range", "rank", "regionHierarchy", "regionIn", "regionToContinent", "regionToCountry", "regionToName", "regionToPopulation", "reinterpretAsDate", "reinterpretAsDateTime", "reinterpretAsFloat32", "reinterpretAsFloat64", "reinterpretAsInt16", "reinterpretAsInt32", "reinterpretAsInt64", "reinterpretAsInt8", "reinterpretAsString", "reinterpretAsUInt16", "reinterpretAsUInt32", "reinterpretAsUInt64", "reinterpretAsUInt8", "replaceAll", "replaceOne", "replaceRegexpOne", "reverse", "reverseUTF8", "round", "round", "roundAge", "roundDuration", "roundToExp2", "sequenceCount", "sequenceMatch", "sipHash128", "sipHash64", "splitByChar", "splitByString", "stddevPop", "stddevPopIf", "stddevSamp", "stddevSampIf", "substring", "substringUTF8", "sum", "sumArray", "sumArrayIf", "sumIf", "timeSlot", "toDate", "toDateTime", "toDayOfMonth", "toDayOfWeek", "toFixedString", "toFloat32", "toFloat64", "toHour", "toInt16", "toInt32", "toInt64", "toInt8", "toMinute", "toMonday", "toMonth", "toRelativeDayNum", "toRelativeHourNum", "toRelativeMinuteNum", "toRelativeMonthNum", "toRelativeSecondNum", "toRelativeWeekNum", "toRelativeYearNum", "toSecond", "toStartOfFiveMinute", "toStartOfHour", "toStartOfMinute", "toStartOfMonth", "toStartOfQuarter", "toStartOfYear", "toString", "toStringCutToZero", "toTime", "toUInt16", "toUInt32", "toUInt64", "toUInt8", "toYear", "today", "topLevelDomain", "ucase", "unhex", "uniq", "uniqArray", "uniqArrayIf", "uniqArrayIf", "uniqCombined", "uniqCombinedIf", "uniqExact", "uniqExactIf", "uniqHLL12", "uniqUpTo", "upper", "upperUTF8", "varPop", "varPopIf", "varSamp", "varSampIf", "yesterday"]; - p.Macros = ["$table", "$timeCol", "$dateTimeCol", "$from", "$to", "$interval", "$timeFilter", "$timeSeries", "$rate", "$columns", "$rateColumns", "$unescape"]; - p.KeywordsRe = function () { - return this.re(p.Keywords); - }; - p.ConstantsRe = function () { - return this.re(p.Constants); - }; - p.FunctionsRe = function () { - return this.re(p.Funcs).concat(this.re(p.Macros)); - }; - p.DataTypesRe = function () { - return this.re(p.DataTypes); - }; - p.FunctionsCompletions = function () { - return [{ - "name": "corr", - "def": "corr(x, y)", - "docText": "Calculates the Pearson correlation coefficient: Σ((x - x̅)(y - y̅)) / sqrt(Σ((x - x̅)2) * Σ((y - y̅)2))." - }, { - "name": "stddevPop", - "def": "stddevPop(x)", - "docText": "The result is equal to the square root of `varPop(x)`." - }, { - "name": "varPop", - "def": "varPop(x, y)", - "docText": "Calculates the value of `Σ((x - x̅)(y - y̅)) / n`." - }, { - "name": "argMin", - "def": "argMin(arg, val)", - "docText": "Calculates the `arg` value for a minimal `val` value. If there are several different values of `arg` for minimal values of `val`, the first of these values encountered is output." - }, { - "name": "quantileExactWeighted", - "def": "quantileExactWeighted(level)(x, weight)", - "docText": "Computes the level quantile exactly. In this case, each value is taken into account with the weight weight - as if it is present weight once. The arguments of the function can be considered as histograms, where the value \"x\" corresponds to the \"column\" of the histogram of the height weight, and the function itself can be considered as the summation of histograms.\n" + "\n" + "The algorithm is a hash table. Because of this, in case the transmitted values ​​are often repeated, the function consumes less RAM than the quantileExact. You can use this function instead of quantileExact, specifying the number 1 as the weight." - }, { - "name": "min", - "def": "min(v)", - "docText": "Minimal execution speed in rows per second. Checked on every data block when `timeout_before_checking_execution_speed` expires. If the execution speed is lower, an exception is thrown." - }, { - "name": "varSamp", - "def": "varSamp(x)", - "docText": "Calculates the amount Σ((x - x̅)2) / (n - 1), where 'n' is the sample size and 'x̅' is the average value of 'x'.\n" + "\n" + "It represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount.\n" + "\n" + "Returns Float64. If n <= 1, it returns +∞." - }, { - "name": "anyLast", - "def": "anyLast(x)", - "docText": "Selects the last value encountered. \n" + "\n" + " The result is just as indeterminate as for the `any` function." - }, { - "name": "argMax", - "def": "argMax(arg, val)", - "docText": "Calculates the `arg` value for a maximum `val` value. If there are several different values of `arg` for maximum values of `val`, the first of these values encountered is output." - }, { - "name": "count", - "def": "count()", - "docText": "Counts the number of rows. " - }, { - "name": "any", - "def": "any(x)", - "docText": "Selects the last value encountered. \n" + "\n" + " The result is just as indeterminate as for the `any` function." - }, { - "name": "avg", - "def": "avg(x)", - "docText": "Calculates the average. \n" + "\n" + " Only works for numbers. \n" + "\n" + " The result is always Float64." - }, { - "name": "sequenceMatch", - "def": "sequenceMatch(pattern)(time, cond1, cond2, ...)", - "docText": "Pattern matching for event chains.\n" + "\n" + "'pattern' is a string containing a pattern to match. The pattern is similar to a regular expression. 'time' is the event time of the DateTime type. 'cond1, cond2 ...' are from one to 32 arguments of the UInt8 type that indicate whether an event condition was met.\n" + "\n" + "The function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern. It returns UInt8 - 0 if the pattern isn't matched, or 1 if it matches.\n" + "\n" + "Example: sequenceMatch('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%') - whether there was a chain of events in which pages with the address in company were visited earlier than pages with the address in cart." - }, { - "name": "stddevSamp", - "def": "stddevSamp(x)", - "docText": "The result is equal to the square root of `varSamp(x)`." - }, { - "name": "medianDeterministic", - "def": "medianDeterministic(x, determinator)", - "docText": "This function works similarly to the `median` function - it approximates the median." - }, { - "name": "quantilesTimingWeighted", - "def": "quantilesTimingWeighted(level1, level2, ...)(x, weight)", - "docText": "Calculates the quantiles of all specified levels using the same algorithm as the `medianTimingWeighted` function." - }, { - "name": "uniq", - "def": "uniq(N)(x)", - "docText": "Calculates the number of different argument values, if it is less than or equal to N. \n" + "\n" + " If the number of different argument values is greater than N, it returns N + 1." - }, { - "name": "covarSamp", - "def": "covarSamp(x, y)", - "docText": "Calculates the value of `Σ((x - x̅)(y - y̅)) / (n - 1)`. " + "Returns Float64. If n <= 1, it returns +∞." - }, { - "name": "max", - "def": "max(v)", - "docText": "Maximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN." - }, { - "name": "quantileTDigest", - "def": "quantileTDigest(level)(x)", - "docText": "t-digest" - }, { - "name": "quantilesTiming", - "def": "quantilesTiming(level1, level2, ...)(x, weight)", - "docText": "Calculates the quantiles of all specified levels using the same algorithm as the `medianTimingWeighted` function." - }, { - "name": "quantiles", - "def": "quantiles(level1, level2, ...)(x, determinator)", - "docText": "Calculates the quantiles of all specified levels using the same algorithm as the `medianDeterministic` function." - }, { - "name": "quantile", - "def": "quantile(level1, level2, ...)(x, determinator)", - "docText": "Calculates the quantiles of all specified levels using the same algorithm as the `medianDeterministic` function." - }, { - "name": "groupArray", - "def": "groupArray(x)", - "docText": "Creates an array of argument values. \n" + "\n" + " Values can be added to the array in any (indeterminate) order. \n" + "\n" + " In some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY." - }, { - "name": "sum", - "def": "sum(x)", - "docText": "Calculates the sum. \n" + "\n" + " Only works for numbers." - }, { - "name": "median", - "def": "median(x, weight)", - "docText": "Differs from the `medianTiming` function in that it has a second argument - `weights`. Weight is a non-negative integer. \n" + "\n" + " The result is calculated as if the `x` value were passed `weight` number of times to the `medianTiming` function." - }, { - "name": "quantileTiming", - "def": "quantileTiming(level)(x, weight)", - "docText": "Calculates the quantile of `level` using the same algorithm as the `medianTimingWeighted` function." - }, { - "name": "quantileTimingWeighted", - "def": "quantileTimingWeighted(level)(x, weight)", - "docText": "Calculates the quantile of `level` using the same algorithm as the `medianTimingWeighted` function." - }, { - "name": "groupUniqArray", - "def": "groupUniqArray(x)", - "docText": "Creates an array from different argument values. Memory consumption is the same as for the `uniqExact` function." - }, { - "name": "uniqHLL12", - "def": "uniqHLL12(x)", - "docText": "Uses the HyperLogLog algorithm to approximate the number of different values of the argument. " - }, { - "name": "covarPop", - "def": "covarPop(x, y)", - "docText": "Calculates the value of `Σ((x - x̅)(y - y̅)) / n`." - }, { - "name": "sequenceCount", - "def": "sequenceCount(pattern)(time, cond1, cond2, ...)", - "docText": "sequenceMatch" - }, { - "name": "quantileDeterministic", - "def": "quantileDeterministic(level)(x, determinator)", - "docText": "Calculates the quantile of `level` using the same algorithm as the `medianDeterministic` function." - }, { - "name": "quantileExact", - "def": "quantileExact(level)(x, weight)", - "docText": "" - }, { - "name": "quantilesDeterministic", - "def": "quantilesDeterministic(level1, level2, ...)(x, determinator)", - "docText": "Calculates the quantiles of all specified levels using the same algorithm as the `medianDeterministic` function." - }, { - "name": "medianTiming", - "def": "medianTiming(x, weight)", - "docText": "Differs from the `medianTiming` function in that it has a second argument - `weights`." - }, { - "name": "medianTimingWeighted", - "def": "medianTimingWeighted(x, weight)", - "docText": "Differs from the `medianTiming` function in that it has a second argument - `weights`." - }, { - "name": "uniqExact", - "def": "uniqExact(x)", - "docText": "Calculates the number of different values of the argument, exactly. \n" + "\n" + " There is no reason to fear approximations, so it`s better to use the `uniq` function. \n" + "\n" + " You should use the `uniqExact` function if you definitely need an exact result. " - }, { - "name": "uniqCombined", - "def": "uniqCombined(x)", - "docText": "" - }, { - "name": "uniqCombinedIf", - "def": "uniqCombinedIf(x,v)", - "docText": "" - }, { - "name": "uniqUpTo", - "def": "uniqUpTo(N)(x)", - "docText": "Calculates the number of different argument values, if it is less than or equal to N. \n" + "\n" + " If the number of different argument values is greater than N, it returns N + 1." - }, { - "name": "substring", - "def": "substring(s, offset, length)", - "docText": "The same as `substring`, but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception)." - }, { - "name": "notLike", - "def": "notLike(haystack, pattern), haystack NOT LIKE pattern operator", - "docText": "The same thing as `like`, but negative." - }, { - "name": "hostName", - "def": "hostName()", - "docText": "Returns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server." - }, { - "name": "globalNotIn", - "def": "globalNotIn(v)", - "docText": "See the section `IN operators`." - }, { - "name": "or", - "def": "or(v)", - "docText": "The same thing as `max_temporary_columns`, but without counting constant columns. \n" + "\n" + " Note that constant columns are formed fairly often when running a query, but they require approximately zero computing resources." - }, { - "name": "extractAll", - "def": "extractAll(haystack, pattern)", - "docText": "Extracts all the fragments of a string using a regular expression. If `haystack` doesn`t match the `pattern` regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex." - }, { - "name": "arrayFirst", - "def": "arrayFirst(func, arr1, ...)", - "docText": "Returns the index of the first element in the `arr1` array for which `func` returns something other than 0." - }, { - "name": "arrayExists", - "def": "arrayExists([func,] arr1, ...)", - "docText": "Returns 1 if there is at least one element in `arr` for which `func` returns something other than 0. Otherwise, it returns 0." - }, { - "name": "arrayCount", - "def": "arrayCount([func,] arr1, ...)", - "docText": "Returns the number of elements in `arr` for which `func` returns something other than 0. If `func` is not specified, it returns the number of non-zero items in the array." - }, { - "name": "arrayMap", - "def": "arrayMap(func, arr1, ...)", - "docText": "Returns an array obtained from the original application of the `func` function to each element in the `arr` array." - }, { - "name": "now", - "def": "now(v)", - "docText": "If the parameter is true, INSERT operation will skip columns with unknown names from input. \n" + "\n" + " Otherwise, an exception will be generated, it is default behavior. \n" + "\n" + " The parameter works only for JSONEachRow and TSKV input formats." - }, { - "name": "intDiv", - "def": "intDiv(a, b)", - "docText": "Differs from `intDiv` in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one." - }, { - "name": "topLevelDomain", - "def": "topLevelDomain(v)", - "docText": "Selects the top-level domain. Example: .ru." - }, { - "name": "intHash32", - "def": "intHash32(v)", - "docText": "Calculates a 32-bit hash code from any type of integer. \n" + "\n" + " This is a relatively fast non-cryptographic hash function of average quality for numbers." - }, { - "name": "replaceOne", - "def": "replaceOne(haystack, pattern, replacement)", - "docText": "Replaces the first occurrence, if it exists, of the `pattern` substring in `haystack` with the `replacement` substring. \n" + "\n" + " Hereafter, `pattern` and `replacement` must be constants." - }, { - "name": "cityHash64", - "def": "cityHash64(v)", - "docText": "Calculates CityHash64 from a string or a similar hash function for any number of any type of arguments. \n" + "\n" + " For String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality" - }, { - "name": "OSToRoot", - "def": "OSToRoot(v)", - "docText": "Accepts a UInt8 number - the ID of the operating system from the Yandex.Metrica dictionary. If any OS matches the passed number, it returns a UInt8 number - the ID of the corresponding root OS (for example, it converts Windows Vista to Windows). Otherwise, returns 0." - }, { - "name": "sipHash128", - "def": "sipHash128(v)", - "docText": "Calculates SipHash from a string. \n" + "\n" + " Accepts a String-type argument. Returns FixedString(16). \n" + "\n" + " Differs from sipHash64 in that the final xor-folding state is only done up to 128 bits." - }, { - "name": "SHA1", - "def": "SHA1(v)", - "docText": "Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32)." - }, { - "name": "asin", - "def": "asin(x)", - "docText": "The arc sine." - }, { - "name": "SHA256", - "def": "SHA256(v)", - "docText": "Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32)." - }, { - "name": "MD5", - "def": "MD5(v)", - "docText": "Calculates the MD5 from a string and returns the resulting set of bytes as FixedString(16)." - }, { - "name": "bitmaskToList", - "def": "bitmaskToList(num)", - "docText": "Accepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order." - }, { - "name": "array", - "def": "array(v)", - "docText": "The -`Array` suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the `Array(T)` type (arrays) instead of `T` type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. " - }, { - "name": "dictGetStringOrDefault", - "def": "dictGetStringOrDefault(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "greaterOrEquals", - "def": "greaterOrEquals(v)", - "docText": "" - }, { - "name": "e", - "def": "e(v)", - "docText": "What to do when the amount of data exceeds one of the limits: `throw` or `break`. By default, throw." - }, { - "name": "runningDifference", - "def": "runningDifference(x)", - "docText": "Calculates the difference between consecutive values in the data block. \n" + "\n" + " Result of the function depends on the order of the data in the blocks. \n" + "\n" + " It works only inside of the each processed block of data. Data splitting in the blocks is not explicitly controlled by the user. " - }, { - "name": "not", - "def": "not(v)", - "docText": "See the section `IN operators`." - }, { - "name": "intHash64", - "def": "intHash64(v)", - "docText": "Calculates a 64-bit hash code from any type of integer. \n" + "\n" + " It works faster than intHash32. Average quality." - }, { - "name": "acos", - "def": "acos(x)", - "docText": "The arc cosine." - }, { - "name": "dictGetString", - "def": "dictGetString(v)", - "docText": "" - }, { - "name": "and", - "def": "and(x, determinator)", - "docText": "This function works similarly to the `median` function - it approximates the median. However, in contrast to `median`, the result is deterministic and does not depend on the order of query execution. \n" + "\n" + " To achieve this, the function takes a second argument - the `determinator`. " - }, { - "name": "dictGetDate", - "def": "dictGetDate(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "dictGetFloat32", - "def": "dictGetFloat32(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "dictGetInt8", - "def": "dictGetInt8(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "dictGetUInt32", - "def": "dictGetUInt32(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "OSIn", - "def": "OSIn(lhs, rhs)", - "docText": "Checks whether the `lhs` operating system belongs to the `rhs` operating system." - }, { - "name": "arrayFirstIndex", - "def": "arrayFirstIndex(func, arr1, ...)", - "docText": "Returns the index of the first element in the `arr1` array for which `func` returns something other than 0." - }, { - "name": "ceil", - "def": "ceil(x[, N])", - "docText": "Returns the smallest round number that is greater than or equal to `x`. In every other way, it is the same as the `floor` function (see above)." - }, { - "name": "fragment", - "def": "fragment(v)", - "docText": "Removes the query-string and fragment identifier. The question mark and number sign are also removed." - }, { - "name": "dictGetUInt8", - "def": "dictGetUInt8(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "dictHas", - "def": "dictHas(v)", - "docText": "dictHas('dict_name', id) check the presence of a key in the dictionary. Returns a value of type UInt8, equal to 0, if there is no key and 1 if there is a key." - }, { - "name": "arraySum", - "def": "arraySum([func,] arr1, ...)", - "docText": "Returns the sum of the `func` values. If the function is omitted, it just returns the sum of the array elements." - }, { - "name": "emptyArrayDateTime", - "def": "emptyArrayDateTime(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "intDivOrZero", - "def": "intDivOrZero(a, b)", - "docText": "Differs from `intDiv` in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one." - }, { - "name": "SEHierarchy", - "def": "SEHierarchy(v)", - "docText": "Accepts a UInt8 number - the ID of the search engine from the Yandex.Metrica dictionary. Returns an array with a hierarchy of search engines. Similar to the `regionHierarchy` function." - }, { - "name": "regionToContinent", - "def": "regionToContinent(id[, geobase])", - "docText": "Converts a region to a continent. In every other way, this function is the same as `regionToCity`. \n" + "\n" + " Example: `regionToContinent(toUInt32(213)) = 10001` converts Moscow (213) to Eurasia (10001)." - }, { - "name": "dictGetInt32", - "def": "dictGetInt32(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "toInt8", - "def": "toInt8(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "materialize", - "def": "materialize(x)", - "docText": "Turns a constant into a full column containing just one value. \n" + "\n" + " In ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior." - }, { - "name": "regionToCountry", - "def": "regionToCountry(id[, geobase])", - "docText": "Converts a region to a country. In every other way, this function is the same as `regionToCity`. \n" + "\n" + " Example: `regionToCountry(toUInt32(213)) = 225` converts Moscow (213) to Russia (225)." - }, { - "name": "dictGetDateTime", - "def": "dictGetDateTime(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "xor", - "def": "xor(v)", - "docText": "" - }, { - "name": "regionToCity", - "def": "regionToCity(id[, geobase])", - "docText": "Accepts a UInt32 number - the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0." - }, { - "name": "dictGetFloat64", - "def": "dictGetFloat64(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "timeSlot", - "def": "timeSlot(StartTime, Duration)", - "docText": "For a time interval starting at `StartTime` and continuing for `Duration` seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour." - }, { - "name": "toTime", - "def": "toTime(v)", - "docText": "Converts a date with time to the date of the start of the Unix Epoch, while preserving the time." - }, { - "name": "log2", - "def": "log2(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument." - }, { - "name": "toRelativeHourNum", - "def": "toRelativeHourNum(v)", - "docText": "Converts a date with time or date to the number of the hour, starting from a certain fixed point in the past." - }, { - "name": "toRelativeDayNum", - "def": "toRelativeDayNum(v)", - "docText": "Converts a date with time or date to the number of the day, starting from a certain fixed point in the past." - }, { - "name": "toRelativeWeekNum", - "def": "toRelativeWeekNum(v)", - "docText": "Converts a date with time or date to the number of the week, starting from a certain fixed point in the past." - }, { - "name": "splitByString", - "def": "splitByString(separator, s)", - "docText": "The same as above, but it uses a string of multiple characters as the separator. The string must be non-empty." - }, { - "name": "currentDatabase", - "def": "currentDatabase()", - "docText": "Returns the name of the current database. \n" + "\n" + " You can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database." - }, { - "name": "toRelativeMonthNum", - "def": "toRelativeMonthNum(v)", - "docText": "Converts a date with time or date to the number of the month, starting from a certain fixed point in the past." - }, { - "name": "visibleWidth", - "def": "visibleWidth(x)", - "docText": "Calculates the approximate width when outputting values to the console in text format (tab-separated). This function is used by the system for implementing Pretty formats." - }, { - "name": "bitShiftRight", - "def": "bitShiftRight(a, b)", - "docText": "" - }, { - "name": "toRelativeYearNum", - "def": "toRelativeYearNum(v)", - "docText": "Converts a date with time or date to the number of the year, starting from a certain fixed point in the past." - }, { - "name": "toStartOfHour", - "def": "toStartOfHour(v)", - "docText": "Rounds down a date with time to the start of the hour." - }, { - "name": "halfMD5", - "def": "halfMD5(v)", - "docText": "Calculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian." - }, { - "name": "toStartOfFiveMinute", - "def": "toStartOfFiveMinute(v)", - "docText": "Rounds down a date with time to the start of the 5 minute (00:00, 00:05, 00:10...)." - }, { - "name": "toUInt16OrZero", - "def": "toUInt16OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument." - }, { - "name": "toMonday", - "def": "toMonday(v)", - "docText": "Rounds down a date or date with time to the nearest Monday. \n" + "\n" + " Returns the date." - }, { - "name": "IPv6NumToString", - "def": "IPv6NumToString(x)", - "docText": "Accepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format. " - }, { - "name": "indexOf", - "def": "indexOf(arr, x)", - "docText": "Returns the index of the `x` element (starting from 1) if it is in the array, or 0 if it is not." - }, { - "name": "today", - "def": "today(v)", - "docText": "Accepts zero arguments and returns the current date at one of the moments of request execution. \n" + "\n" + " The same as `toDate(now())`." - }, { - "name": "emptyArrayToSingle", - "def": "emptyArrayToSingle(v)", - "docText": "Accepts an empty array as argument and returns an array of one element equal to the default value." - }, { - "name": "sleep", - "def": "sleep(seconds)", - "docText": "Sleeps `seconds` seconds on each data block. You can specify an integer or a floating-point number." - }, { - "name": "extract", - "def": "extract(params, name)", - "docText": "Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string. Examples: " - }, { - "name": "emptyArrayInt8", - "def": "emptyArrayInt8(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "regionToName", - "def": "regionToName(id[, lang])", - "docText": "Accepts a UInt32 number - the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language `ru` is used. " - }, { - "name": "concat", - "def": "concat(arr[, separator])", - "docText": "Concatenates strings from the array elements, using `separator` as the separator. \n" + "\n" + " `separator` is a string constant, an optional parameter. By default it is an empty string. \n" + "\n" + " Returns a string." - }, { - "name": "convertCharset", - "def": "convertCharset(s, from, to)", - "docText": "Returns a string with the data `s` (encoded as `from` charset) that was converted to the `to` charset." - }, { - "name": "toMonth", - "def": "toMonth(v)", - "docText": "Converts a date or date with time to a UInt8 number containing the month number (1-12)." - }, { - "name": "IPv6StringToNum", - "def": "IPv6StringToNum(s)", - "docText": "The reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes. \n" + "\n" + " HEX can be uppercase or lowercase." - }, { - "name": "emptyArrayString", - "def": "emptyArrayString(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "uptime", - "def": "uptime()", - "docText": "Returns server's uptime in seconds." - }, { - "name": "blockSize", - "def": "blockSize()", - "docText": "Gets the size of the block. \n" + "\n" + " In ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for." - }, { - "name": "toInt64OrZero", - "def": "toInt64OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "pathFull", - "def": "pathFull(v)", - "docText": "Selects the path, but including query-string and fragment. Example: /top/news.html?page=2#comments" - }, { - "name": "emptyArrayDate", - "def": "emptyArrayDate(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "emptyArrayInt64", - "def": "emptyArrayInt64(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "toInt32OrZero", - "def": "toInt32OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "greater", - "def": "greater(v)", - "docText": "" - }, { - "name": "emptyArrayInt32", - "def": "emptyArrayInt32(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "toString", - "def": "toString(str)", - "docText": "Accepts a FixedString(16) value containing the UUID in the binary format. Returns a readable string containing the UUID in the text format." - }, { - "name": "greatest", - "def": "greatest(a, b)", - "docText": "Returns the greatest element of a and b." - }, { - "name": "emptyArrayUInt64", - "def": "emptyArrayUInt64(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "emptyArrayUInt32", - "def": "emptyArrayUInt32(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "formatReadableSize", - "def": "formatReadableSize(x)", - "docText": "Gets a size (number of bytes). Returns a string that contains rounded size with the suffix (KiB, MiB etc.).\n" + "\n" + "Example:\n" + "\n" + "SELECT\n" + " arrayJoin([1, 1024, 1024*1024, 192851925]) AS f_bytes,\n" + " formatReadableSize(filesize_bytes) AS filesize\n" + "┌─f_bytes-─┬─filesize───┐\n" + "│ 1 │ 1.00 B │\n" + "│ 1024 │ 1.00 KiB │\n" + "│ 1048576 │ 1.00 MiB │\n" + "│192851925 │ 183.92 MiB │\n" + "└──────────┴────────────┘\n" - }, { - "name": "toInt16OrZero", - "def": "toInt16OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "emptyArrayUInt8", - "def": "emptyArrayUInt8(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "protocol", - "def": "protocol(v)", - "docText": "Selects the protocol. Examples: http, ftp, mailto, magnet..." - }, { - "name": "regionToPopulation", - "def": "regionToPopulation(id[, geobase])", - "docText": "Gets the population for a region. \n" + "\n" + " The population can be recorded in files with the geobase. See the section `External dictionaries`. \n" + "\n" + " If the population is not recorded for the region, it returns 0. \n" + "\n" + " In the Yandex geobase, the population might be recorded for child regions, but not for parent regions." - }, { - "name": "notIn", - "def": "notIn(v)", - "docText": "See the section `IN operators`." - }, { - "name": "position", - "def": "position(haystack, needle)", - "docText": "The same as `position`, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception). \n" + "\n" + " There's also positionCaseInsensitiveUTF8 function." - }, { - "name": "arrayElement", - "def": "arrayElement(arr, n), arr[n] operator", - "docText": "Get the element with the index `n` from the array `arr`. \n" + "\n" + " `n` should be any integer type. \n" + "\n" + " Indexes in an array begin from one. \n" + "\n" + " Negative indexes are supported - in this case, it selects the corresponding element numbered from the end. " - }, { - "name": "toStringCutToZero", - "def": "toStringCutToZero(s)", - "docText": "Accepts a String or FixedString argument. Returns a String that is cut to a first null byte occurrence." - }, { - "name": "log", - "def": "log(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument." - }, { - "name": "SEIn", - "def": "SEIn(lhs, rhs)", - "docText": "Checks whether the `lhs` search engine belongs to the `rhs` search engine." - }, { - "name": "replicate", - "def": "replicate(v)", - "docText": "Create a MergeTree table with a different name. Move all the data from the directory with the ReplicatedMergeTree table data to the new table`s data directory." - }, { - "name": "sipHash64", - "def": "sipHash64(v)", - "docText": "Calculates SipHash from a string. \n" + "\n" + " Accepts a String-type argument. Returns UInt64. \n" + "\n" + " SipHash is a cryptographic hash function. It works at least three times faster than MD5. For more information, see https://131002.net/siphash/" - }, { - "name": "emptyArrayUInt16", - "def": "emptyArrayUInt16(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "hex", - "def": "hex(str)", - "docText": "Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits doesn`t have to be even." - }, { - "name": "regionToDistrict", - "def": "regionToDistrict(id[, geobase])", - "docText": "Converts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as `regionToCity`. " - }, { - "name": "arrayFilter", - "def": "arrayFilter(func, arr1, ...)", - "docText": "Returns an array containing only the elements in `arr1` for which `func` returns something other than 0. \n" + "\n" + " Examples: \n" + "\n" + " SELECT arrayFilter(x -> x LIKE `%World%`, [`Hello`, `abc World`]) AS res " - }, { - "name": "toStartOfQuarter", - "def": "toStartOfQuarter(v)", - "docText": "Rounds down a date or date with time to the first day of the quarter. \n" + "\n" + " The first day of the quarter is either 1 January, 1 April, 1 July, or 1 October. Returns the date." - }, { - "name": "divide", - "def": "divide(a, b), a / b operator", - "docText": "Calculates the quotient of the numbers. The result type is always a floating-point type. \n" + "\n" + " It is not integer division. For integer division, use the `intDiv` function. \n" + "\n" + " When dividing by zero you get `inf`, `-inf`, or `nan`." - }, { - "name": "reverseUTF8", - "def": "reverseUTF8(v)", - "docText": "Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn`t throw an exception)." - }, { - "name": "toDate", - "def": "toDate(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "emptyArrayFloat64", - "def": "emptyArrayFloat64(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "abs", - "def": "abs(s, c)", - "docText": "If the `s` string is non-empty and does not contain the `c` character at the end, it appends the `c` character to the end." - }, { - "name": "yesterday", - "def": "yesterday(v)", - "docText": "Accepts zero arguments and returns yesterday`s date at one of the moments of request execution. \n" + "\n" + " The same as `today() - 1`." - }, { - "name": "toMinute", - "def": "toMinute(v)", - "docText": "Converts a date with time to a UInt8 number containing the number of the minute of the hour (0-59)." - }, { - "name": "bitXor", - "def": "bitXor(a, b)", - "docText": "" - }, { - "name": "minus", - "def": "minus(a, b), a - b operator", - "docText": "Calculates the difference. The result is always signed. \n" + "\n" + " You can also calculate whole numbers from a date or date with time. The idea is the same - see above for `plus`." - }, { - "name": "toDateTime", - "def": "toDateTime(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "modulo", - "def": "modulo(a, b), a % b operator", - "docText": "Calculates the remainder after division. \n" + "\n" + " If arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion. The remainder is taken in the same sense as in C++. Truncated division is used for negative numbers. \n" + "\n" + " An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one." - }, { - "name": "bitmaskToArray", - "def": "bitmaskToArray(num)", - "docText": "Accepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order." - }, { - "name": "negate", - "def": "negate(a), -a operator", - "docText": "Calculates a number with the reverse sign. The result is always signed." - }, { - "name": "emptyArrayFloat32", - "def": "emptyArrayFloat32(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "range", - "def": "range(N)", - "docText": "Returns an array of numbers from 0 to N-1. \n" + "\n" + " Just in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block." - }, { - "name": "arrayAll", - "def": "arrayAll([func,] arr1, ...)", - "docText": "Returns 1 if `func` returns something other than 0 for all the elements in `arr`. Otherwise, it returns 0." - }, { - "name": "toInt32", - "def": "toInt32(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "unhex", - "def": "unhex(str)", - "docText": "Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits doesn`t have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn`t thrown). \n" + "\n" + " If you want to convert the result to a number, you can use the functions `reverse` and `reinterpretAsType`." - }, { - "name": "toFloat64", - "def": "toFloat64(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "bitAnd", - "def": "bitAnd(a, b)", - "docText": "" - }, { - "name": "toStartOfYear", - "def": "toStartOfYear(v)", - "docText": "Rounds down a date or date with time to the first day of the year. \n" + "\n" + " Returns the date." - }, { - "name": "bitOr", - "def": "bitOr(a, b)", - "docText": "" - }, { - "name": "SEToRoot", - "def": "SEToRoot(v)", - "docText": "Accepts a UInt8 number - the ID of the search engine from the Yandex.Metrica dictionary. If any search engine matches the passed number, it returns a UInt8 number - the ID of the corresponding root search engine (for example, it converts Yandex.Images to Yandex). Otherwise, returns 0." - }, { - "name": "toRelativeMinuteNum", - "def": "toRelativeMinuteNum(v)", - "docText": "Converts a date with time or date to the number of the minute, starting from a certain fixed point in the past." - }, { - "name": "visitParamExtractInt", - "def": "visitParamExtractInt(params, name)", - "docText": "The same as for Int64." - }, { - "name": "emptyArrayInt16", - "def": "emptyArrayInt16(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, { - "name": "visitParamExtractString", - "def": "visitParamExtractString(params, name)", - "docText": "Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string. " - }, { - "name": "arrayEnumerateUniq", - "def": "arrayEnumerateUniq(arr, ...)", - "docText": "Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value. " - }, { - "name": "visitParamExtractUInt", - "def": "visitParamExtractUInt(params, name)", - "docText": "Parses UInt64 from the value of the field named `name`. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn`t exist, or it exists but doesn`t contain a number, it returns 0." - }, { - "name": "toTypeName", - "def": "toTypeName(x)", - "docText": "Gets the type name. Returns a string containing the type name of the passed argument." - }, { - "name": "empty", - "def": "empty(v)", - "docText": "Accepts an empty array as argument and returns an array of one element equal to the default value." - }, { - "name": "multiply", - "def": "multiply(a, b), a * b operator", - "docText": "Calculates the product of the numbers." - }, { - "name": "has", - "def": "has('database', 'table', 'column')", - "docText": "Accepts constant String columns - database name, table name and column name. Returns constant UInt8 value, equal to 1 if column exists, otherwise 0.\n" + "\n" + " If table doesn't exist than exception is thrown. \n" + "\n" + " For elements of nested data structure function checks existence of column. For nested data structure 0 is returned." - }, { - "name": "bitNot", - "def": "bitNot(a)", - "docText": "" - }, { - "name": "lessOrEquals", - "def": "lessOrEquals(v)", - "docText": "greaterOrEquals, >= operator" - }, { - "name": "reinterpretAsInt64", - "def": "reinterpretAsInt64(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, { - "name": "IPv4NumToString", - "def": "IPv4NumToString(num)", - "docText": "Similar to IPv4NumToString, but using `xxx` instead of the last octet. " - }, { - "name": "bitShiftLeft", - "def": "bitShiftLeft(a, b)", - "docText": "" - }, { - "name": "dictGetInt16", - "def": "dictGetInt16(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "toUInt32OrZero", - "def": "toUInt32OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "regionIn", - "def": "regionIn(lhs, rhs[, geobase])", - "docText": "Checks whether a `lhs` region belongs to a `rhs` region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn`t belong. \n" + "\n" + " The relationship is reflexive - any region also belongs to itself." - }, { - "name": "reinterpretAsUInt8", - "def": "reinterpretAsUInt8(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, { - "name": "dictIsIn", - "def": "dictIsIn(v)", - "docText": "`dictIsIn(`dict_name`, child_id, ancestor_id)` \n" + "\n" + " - For the `dict_name` hierarchical dictionary, finds out whether the `child_id` key is located inside `ancestor_id` (or matches `ancestor_id`). Returns UInt8." - }, { - "name": "toSecond", - "def": "toSecond(v)", - "docText": "Converts a date with time to a UInt8 number containing the number of the second in the minute (0-59). \n" + "\n" + " Leap seconds are not accounted for." - }, { - "name": "least", - "def": "least(a, b)", - "docText": "Returns the least element of a and b." - }, { - "name": "countEqual", - "def": "countEqual(arr, x)", - "docText": "Returns the number of elements in the array equal to `x`. Equivalent to arrayCount(elem -> elem = x, arr)." - }, { - "name": "IPv4StringToNum", - "def": "IPv4StringToNum(s)", - "docText": "The reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0." - }, { - "name": "replaceRegexpAll", - "def": "replaceRegexpAll(haystack, pattern, replacement)", - "docText": "This does the same thing, but replaces all the occurrences" - }, { - "name": "SHA224", - "def": "SHA224(v)", - "docText": "Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32). \n" + "\n" + " The function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million). " - }, { - "name": "URLHash", - "def": "URLHash(url[, N])", - "docText": "A fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization. \n" + "\n" + " URLHash(s) - Calculates a hash from a string without one of the trailing symbols /,? or # at the end, if present" - }, { - "name": "equals", - "def": "equals(v)", - "docText": "

greaterOrEquals, >= operator

" - }, { - "name": "plus", - "def": "plus(a, b), a + b operator", - "docText": "Calculates the sum of the numbers. \n" + "\n" + " You can also add whole numbers with a date or date and time. In the case of a date, adding a whole number means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds." - }, { - "name": "less", - "def": "less(v)", - "docText": "

greaterOrEquals, >= operator

" - }, { - "name": "regionHierarchy", - "def": "regionHierarchy(id[, geobase])", - "docText": "Accepts a UInt32 number - the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain. \n" + "\n" + " Example: `regionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000]`." - }, { - "name": "toUInt64OrZero", - "def": "toUInt64OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "rowNumberInAllBlocks", - "def": "rowNumberInAllBlocks()", - "docText": "Returns an incremental row number within all blocks that were processed by this function." - }, { - "name": "toDayOfWeek", - "def": "toDayOfWeek(v)", - "docText": "Converts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7)." - }, { - "name": "bar", - "def": "bar(v)", - "docText": "Allows building a unicode-art diagram. \n" + "\n" + " bar(x, min, max, width) - Draws a band with a width proportional to (x - min) and equal to `width` characters when x" - }, { - "name": "if", - "def": "if(v)", - "docText": "The suffix -`If` can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument - a condition (Uint8 type). " - }, { - "name": "regionToArea", - "def": "regionToArea(id[, geobase])", - "docText": "Converts a region to an area (type 5 in the geobase). In every other way, this function is the same as `regionToCity`." - }, { - "name": "dictGetUInt16", - "def": "dictGetUInt16(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "toUInt8", - "def": "toUInt8(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "toUInt16", - "def": "toUInt16(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "path", - "def": "path(URL)", - "docText": "- The same thing, but without the protocol and host in the result. The / element (root) is not included." - }, { - "name": "extractURLParameters", - "def": "extractURLParameters(URL)", - "docText": "- Gets an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way." - }, { - "name": "OSHierarchy", - "def": "OSHierarchy(v)", - "docText": "Accepts a UInt8 number - the ID of the operating system from the Yandex.Metrica dictionary. Returns an array with a hierarchy of operating systems. Similar to the `regionHierarchy` function." - }, { - "name": "cutQueryStringAndFragment", - "def": "cutQueryStringAndFragment(v)", - "docText": "Removes the query-string and fragment identifier. The question mark and number sign are also removed." - }, { - "name": "timeSlots", - "def": "timeSlots(StartTime, Duration)", - "docText": "For a time interval starting at `StartTime` and continuing for `Duration` seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour. \n" + "\n" + " For example, `timeSlots(toDateTime(`2012-01-01 12:20:00`), toUInt32(600)) = [toDateTime(`2012-01-01 12:00:00`), toDateTime(`2012-01-01 12:30:00`)]`. \n" + "\n" + " This is necessary for searching for pageviews in the corresponding session." - }, { - "name": "toUInt32", - "def": "toUInt32(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "toStartOfMinute", - "def": "toStartOfMinute(v)", - "docText": "Rounds down a date with time to the start of the minute." - }, { - "name": "version", - "def": "version()", - "docText": "Returns server's version as a string." - }, { - "name": "toUInt64", - "def": "toUInt64(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "toInt16", - "def": "toInt16(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "reinterpretAsInt16", - "def": "reinterpretAsInt16(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, { - "name": "toInt64", - "def": "toInt64(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "toFixedString", - "def": "toFixedString(s, N)", - "docText": "Converts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant. If the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown." - }, { - "name": "toFloat32", - "def": "toFloat32(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "dictGetHierarchy", - "def": "dictGetHierarchy(v)", - "docText": "`dictGetHierarchy(`dict_name`, id)` \n" + "\n" + " - For the `dict_name` hierarchical dictionary, returns an array of dictionary keys starting from `id` and continuing along the chain of parent elements. Returns Array(UInt64)." - }, { - "name": "dictGetInt64", - "def": "dictGetInt64(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "CAST", - "def": "CAST(x, t)", - "docText": "Casts `x` to the `t` data type. \n" + "\n" + " The syntax `CAST(x AS t)` is also supported. \n" - }, { - "name": "toRelativeSecondNum", - "def": "toRelativeSecondNum(v)", - "docText": "Converts a date with time or date to the number of the second, starting from a certain fixed point in the past." - }, { - "name": "toUInt8OrZero", - "def": "toUInt8OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "toStartOfMonth", - "def": "toStartOfMonth(v)", - "docText": "Rounds down a date or date with time to the first day of the month. \n" + "\n" + " Returns the date." - }, { - "name": "rand64", - "def": "rand64(v)", - "docText": "Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers. \n" + "\n" + " Uses a linear congruential generator." - }, { - "name": "toInt8OrZero", - "def": "toInt8OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "toFloat32OrZero", - "def": "toFloat32OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "toYear", - "def": "toYear(v)", - "docText": "Converts a date or date with time to a UInt16 number containing the year number (AD)." - }, { - "name": "atan", - "def": "atan(x)", - "docText": "The arc tangent." - }, { - "name": "toFloat64OrZero", - "def": "toFloat64OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, { - "name": "arrayEnumerate", - "def": "arrayEnumerate(arr, ...)", - "docText": "Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value." - }, { - "name": "toDayOfMonth", - "def": "toDayOfMonth(v)", - "docText": "Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31)." - }, { - "name": "dictGetUInt64", - "def": "dictGetUInt64(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, { - "name": "IPv4NumToStringClassC", - "def": "IPv4NumToStringClassC(num)", - "docText": "Similar to IPv4NumToString, but using `xxx` instead of the last octet. " - }, { - "name": "reinterpretAsString", - "def": "reinterpretAsString(v)", - "docText": "This function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long." - }, { - "name": "toHour", - "def": "toHour(v)", - "docText": "Converts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23). \n" + "\n" + " This function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true - even in Moscow the clocks were once changed at a different time)." - }, { - "name": "ignore", - "def": "ignore(...)", - "docText": "A function that accepts any arguments and always returns 0. \n" + "\n" + " However, the argument is still calculated. This can be used for benchmarks." - }, { - "name": "arrayJoin", - "def": "arrayJoin(v)", - "docText": "This is a very unusual function.\n" + "\n" + "Normal functions don't change a set of rows, but just change the values in each row (map). Aggregate functions compress a set of rows (fold or reduce). The 'arrayJoin' function takes each row and generates a set of rows (unfold).\n" + "\n" + "This function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array. All the values in columns are simply copied, except the values in the column where this function is applied - it is replaced with the corresponding array value.\n" + "\n" + "A query can use multiple 'arrayJoin' functions. In this case, the transformation is performed multiple times.\n" + "\n" + "Note the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities." - }, { - "name": "length", - "def": "length(v)", - "docText": "Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception). \n" + "\n" + " The result type is UInt64." - }, { - "name": "tuple", - "def": "tuple(tuple, n), operator x.N", - "docText": "A function that allows getting columns from a tuple. \n" + "\n" + " `N` is the column index, starting from 1. `N` must be a constant. `N` must be a strict postive integer no greater than the size of the tuple. \n" + "\n" + " There is no cost to execute the function." - }, { - "name": "extractURLParameterNames", - "def": "extractURLParameterNames(URL)", - "docText": "- Gets an array of name=value strings corresponding to the names of URL parameters. The values are not decoded in any way." - }, { - "name": "tupleElement", - "def": "tupleElement(tuple, n), operator x.N", - "docText": "A function that allows getting columns from a tuple. \n" + "\n" + " `N` is the column index, starting from 1. `N` must be a constant. `N` must be a strict postive integer no greater than the size of the tuple. \n" + "\n" + " There is no cost to execute the function." - }, { - "name": "in", - "def": "in(v)", - "docText": "What to do when the amount of data exceeds one of the limits: `throw` or `break`. By default, throw." - }, { - "name": "globalIn", - "def": "globalIn(v)", - "docText": "See the section `IN operators`." - }, { - "name": "isFinite", - "def": "isFinite(x)", - "docText": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0." - }, { - "name": "isNaN", - "def": "isNaN(x)", - "docText": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0." - }, { - "name": "isInfinite", - "def": "isInfinite(x)", - "docText": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. \n" + "\n" + " Note that 0 is returned for a NaN." - }, { - "name": "transform", - "def": "transform(v)", - "docText": "Transforms a value according to the explicitly defined mapping of some elements to other ones. \n" + "\n" + " There are two variations of this function: \n" + "\n" + " 1. `transform(x, array_from, array_to, default)` " - }, { - "name": "rand", - "def": "rand(v)", - "docText": "Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers. \n" + "\n" + " Uses a linear congruential generator." - }, { - "name": "reinterpretAsUInt16", - "def": "reinterpretAsUInt16(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, { - "name": "pi", - "def": "pi(v)", - "docText": "Maximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000." - }, { - "name": "reinterpretAsUInt32", - "def": "reinterpretAsUInt32(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, { - "name": "reinterpretAsUInt64", - "def": "reinterpretAsUInt64(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, { - "name": "reinterpretAsInt8", - "def": "reinterpretAsInt8(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, { - "name": "upperUTF8", - "def": "upperUTF8(v)", - "docText": "Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. It doesn`t detect the language. So for Turkish the result might not be exactly correct. " - }, { - "name": "reinterpretAsInt32", - "def": "reinterpretAsInt32(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, { - "name": "reinterpretAsFloat32", - "def": "reinterpretAsFloat32(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, { - "name": "reinterpretAsFloat64", - "def": "reinterpretAsFloat64(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, { - "name": "reinterpretAsDate", - "def": "reinterpretAsDate(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, { - "name": "reinterpretAsDateTime", - "def": "reinterpretAsDateTime(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, { - "name": "roundToExp2", - "def": "roundToExp2(num)", - "docText": "Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two." - }, { - "name": "upper", - "def": "upper(v)", - "docText": "Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. It doesn`t detect the language. So for Turkish the result might not be exactly correct." - }, { - "name": "positionUTF8", - "def": "positionUTF8(haystack, needle)", - "docText": "The same as `position`, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception). \n" + "\n" + " There's also positionCaseInsensitiveUTF8 function." - }, { - "name": "roundDuration", - "def": "roundDuration(num)", - "docText": "Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length." - }, { - "name": "roundAge", - "def": "roundAge(num)", - "docText": "Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to numbers from the set: 18, 25, 35, 45. This function is specific to Yandex.Metrica and used for implementing the report on user age." - }, { - "name": "round", - "def": "round(num)", - "docText": "Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to numbers from the set: 18, 25, 35, 45. This function is specific to Yandex.Metrica and used for implementing the report on user age." - }, { - "name": "floor", - "def": "floor(x[, N])", - "docText": "Returns a rounder number that is less than or equal to `x`." - }, { - "name": "notEmpty", - "def": "notEmpty(v)", - "docText": "Returns 0 for an empty array, or 1 for a non-empty array. \n" + "\n" + " The result type is UInt8. \n" + "\n" + " The function also works for strings." - }, { - "name": "lengthUTF8", - "def": "lengthUTF8(v)", - "docText": "Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception). \n" + "\n" + " The result type is UInt64." - }, { - "name": "lower", - "def": "lower(v)", - "docText": "Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. It doesn`t detect the language. " - }, { - "name": "lowerUTF8", - "def": "lowerUTF8(v)", - "docText": "Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. It doesn`t detect the language. " - }, { - "name": "reverse", - "def": "reverse(v)", - "docText": "Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn`t throw an exception)." - }, { - "name": "URLPathHierarchy", - "def": "URLPathHierarchy(URL)", - "docText": "- The same thing, but without the protocol and host in the result. The / element (root) is not included. " - }, { - "name": "substringUTF8", - "def": "substringUTF8(s, offset, length)", - "docText": "The same as `substring`, but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception)." - }, { - "name": "appendTrailingCharIfAbsent", - "def": "appendTrailingCharIfAbsent(s, c)", - "docText": "If the `s` string is non-empty and does not contain the `c` character at the end, it appends the `c` character to the end." - }, { - "name": "alphaTokens", - "def": "alphaTokens(s)", - "docText": "Selects substrings of consecutive bytes from the range a-z and A-Z. \n" + "\n" + " Returns an array of selected substrings." - }, { - "name": "splitByChar", - "def": "splitByChar(separator, s)", - "docText": "Splits a string into substrings, using `separator` as the separator. \n" + "\n" + " `separator` must be a string constant consisting of exactly one character. \n" + "\n" + " Returns an array of selected substrings" - }, { - "name": "arrayStringConcat", - "def": "arrayStringConcat(arr[, separator])", - "docText": "Concatenates strings from the array elements, using `separator` as the separator. \n" + "\n" + " `separator` is a string constant, an optional parameter. By default it is an empty string. \n" + "\n" + " Returns a string." - }, { - "name": "replaceAll", - "def": "replaceAll(haystack, pattern, replacement)", - "docText": "Replaces all occurrences of the `pattern` substring in `haystack` with the `replacement` substring." - }, { - "name": "replaceRegexpOne", - "def": "replaceRegexpOne(haystack, pattern, replacement)", - "docText": "Replacement using the `pattern` regular expression. A re2 regular expression. Replaces only the first occurrence, if it exists. \n" + "\n" + " A pattern can be specified as `replacement`. " - }, { - "name": "cbrt", - "def": "cbrt(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to the cubic root of the argument." - }, { - "name": "match", - "def": "match(pattern)(time, cond1, cond2, ...)", - "docText": "Pattern matching for event chains. \n" + "\n" + " `pattern` is a string containing a pattern to match. The pattern is similar to a regular expression." - }, { - "name": "cutURLParameter", - "def": "cutURLParameter(URL, name)", - "docText": "Removes the URL parameter named `name`, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument." - }, { - "name": "like", - "def": "like(haystack, pattern), haystack NOT LIKE pattern operator", - "docText": "The same thing as `like`, but negative." - }, { - "name": "domain", - "def": "domain(v)", - "docText": "- Selects the part of the domain that includes top-level subdomains up to the `first significant subdomain` (see the explanation above). \n" + "\n" + " For example, cutToFirstSignificantSubdomain(`https://news.yandex.com.tr/`) = `yandex.com.tr`." - }, { - "name": "domainWithoutWWW", - "def": "domainWithoutWWW(v)", - "docText": "- Selects the domain and removes no more than one `www.` from the beginning of it, if present." - }, { - "name": "firstSignificantSubdomain", - "def": "firstSignificantSubdomain(v)", - "docText": "- Selects the part of the domain that includes top-level subdomains up to the `first significant subdomain` (see the explanation above). \n" + "\n" + " For example, cutToFirstSignificantSubdomain(`https://news.yandex.com.tr/`) = `yandex.com.tr`." - }, { - "name": "queryString", - "def": "queryString(v)", - "docText": "Removes the query-string and fragment identifier. The question mark and number sign are also removed." - }, { - "name": "queryStringAndFragment", - "def": "queryStringAndFragment(v)", - "docText": "Removes the query-string and fragment identifier. The question mark and number sign are also removed." - }, { - "name": "extractURLParameter", - "def": "extractURLParameter(URL)", - "docText": "- Gets an array of name=value strings corresponding to the names of URL parameters. The values are not decoded in any way." - }, { - "name": "URLHierarchy", - "def": "URLHierarchy(URL)", - "docText": "- Gets an array containing the URL trimmed to the `/`, `?` characters in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:" - }, { - "name": "cutToFirstSignificantSubdomain", - "def": "cutToFirstSignificantSubdomain(v)", - "docText": "- Selects the part of the domain that includes top-level subdomains up to the `first significant subdomain` (see the explanation above). \n" + "\n" + " For example, cutToFirstSignificantSubdomain(`https://news.yandex.com.tr/`) = `yandex.com.tr`." - }, { - "name": "cutWWW", - "def": "cutWWW(v)", - "docText": "Removes no more than one `www.` from the beginning of the URL`s domain, if present." - }, { - "name": "cutQueryString", - "def": "cutQueryString(v)", - "docText": "Removes the query-string and fragment identifier. The question mark and number sign are also removed." - }, { - "name": "cutFragment", - "def": "cutFragment(v)", - "docText": "Removes the fragment identifier. The number sign is also removed." - }, { - "name": "visitParamHas", - "def": "visitParamHas(params, name)", - "docText": "Checks whether there is a field with the `name` name." - }, { - "name": "visitParamExtractFloat", - "def": "visitParamExtractFloat(params, name)", - "docText": "The same as for Float64." - }, { - "name": "visitParamExtractBool", - "def": "visitParamExtractBool(params, name)", - "docText": "Parses a true/false value. The result is UInt8." - }, { - "name": "visitParamExtractRaw", - "def": "visitParamExtractRaw(params, name)", - "docText": "Returns the value of a field, including separators." - }, { - "name": "exp", - "def": "exp(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to 10 in power of x." - }, { - "name": "exp2", - "def": "exp2(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to 2 in power of x." - }, { - "name": "exp10", - "def": "exp10(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to 10 in power of x." - }, { - "name": "tgamma", - "def": "tgamma(x)", - "docText": "Gamma function." - }, { - "name": "log10", - "def": "log10(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument." - }, { - "name": "sqrt", - "def": "sqrt(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to the square root of the argument." - }, { - "name": "erf", - "def": "erf(v)", - "docText": "What to do when the amount of data exceeds one of the limits: `throw` or `break`. By default, throw." - }, { - "name": "erfc", - "def": "erfc(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large `x` values." - }, { - "name": "lgamma", - "def": "lgamma(x)", - "docText": "The logarithm of the gamma function." - }, { - "name": "sin", - "def": "sin(x)", - "docText": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. \n" + "\n" + " Note that 0 is returned for a NaN." - }, { - "name": "cos", - "def": "cos(x)", - "docText": "The arc cosine." - }, { - "name": "tan", - "def": "tan(x)", - "docText": "The arc tangent." - }, { - "name": "pow", - "def": "pow(x, y)", - "docText": "x in power of y." - }]; - }; - - p.MacrosCompletions = function () { - return [{ - "name": "$table", - "def": "$table", - "docText": "Replaced with selected table name from Query Builder" - }, { - "name": "$timeCol", - "def": "$timeCol", - "docText": "Replaced with `Date:Col` value from Query Builder" - }, { - "name": "$dateTimeCol", - "def": "$dateTimeCol", - "docText": "Replaced with `Column:DateTime` or `Column:TimeStamp` value from Query Builder" - }, { - "name": "$from", - "def": "$from", - "docText": "Replaced with timestamp/1000 value of selected `Time Range:From`" - }, { - "name": "$to", - "def": "$to", - "docText": "Replaced with timestamp/1000 value of selected `Time Range:To`" - }, { - "name": "$interval", - "def": "$interval", - "docText": "Replaced with selected `Group by time interval` value (as a number of seconds)" - }, { - "name": "$timeFilter", - "def": "$timeFilter", - "docText": "Replaced with currently selected `Time Range`. Requires `Column:Date` and `Column:DateTime` or `Column:TimeStamp` to be selected" - }, { - "name": "$timeSeries", - "def": "$timeSeries", - "docText": "Replaced with special ClickHouse construction to convert results as time-series data. Use it as `SELECT $timeSeries...`. Require `Column:DateTime` or `Column:TimeStamp` to be selected" - }, { - "name": "$rate", - "def": "$rate(cols...)", - "docText": "Converts query results as `change rate per interval`. Can be used to display changes-per-second." + "\n" + "Example:\n $rate(countIf(Type = 200) AS good, countIf(Type != 200) AS bad) FROM requests" - }, { - "name": "$columns", - "def": "$columns(key, value)", - "docText": "Query values as array of [key, value], where key will be used as label. Can be used to display multiple lines at graph" + "\n" + "Example:\n $columns(OSName, count(*) c) FROM requests" - }, { - "name": "$rateColumns", - "def": "$rateColumns(key, value)", - "docText": "Is a combination of `$columns` and `$rate` ." + "\n" + "Example:\n $rateColumns(OS, count(*) c) FROM requests" - }, { - "name": "$unescape", - "def": "$unescape($variable)", - "docText": "Unescapes variable value by removing single quotes" + "\n" + "Example:\n SELECT $unescape($column) FROM requests WHERE $unescape($column) = 5" - }]; - }; - - p.re = function (list) { - return list.join("|"); - }; - - exports.ClickhouseInfo = p; - }); - } - }; -}); -//# sourceMappingURL=clickhouse-info.js.map diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/clickhouse-info.js.map b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/clickhouse-info.js.map deleted file mode 100644 index 242e190..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/clickhouse-info.js.map +++ /dev/null @@ -1 +0,0 @@ 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jshint ignore: start\r\n// jscs: disable\r\n\r\nace.define(\"ace/mode/clickhouse_info\", [\"require\", \"exports\", \"module\"], function (require, exports, module) {\r\n \"use strict\";\r\n\r\n var p = {};\r\n p.Keywords = [\r\n \"SELECT\",\r\n \"CASE\",\r\n \"THEN\",\r\n \"DISTINCT\",\r\n \"INSERT\",\r\n \"UPDATE\",\r\n \"DELETE\",\r\n \"WHERE\",\r\n \"AND\",\r\n \"OR\",\r\n \"OFFSET\",\r\n \"HAVING\",\r\n \"AS\",\r\n \"GLOBAL\",\r\n \"FROM\",\r\n \"WHEN\",\r\n \"ELSE\",\r\n \"END\",\r\n \"TYPE\",\r\n \"LEFT\",\r\n \"RIGHT\",\r\n \"USING\",\r\n \"JOIN\",\r\n \"ON\",\r\n \"OUTER\",\r\n \"DESC\",\r\n \"ASC\",\r\n \"UNION\",\r\n \"CREATE\",\r\n \"TABLE\",\r\n \"PRIMARY\",\r\n \"KEY\",\r\n \"FOREIGN\",\r\n \"NOT\",\r\n \"REFERENCES\",\r\n \"DEFAULT\",\r\n \"INNER\",\r\n \"CROSS\",\r\n \"NATURAL\",\r\n \"DATABASE\",\r\n \"DROP\",\r\n \"GRANT\",\r\n \"ANY\",\r\n \"BETWEEN\",\r\n \"ATTACH\",\r\n \"DETACH\",\r\n \"CAST\",\r\n \"WITH\",\r\n \"BIT_AND\",\r\n \"BIT_OR\",\r\n \"BIT_XOR\",\r\n \"DESCRIBE\",\r\n \"OPTIMIZE\",\r\n \"PREWHERE\",\r\n \"TOTALS\",\r\n \"DATABASES\",\r\n \"PROCESSLIST\",\r\n \"SHOW\",\r\n \"LIMIT\",\r\n \"IF\",\r\n \"IF NOT EXISTS\",\r\n \"IF EXISTS\",\r\n \"GROUP\",\r\n \"ORDER\",\r\n \"BY\",\r\n \"IN\",\r\n \"FORMAT\",\r\n \"GROUP BY\",\r\n \"ORDER BY\",\r\n \"UNION ALL\"\r\n ];\r\n p.DataTypes = [\r\n \"int\",\r\n \"numeric\",\r\n \"decimal\",\r\n \"date\",\r\n \"varchar\",\r\n \"char\",\r\n \"bigint\",\r\n \"float\",\r\n \"double\",\r\n \"bit\",\r\n \"binary\",\r\n \"text\",\r\n \"set\",\r\n \"timestamp\",\r\n \"uint8\",\r\n \"uint16\",\r\n \"uint32\",\r\n \"uint64\",\r\n \"int8\",\r\n \"int16\",\r\n \"int32\",\r\n \"int64\",\r\n \"float32\",\r\n \"float64\",\r\n \"datetime\",\r\n \"enum8\",\r\n \"enum16\",\r\n \"array\",\r\n \"tuple\",\r\n \"string\"\r\n ];\r\n p.Constants = [\r\n \"true\",\r\n \"false\",\r\n \"NULL\"\r\n ];\r\n p.Funcs = [\r\n \"IPv4NumToString\", \"IPv4NumToStringClassC\", \"IPv4StringToNum\", \"IPv6NumToString\", \"IPv6StringToNum\",\r\n \"MD5\", \"OSHierarchy\", \"OSIn\", \"OSToRoot\", \"SEHierarchy\", \"SEIn\", \"SEToRoot\", \"SHA1\", \"SHA224\", \"SHA256\",\r\n \"URLHash\", \"URLHierarchy\", \"URLPathHierarchy\", \"abs\", \"alphaTokens\", \"appendTrailingCharIfAbsent\",\r\n \"argMax\", \"argMin\", \"argMinIf\", \"arrayAll\", \"arrayCount\", \"arrayElement\", \"arrayEnumerate\", \"arrayEnumerateUniq\",\r\n \"arrayExists\", \"arrayFilter\", \"arrayFirst\", \"arrayJoin\", \"arrayMap\", \"arraySum\", \"avg\", \"avgIf\",\r\n \"bitAnd\", \"bitNot\", \"bitOr\", \"bitShiftLeft\", \"bitShiftRight\", \"bitXor\", \"bitmaskToArray\", \"bitmaskToList\",\r\n \"ceil\", \"cityHash64\", \"coalesce\", \"concat\", \"corr\", \"corrIf\", \"count\", \"countEqual\", \"countIf\", \"countIf\",\r\n \"covarPop\", \"covarPopIf\", \"covarSamp\", \"covarSampIf\", \"cutFragment\", \"cutQueryString\", \"cutQueryStringAndFragment\",\r\n \"cutToFirstSignificantSubdomain\", \"cutURLParameter\", \"cutWWW\", \"dictGetDate\", \"dictGetDateTime\", \"dictGetFloat32\",\r\n \"dictGetFloat64\", \"dictGetHierarchy\", \"dictGetInt16\", \"dictGetInt32\", \"dictGetInt64\", \"dictGetInt8\", \"dictGetString\",\r\n \"dictGetUInt16\", \"dictGetUInt32\", \"dictGetUInt64\", \"dictGetUInt8\", \"dictHas\", \"dictIsIn\", \"divide\", \"domainWithoutWWW\",\r\n \"empty\", \"extract\", \"extractAll\", \"extractURLParameter\", \"extractURLParameterNames\", \"extractURLParameters\",\r\n \"first\", \"firstSignificantSubdomain\", \"floor\", \"groupArray\", \"groupUniqArray\", \"halfMD5\", \"has\", \"hex\", \"ifnull\",\r\n \"indexOf\", \"intDiv\", \"intDivOrZero\", \"intHash32\", \"intHash64\", \"isnull\", \"last\", \"lcase\", \"length\", \"lengthUTF8\",\r\n \"like\", \"lower\", \"lowerUTF8\", \"match\", \"max\", \"median\", \"medianIf\", \"mid\", \"min\", \"minus\", \"modulo\", \"multiply\",\r\n \"negate\", \"notEmpty\", \"notLike\", \"now\", \"now\", \"nvl\", \"plus\", \"position\", \"positionUTF8\", \"quantile\",\r\n \"quantileDeterministic\", \"quantileExact\", \"quantileExactWeighted\", \"quantileTDigest\", \"quantileTiming\",\r\n \"quantileTimingWeighted\", \"quantiles\", \"quantilesIf\", \"quantilesTimingArrayIf\", \"quantilesTimingIf\", \"queryString\",\r\n \"queryStringAndFragment\", \"rand\", \"rand64\", \"range\", \"rank\", \"regionHierarchy\", \"regionIn\", \"regionToContinent\",\r\n \"regionToCountry\", \"regionToName\", \"regionToPopulation\", \"reinterpretAsDate\", \"reinterpretAsDateTime\", \"reinterpretAsFloat32\",\r\n \"reinterpretAsFloat64\", \"reinterpretAsInt16\", \"reinterpretAsInt32\", \"reinterpretAsInt64\", \"reinterpretAsInt8\", \"reinterpretAsString\",\r\n \"reinterpretAsUInt16\", \"reinterpretAsUInt32\", \"reinterpretAsUInt64\", \"reinterpretAsUInt8\", \"replaceAll\", \"replaceOne\",\r\n \"replaceRegexpOne\", \"reverse\", \"reverseUTF8\", \"round\", \"round\", \"roundAge\", \"roundDuration\", \"roundToExp2\", \"sequenceCount\",\r\n \"sequenceMatch\", \"sipHash128\", \"sipHash64\", \"splitByChar\", \"splitByString\", \"stddevPop\", \"stddevPopIf\", \"stddevSamp\",\r\n \"stddevSampIf\", \"substring\", \"substringUTF8\", \"sum\", \"sumArray\", \"sumArrayIf\", \"sumIf\", \"timeSlot\", \"toDate\", \"toDateTime\",\r\n \"toDayOfMonth\", \"toDayOfWeek\", \"toFixedString\", \"toFloat32\", \"toFloat64\", \"toHour\", \"toInt16\", \"toInt32\", \"toInt64\", \"toInt8\",\r\n \"toMinute\", \"toMonday\", \"toMonth\", \"toRelativeDayNum\", \"toRelativeHourNum\", \"toRelativeMinuteNum\", \"toRelativeMonthNum\",\r\n \"toRelativeSecondNum\", \"toRelativeWeekNum\", \"toRelativeYearNum\", \"toSecond\", \"toStartOfFiveMinute\", \"toStartOfHour\",\r\n \"toStartOfMinute\", \"toStartOfMonth\", \"toStartOfQuarter\", \"toStartOfYear\", \"toString\", \"toStringCutToZero\", \"toTime\", \"toUInt16\",\r\n \"toUInt32\", \"toUInt64\", \"toUInt8\", \"toYear\", \"today\", \"topLevelDomain\", \"ucase\", \"unhex\", \"uniq\", \"uniqArray\", \"uniqArrayIf\",\r\n \"uniqArrayIf\", \"uniqCombined\", \"uniqCombinedIf\", \"uniqExact\", \"uniqExactIf\", \"uniqHLL12\", \"uniqUpTo\", \"upper\", \"upperUTF8\", \"varPop\", \"varPopIf\",\r\n \"varSamp\", \"varSampIf\", \"yesterday\"\r\n ];\r\n p.Macros = [\r\n \"$table\",\r\n \"$timeCol\",\r\n \"$dateTimeCol\",\r\n \"$from\",\r\n \"$to\",\r\n \"$interval\",\r\n \"$timeFilter\",\r\n \"$timeSeries\",\r\n \"$rate\",\r\n \"$columns\",\r\n \"$rateColumns\",\r\n \"$unescape\"\r\n ];\r\n p.KeywordsRe = function () {\r\n return this.re(p.Keywords)\r\n };\r\n p.ConstantsRe = function () {\r\n return this.re(p.Constants)\r\n };\r\n p.FunctionsRe = function () {\r\n return this.re(p.Funcs).concat(this.re(p.Macros))\r\n };\r\n p.DataTypesRe = function () {\r\n return this.re(p.DataTypes);\r\n };\r\n p.FunctionsCompletions = function () {\r\n return [\r\n {\r\n \"name\": \"corr\",\r\n \"def\": \"corr(x, y)\",\r\n \"docText\": \"Calculates the Pearson correlation coefficient: Σ((x - x̅)(y - y̅)) / sqrt(Σ((x - x̅)2) * Σ((y - y̅)2)).\"\r\n },\r\n {\r\n \"name\": \"stddevPop\",\r\n \"def\": \"stddevPop(x)\",\r\n \"docText\": \"The result is equal to the square root of `varPop(x)`.\"\r\n },\r\n {\r\n \"name\": \"varPop\",\r\n \"def\": \"varPop(x, y)\",\r\n \"docText\": \"Calculates the value of `Σ((x - x̅)(y - y̅)) / n`.\"\r\n },\r\n {\r\n \"name\": \"argMin\",\r\n \"def\": \"argMin(arg, val)\",\r\n \"docText\": \"Calculates the `arg` value for a minimal `val` value. If there are several different values of `arg` for minimal values of `val`, the first of these values encountered is output.\"\r\n },\r\n {\r\n \"name\": \"quantileExactWeighted\",\r\n \"def\": \"quantileExactWeighted(level)(x, weight)\",\r\n \"docText\": \"Computes the level quantile exactly. In this case, each value is taken into account with the weight weight - as if it is present weight once. The arguments of the function can be considered as histograms, where the value \\\"x\\\" corresponds to the \\\"column\\\" of the histogram of the height weight, and the function itself can be considered as the summation of histograms.\\n\" +\r\n \"\\n\" +\r\n \"The algorithm is a hash table. Because of this, in case the transmitted values ​​are often repeated, the function consumes less RAM than the quantileExact. You can use this function instead of quantileExact, specifying the number 1 as the weight.\"\r\n },\r\n {\r\n \"name\": \"min\",\r\n \"def\": \"min(v)\",\r\n \"docText\": \"Minimal execution speed in rows per second. Checked on every data block when `timeout_before_checking_execution_speed` expires. If the execution speed is lower, an exception is thrown.\"\r\n },\r\n {\r\n \"name\": \"varSamp\",\r\n \"def\": \"varSamp(x)\",\r\n \"docText\": \"Calculates the amount Σ((x - x̅)2) / (n - 1), where 'n' is the sample size and 'x̅' is the average value of 'x'.\\n\" +\r\n \"\\n\" +\r\n \"It represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount.\\n\" +\r\n \"\\n\" +\r\n \"Returns Float64. If n <= 1, it returns +∞.\"\r\n },\r\n {\r\n \"name\": \"anyLast\",\r\n \"def\": \"anyLast(x)\",\r\n \"docText\": \"Selects the last value encountered. \\n\" +\r\n \"\\n\" +\r\n \" The result is just as indeterminate as for the `any` function.\"\r\n },\r\n {\r\n \"name\": \"argMax\",\r\n \"def\": \"argMax(arg, val)\",\r\n \"docText\": \"Calculates the `arg` value for a maximum `val` value. If there are several different values of `arg` for maximum values of `val`, the first of these values encountered is output.\"\r\n },\r\n {\r\n \"name\": \"count\",\r\n \"def\": \"count()\",\r\n \"docText\": \"Counts the number of rows. \"\r\n },\r\n {\r\n \"name\": \"any\",\r\n \"def\": \"any(x)\",\r\n \"docText\": \"Selects the last value encountered. \\n\" +\r\n \"\\n\" +\r\n \" The result is just as indeterminate as for the `any` function.\"\r\n },\r\n {\r\n \"name\": \"avg\",\r\n \"def\": \"avg(x)\",\r\n \"docText\": \"Calculates the average. \\n\" +\r\n \"\\n\" +\r\n \" Only works for numbers. \\n\" +\r\n \"\\n\" +\r\n \" The result is always Float64.\"\r\n },\r\n {\r\n \"name\": \"sequenceMatch\",\r\n \"def\": \"sequenceMatch(pattern)(time, cond1, cond2, ...)\",\r\n \"docText\": \"Pattern matching for event chains.\\n\" +\r\n \"\\n\" +\r\n \"'pattern' is a string containing a pattern to match. The pattern is similar to a regular expression. 'time' is the event time of the DateTime type. 'cond1, cond2 ...' are from one to 32 arguments of the UInt8 type that indicate whether an event condition was met.\\n\" +\r\n \"\\n\" +\r\n \"The function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern. It returns UInt8 - 0 if the pattern isn't matched, or 1 if it matches.\\n\" +\r\n \"\\n\" +\r\n \"Example: sequenceMatch('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%') - whether there was a chain of events in which pages with the address in company were visited earlier than pages with the address in cart.\"\r\n },\r\n {\r\n \"name\": \"stddevSamp\",\r\n \"def\": \"stddevSamp(x)\",\r\n \"docText\": \"The result is equal to the square root of `varSamp(x)`.\"\r\n },\r\n {\r\n \"name\": \"medianDeterministic\",\r\n \"def\": \"medianDeterministic(x, determinator)\",\r\n \"docText\": \"This function works similarly to the `median` function - it approximates the median.\"\r\n },\r\n {\r\n \"name\": \"quantilesTimingWeighted\",\r\n \"def\": \"quantilesTimingWeighted(level1, level2, ...)(x, weight)\",\r\n \"docText\": \"Calculates the quantiles of all specified levels using the same algorithm as the `medianTimingWeighted` function.\"\r\n },\r\n {\r\n \"name\": \"uniq\",\r\n \"def\": \"uniq(N)(x)\",\r\n \"docText\": \"Calculates the number of different argument values, if it is less than or equal to N. \\n\" +\r\n \"\\n\" +\r\n \" If the number of different argument values is greater than N, it returns N + 1.\"\r\n },\r\n {\r\n \"name\": \"covarSamp\",\r\n \"def\": \"covarSamp(x, y)\",\r\n \"docText\": \"Calculates the value of `Σ((x - x̅)(y - y̅)) / (n - 1)`. \" +\r\n \"Returns Float64. If n <= 1, it returns +∞.\"\r\n },\r\n {\r\n \"name\": \"max\",\r\n \"def\": \"max(v)\",\r\n \"docText\": \"Maximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN.\"\r\n },\r\n {\r\n \"name\": \"quantileTDigest\",\r\n \"def\": \"quantileTDigest(level)(x)\",\r\n \"docText\": \"t-digest\"\r\n },\r\n {\r\n \"name\": \"quantilesTiming\",\r\n \"def\": \"quantilesTiming(level1, level2, ...)(x, weight)\",\r\n \"docText\": \"Calculates the quantiles of all specified levels using the same algorithm as the `medianTimingWeighted` function.\"\r\n },\r\n {\r\n \"name\": \"quantiles\",\r\n \"def\": \"quantiles(level1, level2, ...)(x, determinator)\",\r\n \"docText\": \"Calculates the quantiles of all specified levels using the same algorithm as the `medianDeterministic` function.\"\r\n },\r\n {\r\n \"name\": \"quantile\",\r\n \"def\": \"quantile(level1, level2, ...)(x, determinator)\",\r\n \"docText\": \"Calculates the quantiles of all specified levels using the same algorithm as the `medianDeterministic` function.\"\r\n },\r\n {\r\n \"name\": \"groupArray\",\r\n \"def\": \"groupArray(x)\",\r\n \"docText\": \"Creates an array of argument values. \\n\" +\r\n \"\\n\" +\r\n \" Values can be added to the array in any (indeterminate) order. \\n\" +\r\n \"\\n\" +\r\n \" In some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.\"\r\n },\r\n {\r\n \"name\": \"sum\",\r\n \"def\": \"sum(x)\",\r\n \"docText\": \"Calculates the sum. \\n\" +\r\n \"\\n\" +\r\n \" Only works for numbers.\"\r\n },\r\n {\r\n \"name\": \"median\",\r\n \"def\": \"median(x, weight)\",\r\n \"docText\": \"Differs from the `medianTiming` function in that it has a second argument - `weights`. Weight is a non-negative integer. \\n\" +\r\n \"\\n\" +\r\n \" The result is calculated as if the `x` value were passed `weight` number of times to the `medianTiming` function.\"\r\n },\r\n {\r\n \"name\": \"quantileTiming\",\r\n \"def\": \"quantileTiming(level)(x, weight)\",\r\n \"docText\": \"Calculates the quantile of `level` using the same algorithm as the `medianTimingWeighted` function.\"\r\n },\r\n {\r\n \"name\": \"quantileTimingWeighted\",\r\n \"def\": \"quantileTimingWeighted(level)(x, weight)\",\r\n \"docText\": \"Calculates the quantile of `level` using the same algorithm as the `medianTimingWeighted` function.\"\r\n },\r\n {\r\n \"name\": \"groupUniqArray\",\r\n \"def\": \"groupUniqArray(x)\",\r\n \"docText\": \"Creates an array from different argument values. Memory consumption is the same as for the `uniqExact` function.\"\r\n },\r\n {\r\n \"name\": \"uniqHLL12\",\r\n \"def\": \"uniqHLL12(x)\",\r\n \"docText\": \"Uses the HyperLogLog algorithm to approximate the number of different values of the argument. \"\r\n },\r\n {\r\n \"name\": \"covarPop\",\r\n \"def\": \"covarPop(x, y)\",\r\n \"docText\": \"Calculates the value of `Σ((x - x̅)(y - y̅)) / n`.\"\r\n },\r\n {\r\n \"name\": \"sequenceCount\",\r\n \"def\": \"sequenceCount(pattern)(time, cond1, cond2, ...)\",\r\n \"docText\": \"sequenceMatch\"\r\n },\r\n {\r\n \"name\": \"quantileDeterministic\",\r\n \"def\": \"quantileDeterministic(level)(x, determinator)\",\r\n \"docText\": \"Calculates the quantile of `level` using the same algorithm as the `medianDeterministic` function.\"\r\n },\r\n {\r\n \"name\": \"quantileExact\",\r\n \"def\": \"quantileExact(level)(x, weight)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"quantilesDeterministic\",\r\n \"def\": \"quantilesDeterministic(level1, level2, ...)(x, determinator)\",\r\n \"docText\": \"Calculates the quantiles of all specified levels using the same algorithm as the `medianDeterministic` function.\"\r\n },\r\n {\r\n \"name\": \"medianTiming\",\r\n \"def\": \"medianTiming(x, weight)\",\r\n \"docText\": \"Differs from the `medianTiming` function in that it has a second argument - `weights`.\"\r\n },\r\n {\r\n \"name\": \"medianTimingWeighted\",\r\n \"def\": \"medianTimingWeighted(x, weight)\",\r\n \"docText\": \"Differs from the `medianTiming` function in that it has a second argument - `weights`.\"\r\n },\r\n {\r\n \"name\": \"uniqExact\",\r\n \"def\": \"uniqExact(x)\",\r\n \"docText\": \"Calculates the number of different values of the argument, exactly. \\n\" +\r\n \"\\n\" +\r\n \" There is no reason to fear approximations, so it`s better to use the `uniq` function. \\n\" +\r\n \"\\n\" +\r\n \" You should use the `uniqExact` function if you definitely need an exact result. \"\r\n },\r\n {\r\n \"name\": \"uniqCombined\",\r\n \"def\": \"uniqCombined(x)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"uniqCombinedIf\",\r\n \"def\": \"uniqCombinedIf(x,v)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"uniqUpTo\",\r\n \"def\": \"uniqUpTo(N)(x)\",\r\n \"docText\": \"Calculates the number of different argument values, if it is less than or equal to N. \\n\" +\r\n \"\\n\" +\r\n \" If the number of different argument values is greater than N, it returns N + 1.\"\r\n },\r\n {\r\n \"name\": \"substring\",\r\n \"def\": \"substring(s, offset, length)\",\r\n \"docText\": \"The same as `substring`, but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception).\"\r\n },\r\n {\r\n \"name\": \"notLike\",\r\n \"def\": \"notLike(haystack, pattern), haystack NOT LIKE pattern operator\",\r\n \"docText\": \"The same thing as `like`, but negative.\"\r\n },\r\n {\r\n \"name\": \"hostName\",\r\n \"def\": \"hostName()\",\r\n \"docText\": \"Returns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server.\"\r\n },\r\n {\r\n \"name\": \"globalNotIn\",\r\n \"def\": \"globalNotIn(v)\",\r\n \"docText\": \"See the section `IN operators`.\"\r\n },\r\n {\r\n \"name\": \"or\",\r\n \"def\": \"or(v)\",\r\n \"docText\": \"The same thing as `max_temporary_columns`, but without counting constant columns. \\n\" +\r\n \"\\n\" +\r\n \" Note that constant columns are formed fairly often when running a query, but they require approximately zero computing resources.\"\r\n },\r\n {\r\n \"name\": \"extractAll\",\r\n \"def\": \"extractAll(haystack, pattern)\",\r\n \"docText\": \"Extracts all the fragments of a string using a regular expression. If `haystack` doesn`t match the `pattern` regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex.\"\r\n },\r\n {\r\n \"name\": \"arrayFirst\",\r\n \"def\": \"arrayFirst(func, arr1, ...)\",\r\n \"docText\": \"Returns the index of the first element in the `arr1` array for which `func` returns something other than 0.\"\r\n },\r\n {\r\n \"name\": \"arrayExists\",\r\n \"def\": \"arrayExists([func,] arr1, ...)\",\r\n \"docText\": \"Returns 1 if there is at least one element in `arr` for which `func` returns something other than 0. Otherwise, it returns 0.\"\r\n },\r\n {\r\n \"name\": \"arrayCount\",\r\n \"def\": \"arrayCount([func,] arr1, ...)\",\r\n \"docText\": \"Returns the number of elements in `arr` for which `func` returns something other than 0. If `func` is not specified, it returns the number of non-zero items in the array.\"\r\n },\r\n {\r\n \"name\": \"arrayMap\",\r\n \"def\": \"arrayMap(func, arr1, ...)\",\r\n \"docText\": \"Returns an array obtained from the original application of the `func` function to each element in the `arr` array.\"\r\n },\r\n {\r\n \"name\": \"now\",\r\n \"def\": \"now(v)\",\r\n \"docText\": \"If the parameter is true, INSERT operation will skip columns with unknown names from input. \\n\" +\r\n \"\\n\" +\r\n \" Otherwise, an exception will be generated, it is default behavior. \\n\" +\r\n \"\\n\" +\r\n \" The parameter works only for JSONEachRow and TSKV input formats.\"\r\n },\r\n {\r\n \"name\": \"intDiv\",\r\n \"def\": \"intDiv(a, b)\",\r\n \"docText\": \"Differs from `intDiv` in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.\"\r\n },\r\n {\r\n \"name\": \"topLevelDomain\",\r\n \"def\": \"topLevelDomain(v)\",\r\n \"docText\": \"Selects the top-level domain. Example: .ru.\"\r\n },\r\n {\r\n \"name\": \"intHash32\",\r\n \"def\": \"intHash32(v)\",\r\n \"docText\": \"Calculates a 32-bit hash code from any type of integer. \\n\" +\r\n \"\\n\" +\r\n \" This is a relatively fast non-cryptographic hash function of average quality for numbers.\"\r\n },\r\n {\r\n \"name\": \"replaceOne\",\r\n \"def\": \"replaceOne(haystack, pattern, replacement)\",\r\n \"docText\": \"Replaces the first occurrence, if it exists, of the `pattern` substring in `haystack` with the `replacement` substring. \\n\" +\r\n \"\\n\" +\r\n \" Hereafter, `pattern` and `replacement` must be constants.\"\r\n },\r\n {\r\n \"name\": \"cityHash64\",\r\n \"def\": \"cityHash64(v)\",\r\n \"docText\": \"Calculates CityHash64 from a string or a similar hash function for any number of any type of arguments. \\n\" +\r\n \"\\n\" +\r\n \" For String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality\"\r\n },\r\n {\r\n \"name\": \"OSToRoot\",\r\n \"def\": \"OSToRoot(v)\",\r\n \"docText\": \"Accepts a UInt8 number - the ID of the operating system from the Yandex.Metrica dictionary. If any OS matches the passed number, it returns a UInt8 number - the ID of the corresponding root OS (for example, it converts Windows Vista to Windows). Otherwise, returns 0.\"\r\n },\r\n {\r\n \"name\": \"sipHash128\",\r\n \"def\": \"sipHash128(v)\",\r\n \"docText\": \"Calculates SipHash from a string. \\n\" +\r\n \"\\n\" +\r\n \" Accepts a String-type argument. Returns FixedString(16). \\n\" +\r\n \"\\n\" +\r\n \" Differs from sipHash64 in that the final xor-folding state is only done up to 128 bits.\"\r\n },\r\n {\r\n \"name\": \"SHA1\",\r\n \"def\": \"SHA1(v)\",\r\n \"docText\": \"Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32).\"\r\n },\r\n {\r\n \"name\": \"asin\",\r\n \"def\": \"asin(x)\",\r\n \"docText\": \"The arc sine.\"\r\n },\r\n {\r\n \"name\": \"SHA256\",\r\n \"def\": \"SHA256(v)\",\r\n \"docText\": \"Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32).\"\r\n },\r\n {\r\n \"name\": \"MD5\",\r\n \"def\": \"MD5(v)\",\r\n \"docText\": \"Calculates the MD5 from a string and returns the resulting set of bytes as FixedString(16).\"\r\n },\r\n {\r\n \"name\": \"bitmaskToList\",\r\n \"def\": \"bitmaskToList(num)\",\r\n \"docText\": \"Accepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order.\"\r\n },\r\n {\r\n \"name\": \"array\",\r\n \"def\": \"array(v)\",\r\n \"docText\": \"The -`Array` suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the `Array(T)` type (arrays) instead of `T` type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. \"\r\n },\r\n {\r\n \"name\": \"dictGetStringOrDefault\",\r\n \"def\": \"dictGetStringOrDefault(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"greaterOrEquals\",\r\n \"def\": \"greaterOrEquals(v)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"e\",\r\n \"def\": \"e(v)\",\r\n \"docText\": \"What to do when the amount of data exceeds one of the limits: `throw` or `break`. By default, throw.\"\r\n },\r\n {\r\n \"name\": \"runningDifference\",\r\n \"def\": \"runningDifference(x)\",\r\n \"docText\": \"Calculates the difference between consecutive values in the data block. \\n\" +\r\n \"\\n\" +\r\n \" Result of the function depends on the order of the data in the blocks. \\n\" +\r\n \"\\n\" +\r\n \" It works only inside of the each processed block of data. Data splitting in the blocks is not explicitly controlled by the user. \"\r\n },\r\n {\r\n \"name\": \"not\",\r\n \"def\": \"not(v)\",\r\n \"docText\": \"See the section `IN operators`.\"\r\n },\r\n {\r\n \"name\": \"intHash64\",\r\n \"def\": \"intHash64(v)\",\r\n \"docText\": \"Calculates a 64-bit hash code from any type of integer. \\n\" +\r\n \"\\n\" +\r\n \" It works faster than intHash32. Average quality.\"\r\n },\r\n {\r\n \"name\": \"acos\",\r\n \"def\": \"acos(x)\",\r\n \"docText\": \"The arc cosine.\"\r\n },\r\n {\r\n \"name\": \"dictGetString\",\r\n \"def\": \"dictGetString(v)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"and\",\r\n \"def\": \"and(x, determinator)\",\r\n \"docText\": \"This function works similarly to the `median` function - it approximates the median. However, in contrast to `median`, the result is deterministic and does not depend on the order of query execution. \\n\" +\r\n \"\\n\" +\r\n \" To achieve this, the function takes a second argument - the `determinator`. \"\r\n },\r\n {\r\n \"name\": \"dictGetDate\",\r\n \"def\": \"dictGetDate(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"dictGetFloat32\",\r\n \"def\": \"dictGetFloat32(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"dictGetInt8\",\r\n \"def\": \"dictGetInt8(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"dictGetUInt32\",\r\n \"def\": \"dictGetUInt32(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"OSIn\",\r\n \"def\": \"OSIn(lhs, rhs)\",\r\n \"docText\": \"Checks whether the `lhs` operating system belongs to the `rhs` operating system.\"\r\n },\r\n {\r\n \"name\": \"arrayFirstIndex\",\r\n \"def\": \"arrayFirstIndex(func, arr1, ...)\",\r\n \"docText\": \"Returns the index of the first element in the `arr1` array for which `func` returns something other than 0.\"\r\n },\r\n {\r\n \"name\": \"ceil\",\r\n \"def\": \"ceil(x[, N])\",\r\n \"docText\": \"Returns the smallest round number that is greater than or equal to `x`. In every other way, it is the same as the `floor` function (see above).\"\r\n },\r\n {\r\n \"name\": \"fragment\",\r\n \"def\": \"fragment(v)\",\r\n \"docText\": \"Removes the query-string and fragment identifier. The question mark and number sign are also removed.\"\r\n },\r\n {\r\n \"name\": \"dictGetUInt8\",\r\n \"def\": \"dictGetUInt8(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"dictHas\",\r\n \"def\": \"dictHas(v)\",\r\n \"docText\": \"dictHas('dict_name', id) check the presence of a key in the dictionary. Returns a value of type UInt8, equal to 0, if there is no key and 1 if there is a key.\"\r\n },\r\n {\r\n \"name\": \"arraySum\",\r\n \"def\": \"arraySum([func,] arr1, ...)\",\r\n \"docText\": \"Returns the sum of the `func` values. If the function is omitted, it just returns the sum of the array elements.\"\r\n },\r\n {\r\n \"name\": \"emptyArrayDateTime\",\r\n \"def\": \"emptyArrayDateTime(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"intDivOrZero\",\r\n \"def\": \"intDivOrZero(a, b)\",\r\n \"docText\": \"Differs from `intDiv` in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one.\"\r\n },\r\n {\r\n \"name\": \"SEHierarchy\",\r\n \"def\": \"SEHierarchy(v)\",\r\n \"docText\": \"Accepts a UInt8 number - the ID of the search engine from the Yandex.Metrica dictionary. Returns an array with a hierarchy of search engines. Similar to the `regionHierarchy` function.\"\r\n },\r\n {\r\n \"name\": \"regionToContinent\",\r\n \"def\": \"regionToContinent(id[, geobase])\",\r\n \"docText\": \"Converts a region to a continent. In every other way, this function is the same as `regionToCity`. \\n\" +\r\n \"\\n\" +\r\n \" Example: `regionToContinent(toUInt32(213)) = 10001` converts Moscow (213) to Eurasia (10001).\"\r\n },\r\n {\r\n \"name\": \"dictGetInt32\",\r\n \"def\": \"dictGetInt32(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"toInt8\",\r\n \"def\": \"toInt8(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"materialize\",\r\n \"def\": \"materialize(x)\",\r\n \"docText\": \"Turns a constant into a full column containing just one value. \\n\" +\r\n \"\\n\" +\r\n \" In ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior.\"\r\n },\r\n {\r\n \"name\": \"regionToCountry\",\r\n \"def\": \"regionToCountry(id[, geobase])\",\r\n \"docText\": \"Converts a region to a country. In every other way, this function is the same as `regionToCity`. \\n\" +\r\n \"\\n\" +\r\n \" Example: `regionToCountry(toUInt32(213)) = 225` converts Moscow (213) to Russia (225).\"\r\n },\r\n {\r\n \"name\": \"dictGetDateTime\",\r\n \"def\": \"dictGetDateTime(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"xor\",\r\n \"def\": \"xor(v)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"regionToCity\",\r\n \"def\": \"regionToCity(id[, geobase])\",\r\n \"docText\": \"Accepts a UInt32 number - the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0.\"\r\n },\r\n {\r\n \"name\": \"dictGetFloat64\",\r\n \"def\": \"dictGetFloat64(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"timeSlot\",\r\n \"def\": \"timeSlot(StartTime, Duration)\",\r\n \"docText\": \"For a time interval starting at `StartTime` and continuing for `Duration` seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour.\"\r\n },\r\n {\r\n \"name\": \"toTime\",\r\n \"def\": \"toTime(v)\",\r\n \"docText\": \"Converts a date with time to the date of the start of the Unix Epoch, while preserving the time.\"\r\n },\r\n {\r\n \"name\": \"log2\",\r\n \"def\": \"log2(x)\",\r\n \"docText\": \"Accepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument.\"\r\n },\r\n {\r\n \"name\": \"toRelativeHourNum\",\r\n \"def\": \"toRelativeHourNum(v)\",\r\n \"docText\": \"Converts a date with time or date to the number of the hour, starting from a certain fixed point in the past.\"\r\n },\r\n {\r\n \"name\": \"toRelativeDayNum\",\r\n \"def\": \"toRelativeDayNum(v)\",\r\n \"docText\": \"Converts a date with time or date to the number of the day, starting from a certain fixed point in the past.\"\r\n },\r\n {\r\n \"name\": \"toRelativeWeekNum\",\r\n \"def\": \"toRelativeWeekNum(v)\",\r\n \"docText\": \"Converts a date with time or date to the number of the week, starting from a certain fixed point in the past.\"\r\n },\r\n {\r\n \"name\": \"splitByString\",\r\n \"def\": \"splitByString(separator, s)\",\r\n \"docText\": \"The same as above, but it uses a string of multiple characters as the separator. The string must be non-empty.\"\r\n },\r\n {\r\n \"name\": \"currentDatabase\",\r\n \"def\": \"currentDatabase()\",\r\n \"docText\": \"Returns the name of the current database. \\n\" +\r\n \"\\n\" +\r\n \" You can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database.\"\r\n },\r\n {\r\n \"name\": \"toRelativeMonthNum\",\r\n \"def\": \"toRelativeMonthNum(v)\",\r\n \"docText\": \"Converts a date with time or date to the number of the month, starting from a certain fixed point in the past.\"\r\n },\r\n {\r\n \"name\": \"visibleWidth\",\r\n \"def\": \"visibleWidth(x)\",\r\n \"docText\": \"Calculates the approximate width when outputting values to the console in text format (tab-separated). This function is used by the system for implementing Pretty formats.\"\r\n },\r\n {\r\n \"name\": \"bitShiftRight\",\r\n \"def\": \"bitShiftRight(a, b)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"toRelativeYearNum\",\r\n \"def\": \"toRelativeYearNum(v)\",\r\n \"docText\": \"Converts a date with time or date to the number of the year, starting from a certain fixed point in the past.\"\r\n },\r\n {\r\n \"name\": \"toStartOfHour\",\r\n \"def\": \"toStartOfHour(v)\",\r\n \"docText\": \"Rounds down a date with time to the start of the hour.\"\r\n },\r\n {\r\n \"name\": \"halfMD5\",\r\n \"def\": \"halfMD5(v)\",\r\n \"docText\": \"Calculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian.\"\r\n },\r\n {\r\n \"name\": \"toStartOfFiveMinute\",\r\n \"def\": \"toStartOfFiveMinute(v)\",\r\n \"docText\": \"Rounds down a date with time to the start of the 5 minute (00:00, 00:05, 00:10...).\"\r\n },\r\n {\r\n \"name\": \"toUInt16OrZero\",\r\n \"def\": \"toUInt16OrZero(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument.\"\r\n },\r\n {\r\n \"name\": \"toMonday\",\r\n \"def\": \"toMonday(v)\",\r\n \"docText\": \"Rounds down a date or date with time to the nearest Monday. \\n\" +\r\n \"\\n\" +\r\n \" Returns the date.\"\r\n },\r\n {\r\n \"name\": \"IPv6NumToString\",\r\n \"def\": \"IPv6NumToString(x)\",\r\n \"docText\": \"Accepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format. \"\r\n },\r\n {\r\n \"name\": \"indexOf\",\r\n \"def\": \"indexOf(arr, x)\",\r\n \"docText\": \"Returns the index of the `x` element (starting from 1) if it is in the array, or 0 if it is not.\"\r\n },\r\n {\r\n \"name\": \"today\",\r\n \"def\": \"today(v)\",\r\n \"docText\": \"Accepts zero arguments and returns the current date at one of the moments of request execution. \\n\" +\r\n \"\\n\" +\r\n \" The same as `toDate(now())`.\"\r\n },\r\n {\r\n \"name\": \"emptyArrayToSingle\",\r\n \"def\": \"emptyArrayToSingle(v)\",\r\n \"docText\": \"Accepts an empty array as argument and returns an array of one element equal to the default value.\"\r\n },\r\n {\r\n \"name\": \"sleep\",\r\n \"def\": \"sleep(seconds)\",\r\n \"docText\": \"Sleeps `seconds` seconds on each data block. You can specify an integer or a floating-point number.\"\r\n },\r\n {\r\n \"name\": \"extract\",\r\n \"def\": \"extract(params, name)\",\r\n \"docText\": \"Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string. Examples: \"\r\n },\r\n {\r\n \"name\": \"emptyArrayInt8\",\r\n \"def\": \"emptyArrayInt8(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"regionToName\",\r\n \"def\": \"regionToName(id[, lang])\",\r\n \"docText\": \"Accepts a UInt32 number - the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language `ru` is used. \"\r\n },\r\n {\r\n \"name\": \"concat\",\r\n \"def\": \"concat(arr[, separator])\",\r\n \"docText\": \"Concatenates strings from the array elements, using `separator` as the separator. \\n\" +\r\n \"\\n\" +\r\n \" `separator` is a string constant, an optional parameter. By default it is an empty string. \\n\" +\r\n \"\\n\" +\r\n \" Returns a string.\"\r\n },\r\n {\r\n \"name\": \"convertCharset\",\r\n \"def\": \"convertCharset(s, from, to)\",\r\n \"docText\": \"Returns a string with the data `s` (encoded as `from` charset) that was converted to the `to` charset.\"\r\n },\r\n {\r\n \"name\": \"toMonth\",\r\n \"def\": \"toMonth(v)\",\r\n \"docText\": \"Converts a date or date with time to a UInt8 number containing the month number (1-12).\"\r\n },\r\n {\r\n \"name\": \"IPv6StringToNum\",\r\n \"def\": \"IPv6StringToNum(s)\",\r\n \"docText\": \"The reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes. \\n\" +\r\n \"\\n\" +\r\n \" HEX can be uppercase or lowercase.\"\r\n },\r\n {\r\n \"name\": \"emptyArrayString\",\r\n \"def\": \"emptyArrayString(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"uptime\",\r\n \"def\": \"uptime()\",\r\n \"docText\": \"Returns server's uptime in seconds.\"\r\n },\r\n {\r\n \"name\": \"blockSize\",\r\n \"def\": \"blockSize()\",\r\n \"docText\": \"Gets the size of the block. \\n\" +\r\n \"\\n\" +\r\n \" In ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for.\"\r\n },\r\n {\r\n \"name\": \"toInt64OrZero\",\r\n \"def\": \"toInt64OrZero(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"pathFull\",\r\n \"def\": \"pathFull(v)\",\r\n \"docText\": \"Selects the path, but including query-string and fragment. Example: /top/news.html?page=2#comments\"\r\n },\r\n {\r\n \"name\": \"emptyArrayDate\",\r\n \"def\": \"emptyArrayDate(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"emptyArrayInt64\",\r\n \"def\": \"emptyArrayInt64(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"toInt32OrZero\",\r\n \"def\": \"toInt32OrZero(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"greater\",\r\n \"def\": \"greater(v)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"emptyArrayInt32\",\r\n \"def\": \"emptyArrayInt32(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"toString\",\r\n \"def\": \"toString(str)\",\r\n \"docText\": \"Accepts a FixedString(16) value containing the UUID in the binary format. Returns a readable string containing the UUID in the text format.\"\r\n },\r\n {\r\n \"name\": \"greatest\",\r\n \"def\": \"greatest(a, b)\",\r\n \"docText\": \"Returns the greatest element of a and b.\"\r\n },\r\n {\r\n \"name\": \"emptyArrayUInt64\",\r\n \"def\": \"emptyArrayUInt64(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"emptyArrayUInt32\",\r\n \"def\": \"emptyArrayUInt32(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"formatReadableSize\",\r\n \"def\": \"formatReadableSize(x)\",\r\n \"docText\": \"Gets a size (number of bytes). Returns a string that contains rounded size with the suffix (KiB, MiB etc.).\\n\" +\r\n \"\\n\" +\r\n \"Example:\\n\" +\r\n \"\\n\" +\r\n \"SELECT\\n\" +\r\n \" arrayJoin([1, 1024, 1024*1024, 192851925]) AS f_bytes,\\n\" +\r\n \" formatReadableSize(filesize_bytes) AS filesize\\n\" +\r\n \"┌─f_bytes-─┬─filesize───┐\\n\" +\r\n \"│ 1 │ 1.00 B │\\n\" +\r\n \"│ 1024 │ 1.00 KiB │\\n\" +\r\n \"│ 1048576 │ 1.00 MiB │\\n\" +\r\n \"│192851925 │ 183.92 MiB │\\n\" +\r\n \"└──────────┴────────────┘\\n\"\r\n },\r\n {\r\n \"name\": \"toInt16OrZero\",\r\n \"def\": \"toInt16OrZero(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"emptyArrayUInt8\",\r\n \"def\": \"emptyArrayUInt8(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"protocol\",\r\n \"def\": \"protocol(v)\",\r\n \"docText\": \"Selects the protocol. Examples: http, ftp, mailto, magnet...\"\r\n },\r\n {\r\n \"name\": \"regionToPopulation\",\r\n \"def\": \"regionToPopulation(id[, geobase])\",\r\n \"docText\": \"Gets the population for a region. \\n\" +\r\n \"\\n\" +\r\n \" The population can be recorded in files with the geobase. See the section `External dictionaries`. \\n\" +\r\n \"\\n\" +\r\n \" If the population is not recorded for the region, it returns 0. \\n\" +\r\n \"\\n\" +\r\n \" In the Yandex geobase, the population might be recorded for child regions, but not for parent regions.\"\r\n },\r\n {\r\n \"name\": \"notIn\",\r\n \"def\": \"notIn(v)\",\r\n \"docText\": \"See the section `IN operators`.\"\r\n },\r\n {\r\n \"name\": \"position\",\r\n \"def\": \"position(haystack, needle)\",\r\n \"docText\": \"The same as `position`, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception). \\n\" +\r\n \"\\n\" +\r\n \" There's also positionCaseInsensitiveUTF8 function.\"\r\n },\r\n {\r\n \"name\": \"arrayElement\",\r\n \"def\": \"arrayElement(arr, n), arr[n] operator\",\r\n \"docText\": \"Get the element with the index `n` from the array `arr`. \\n\" +\r\n \"\\n\" +\r\n \" `n` should be any integer type. \\n\" +\r\n \"\\n\" +\r\n \" Indexes in an array begin from one. \\n\" +\r\n \"\\n\" +\r\n \" Negative indexes are supported - in this case, it selects the corresponding element numbered from the end. \"\r\n },\r\n {\r\n \"name\": \"toStringCutToZero\",\r\n \"def\": \"toStringCutToZero(s)\",\r\n \"docText\": \"Accepts a String or FixedString argument. Returns a String that is cut to a first null byte occurrence.\"\r\n },\r\n {\r\n \"name\": \"log\",\r\n \"def\": \"log(x)\",\r\n \"docText\": \"Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.\"\r\n },\r\n {\r\n \"name\": \"SEIn\",\r\n \"def\": \"SEIn(lhs, rhs)\",\r\n \"docText\": \"Checks whether the `lhs` search engine belongs to the `rhs` search engine.\"\r\n },\r\n {\r\n \"name\": \"replicate\",\r\n \"def\": \"replicate(v)\",\r\n \"docText\": \"Create a MergeTree table with a different name. Move all the data from the directory with the ReplicatedMergeTree table data to the new table`s data directory.\"\r\n },\r\n {\r\n \"name\": \"sipHash64\",\r\n \"def\": \"sipHash64(v)\",\r\n \"docText\": \"Calculates SipHash from a string. \\n\" +\r\n \"\\n\" +\r\n \" Accepts a String-type argument. Returns UInt64. \\n\" +\r\n \"\\n\" +\r\n \" SipHash is a cryptographic hash function. It works at least three times faster than MD5. For more information, see https://131002.net/siphash/\"\r\n },\r\n {\r\n \"name\": \"emptyArrayUInt16\",\r\n \"def\": \"emptyArrayUInt16(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"hex\",\r\n \"def\": \"hex(str)\",\r\n \"docText\": \"Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits doesn`t have to be even.\"\r\n },\r\n {\r\n \"name\": \"regionToDistrict\",\r\n \"def\": \"regionToDistrict(id[, geobase])\",\r\n \"docText\": \"Converts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as `regionToCity`. \"\r\n },\r\n {\r\n \"name\": \"arrayFilter\",\r\n \"def\": \"arrayFilter(func, arr1, ...)\",\r\n \"docText\": \"Returns an array containing only the elements in `arr1` for which `func` returns something other than 0. \\n\" +\r\n \"\\n\" +\r\n \" Examples: \\n\" +\r\n \"\\n\" +\r\n \" SELECT arrayFilter(x -> x LIKE `%World%`, [`Hello`, `abc World`]) AS res \"\r\n },\r\n {\r\n \"name\": \"toStartOfQuarter\",\r\n \"def\": \"toStartOfQuarter(v)\",\r\n \"docText\": \"Rounds down a date or date with time to the first day of the quarter. \\n\" +\r\n \"\\n\" +\r\n \" The first day of the quarter is either 1 January, 1 April, 1 July, or 1 October. Returns the date.\"\r\n },\r\n {\r\n \"name\": \"divide\",\r\n \"def\": \"divide(a, b), a / b operator\",\r\n \"docText\": \"Calculates the quotient of the numbers. The result type is always a floating-point type. \\n\" +\r\n \"\\n\" +\r\n \" It is not integer division. For integer division, use the `intDiv` function. \\n\" +\r\n \"\\n\" +\r\n \" When dividing by zero you get `inf`, `-inf`, or `nan`.\"\r\n },\r\n {\r\n \"name\": \"reverseUTF8\",\r\n \"def\": \"reverseUTF8(v)\",\r\n \"docText\": \"Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn`t throw an exception).\"\r\n },\r\n {\r\n \"name\": \"toDate\",\r\n \"def\": \"toDate(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"emptyArrayFloat64\",\r\n \"def\": \"emptyArrayFloat64(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"abs\",\r\n \"def\": \"abs(s, c)\",\r\n \"docText\": \"If the `s` string is non-empty and does not contain the `c` character at the end, it appends the `c` character to the end.\"\r\n },\r\n {\r\n \"name\": \"yesterday\",\r\n \"def\": \"yesterday(v)\",\r\n \"docText\": \"Accepts zero arguments and returns yesterday`s date at one of the moments of request execution. \\n\" +\r\n \"\\n\" +\r\n \" The same as `today() - 1`.\"\r\n },\r\n {\r\n \"name\": \"toMinute\",\r\n \"def\": \"toMinute(v)\",\r\n \"docText\": \"Converts a date with time to a UInt8 number containing the number of the minute of the hour (0-59).\"\r\n },\r\n {\r\n \"name\": \"bitXor\",\r\n \"def\": \"bitXor(a, b)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"minus\",\r\n \"def\": \"minus(a, b), a - b operator\",\r\n \"docText\": \"Calculates the difference. The result is always signed. \\n\" +\r\n \"\\n\" +\r\n \" You can also calculate whole numbers from a date or date with time. The idea is the same - see above for `plus`.\"\r\n },\r\n {\r\n \"name\": \"toDateTime\",\r\n \"def\": \"toDateTime(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"modulo\",\r\n \"def\": \"modulo(a, b), a % b operator\",\r\n \"docText\": \"Calculates the remainder after division. \\n\" +\r\n \"\\n\" +\r\n \" If arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion. The remainder is taken in the same sense as in C++. Truncated division is used for negative numbers. \\n\" +\r\n \"\\n\" +\r\n \" An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one.\"\r\n },\r\n {\r\n \"name\": \"bitmaskToArray\",\r\n \"def\": \"bitmaskToArray(num)\",\r\n \"docText\": \"Accepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order.\"\r\n },\r\n {\r\n \"name\": \"negate\",\r\n \"def\": \"negate(a), -a operator\",\r\n \"docText\": \"Calculates a number with the reverse sign. The result is always signed.\"\r\n },\r\n {\r\n \"name\": \"emptyArrayFloat32\",\r\n \"def\": \"emptyArrayFloat32(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"range\",\r\n \"def\": \"range(N)\",\r\n \"docText\": \"Returns an array of numbers from 0 to N-1. \\n\" +\r\n \"\\n\" +\r\n \" Just in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block.\"\r\n },\r\n {\r\n \"name\": \"arrayAll\",\r\n \"def\": \"arrayAll([func,] arr1, ...)\",\r\n \"docText\": \"Returns 1 if `func` returns something other than 0 for all the elements in `arr`. Otherwise, it returns 0.\"\r\n },\r\n {\r\n \"name\": \"toInt32\",\r\n \"def\": \"toInt32(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"unhex\",\r\n \"def\": \"unhex(str)\",\r\n \"docText\": \"Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits doesn`t have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn`t thrown). \\n\" +\r\n \"\\n\" +\r\n \" If you want to convert the result to a number, you can use the functions `reverse` and `reinterpretAsType`.\"\r\n },\r\n {\r\n \"name\": \"toFloat64\",\r\n \"def\": \"toFloat64(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"bitAnd\",\r\n \"def\": \"bitAnd(a, b)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"toStartOfYear\",\r\n \"def\": \"toStartOfYear(v)\",\r\n \"docText\": \"Rounds down a date or date with time to the first day of the year. \\n\" +\r\n \"\\n\" +\r\n \" Returns the date.\"\r\n },\r\n {\r\n \"name\": \"bitOr\",\r\n \"def\": \"bitOr(a, b)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"SEToRoot\",\r\n \"def\": \"SEToRoot(v)\",\r\n \"docText\": \"Accepts a UInt8 number - the ID of the search engine from the Yandex.Metrica dictionary. If any search engine matches the passed number, it returns a UInt8 number - the ID of the corresponding root search engine (for example, it converts Yandex.Images to Yandex). Otherwise, returns 0.\"\r\n },\r\n {\r\n \"name\": \"toRelativeMinuteNum\",\r\n \"def\": \"toRelativeMinuteNum(v)\",\r\n \"docText\": \"Converts a date with time or date to the number of the minute, starting from a certain fixed point in the past.\"\r\n },\r\n {\r\n \"name\": \"visitParamExtractInt\",\r\n \"def\": \"visitParamExtractInt(params, name)\",\r\n \"docText\": \"The same as for Int64.\"\r\n },\r\n {\r\n \"name\": \"emptyArrayInt16\",\r\n \"def\": \"emptyArrayInt16(v)\",\r\n \"docText\": \"Accepts zero arguments and returns an empty array of the appropriate type.\"\r\n },\r\n {\r\n \"name\": \"visitParamExtractString\",\r\n \"def\": \"visitParamExtractString(params, name)\",\r\n \"docText\": \"Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string. \"\r\n },\r\n {\r\n \"name\": \"arrayEnumerateUniq\",\r\n \"def\": \"arrayEnumerateUniq(arr, ...)\",\r\n \"docText\": \"Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value. \"\r\n },\r\n {\r\n \"name\": \"visitParamExtractUInt\",\r\n \"def\": \"visitParamExtractUInt(params, name)\",\r\n \"docText\": \"Parses UInt64 from the value of the field named `name`. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn`t exist, or it exists but doesn`t contain a number, it returns 0.\"\r\n },\r\n {\r\n \"name\": \"toTypeName\",\r\n \"def\": \"toTypeName(x)\",\r\n \"docText\": \"Gets the type name. Returns a string containing the type name of the passed argument.\"\r\n },\r\n {\r\n \"name\": \"empty\",\r\n \"def\": \"empty(v)\",\r\n \"docText\": \"Accepts an empty array as argument and returns an array of one element equal to the default value.\"\r\n },\r\n {\r\n \"name\": \"multiply\",\r\n \"def\": \"multiply(a, b), a * b operator\",\r\n \"docText\": \"Calculates the product of the numbers.\"\r\n },\r\n {\r\n \"name\": \"has\",\r\n \"def\": \"has('database', 'table', 'column')\",\r\n \"docText\": \"Accepts constant String columns - database name, table name and column name. Returns constant UInt8 value, equal to 1 if column exists, otherwise 0.\\n\" +\r\n \"\\n\" +\r\n \" If table doesn't exist than exception is thrown. \\n\" +\r\n \"\\n\" +\r\n \" For elements of nested data structure function checks existence of column. For nested data structure 0 is returned.\"\r\n },\r\n {\r\n \"name\": \"bitNot\",\r\n \"def\": \"bitNot(a)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"lessOrEquals\",\r\n \"def\": \"lessOrEquals(v)\",\r\n \"docText\": \"greaterOrEquals, >= operator\"\r\n },\r\n {\r\n \"name\": \"reinterpretAsInt64\",\r\n \"def\": \"reinterpretAsInt64(v)\",\r\n \"docText\": \"Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\"\r\n },\r\n {\r\n \"name\": \"IPv4NumToString\",\r\n \"def\": \"IPv4NumToString(num)\",\r\n \"docText\": \"Similar to IPv4NumToString, but using `xxx` instead of the last octet. \"\r\n },\r\n {\r\n \"name\": \"bitShiftLeft\",\r\n \"def\": \"bitShiftLeft(a, b)\",\r\n \"docText\": \"\"\r\n },\r\n {\r\n \"name\": \"dictGetInt16\",\r\n \"def\": \"dictGetInt16(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"toUInt32OrZero\",\r\n \"def\": \"toUInt32OrZero(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"regionIn\",\r\n \"def\": \"regionIn(lhs, rhs[, geobase])\",\r\n \"docText\": \"Checks whether a `lhs` region belongs to a `rhs` region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn`t belong. \\n\" +\r\n \"\\n\" +\r\n \" The relationship is reflexive - any region also belongs to itself.\"\r\n },\r\n {\r\n \"name\": \"reinterpretAsUInt8\",\r\n \"def\": \"reinterpretAsUInt8(v)\",\r\n \"docText\": \"Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\"\r\n },\r\n {\r\n \"name\": \"dictIsIn\",\r\n \"def\": \"dictIsIn(v)\",\r\n \"docText\": \"`dictIsIn(`dict_name`, child_id, ancestor_id)` \\n\" +\r\n \"\\n\" +\r\n \" - For the `dict_name` hierarchical dictionary, finds out whether the `child_id` key is located inside `ancestor_id` (or matches `ancestor_id`). Returns UInt8.\"\r\n },\r\n {\r\n \"name\": \"toSecond\",\r\n \"def\": \"toSecond(v)\",\r\n \"docText\": \"Converts a date with time to a UInt8 number containing the number of the second in the minute (0-59). \\n\" +\r\n \"\\n\" +\r\n \" Leap seconds are not accounted for.\"\r\n },\r\n {\r\n \"name\": \"least\",\r\n \"def\": \"least(a, b)\",\r\n \"docText\": \"Returns the least element of a and b.\"\r\n },\r\n {\r\n \"name\": \"countEqual\",\r\n \"def\": \"countEqual(arr, x)\",\r\n \"docText\": \"Returns the number of elements in the array equal to `x`. Equivalent to arrayCount(elem -> elem = x, arr).\"\r\n },\r\n {\r\n \"name\": \"IPv4StringToNum\",\r\n \"def\": \"IPv4StringToNum(s)\",\r\n \"docText\": \"The reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0.\"\r\n },\r\n {\r\n \"name\": \"replaceRegexpAll\",\r\n \"def\": \"replaceRegexpAll(haystack, pattern, replacement)\",\r\n \"docText\": \"This does the same thing, but replaces all the occurrences\"\r\n },\r\n {\r\n \"name\": \"SHA224\",\r\n \"def\": \"SHA224(v)\",\r\n \"docText\": \"Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32). \\n\" +\r\n \"\\n\" +\r\n \" The function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million). \"\r\n },\r\n {\r\n \"name\": \"URLHash\",\r\n \"def\": \"URLHash(url[, N])\",\r\n \"docText\": \"A fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization. \\n\" +\r\n \"\\n\" +\r\n \" URLHash(s) - Calculates a hash from a string without one of the trailing symbols /,? or # at the end, if present\"\r\n },\r\n {\r\n \"name\": \"equals\",\r\n \"def\": \"equals(v)\",\r\n \"docText\": \"

greaterOrEquals, >= operator

\"\r\n },\r\n {\r\n \"name\": \"plus\",\r\n \"def\": \"plus(a, b), a + b operator\",\r\n \"docText\": \"Calculates the sum of the numbers. \\n\" +\r\n \"\\n\" +\r\n \" You can also add whole numbers with a date or date and time. In the case of a date, adding a whole number means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds.\"\r\n },\r\n {\r\n \"name\": \"less\",\r\n \"def\": \"less(v)\",\r\n \"docText\": \"

greaterOrEquals, >= operator

\"\r\n },\r\n {\r\n \"name\": \"regionHierarchy\",\r\n \"def\": \"regionHierarchy(id[, geobase])\",\r\n \"docText\": \"Accepts a UInt32 number - the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain. \\n\" +\r\n \"\\n\" +\r\n \" Example: `regionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000]`.\"\r\n },\r\n {\r\n \"name\": \"toUInt64OrZero\",\r\n \"def\": \"toUInt64OrZero(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"rowNumberInAllBlocks\",\r\n \"def\": \"rowNumberInAllBlocks()\",\r\n \"docText\": \"Returns an incremental row number within all blocks that were processed by this function.\"\r\n },\r\n {\r\n \"name\": \"toDayOfWeek\",\r\n \"def\": \"toDayOfWeek(v)\",\r\n \"docText\": \"Converts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7).\"\r\n },\r\n {\r\n \"name\": \"bar\",\r\n \"def\": \"bar(v)\",\r\n \"docText\": \"Allows building a unicode-art diagram. \\n\" +\r\n \"\\n\" +\r\n \" bar(x, min, max, width) - Draws a band with a width proportional to (x - min) and equal to `width` characters when x\"\r\n },\r\n {\r\n \"name\": \"if\",\r\n \"def\": \"if(v)\",\r\n \"docText\": \"The suffix -`If` can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument - a condition (Uint8 type). \"\r\n },\r\n {\r\n \"name\": \"regionToArea\",\r\n \"def\": \"regionToArea(id[, geobase])\",\r\n \"docText\": \"Converts a region to an area (type 5 in the geobase). In every other way, this function is the same as `regionToCity`.\"\r\n },\r\n {\r\n \"name\": \"dictGetUInt16\",\r\n \"def\": \"dictGetUInt16(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"toUInt8\",\r\n \"def\": \"toUInt8(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"toUInt16\",\r\n \"def\": \"toUInt16(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"path\",\r\n \"def\": \"path(URL)\",\r\n \"docText\": \"- The same thing, but without the protocol and host in the result. The / element (root) is not included.\"\r\n },\r\n {\r\n \"name\": \"extractURLParameters\",\r\n \"def\": \"extractURLParameters(URL)\",\r\n \"docText\": \"- Gets an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way.\"\r\n },\r\n {\r\n \"name\": \"OSHierarchy\",\r\n \"def\": \"OSHierarchy(v)\",\r\n \"docText\": \"Accepts a UInt8 number - the ID of the operating system from the Yandex.Metrica dictionary. Returns an array with a hierarchy of operating systems. Similar to the `regionHierarchy` function.\"\r\n },\r\n {\r\n \"name\": \"cutQueryStringAndFragment\",\r\n \"def\": \"cutQueryStringAndFragment(v)\",\r\n \"docText\": \"Removes the query-string and fragment identifier. The question mark and number sign are also removed.\"\r\n },\r\n {\r\n \"name\": \"timeSlots\",\r\n \"def\": \"timeSlots(StartTime, Duration)\",\r\n \"docText\": \"For a time interval starting at `StartTime` and continuing for `Duration` seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour. \\n\" +\r\n \"\\n\" +\r\n \" For example, `timeSlots(toDateTime(`2012-01-01 12:20:00`), toUInt32(600)) = [toDateTime(`2012-01-01 12:00:00`), toDateTime(`2012-01-01 12:30:00`)]`. \\n\" +\r\n \"\\n\" +\r\n \" This is necessary for searching for pageviews in the corresponding session.\"\r\n },\r\n {\r\n \"name\": \"toUInt32\",\r\n \"def\": \"toUInt32(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"toStartOfMinute\",\r\n \"def\": \"toStartOfMinute(v)\",\r\n \"docText\": \"Rounds down a date with time to the start of the minute.\"\r\n },\r\n {\r\n \"name\": \"version\",\r\n \"def\": \"version()\",\r\n \"docText\": \"Returns server's version as a string.\"\r\n },\r\n {\r\n \"name\": \"toUInt64\",\r\n \"def\": \"toUInt64(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"toInt16\",\r\n \"def\": \"toInt16(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"reinterpretAsInt16\",\r\n \"def\": \"reinterpretAsInt16(v)\",\r\n \"docText\": \"Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\"\r\n },\r\n {\r\n \"name\": \"toInt64\",\r\n \"def\": \"toInt64(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"toFixedString\",\r\n \"def\": \"toFixedString(s, N)\",\r\n \"docText\": \"Converts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant. If the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown.\"\r\n },\r\n {\r\n \"name\": \"toFloat32\",\r\n \"def\": \"toFloat32(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"dictGetHierarchy\",\r\n \"def\": \"dictGetHierarchy(v)\",\r\n \"docText\": \"`dictGetHierarchy(`dict_name`, id)` \\n\" +\r\n \"\\n\" +\r\n \" - For the `dict_name` hierarchical dictionary, returns an array of dictionary keys starting from `id` and continuing along the chain of parent elements. Returns Array(UInt64).\"\r\n },\r\n {\r\n \"name\": \"dictGetInt64\",\r\n \"def\": \"dictGetInt64(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"CAST\",\r\n \"def\": \"CAST(x, t)\",\r\n \"docText\": \"Casts `x` to the `t` data type. \\n\" +\r\n \"\\n\" +\r\n \" The syntax `CAST(x AS t)` is also supported. \\n\"\r\n },\r\n {\r\n \"name\": \"toRelativeSecondNum\",\r\n \"def\": \"toRelativeSecondNum(v)\",\r\n \"docText\": \"Converts a date with time or date to the number of the second, starting from a certain fixed point in the past.\"\r\n },\r\n {\r\n \"name\": \"toUInt8OrZero\",\r\n \"def\": \"toUInt8OrZero(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"toStartOfMonth\",\r\n \"def\": \"toStartOfMonth(v)\",\r\n \"docText\": \"Rounds down a date or date with time to the first day of the month. \\n\" +\r\n \"\\n\" +\r\n \" Returns the date.\"\r\n },\r\n {\r\n \"name\": \"rand64\",\r\n \"def\": \"rand64(v)\",\r\n \"docText\": \"Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers. \\n\" +\r\n \"\\n\" +\r\n \" Uses a linear congruential generator.\"\r\n },\r\n {\r\n \"name\": \"toInt8OrZero\",\r\n \"def\": \"toInt8OrZero(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"toFloat32OrZero\",\r\n \"def\": \"toFloat32OrZero(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"toYear\",\r\n \"def\": \"toYear(v)\",\r\n \"docText\": \"Converts a date or date with time to a UInt16 number containing the year number (AD).\"\r\n },\r\n {\r\n \"name\": \"atan\",\r\n \"def\": \"atan(x)\",\r\n \"docText\": \"The arc tangent.\"\r\n },\r\n {\r\n \"name\": \"toFloat64OrZero\",\r\n \"def\": \"toFloat64OrZero(v)\",\r\n \"docText\": \"Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. \"\r\n },\r\n {\r\n \"name\": \"arrayEnumerate\",\r\n \"def\": \"arrayEnumerate(arr, ...)\",\r\n \"docText\": \"Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value.\"\r\n },\r\n {\r\n \"name\": \"toDayOfMonth\",\r\n \"def\": \"toDayOfMonth(v)\",\r\n \"docText\": \"Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31).\"\r\n },\r\n {\r\n \"name\": \"dictGetUInt64\",\r\n \"def\": \"dictGetUInt64(v)\",\r\n \"docText\": \"dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition.\"\r\n },\r\n {\r\n \"name\": \"IPv4NumToStringClassC\",\r\n \"def\": \"IPv4NumToStringClassC(num)\",\r\n \"docText\": \"Similar to IPv4NumToString, but using `xxx` instead of the last octet. \"\r\n },\r\n {\r\n \"name\": \"reinterpretAsString\",\r\n \"def\": \"reinterpretAsString(v)\",\r\n \"docText\": \"This function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long.\"\r\n },\r\n {\r\n \"name\": \"toHour\",\r\n \"def\": \"toHour(v)\",\r\n \"docText\": \"Converts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23). \\n\" +\r\n \"\\n\" +\r\n \" This function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true - even in Moscow the clocks were once changed at a different time).\"\r\n },\r\n {\r\n \"name\": \"ignore\",\r\n \"def\": \"ignore(...)\",\r\n \"docText\": \"A function that accepts any arguments and always returns 0. \\n\" +\r\n \"\\n\" +\r\n \" However, the argument is still calculated. This can be used for benchmarks.\"\r\n },\r\n {\r\n \"name\": \"arrayJoin\",\r\n \"def\": \"arrayJoin(v)\",\r\n \"docText\": \"This is a very unusual function.\\n\" +\r\n \"\\n\" +\r\n \"Normal functions don't change a set of rows, but just change the values in each row (map). Aggregate functions compress a set of rows (fold or reduce). The 'arrayJoin' function takes each row and generates a set of rows (unfold).\\n\" +\r\n \"\\n\" +\r\n \"This function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array. All the values in columns are simply copied, except the values in the column where this function is applied - it is replaced with the corresponding array value.\\n\" +\r\n \"\\n\" +\r\n \"A query can use multiple 'arrayJoin' functions. In this case, the transformation is performed multiple times.\\n\" +\r\n \"\\n\" +\r\n \"Note the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities.\"\r\n },\r\n {\r\n \"name\": \"length\",\r\n \"def\": \"length(v)\",\r\n \"docText\": \"Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception). \\n\" +\r\n \"\\n\" +\r\n \" The result type is UInt64.\"\r\n },\r\n {\r\n \"name\": \"tuple\",\r\n \"def\": \"tuple(tuple, n), operator x.N\",\r\n \"docText\": \"A function that allows getting columns from a tuple. \\n\" +\r\n \"\\n\" +\r\n \" `N` is the column index, starting from 1. `N` must be a constant. `N` must be a strict postive integer no greater than the size of the tuple. \\n\" +\r\n \"\\n\" +\r\n \" There is no cost to execute the function.\"\r\n },\r\n {\r\n \"name\": \"extractURLParameterNames\",\r\n \"def\": \"extractURLParameterNames(URL)\",\r\n \"docText\": \"- Gets an array of name=value strings corresponding to the names of URL parameters. The values are not decoded in any way.\"\r\n },\r\n {\r\n \"name\": \"tupleElement\",\r\n \"def\": \"tupleElement(tuple, n), operator x.N\",\r\n \"docText\": \"A function that allows getting columns from a tuple. \\n\" +\r\n \"\\n\" +\r\n \" `N` is the column index, starting from 1. `N` must be a constant. `N` must be a strict postive integer no greater than the size of the tuple. \\n\" +\r\n \"\\n\" +\r\n \" There is no cost to execute the function.\"\r\n },\r\n {\r\n \"name\": \"in\",\r\n \"def\": \"in(v)\",\r\n \"docText\": \"What to do when the amount of data exceeds one of the limits: `throw` or `break`. By default, throw.\"\r\n },\r\n {\r\n \"name\": \"globalIn\",\r\n \"def\": \"globalIn(v)\",\r\n \"docText\": \"See the section `IN operators`.\"\r\n },\r\n {\r\n \"name\": \"isFinite\",\r\n \"def\": \"isFinite(x)\",\r\n \"docText\": \"Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0.\"\r\n },\r\n {\r\n \"name\": \"isNaN\",\r\n \"def\": \"isNaN(x)\",\r\n \"docText\": \"Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0.\"\r\n },\r\n {\r\n \"name\": \"isInfinite\",\r\n \"def\": \"isInfinite(x)\",\r\n \"docText\": \"Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. \\n\" +\r\n \"\\n\" +\r\n \" Note that 0 is returned for a NaN.\"\r\n },\r\n {\r\n \"name\": \"transform\",\r\n \"def\": \"transform(v)\",\r\n \"docText\": \"Transforms a value according to the explicitly defined mapping of some elements to other ones. \\n\" +\r\n \"\\n\" +\r\n \" There are two variations of this function: \\n\" +\r\n \"\\n\" +\r\n \" 1. `transform(x, array_from, array_to, default)` \"\r\n },\r\n {\r\n \"name\": \"rand\",\r\n \"def\": \"rand(v)\",\r\n \"docText\": \"Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers. \\n\" +\r\n \"\\n\" +\r\n \" Uses a linear congruential generator.\"\r\n },\r\n {\r\n \"name\": \"reinterpretAsUInt16\",\r\n \"def\": \"reinterpretAsUInt16(v)\",\r\n \"docText\": \"Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\"\r\n },\r\n {\r\n \"name\": \"pi\",\r\n \"def\": \"pi(v)\",\r\n \"docText\": \"Maximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000.\"\r\n },\r\n {\r\n \"name\": \"reinterpretAsUInt32\",\r\n \"def\": \"reinterpretAsUInt32(v)\",\r\n \"docText\": \"Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\"\r\n },\r\n {\r\n \"name\": \"reinterpretAsUInt64\",\r\n \"def\": \"reinterpretAsUInt64(v)\",\r\n \"docText\": \"Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\"\r\n },\r\n {\r\n \"name\": \"reinterpretAsInt8\",\r\n \"def\": \"reinterpretAsInt8(v)\",\r\n \"docText\": \"Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\"\r\n },\r\n {\r\n \"name\": \"upperUTF8\",\r\n \"def\": \"upperUTF8(v)\",\r\n \"docText\": \"Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. It doesn`t detect the language. So for Turkish the result might not be exactly correct. \"\r\n },\r\n {\r\n \"name\": \"reinterpretAsInt32\",\r\n \"def\": \"reinterpretAsInt32(v)\",\r\n \"docText\": \"Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\"\r\n },\r\n {\r\n \"name\": \"reinterpretAsFloat32\",\r\n \"def\": \"reinterpretAsFloat32(v)\",\r\n \"docText\": \"Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\"\r\n },\r\n {\r\n \"name\": \"reinterpretAsFloat64\",\r\n \"def\": \"reinterpretAsFloat64(v)\",\r\n \"docText\": \"Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\"\r\n },\r\n {\r\n \"name\": \"reinterpretAsDate\",\r\n \"def\": \"reinterpretAsDate(v)\",\r\n \"docText\": \"Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\"\r\n },\r\n {\r\n \"name\": \"reinterpretAsDateTime\",\r\n \"def\": \"reinterpretAsDateTime(v)\",\r\n \"docText\": \"Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch.\"\r\n },\r\n {\r\n \"name\": \"roundToExp2\",\r\n \"def\": \"roundToExp2(num)\",\r\n \"docText\": \"Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two.\"\r\n },\r\n {\r\n \"name\": \"upper\",\r\n \"def\": \"upper(v)\",\r\n \"docText\": \"Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. It doesn`t detect the language. So for Turkish the result might not be exactly correct.\"\r\n },\r\n {\r\n \"name\": \"positionUTF8\",\r\n \"def\": \"positionUTF8(haystack, needle)\",\r\n \"docText\": \"The same as `position`, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception). \\n\" +\r\n \"\\n\" +\r\n \" There's also positionCaseInsensitiveUTF8 function.\"\r\n },\r\n {\r\n \"name\": \"roundDuration\",\r\n \"def\": \"roundDuration(num)\",\r\n \"docText\": \"Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length.\"\r\n },\r\n {\r\n \"name\": \"roundAge\",\r\n \"def\": \"roundAge(num)\",\r\n \"docText\": \"Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to numbers from the set: 18, 25, 35, 45. This function is specific to Yandex.Metrica and used for implementing the report on user age.\"\r\n },\r\n {\r\n \"name\": \"round\",\r\n \"def\": \"round(num)\",\r\n \"docText\": \"Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to numbers from the set: 18, 25, 35, 45. This function is specific to Yandex.Metrica and used for implementing the report on user age.\"\r\n },\r\n {\r\n \"name\": \"floor\",\r\n \"def\": \"floor(x[, N])\",\r\n \"docText\": \"Returns a rounder number that is less than or equal to `x`.\"\r\n },\r\n {\r\n \"name\": \"notEmpty\",\r\n \"def\": \"notEmpty(v)\",\r\n \"docText\": \"Returns 0 for an empty array, or 1 for a non-empty array. \\n\" +\r\n \"\\n\" +\r\n \" The result type is UInt8. \\n\" +\r\n \"\\n\" +\r\n \" The function also works for strings.\"\r\n },\r\n {\r\n \"name\": \"lengthUTF8\",\r\n \"def\": \"lengthUTF8(v)\",\r\n \"docText\": \"Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception). \\n\" +\r\n \"\\n\" +\r\n \" The result type is UInt64.\"\r\n },\r\n {\r\n \"name\": \"lower\",\r\n \"def\": \"lower(v)\",\r\n \"docText\": \"Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. It doesn`t detect the language. \"\r\n },\r\n {\r\n \"name\": \"lowerUTF8\",\r\n \"def\": \"lowerUTF8(v)\",\r\n \"docText\": \"Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. It doesn`t detect the language. \"\r\n },\r\n {\r\n \"name\": \"reverse\",\r\n \"def\": \"reverse(v)\",\r\n \"docText\": \"Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn`t throw an exception).\"\r\n },\r\n {\r\n \"name\": \"URLPathHierarchy\",\r\n \"def\": \"URLPathHierarchy(URL)\",\r\n \"docText\": \"- The same thing, but without the protocol and host in the result. The / element (root) is not included. \"\r\n },\r\n {\r\n \"name\": \"substringUTF8\",\r\n \"def\": \"substringUTF8(s, offset, length)\",\r\n \"docText\": \"The same as `substring`, but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception).\"\r\n },\r\n {\r\n \"name\": \"appendTrailingCharIfAbsent\",\r\n \"def\": \"appendTrailingCharIfAbsent(s, c)\",\r\n \"docText\": \"If the `s` string is non-empty and does not contain the `c` character at the end, it appends the `c` character to the end.\"\r\n },\r\n {\r\n \"name\": \"alphaTokens\",\r\n \"def\": \"alphaTokens(s)\",\r\n \"docText\": \"Selects substrings of consecutive bytes from the range a-z and A-Z. \\n\" +\r\n \"\\n\" +\r\n \" Returns an array of selected substrings.\"\r\n },\r\n {\r\n \"name\": \"splitByChar\",\r\n \"def\": \"splitByChar(separator, s)\",\r\n \"docText\": \"Splits a string into substrings, using `separator` as the separator. \\n\" +\r\n \"\\n\" +\r\n \" `separator` must be a string constant consisting of exactly one character. \\n\" +\r\n \"\\n\" +\r\n \" Returns an array of selected substrings\"\r\n },\r\n {\r\n \"name\": \"arrayStringConcat\",\r\n \"def\": \"arrayStringConcat(arr[, separator])\",\r\n \"docText\": \"Concatenates strings from the array elements, using `separator` as the separator. \\n\" +\r\n \"\\n\" +\r\n \" `separator` is a string constant, an optional parameter. By default it is an empty string. \\n\" +\r\n \"\\n\" +\r\n \" Returns a string.\"\r\n },\r\n {\r\n \"name\": \"replaceAll\",\r\n \"def\": \"replaceAll(haystack, pattern, replacement)\",\r\n \"docText\": \"Replaces all occurrences of the `pattern` substring in `haystack` with the `replacement` substring.\"\r\n },\r\n {\r\n \"name\": \"replaceRegexpOne\",\r\n \"def\": \"replaceRegexpOne(haystack, pattern, replacement)\",\r\n \"docText\": \"Replacement using the `pattern` regular expression. A re2 regular expression. Replaces only the first occurrence, if it exists. \\n\" +\r\n \"\\n\" +\r\n \" A pattern can be specified as `replacement`. \"\r\n },\r\n {\r\n \"name\": \"cbrt\",\r\n \"def\": \"cbrt(x)\",\r\n \"docText\": \"Accepts a numeric argument and returns a Float64 number close to the cubic root of the argument.\"\r\n },\r\n {\r\n \"name\": \"match\",\r\n \"def\": \"match(pattern)(time, cond1, cond2, ...)\",\r\n \"docText\": \"Pattern matching for event chains. \\n\" +\r\n \"\\n\" +\r\n \" `pattern` is a string containing a pattern to match. The pattern is similar to a regular expression.\"\r\n },\r\n {\r\n \"name\": \"cutURLParameter\",\r\n \"def\": \"cutURLParameter(URL, name)\",\r\n \"docText\": \"Removes the URL parameter named `name`, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument.\"\r\n },\r\n {\r\n \"name\": \"like\",\r\n \"def\": \"like(haystack, pattern), haystack NOT LIKE pattern operator\",\r\n \"docText\": \"The same thing as `like`, but negative.\"\r\n },\r\n {\r\n \"name\": \"domain\",\r\n \"def\": \"domain(v)\",\r\n \"docText\": \"- Selects the part of the domain that includes top-level subdomains up to the `first significant subdomain` (see the explanation above). \\n\" +\r\n \"\\n\" +\r\n \" For example, cutToFirstSignificantSubdomain(`https://news.yandex.com.tr/`) = `yandex.com.tr`.\"\r\n },\r\n {\r\n \"name\": \"domainWithoutWWW\",\r\n \"def\": \"domainWithoutWWW(v)\",\r\n \"docText\": \"- Selects the domain and removes no more than one `www.` from the beginning of it, if present.\"\r\n },\r\n {\r\n \"name\": \"firstSignificantSubdomain\",\r\n \"def\": \"firstSignificantSubdomain(v)\",\r\n \"docText\": \"- Selects the part of the domain that includes top-level subdomains up to the `first significant subdomain` (see the explanation above). \\n\" +\r\n \"\\n\" +\r\n \" For example, cutToFirstSignificantSubdomain(`https://news.yandex.com.tr/`) = `yandex.com.tr`.\"\r\n },\r\n {\r\n \"name\": \"queryString\",\r\n \"def\": \"queryString(v)\",\r\n \"docText\": \"Removes the query-string and fragment identifier. The question mark and number sign are also removed.\"\r\n },\r\n {\r\n \"name\": \"queryStringAndFragment\",\r\n \"def\": \"queryStringAndFragment(v)\",\r\n \"docText\": \"Removes the query-string and fragment identifier. The question mark and number sign are also removed.\"\r\n },\r\n {\r\n \"name\": \"extractURLParameter\",\r\n \"def\": \"extractURLParameter(URL)\",\r\n \"docText\": \"- Gets an array of name=value strings corresponding to the names of URL parameters. The values are not decoded in any way.\"\r\n },\r\n {\r\n \"name\": \"URLHierarchy\",\r\n \"def\": \"URLHierarchy(URL)\",\r\n \"docText\": \"- Gets an array containing the URL trimmed to the `/`, `?` characters in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:\"\r\n },\r\n {\r\n \"name\": \"cutToFirstSignificantSubdomain\",\r\n \"def\": \"cutToFirstSignificantSubdomain(v)\",\r\n \"docText\": \"- Selects the part of the domain that includes top-level subdomains up to the `first significant subdomain` (see the explanation above). \\n\" +\r\n \"\\n\" +\r\n \" For example, cutToFirstSignificantSubdomain(`https://news.yandex.com.tr/`) = `yandex.com.tr`.\"\r\n },\r\n {\r\n \"name\": \"cutWWW\",\r\n \"def\": \"cutWWW(v)\",\r\n \"docText\": \"Removes no more than one `www.` from the beginning of the URL`s domain, if present.\"\r\n },\r\n {\r\n \"name\": \"cutQueryString\",\r\n \"def\": \"cutQueryString(v)\",\r\n \"docText\": \"Removes the query-string and fragment identifier. The question mark and number sign are also removed.\"\r\n },\r\n {\r\n \"name\": \"cutFragment\",\r\n \"def\": \"cutFragment(v)\",\r\n \"docText\": \"Removes the fragment identifier. The number sign is also removed.\"\r\n },\r\n {\r\n \"name\": \"visitParamHas\",\r\n \"def\": \"visitParamHas(params, name)\",\r\n \"docText\": \"Checks whether there is a field with the `name` name.\"\r\n },\r\n {\r\n \"name\": \"visitParamExtractFloat\",\r\n \"def\": \"visitParamExtractFloat(params, name)\",\r\n \"docText\": \"The same as for Float64.\"\r\n },\r\n {\r\n \"name\": \"visitParamExtractBool\",\r\n \"def\": \"visitParamExtractBool(params, name)\",\r\n \"docText\": \"Parses a true/false value. The result is UInt8.\"\r\n },\r\n {\r\n \"name\": \"visitParamExtractRaw\",\r\n \"def\": \"visitParamExtractRaw(params, name)\",\r\n \"docText\": \"Returns the value of a field, including separators.\"\r\n },\r\n {\r\n \"name\": \"exp\",\r\n \"def\": \"exp(x)\",\r\n \"docText\": \"Accepts a numeric argument and returns a Float64 number close to 10 in power of x.\"\r\n },\r\n {\r\n \"name\": \"exp2\",\r\n \"def\": \"exp2(x)\",\r\n \"docText\": \"Accepts a numeric argument and returns a Float64 number close to 2 in power of x.\"\r\n },\r\n {\r\n \"name\": \"exp10\",\r\n \"def\": \"exp10(x)\",\r\n \"docText\": \"Accepts a numeric argument and returns a Float64 number close to 10 in power of x.\"\r\n },\r\n {\r\n \"name\": \"tgamma\",\r\n \"def\": \"tgamma(x)\",\r\n \"docText\": \"Gamma function.\"\r\n },\r\n {\r\n \"name\": \"log10\",\r\n \"def\": \"log10(x)\",\r\n \"docText\": \"Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument.\"\r\n },\r\n {\r\n \"name\": \"sqrt\",\r\n \"def\": \"sqrt(x)\",\r\n \"docText\": \"Accepts a numeric argument and returns a Float64 number close to the square root of the argument.\"\r\n },\r\n {\r\n \"name\": \"erf\",\r\n \"def\": \"erf(v)\",\r\n \"docText\": \"What to do when the amount of data exceeds one of the limits: `throw` or `break`. By default, throw.\"\r\n },\r\n {\r\n \"name\": \"erfc\",\r\n \"def\": \"erfc(x)\",\r\n \"docText\": \"Accepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large `x` values.\"\r\n },\r\n {\r\n \"name\": \"lgamma\",\r\n \"def\": \"lgamma(x)\",\r\n \"docText\": \"The logarithm of the gamma function.\"\r\n },\r\n {\r\n \"name\": \"sin\",\r\n \"def\": \"sin(x)\",\r\n \"docText\": \"Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. \\n\" +\r\n \"\\n\" +\r\n \" Note that 0 is returned for a NaN.\"\r\n },\r\n {\r\n \"name\": \"cos\",\r\n \"def\": \"cos(x)\",\r\n \"docText\": \"The arc cosine.\"\r\n },\r\n {\r\n \"name\": \"tan\",\r\n \"def\": \"tan(x)\",\r\n \"docText\": \"The arc tangent.\"\r\n },\r\n {\r\n \"name\": \"pow\",\r\n \"def\": \"pow(x, y)\",\r\n \"docText\": \"x in power of y.\"\r\n }\r\n ]\r\n };\r\n\r\n p.MacrosCompletions = function () {\r\n return [\r\n {\r\n \"name\": \"$table\",\r\n \"def\": \"$table\",\r\n \"docText\": \"Replaced with selected table name from Query Builder\"\r\n },\r\n {\r\n \"name\": \"$timeCol\",\r\n \"def\": \"$timeCol\",\r\n \"docText\": \"Replaced with `Date:Col` value from Query Builder\"\r\n },\r\n {\r\n \"name\": \"$dateTimeCol\",\r\n \"def\": \"$dateTimeCol\",\r\n \"docText\": \"Replaced with `Column:DateTime` or `Column:TimeStamp` value from Query Builder\"\r\n },\r\n {\r\n \"name\": \"$from\",\r\n \"def\": \"$from\",\r\n \"docText\": \"Replaced with timestamp/1000 value of selected `Time Range:From`\"\r\n },\r\n {\r\n \"name\": \"$to\",\r\n \"def\": \"$to\",\r\n \"docText\": \"Replaced with timestamp/1000 value of selected `Time Range:To`\"\r\n },\r\n {\r\n \"name\": \"$interval\",\r\n \"def\": \"$interval\",\r\n \"docText\": \"Replaced with selected `Group by time interval` value (as a number of seconds)\"\r\n },\r\n {\r\n \"name\": \"$timeFilter\",\r\n \"def\": \"$timeFilter\",\r\n \"docText\": \"Replaced with currently selected `Time Range`. Requires `Column:Date` and `Column:DateTime` or `Column:TimeStamp` to be selected\"\r\n },\r\n {\r\n \"name\": \"$timeSeries\",\r\n \"def\": \"$timeSeries\",\r\n \"docText\": \"Replaced with special ClickHouse construction to convert results as time-series data. Use it as `SELECT $timeSeries...`. Require `Column:DateTime` or `Column:TimeStamp` to be selected\"\r\n },\r\n {\r\n \"name\": \"$rate\",\r\n \"def\": \"$rate(cols...)\",\r\n \"docText\": \"Converts query results as `change rate per interval`. Can be used to display changes-per-second.\" +\r\n \"\\n\" +\r\n \"Example:\\n $rate(countIf(Type = 200) AS good, countIf(Type != 200) AS bad) FROM requests\"\r\n },\r\n {\r\n \"name\": \"$columns\",\r\n \"def\": \"$columns(key, value)\",\r\n \"docText\": \"Query values as array of [key, value], where key will be used as label. Can be used to display multiple lines at graph\" +\r\n \"\\n\" +\r\n \"Example:\\n $columns(OSName, count(*) c) FROM requests\"\r\n },\r\n {\r\n \"name\": \"$rateColumns\",\r\n \"def\": \"$rateColumns(key, value)\",\r\n \"docText\": \"Is a combination of `$columns` and `$rate` .\" +\r\n \"\\n\" +\r\n \"Example:\\n $rateColumns(OS, count(*) c) FROM requests\"\r\n },\r\n {\r\n \"name\": \"$unescape\",\r\n \"def\": \"$unescape($variable)\",\r\n \"docText\": \"Unescapes variable value by removing single quotes\" +\r\n \"\\n\" +\r\n \"Example:\\n SELECT $unescape($column) FROM requests WHERE $unescape($column) = 5\"\r\n }\r\n ];\r\n };\r\n\r\n p.re = function (list) {\r\n return list.join(\"|\")\r\n };\r\n\r\n exports.ClickhouseInfo = p;\r\n});"]} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/datasource.d.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/datasource.d.ts deleted file mode 100644 index ee92930..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/datasource.d.ts +++ /dev/null @@ -1,28 +0,0 @@ -/// -import AdhocCtrl from './adhoc'; -export declare class ClickHouseDatasource { - private $q; - private backendSrv; - private templateSrv; - type: string; - name: string; - supportMetrics: boolean; - url: string; - directUrl: string; - basicAuth: any; - withCredentials: any; - usePOST: boolean; - addCorsHeader: boolean; - responseParser: any; - adhocCtrl: AdhocCtrl; - /** @ngInject */ - constructor(instanceSettings: any, $q: any, backendSrv: any, templateSrv: any); - _request(query: any): any; - query(options: any): any; - metricFindQuery(query: any, options?: any): any; - testDatasource(): any; - _seriesQuery(query: any): any; - targetContainsTemplate(target: any): any; - getTagKeys(): any; - getTagValues(options: any): Promise; -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/datasource.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/datasource.js deleted file mode 100644 index 64ecb81..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/datasource.js +++ /dev/null @@ -1,173 +0,0 @@ -/// -System.register(['lodash', './sql_series', './sql_query', './response_parser', './adhoc'], function(exports_1) { - var lodash_1, sql_series_1, sql_query_1, response_parser_1, adhoc_1; - var ClickHouseDatasource; - return { - setters:[ - function (lodash_1_1) { - lodash_1 = lodash_1_1; - }, - function (sql_series_1_1) { - sql_series_1 = sql_series_1_1; - }, - function (sql_query_1_1) { - sql_query_1 = sql_query_1_1; - }, - function (response_parser_1_1) { - response_parser_1 = response_parser_1_1; - }, - function (adhoc_1_1) { - adhoc_1 = adhoc_1_1; - }], - execute: function() { - ClickHouseDatasource = (function () { - /** @ngInject */ - function ClickHouseDatasource(instanceSettings, $q, backendSrv, templateSrv) { - this.$q = $q; - this.backendSrv = backendSrv; - this.templateSrv = templateSrv; - this.type = 'clickhouse'; - this.name = instanceSettings.name; - this.supportMetrics = true; - this.responseParser = new response_parser_1.default(); - this.url = instanceSettings.url; - this.directUrl = instanceSettings.directUrl; - this.basicAuth = instanceSettings.basicAuth; - this.withCredentials = instanceSettings.withCredentials; - this.addCorsHeader = instanceSettings.jsonData.addCorsHeader; - this.usePOST = instanceSettings.jsonData.usePOST; - this.adhocCtrl = new adhoc_1.default(); - } - ClickHouseDatasource.prototype._request = function (query) { - var options = { - url: this.url - }; - if (this.usePOST) { - options.method = 'POST'; - options.data = query; - } - else { - options.method = 'GET'; - options.url += '/?query=' + encodeURIComponent(query); - } - if (this.basicAuth || this.withCredentials) { - options.withCredentials = true; - } - options.headers = options.headers || {}; - if (this.basicAuth) { - options.headers.Authorization = this.basicAuth; - } - if (this.addCorsHeader) { - if (this.usePOST) { - options.url += "?add_http_cors_header=1"; - } - else { - options.url += "&add_http_cors_header=1"; - } - } - return this.backendSrv.datasourceRequest(options).then(function (result) { - return result.data; - }); - }; - ; - ClickHouseDatasource.prototype.query = function (options) { - var _this = this; - var queries = [], q, adhocFilters = this.templateSrv.getAdhocFilters(this.name); - lodash_1.default.map(options.targets, function (target) { - if (!target.hide && target.query) { - var queryModel = new sql_query_1.default(target, _this.templateSrv, options); - q = queryModel.replace(options, adhocFilters); - queries.push(q); - } - }); - // No valid targets, return the empty result to save a round trip. - if (lodash_1.default.isEmpty(queries)) { - var d = this.$q.defer(); - d.resolve({ data: [] }); - return d.promise; - } - var allQueryPromise = lodash_1.default.map(queries, function (query) { - return _this._seriesQuery(query); - }); - return this.$q.all(allQueryPromise).then(function (responses) { - var result = [], i = 0; - lodash_1.default.each(responses, function (response) { - var target = options.targets[i]; - i++; - if (!response || !response.rows) { - return; - } - var sqlSeries = new sql_series_1.default({ - series: response.data, - meta: response.meta, - tillNow: options.rangeRaw.to === 'now', - from: sql_query_1.default.convertTimestamp(options.range.from, target.dateTimeType), - to: sql_query_1.default.convertTimestamp(options.range.to, target.dateTimeType) - }); - if (target.format === 'table') { - lodash_1.default.each(sqlSeries.toTable(), function (data) { - result.push(data); - }); - } - else { - lodash_1.default.each(sqlSeries.toTimeSeries(), function (data) { - result.push(data); - }); - } - }); - return { data: result }; - }); - }; - ; - ClickHouseDatasource.prototype.metricFindQuery = function (query, options) { - var interpolated; - try { - var scopedVars = {}; - if (options && options.range) { - scopedVars['from'] = { - text: options.range.from.valueOf(), - value: options.range.from.valueOf() - }; - scopedVars['to'] = { - text: options.range.to.valueOf(), - value: options.range.to.valueOf() - }; - } - interpolated = this.templateSrv.replace(query, scopedVars, sql_query_1.default.interpolateQueryExpr); - } - catch (err) { - return this.$q.reject(err); - } - return this._seriesQuery(interpolated) - .then(lodash_1.default.curry(this.responseParser.parse)(query)); - }; - ; - ClickHouseDatasource.prototype.testDatasource = function () { - return this.metricFindQuery('SELECT 1').then(function () { - return { status: "success", message: "Data source is working", title: "Success" }; - }); - }; - ; - ClickHouseDatasource.prototype._seriesQuery = function (query) { - query = query.replace(/(?:\r\n|\r|\n)/g, ' '); - query += ' FORMAT JSON'; - return this._request(query); - }; - ; - ClickHouseDatasource.prototype.targetContainsTemplate = function (target) { - return this.templateSrv.variableExists(target.expr); - }; - ; - ClickHouseDatasource.prototype.getTagKeys = function () { - return this.adhocCtrl.GetTagKeys(this); - }; - ClickHouseDatasource.prototype.getTagValues = function (options) { - return this.adhocCtrl.GetTagValues(options); - }; - return ClickHouseDatasource; - })(); - exports_1("ClickHouseDatasource", ClickHouseDatasource); - } - } -}); -//# sourceMappingURL=datasource.js.map \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/datasource.js.map b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/datasource.js.map deleted file mode 100644 index 0788e1b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/datasource.js.map +++ /dev/null @@ -1 +0,0 @@ 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\ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/datasource.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/datasource.ts deleted file mode 100644 index 9989b6f..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/datasource.ts +++ /dev/null @@ -1,182 +0,0 @@ -/// - -import _ from 'lodash'; - -import SqlSeries from './sql_series'; -import SqlQuery from './sql_query'; -import ResponseParser from './response_parser'; -import AdhocCtrl from './adhoc'; - -export class ClickHouseDatasource { - type: string; - name: string; - supportMetrics: boolean; - url: string; - directUrl: string; - basicAuth: any; - withCredentials: any; - usePOST: boolean; - addCorsHeader: boolean; - responseParser: any; - adhocCtrl: AdhocCtrl; - - /** @ngInject */ - constructor(instanceSettings, - private $q, - private backendSrv, - private templateSrv) { - this.type = 'clickhouse'; - this.name = instanceSettings.name; - this.supportMetrics = true; - this.responseParser = new ResponseParser(); - this.url = instanceSettings.url; - this.directUrl = instanceSettings.directUrl; - this.basicAuth = instanceSettings.basicAuth; - this.withCredentials = instanceSettings.withCredentials; - this.addCorsHeader = instanceSettings.jsonData.addCorsHeader; - this.usePOST = instanceSettings.jsonData.usePOST; - this.adhocCtrl = new AdhocCtrl(); - } - - _request(query) { - let options: any = { - url: this.url - }; - - if (this.usePOST) { - options.method = 'POST'; - options.data = query; - } else { - options.method = 'GET'; - options.url += '/?query=' + encodeURIComponent(query); - } - - if (this.basicAuth || this.withCredentials) { - options.withCredentials = true; - } - - options.headers = options.headers || {}; - if (this.basicAuth) { - options.headers.Authorization = this.basicAuth; - } - - if (this.addCorsHeader) { - if (this.usePOST) { - options.url += "?add_http_cors_header=1"; - } else { - options.url += "&add_http_cors_header=1"; - } - } - - return this.backendSrv.datasourceRequest(options).then(result => { - return result.data; - }); - }; - - query(options) { - var queries = [], q, - adhocFilters = this.templateSrv.getAdhocFilters(this.name); - - _.map(options.targets, (target) => { - if (!target.hide && target.query) { - var queryModel = new SqlQuery(target, this.templateSrv, options); - q = queryModel.replace(options, adhocFilters); - queries.push(q); - } - }); - - // No valid targets, return the empty result to save a round trip. - if (_.isEmpty(queries)) { - var d = this.$q.defer(); - d.resolve({data: []}); - return d.promise; - } - - var allQueryPromise = _.map(queries, query => { - return this._seriesQuery(query); - }); - - - return this.$q.all(allQueryPromise).then((responses): any => { - var result = [], i = 0; - _.each(responses, (response) => { - var target = options.targets[i]; - i++; - if (!response || !response.rows) { - return; - } - - var sqlSeries = new SqlSeries({ - series: response.data, - meta: response.meta, - tillNow: options.rangeRaw.to === 'now', - from: SqlQuery.convertTimestamp(options.range.from, target.dateTimeType), - to: SqlQuery.convertTimestamp(options.range.to, target.dateTimeType) - }); - if (target.format === 'table') { - _.each(sqlSeries.toTable(), (data) => { - result.push(data); - }); - } else { - _.each(sqlSeries.toTimeSeries(), (data) => { - result.push(data); - }); - } - }); - return {data: result}; - }); - }; - - metricFindQuery(query, options?: any) { - var interpolated; - try { - var scopedVars = {}; - - if (options && options.range) { - scopedVars['from'] = { - text: options.range.from.valueOf(), - value: options.range.from.valueOf() - } - - scopedVars['to'] = { - text: options.range.to.valueOf(), - value: options.range.to.valueOf() - } - } - - interpolated = this.templateSrv.replace(query, scopedVars, SqlQuery.interpolateQueryExpr); - } catch (err) { - return this.$q.reject(err); - } - - return this._seriesQuery(interpolated) - .then(_.curry(this.responseParser.parse)(query)); - }; - - testDatasource() { - return this.metricFindQuery('SELECT 1').then( - () => { - return {status: "success", message: "Data source is working", title: "Success"}; - }); - }; - - _seriesQuery(query) { - query = query.replace(/(?:\r\n|\r|\n)/g, ' '); - query += ' FORMAT JSON'; - return this._request(query); - }; - - - targetContainsTemplate(target) { - return this.templateSrv.variableExists(target.expr); - }; - - getTagKeys() { - return this.adhocCtrl.GetTagKeys(this); - - } - - getTagValues(options) { - return this.adhocCtrl.GetTagValues(options); - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/img/clickhouse_logo.svg b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/img/clickhouse_logo.svg deleted file mode 100644 index 3ab95fe..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/img/clickhouse_logo.svg +++ /dev/null @@ -1 +0,0 @@ -Clickhouse \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/mode-clickhouse.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/mode-clickhouse.js deleted file mode 100644 index 4d0cc50..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/mode-clickhouse.js +++ /dev/null @@ -1,192 +0,0 @@ -"use strict"; - -System.register([], function (_export, _context) { - "use strict"; - - return { - setters: [], - execute: function () { - // jshint ignore: start - // jscs: disable - - ace.define("ace/mode/clickhouse_highlight_rules", ["require", "exports", "module", "ace/lib/oop", "ace/mode/text_highlight_rules"], function (require, exports, module) { - "use strict"; - - var oop = require("../lib/oop"); - var TextHighlightRules = require("./text_highlight_rules").TextHighlightRules; - var ClickhouseInfo = require("./clickhouse_info").ClickhouseInfo; - - var ClickHouseHighlightRules = function ClickHouseHighlightRules() { - var keywords = ClickhouseInfo.KeywordsRe(), - builtinConstants = ClickhouseInfo.ConstantsRe(), - builtinFunctions = ClickhouseInfo.FunctionsRe(), - dataTypes = ClickhouseInfo.DataTypesRe(); - - var keywordMapper = this.createKeywordMapper({ - "support.function": builtinFunctions, - "keyword": keywords, - "constant.language": builtinConstants, - "storage.type": dataTypes - }, "identifier", true); - - this.$rules = { - "start": [{ - token: "comment", - regex: "--.*$" - }, { - token: "comment.block", - start: "```", - end: "```" - }, { - token: "string", // ' string - regex: "'.*?'" - }, { - token: "variable", - regex: "\\$\\w+" - }, { - token: "keyword.operator", - regex: "\\+|\\-|\\/|\\/\\/|%|<@>|@>|<@|&|\\^|~|<|>|<=|=>|==|!=|<>|=|\\?|:" - }, { - token: "constant.numeric", // float - regex: "[+-]?\\d+(?:(?:\\.\\d*)?(?:[eE][+-]?\\d+)?)?\\b" - }, { - token: keywordMapper, - regex: "[a-zA-Z_$][a-zA-Z0-9_$]*\\b" - }, { - token: "paren.lparen", - regex: "[\\(]" - }, { - token: "paren.rparen", - regex: "[\\)]" - }, { - token: "text", - regex: "\\s+" - }] - }; - this.normalizeRules(); - }; - - oop.inherits(ClickHouseHighlightRules, TextHighlightRules); - - exports.ClickHouseHighlightRules = ClickHouseHighlightRules; - }); - - ace.define("ace/mode/clickhouse_completions", ["require", "exports", "module", "ace/token_iterator", "ace/lib/lang"], function (require, exports, module) { - "use strict"; - - var lang = require("../lib/lang"), - ClickhouseInfo = require("./clickhouse_info").ClickhouseInfo; - - var keyWordsCompletions = ClickhouseInfo.Keywords.map(function (word) { - return { - caption: word, - value: word, - meta: "keyword", - score: Number.MAX_VALUE - }; - }); - - var constantCompletions = ClickhouseInfo.Constants.map(function (word) { - return { - caption: word, - value: word, - meta: "constant", - score: Number.MAX_VALUE - }; - }); - - var macrosCompletions = ClickhouseInfo.MacrosCompletions().map(function (item) { - return { - caption: item.name, - value: item.name, - docHTML: convertToHTML(item), - meta: "macros", - score: Number.MAX_VALUE - }; - }); - - var functionsCompletions = ClickhouseInfo.FunctionsCompletions().map(function (item) { - return { - caption: item.name, - value: item.name + "()", - docHTML: convertToHTML(item), - meta: "function", - score: Number.MAX_VALUE - }; - }); - - function wrapText(str, len) { - len = len || 90; - var lines = []; - var space_index = 0; - var line_start = 0; - var next_line_end = len; - var line = ""; - for (var i = 0; i < str.length; i++) { - if (str[i] === ' ') { - space_index = i; - } else if (i >= next_line_end && space_index !== 0) { - line = str.slice(line_start, space_index); - lines.push(line); - line_start = space_index + 1; - next_line_end = i + len; - space_index = 0; - } - } - line = str.slice(line_start); - lines.push(line); - return lines.join(" 
"); - } - - function convertMarkDownTags(text) { - text = text.replace(/```(.+)```/, "
$1
"); - text = text.replace(/`([^`]+)`/, "$1"); - return text; - } - - function convertToHTML(item) { - var docText = lang.escapeHTML(item.docText); - docText = convertMarkDownTags(wrapText(docText, 90)); - return ["", lang.escapeHTML(item.def), "", "
", docText, "
 "].join(""); - } - - var ClickhouseCompletions = function ClickhouseCompletions() {}; - - (function () { - this.getCompletions = function (state, session, pos, prefix, callback) { - var completions = keyWordsCompletions.concat(functionsCompletions).concat(constantCompletions); - completions = completions.concat(macrosCompletions); - callback(null, completions); - }; - }).call(ClickhouseCompletions.prototype); - - exports.ClickhouseCompletions = ClickhouseCompletions; - }); - - ace.define("ace/mode/clickhouse", ["require", "exports", "module", "ace/lib/oop", "ace/mode/text", "ace/mode/clickhouse_highlight_rules"], function (require, exports, module) { - "use strict"; - - var oop = require("../lib/oop"); - var TextMode = require("./text").Mode; - var ClickHouseHighlightRules = require("./clickhouse_highlight_rules").ClickHouseHighlightRules; - var ClickhouseCompletions = require("./clickhouse_completions").ClickhouseCompletions; - - var Mode = function Mode() { - this.HighlightRules = ClickHouseHighlightRules; - this.$completer = new ClickhouseCompletions(); - // replace keyWordCompleter - this.completer = this.$completer; - }; - - oop.inherits(Mode, TextMode); - - (function () { - this.$id = "ace/mode/clickhouse"; - }).call(Mode.prototype); - - exports.Mode = Mode; - }); - } - }; -}); -//# sourceMappingURL=mode-clickhouse.js.map diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/mode-clickhouse.js.map b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/mode-clickhouse.js.map deleted file mode 100644 index 0fe92a4..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/mode-clickhouse.js.map +++ /dev/null @@ -1 +0,0 @@ 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jshint ignore: start\r\n// jscs: disable\r\n\r\nace.define(\"ace/mode/clickhouse_highlight_rules\", [\"require\", \"exports\", \"module\", \"ace/lib/oop\", \"ace/mode/text_highlight_rules\"], function (require, exports, module) {\r\n \"use strict\";\r\n\r\n var oop = require(\"../lib/oop\");\r\n var TextHighlightRules = require(\"./text_highlight_rules\").TextHighlightRules;\r\n var ClickhouseInfo = require(\"./clickhouse_info\").ClickhouseInfo;\r\n\r\n var ClickHouseHighlightRules = function () {\r\n var keywords = ClickhouseInfo.KeywordsRe(),\r\n builtinConstants = ClickhouseInfo.ConstantsRe(),\r\n builtinFunctions = ClickhouseInfo.FunctionsRe(),\r\n dataTypes = ClickhouseInfo.DataTypesRe();\r\n\r\n var keywordMapper = this.createKeywordMapper({\r\n \"support.function\": builtinFunctions,\r\n \"keyword\": keywords,\r\n \"constant.language\": builtinConstants,\r\n \"storage.type\": dataTypes\r\n }, \"identifier\", true);\r\n\r\n this.$rules = {\r\n \"start\": [{\r\n token: \"comment\",\r\n regex: \"--.*$\"\r\n }, {\r\n token: \"comment.block\",\r\n start: \"```\",\r\n end: \"```\"\r\n }, {\r\n token: \"string\", // ' string\r\n regex: \"'.*?'\"\r\n }, {\r\n token: \"variable\",\r\n regex: \"\\\\$\\\\w+\"\r\n }, {\r\n token: \"keyword.operator\",\r\n regex: \"\\\\+|\\\\-|\\\\/|\\\\/\\\\/|%|<@>|@>|<@|&|\\\\^|~|<|>|<=|=>|==|!=|<>|=|\\\\?|:\"\r\n }, {\r\n token: \"constant.numeric\", // float\r\n regex: \"[+-]?\\\\d+(?:(?:\\\\.\\\\d*)?(?:[eE][+-]?\\\\d+)?)?\\\\b\"\r\n }, {\r\n token: keywordMapper,\r\n regex: \"[a-zA-Z_$][a-zA-Z0-9_$]*\\\\b\"\r\n }, {\r\n token: \"paren.lparen\",\r\n regex: \"[\\\\(]\"\r\n }, {\r\n token: \"paren.rparen\",\r\n regex: \"[\\\\)]\"\r\n }, {\r\n token: \"text\",\r\n regex: \"\\\\s+\"\r\n }]\r\n };\r\n this.normalizeRules();\r\n };\r\n\r\n oop.inherits(ClickHouseHighlightRules, TextHighlightRules);\r\n\r\n exports.ClickHouseHighlightRules = ClickHouseHighlightRules;\r\n});\r\n\r\nace.define(\"ace/mode/clickhouse_completions\", [\"require\", \"exports\", \"module\", \"ace/token_iterator\", \"ace/lib/lang\"], function (require, exports, module) {\r\n \"use strict\";\r\n\r\n var lang = require(\"../lib/lang\"),\r\n ClickhouseInfo = require(\"./clickhouse_info\").ClickhouseInfo;\r\n\r\n var keyWordsCompletions = ClickhouseInfo.Keywords.map(function (word) {\r\n return {\r\n caption: word,\r\n value: word,\r\n meta: \"keyword\",\r\n score: Number.MAX_VALUE\r\n }\r\n });\r\n\r\n var constantCompletions = ClickhouseInfo.Constants.map(function (word) {\r\n return {\r\n caption: word,\r\n value: word,\r\n meta: \"constant\",\r\n score: Number.MAX_VALUE\r\n };\r\n });\r\n\r\n var macrosCompletions = ClickhouseInfo.MacrosCompletions().map(function (item) {\r\n return {\r\n caption: item.name,\r\n value: item.name,\r\n docHTML: convertToHTML(item),\r\n meta: \"macros\",\r\n score: Number.MAX_VALUE\r\n };\r\n });\r\n\r\n\r\n var functionsCompletions = ClickhouseInfo.FunctionsCompletions().map(function (item) {\r\n return {\r\n caption: item.name,\r\n value: item.name+\"()\",\r\n docHTML: convertToHTML(item),\r\n meta: \"function\",\r\n score: Number.MAX_VALUE\r\n };\r\n });\r\n\r\n function wrapText(str, len) {\r\n len = len || 90;\r\n var lines = [];\r\n var space_index = 0;\r\n var line_start = 0;\r\n var next_line_end = len;\r\n var line = \"\";\r\n for (var i = 0; i < str.length; i++) {\r\n if (str[i] === ' ') {\r\n space_index = i;\r\n } else if (i >= next_line_end && space_index !== 0) {\r\n line = str.slice(line_start, space_index);\r\n lines.push(line);\r\n line_start = space_index + 1;\r\n next_line_end = i + len;\r\n space_index = 0;\r\n }\r\n }\r\n line = str.slice(line_start);\r\n lines.push(line);\r\n return lines.join(\" 
\");\r\n }\r\n\r\n function convertMarkDownTags(text) {\r\n text = text.replace(/```(.+)```/, \"
$1
\");\r\n text = text.replace(/`([^`]+)`/, \"$1\");\r\n return text;\r\n }\r\n\r\n function convertToHTML(item) {\r\n var docText = lang.escapeHTML(item.docText);\r\n docText = convertMarkDownTags(wrapText(docText, 90));\r\n return [\r\n \"\", lang.escapeHTML(item.def), \"\", \"
\", docText, \"
 \"\r\n ].join(\"\");\r\n }\r\n\r\n var ClickhouseCompletions = function () {\r\n };\r\n\r\n (function () {\r\n this.getCompletions = function (state, session, pos, prefix, callback) {\r\n var completions = keyWordsCompletions.concat(functionsCompletions).concat(constantCompletions);\r\n completions = completions.concat(macrosCompletions);\r\n callback(null, completions);\r\n };\r\n\r\n }).call(ClickhouseCompletions.prototype);\r\n\r\n exports.ClickhouseCompletions = ClickhouseCompletions;\r\n});\r\n\r\n\r\nace.define(\"ace/mode/clickhouse\", [\"require\", \"exports\", \"module\", \"ace/lib/oop\", \"ace/mode/text\", \"ace/mode/clickhouse_highlight_rules\"], function (require, exports, module) {\r\n \"use strict\";\r\n\r\n var oop = require(\"../lib/oop\");\r\n var TextMode = require(\"./text\").Mode;\r\n var ClickHouseHighlightRules = require(\"./clickhouse_highlight_rules\").ClickHouseHighlightRules;\r\n var ClickhouseCompletions = require(\"./clickhouse_completions\").ClickhouseCompletions;\r\n\r\n\r\n var Mode = function () {\r\n this.HighlightRules = ClickHouseHighlightRules;\r\n this.$completer = new ClickhouseCompletions();\r\n // replace keyWordCompleter\r\n this.completer = this.$completer;\r\n };\r\n\r\n oop.inherits(Mode, TextMode);\r\n\r\n (function () {\r\n this.$id = \"ace/mode/clickhouse\";\r\n }).call(Mode.prototype);\r\n\r\n exports.Mode = Mode;\r\n});\r\n\r\n"]} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/module.d.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/module.d.ts deleted file mode 100644 index a51a6c1..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/module.d.ts +++ /dev/null @@ -1,6 +0,0 @@ -import { ClickHouseDatasource } from './datasource'; -import { SqlQueryCtrl } from './query_ctrl'; -declare class SqlConfigCtrl { - static templateUrl: string; -} -export { ClickHouseDatasource as Datasource, SqlQueryCtrl as QueryCtrl, SqlConfigCtrl as ConfigCtrl }; diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/module.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/module.js deleted file mode 100644 index 81ace39..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/module.js +++ /dev/null @@ -1,25 +0,0 @@ -System.register(['./datasource', './query_ctrl'], function(exports_1) { - var datasource_1, query_ctrl_1; - var SqlConfigCtrl; - return { - setters:[ - function (datasource_1_1) { - datasource_1 = datasource_1_1; - }, - function (query_ctrl_1_1) { - query_ctrl_1 = query_ctrl_1_1; - }], - execute: function() { - SqlConfigCtrl = (function () { - function SqlConfigCtrl() { - } - SqlConfigCtrl.templateUrl = 'partials/config.html'; - return SqlConfigCtrl; - })(); - exports_1("Datasource", datasource_1.ClickHouseDatasource); - exports_1("QueryCtrl", query_ctrl_1.SqlQueryCtrl); - exports_1("ConfigCtrl", SqlConfigCtrl); - } - } -}); -//# sourceMappingURL=module.js.map \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/module.js.map b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/module.js.map deleted file mode 100644 index 7f28d2a..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/module.js.map +++ /dev/null @@ -1 +0,0 @@ -{"version":3,"file":"module.js","sourceRoot":"","sources":["module.ts"],"names":["SqlConfigCtrl","SqlConfigCtrl.constructor"],"mappings":";;;;;;;;;;;;YAGA;gBAAAA;gBAEAC,CAACA;gBADQD,yBAAWA,GAAGA,sBAAsBA,CAACA;gBAC9CA,oBAACA;YAADA,CAACA,AAFD,IAEC;YAGyB,0DAAU;YAClB,iDAAS;YACR,sCAAU"} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/module.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/module.ts deleted file mode 100644 index 17708c1..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/module.ts +++ /dev/null @@ -1,12 +0,0 @@ -import {ClickHouseDatasource} from './datasource'; -import {SqlQueryCtrl} from './query_ctrl'; - -class SqlConfigCtrl { - static templateUrl = 'partials/config.html'; -} - -export { - ClickHouseDatasource as Datasource, - SqlQueryCtrl as QueryCtrl, - SqlConfigCtrl as ConfigCtrl, -}; diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/partials/config.html b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/partials/config.html deleted file mode 100644 index 47d03ab..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/partials/config.html +++ /dev/null @@ -1,21 +0,0 @@ - - - -

Additional

- -
- - - -
- -
- - -
diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/partials/query.editor.html b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/partials/query.editor.html deleted file mode 100644 index b66c237..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/partials/query.editor.html +++ /dev/null @@ -1,209 +0,0 @@ - - -
- - -
- -
-
-
- - - - -
- -
-
-
-
- -
-
- -
- -
- -
- -
- - - - Select - DateTime - or UInt32 column for binding with Grafana's range selector - -
- -
- - -
- -
-
-
-
- -
- -
- -
- -
-
- - - - Leave blank for auto handling based on time range and panel width - -
-
- -
- -
-
- -
- - - - Set rounding for `$from` and `$to` timestamps.
- For example, if set `1m` - both `$from` and `$to` will be rounded to beginning of minute.
- It will make all requests similar during one minute which is good for caching. - -
- -
-
-
-
- -
-
- -
- -
-
- -
- -
- -
- -
- -
- -
-
- -
-
- 
{{ctrl.target.rawQuery}}
-
- -
- 
-Macros
-
-$table - replaced with selected table name from Query Builder
-$timeCol - replaced with Date:Col value from Query Builder
-$dateTimeCol - replaced with Column:DateTime or Column:TimeStamp value from Query Builder
-$from - replaced with timestamp/1000 value of selected "Time Range:From"
-$to - replaced with timestamp/1000 value of selected "Time Range:To"
-$interval - replaced with selected "Group by time interval" value (as a number of seconds)
-$timeFilter - replaced with currently selected "Time Range".
-            Require Column:Date and Column:DateTime or Column:TimeStamp to be selected
-$timeSeries - replaced with special ClickHouse construction to convert results as time-series data.
-            Use it as "SELECT $timeSeries...". Require Column:DateTime or Column:TimeStamp to be selected
-$unescape - unescapes variable value by removing single quotes.
-            Used for multiple-value string variables: "SELECT $unescape($column) FROM requests WHERE $unescape($column) = 5"
-A description of macros is available by typing their names in Raw Editor
-
-
-Functions (Only one function per query allowed)
-
-$rate(cols...) - function to convert query results as "change rate per interval".
-Example usage: $rate(countIf(Type = 200) * 60 AS good, countIf(Type != 200) * 60 AS bad) FROM requests
-
-$columns(key, value) - function to query values as array of [key, value], where key would be used as label.
-Example usage: $columns(Type, count() c) FROM requests
-
-$rateColumns(key, value) - is an combination of $columns and $rate.
-Example usage: $rateColumns(Type, count() c) FROM requests
-
-
-
- - diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/plugin.json b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/plugin.json deleted file mode 100644 index 8d6ce7b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/plugin.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "id": "vertamedia-clickhouse-datasource", - "type": "datasource", - "name": "ClickHouse", - - "metrics": true, - "annotations": false, - "alerting": false, - - "info": { - "description": "ClickHouse datasource for Grafana", - "author": { - "name": "Vertamedia", - "url": "https://vertamedia.com/" - }, - "keywords": ["clickhouse"], - "logos": { - "small": "img/clickhouse_logo.svg", - "large": "img/clickhouse_logo.svg" - }, - "links": [ - { - "name": "GitHub", - "url": "https://github.com/Vertamedia/clickhouse-grafana" - } - ], - "version": "1.4.2" - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/query_ctrl.d.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/query_ctrl.d.ts deleted file mode 100644 index 029b6a7..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/query_ctrl.d.ts +++ /dev/null @@ -1,56 +0,0 @@ -/// -import SqlQuery from './sql_query'; -import { QueryCtrl } from 'app/plugins/sdk'; -declare class SqlQueryCtrl extends QueryCtrl { - private uiSegmentSrv; - static templateUrl: string; - queryModel: SqlQuery; - databaseSegment: any; - dateTimeType: any; - dateColDataTypeSegment: any; - dateTimeColDataTypeSegment: any; - tableSegment: any; - formats: any[]; - panel: any; - datasource: any; - target: any; - resolutions: any; - scanner: any; - editMode: boolean; - textareaHeight: any; - dateTimeTypeOptions: any; - completerCache: any[]; - tableLoading: boolean; - datetimeLoading: boolean; - dateLoading: boolean; - showLastQuerySQL: boolean; - showHelp: boolean; - /** @ngInject **/ - constructor($scope: any, $injector: any, templateSrv: any, uiSegmentSrv: any); - getCollapsedText(): any; - fakeSegment(value: any): any; - getDateColDataTypeSegments(): any; - dateColDataTypeChanged(): void; - dateTimeTypeChanged(): void; - getDateTimeColDataTypeSegments(): any; - dateTimeColDataTypeChanged(): void; - toggleEditorMode(): void; - toggleEdit(e: any, editMode: boolean): void; - getCompleter(): SqlQueryCtrl; - getCompletions(editor: any, session: any, pos: any, prefix: any, callback: any): void; - static _convertToHTML(item: any): string; - getDatabaseSegments(): any; - databaseChanged(): void; - getTableSegments(): any; - tableChanged(): void; - formatQuery(): void; - toQueryMode(): void; - format(): any; - getScanner(): any; - handleQueryError(err: any): any[]; - queryColumns(): any; - querySegment(type: string): any; - applySegment(dst: any, src: any): void; - buildExploreQuery(type: any): any; -} -export { SqlQueryCtrl }; diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/query_ctrl.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/query_ctrl.js deleted file mode 100644 index 86defc9..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/query_ctrl.js +++ /dev/null @@ -1,305 +0,0 @@ -/// -System.register(['jquery', 'lodash', './clickhouse-info', './mode-clickhouse', './snippets/clickhouse', './sql_query', 'app/plugins/sdk', './scanner'], function(exports_1) { - var __extends = (this && this.__extends) || function (d, b) { - for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; - function __() { this.constructor = d; } - d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __()); - }; - var jquery_1, lodash_1, sql_query_1, sdk_1, scanner_1; - var defaultQuery, SqlQueryCtrl; - return { - setters:[ - function (jquery_1_1) { - jquery_1 = jquery_1_1; - }, - function (lodash_1_1) { - lodash_1 = lodash_1_1; - }, - function (_1) {}, - function (_2) {}, - function (_3) {}, - function (sql_query_1_1) { - sql_query_1 = sql_query_1_1; - }, - function (sdk_1_1) { - sdk_1 = sdk_1_1; - }, - function (scanner_1_1) { - scanner_1 = scanner_1_1; - }], - execute: function() { - defaultQuery = "SELECT $timeSeries as t, count() FROM $table WHERE $timeFilter GROUP BY t ORDER BY t"; - SqlQueryCtrl = (function (_super) { - __extends(SqlQueryCtrl, _super); - /** @ngInject **/ - function SqlQueryCtrl($scope, $injector, templateSrv, uiSegmentSrv) { - _super.call(this, $scope, $injector); - this.uiSegmentSrv = uiSegmentSrv; - this.queryModel = new sql_query_1.default(this.target, templateSrv, this.panel.scopedVars); - this.databaseSegment = uiSegmentSrv.newSegment(this.target.database || { fake: true, value: '-- database --' }); - this.tableSegment = uiSegmentSrv.newSegment(this.target.table || { fake: true, value: '-- table --' }); - this.dateColDataTypeSegment = uiSegmentSrv.newSegment(this.target.dateColDataType || { fake: true, value: '-- date : col --' }); - this.dateTimeColDataTypeSegment = uiSegmentSrv.newSegment(this.target.dateTimeColDataType || { fake: true, value: '-- dateTime : col --' }); - this.resolutions = lodash_1.default.map([1, 2, 3, 4, 5, 10], function (f) { - return { factor: f, label: '1/' + f }; - }); - this.completerCache = []; - this.dateTimeTypeOptions = [ - { text: 'Column:DateTime', value: 'DATETIME' }, - { text: 'Column:TimeStamp', value: 'TIMESTAMP' }, - { text: 'Column:TimeStampMs', value: 'TIMESTAMPMS' }, - ]; - this.formats = [ - { text: 'Time series', value: 'time_series' }, - { text: 'Table', value: 'table' }, - ]; - this.target.format = this.target.format || 'time_series'; - this.target.dateTimeType = this.target.dateTimeType || this.dateTimeTypeOptions[0].value; - this.target.round = this.target.round || "0s"; - this.target.intervalFactor = this.target.intervalFactor || 1; - this.target.query = this.target.query || defaultQuery; - this.target.formattedQuery = this.target.formattedQuery || this.target.query; - this.scanner = new scanner_1.default(this.target.query); - if (this.target.query === defaultQuery) { - this.target.query = this.format(); - } - } - SqlQueryCtrl.prototype.getCollapsedText = function () { - return this.target.query; - }; - SqlQueryCtrl.prototype.fakeSegment = function (value) { - return this.uiSegmentSrv.newSegment({ fake: true, value: value }); - }; - SqlQueryCtrl.prototype.getDateColDataTypeSegments = function () { - var target = this.target; - target.dateLoading = true; - return this.querySegment('DATE').then(function (response) { - target.dateLoading = false; - return response; - }); - }; - SqlQueryCtrl.prototype.dateColDataTypeChanged = function () { - this.target.dateColDataType = this.dateColDataTypeSegment.value; - }; - SqlQueryCtrl.prototype.dateTimeTypeChanged = function () { - var self = this; - this.getDateTimeColDataTypeSegments().then(function (segments) { - if (segments.length === 0) { - return; - } - self.applySegment(self.dateTimeColDataTypeSegment, segments[0]); - self.dateTimeColDataTypeChanged(); - }); - }; - SqlQueryCtrl.prototype.getDateTimeColDataTypeSegments = function () { - var target = this.target; - target.datetimeLoading = true; - return this.querySegment(target.dateTimeType).then(function (response) { - target.datetimeLoading = false; - return response; - }); - }; - SqlQueryCtrl.prototype.dateTimeColDataTypeChanged = function () { - this.target.dateTimeColDataType = this.dateTimeColDataTypeSegment.value; - }; - SqlQueryCtrl.prototype.toggleEditorMode = function () { - this.target.rawQuery = !this.target.rawQuery; - }; - SqlQueryCtrl.prototype.toggleEdit = function (e, editMode) { - if (editMode) { - this.editMode = true; - this.textareaHeight = "height: " + jquery_1.default(e.currentTarget).outerHeight() + "px;"; - return; - } - if (this.editMode === true) { - this.editMode = false; - this.refresh(); - } - }; - SqlQueryCtrl.prototype.getCompleter = function () { - return this; - }; - SqlQueryCtrl.prototype.getCompletions = function (editor, session, pos, prefix, callback) { - if (this.target.database === undefined || this.target.table === undefined) { - callback(null, []); - return; - } - var self = this; - var key = self.target.database + '.' + self.target.table; - if (self.completerCache[key]) { - callback(null, self.completerCache[key]); - return; - } - self.queryColumns().then(function (response) { - self.completerCache[key] = response.map(function (item) { - return { - caption: item.text, - value: item.text, - meta: key, - docHTML: SqlQueryCtrl._convertToHTML(item), - }; - }); - callback(null, self.completerCache[key]); - }); - }; - SqlQueryCtrl._convertToHTML = function (item) { - var desc = item.value, space_index = 0, start = 0, line = "", next_line_end = 60, lines = []; - for (var i = 0; i < desc.length; i++) { - if (desc[i] === ' ') { - space_index = i; - } - else if (i >= next_line_end && space_index !== 0) { - line = desc.slice(start, space_index); - lines.push(line); - start = space_index + 1; - next_line_end = i + 60; - space_index = 0; - } - } - line = desc.slice(start); - lines.push(line); - return ["", item.text, "", "
", lines.join(" 
")].join(""); - }; - SqlQueryCtrl.prototype.getDatabaseSegments = function () { - return this.querySegment('DATABASES'); - }; - SqlQueryCtrl.prototype.databaseChanged = function () { - this.target.database = this.databaseSegment.value; - this.applySegment(this.tableSegment, this.fakeSegment('-- table : col --')); - this.applySegment(this.dateColDataTypeSegment, this.fakeSegment('-- date : col --')); - this.applySegment(this.dateTimeColDataTypeSegment, this.fakeSegment('-- dateTime : col --')); - }; - SqlQueryCtrl.prototype.getTableSegments = function () { - var target = this.target; - target.tableLoading = true; - return this.querySegment('TABLES').then(function (response) { - target.tableLoading = false; - return response; - }); - }; - SqlQueryCtrl.prototype.tableChanged = function () { - this.target.table = this.tableSegment.value; - this.applySegment(this.dateColDataTypeSegment, this.fakeSegment('-- date : col --')); - this.applySegment(this.dateTimeColDataTypeSegment, this.fakeSegment('-- dateTime : col --')); - var self = this; - this.getDateColDataTypeSegments().then(function (segments) { - if (segments.length === 0) { - return; - } - self.applySegment(self.dateColDataTypeSegment, segments[0]); - self.dateColDataTypeChanged(); - }); - this.getDateTimeColDataTypeSegments().then(function (segments) { - if (segments.length === 0) { - return; - } - self.applySegment(self.dateTimeColDataTypeSegment, segments[0]); - self.dateTimeColDataTypeChanged(); - }); - }; - SqlQueryCtrl.prototype.formatQuery = function () { - this.target.query = this.format(); - this.toggleEdit({}, false); - }; - SqlQueryCtrl.prototype.toQueryMode = function () { - this.toggleEditorMode(); - this.refresh(); - }; - SqlQueryCtrl.prototype.format = function () { - try { - return this.getScanner().Format(); - } - catch (err) { - console.log("Parse error: ", err); - return this.getScanner().raw(); - } - }; - SqlQueryCtrl.prototype.getScanner = function () { - if (this.scanner.raw() !== this.target.query) { - this.scanner = new scanner_1.default(this.target.query); - } - return this.scanner; - }; - SqlQueryCtrl.prototype.handleQueryError = function (err) { - this.error = err.message || 'Failed to issue metric query'; - return []; - }; - SqlQueryCtrl.prototype.queryColumns = function () { - var query = this.buildExploreQuery('COLUMNS'); - return this.datasource.metricFindQuery(query); - }; - SqlQueryCtrl.prototype.querySegment = function (type) { - var query = this.buildExploreQuery(type); - return this.datasource.metricFindQuery(query) - .then(this.uiSegmentSrv.transformToSegments(false)) - .catch(this.handleQueryError.bind(this)); - }; - SqlQueryCtrl.prototype.applySegment = function (dst, src) { - dst.value = src.value; - dst.html = src.html || src.value; - dst.fake = src.fake === undefined ? false : src.fake; - }; - SqlQueryCtrl.prototype.buildExploreQuery = function (type) { - var query; - switch (type) { - case 'TABLES': - query = 'SELECT name ' + - 'FROM system.tables ' + - 'WHERE database = \'' + this.target.database + '\' ' + - 'ORDER BY name'; - break; - case 'DATE': - query = 'SELECT name ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\' AND ' + - 'type = \'Date\' ' + - 'ORDER BY name'; - break; - case 'DATETIME': - query = 'SELECT name ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\' AND ' + - 'type LIKE \'DateTime%\' ' + - 'ORDER BY name'; - break; - case 'TIMESTAMP': - query = 'SELECT name ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\' AND ' + - 'type = \'UInt32\' ' + - 'ORDER BY name'; - break; - case 'TIMESTAMPMS': - query = 'SELECT name ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\' AND ' + - 'type = \'UInt64\' ' + - 'ORDER BY name'; - break; - case 'DATABASES': - query = 'SELECT name ' + - 'FROM system.databases ' + - 'ORDER BY name'; - break; - case 'COLUMNS': - query = 'SELECT name text, type value ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\''; - break; - } - return query; - }; - ; - SqlQueryCtrl.templateUrl = 'partials/query.editor.html'; - return SqlQueryCtrl; - })(sdk_1.QueryCtrl); - exports_1("SqlQueryCtrl", SqlQueryCtrl); - } - } -}); -//# sourceMappingURL=query_ctrl.js.map \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/query_ctrl.js.map b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/query_ctrl.js.map deleted file mode 100644 index 07fa260..0000000 --- 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\ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/query_ctrl.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/query_ctrl.ts deleted file mode 100644 index 9ebf95e..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/query_ctrl.ts +++ /dev/null @@ -1,357 +0,0 @@ -/// - -import $ from 'jquery'; -import _ from 'lodash'; -import './clickhouse-info'; -import './mode-clickhouse'; -import './snippets/clickhouse'; -import SqlQuery from './sql_query'; -import {QueryCtrl} from 'app/plugins/sdk'; -import Scanner from './scanner'; - -const defaultQuery = "SELECT $timeSeries as t, count() FROM $table WHERE $timeFilter GROUP BY t ORDER BY t"; - -class SqlQueryCtrl extends QueryCtrl { - static templateUrl = 'partials/query.editor.html'; - - queryModel: SqlQuery; - databaseSegment: any; - - dateTimeType: any; - dateColDataTypeSegment: any; - dateTimeColDataTypeSegment: any; - tableSegment: any; - formats: any[]; - - panel: any; - datasource: any; - target: any; - resolutions: any; - scanner: any; - editMode: boolean; - textareaHeight: any; - dateTimeTypeOptions: any; - - completerCache: any[]; - - tableLoading: boolean; - datetimeLoading: boolean; - dateLoading: boolean; - - showLastQuerySQL: boolean; - showHelp: boolean; - - /** @ngInject **/ - constructor($scope, $injector, templateSrv, private uiSegmentSrv) { - super($scope, $injector); - - this.queryModel = new SqlQuery(this.target, templateSrv, this.panel.scopedVars); - - this.databaseSegment = uiSegmentSrv.newSegment( - this.target.database || {fake: true, value: '-- database --'} - ); - - this.tableSegment = uiSegmentSrv.newSegment( - this.target.table || {fake: true, value: '-- table --'} - ); - - this.dateColDataTypeSegment = uiSegmentSrv.newSegment( - this.target.dateColDataType || {fake: true, value: '-- date : col --'} - ); - - this.dateTimeColDataTypeSegment = uiSegmentSrv.newSegment( - this.target.dateTimeColDataType || {fake: true, value: '-- dateTime : col --'} - ); - - this.resolutions = _.map([1,2,3,4,5,10], function(f) { - return {factor: f, label: '1/' + f}; - }); - - this.completerCache = []; - - this.dateTimeTypeOptions = [ - {text: 'Column:DateTime', value: 'DATETIME'}, - {text: 'Column:TimeStamp', value: 'TIMESTAMP'}, - {text: 'Column:TimeStampMs', value: 'TIMESTAMPMS'}, - ]; - - this.formats = [ - {text: 'Time series', value: 'time_series'}, - {text: 'Table', value: 'table'}, - ]; - - this.target.format = this.target.format || 'time_series'; - this.target.dateTimeType = this.target.dateTimeType || this.dateTimeTypeOptions[0].value; - this.target.round = this.target.round || "0s"; - this.target.intervalFactor = this.target.intervalFactor || 1; - this.target.query = this.target.query || defaultQuery; - this.target.formattedQuery = this.target.formattedQuery || this.target.query; - this.scanner = new Scanner(this.target.query); - if (this.target.query === defaultQuery) { - this.target.query = this.format(); - } - } - - getCollapsedText() { - return this.target.query; - } - - fakeSegment(value) { - return this.uiSegmentSrv.newSegment({fake: true, value: value}); - } - - getDateColDataTypeSegments() { - var target = this.target; - target.dateLoading = true; - return this.querySegment('DATE').then(function(response){ - target.dateLoading = false; - return response; - }); - } - - dateColDataTypeChanged() { - this.target.dateColDataType = this.dateColDataTypeSegment.value; - } - - dateTimeTypeChanged() { - var self = this; - this.getDateTimeColDataTypeSegments().then(function(segments) { - if (segments.length === 0) { - return; - } - self.applySegment(self.dateTimeColDataTypeSegment, segments[0]); - self.dateTimeColDataTypeChanged(); - }); - } - - getDateTimeColDataTypeSegments() { - var target = this.target; - target.datetimeLoading = true; - return this.querySegment(target.dateTimeType).then(function(response){ - target.datetimeLoading = false; - return response; - }); - } - - dateTimeColDataTypeChanged() { - this.target.dateTimeColDataType = this.dateTimeColDataTypeSegment.value; - } - - toggleEditorMode() { - this.target.rawQuery = !this.target.rawQuery; - } - - toggleEdit(e: any, editMode: boolean) { - if (editMode) { - this.editMode = true; - this.textareaHeight = "height: " + $(e.currentTarget).outerHeight() + "px;"; - return; - } - - if ( this.editMode === true ) { - this.editMode = false; - this.refresh(); - } - } - - getCompleter() { - return this; - } - - getCompletions(editor, session, pos, prefix, callback) { - if (this.target.database === undefined || this.target.table === undefined) { - callback(null, []); - return; - } - - let self = this; - let key = self.target.database + '.' + self.target.table; - if (self.completerCache[key]) { - callback(null, self.completerCache[key]); - return; - } - - self.queryColumns().then(function(response){ - self.completerCache[key] = response.map(function (item) { - return { - caption: item.text, - value: item.text, - meta: key, - docHTML: SqlQueryCtrl._convertToHTML(item), - }; - }); - callback(null, self.completerCache[key]); - }); - } - - static _convertToHTML(item: any) { - var desc = item.value, - space_index = 0, - start = 0, - line = "", - next_line_end = 60, - lines = []; - for (var i = 0; i < desc.length; i++) { - if (desc[i] === ' ') { - space_index = i; - } else if (i >= next_line_end && space_index !== 0) { - line = desc.slice(start, space_index); - lines.push(line); - start = space_index + 1; - next_line_end = i + 60; - space_index = 0; - } - } - line = desc.slice(start); - lines.push(line); - return ["", item.text, "", "
", lines.join(" 
")].join(""); - } - - getDatabaseSegments() { - return this.querySegment('DATABASES'); - } - - databaseChanged() { - this.target.database = this.databaseSegment.value; - this.applySegment(this.tableSegment, this.fakeSegment('-- table : col --')); - this.applySegment(this.dateColDataTypeSegment, this.fakeSegment('-- date : col --')); - this.applySegment(this.dateTimeColDataTypeSegment, this.fakeSegment('-- dateTime : col --')); - } - - getTableSegments() { - var target = this.target; - target.tableLoading = true; - return this.querySegment('TABLES').then(function(response){ - target.tableLoading = false; - return response; - }); - } - - tableChanged() { - this.target.table = this.tableSegment.value; - this.applySegment(this.dateColDataTypeSegment, this.fakeSegment('-- date : col --')); - this.applySegment(this.dateTimeColDataTypeSegment, this.fakeSegment('-- dateTime : col --')); - - var self = this; - this.getDateColDataTypeSegments().then(function(segments) { - if (segments.length === 0) { - return; - } - self.applySegment(self.dateColDataTypeSegment, segments[0]); - self.dateColDataTypeChanged(); - }); - this.getDateTimeColDataTypeSegments().then(function(segments) { - if (segments.length === 0) { - return; - } - self.applySegment(self.dateTimeColDataTypeSegment, segments[0]); - self.dateTimeColDataTypeChanged(); - }); - } - - formatQuery() { - this.target.query = this.format(); - this.toggleEdit({}, false); - } - - toQueryMode() { - this.toggleEditorMode(); - this.refresh(); - } - - format() { - try { - return this.getScanner().Format(); - } catch (err) { - console.log("Parse error: ", err); - return this.getScanner().raw(); - } - } - - getScanner() { - if (this.scanner.raw() !== this.target.query) { - this.scanner = new Scanner(this.target.query); - } - return this.scanner; - } - - handleQueryError(err) { - this.error = err.message || 'Failed to issue metric query'; - return []; - } - - queryColumns() { - var query = this.buildExploreQuery('COLUMNS'); - return this.datasource.metricFindQuery(query) - } - - querySegment(type: string) { - var query = this.buildExploreQuery(type); - return this.datasource.metricFindQuery(query) - .then(this.uiSegmentSrv.transformToSegments(false)) - .catch(this.handleQueryError.bind(this)); - } - - applySegment(dst, src) { - dst.value = src.value; - dst.html = src.html || src.value; - dst.fake = src.fake === undefined ? false : src.fake; - } - - buildExploreQuery(type) { - var query; - switch (type){ - case 'TABLES': - query = 'SELECT name ' + - 'FROM system.tables ' + - 'WHERE database = \'' + this.target.database + '\' ' + - 'ORDER BY name'; - break; - case 'DATE': - query = 'SELECT name ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\' AND ' + - 'type = \'Date\' ' + - 'ORDER BY name'; - break; - case 'DATETIME': - query = 'SELECT name ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\' AND ' + - 'type LIKE \'DateTime%\' ' + - 'ORDER BY name'; - break; - case 'TIMESTAMP': - query = 'SELECT name ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\' AND ' + - 'type = \'UInt32\' ' + - 'ORDER BY name'; - break; - case 'TIMESTAMPMS': - query = 'SELECT name ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\' AND ' + - 'type = \'UInt64\' ' + - 'ORDER BY name'; - break; - case 'DATABASES': - query = 'SELECT name ' + - 'FROM system.databases ' + - 'ORDER BY name'; - break; - case 'COLUMNS': - query = 'SELECT name text, type value ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\''; - break; - } - return query; - }; -} -export {SqlQueryCtrl}; diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/response_parser.d.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/response_parser.d.ts deleted file mode 100644 index 8871d08..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/response_parser.d.ts +++ /dev/null @@ -1,4 +0,0 @@ -/// -export default class ResponseParser { - parse(query: any, results: any): any[]; -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/response_parser.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/response_parser.js deleted file mode 100644 index 5003ff0..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/response_parser.js +++ /dev/null @@ -1,40 +0,0 @@ -System.register(['lodash'], function(exports_1) { - var lodash_1; - var ResponseParser; - return { - setters:[ - function (lodash_1_1) { - lodash_1 = lodash_1_1; - }], - execute: function() { - ResponseParser = (function () { - function ResponseParser() { - } - ResponseParser.prototype.parse = function (query, results) { - if (!results || results.data.length === 0) { - return []; - } - var sqlResults = results.data; - var res = []; - lodash_1.default.each(sqlResults, function (r) { - if (!lodash_1.default.isObject(r)) { - res.push({ text: r }); - return; - } - var keys = Object.keys(r); - if (keys.length > 1) { - res.push(r); - } - else { - res.push({ text: r[keys[0]] }); - } - }); - return res; - }; - return ResponseParser; - })(); - exports_1("default", ResponseParser); - } - } -}); -//# sourceMappingURL=response_parser.js.map \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/response_parser.js.map b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/response_parser.js.map deleted file mode 100644 index 09e5cd3..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/response_parser.js.map +++ /dev/null @@ -1 +0,0 @@ -{"version":3,"file":"response_parser.js","sourceRoot":"","sources":["response_parser.ts"],"names":["ResponseParser","ResponseParser.constructor","ResponseParser.parse"],"mappings":";;;;;;;;;YAGA;gBAAAA;gBAqBAC,CAACA;gBApBCD,8BAAKA,GAALA,UAAMA,KAAKA,EAAEA,OAAOA;oBAClBE,EAAEA,CAACA,CAACA,CAACA,OAAOA,IAAIA,OAAOA,CAACA,IAAIA,CAACA,MAAMA,KAAKA,CAACA,CAACA,CAACA,CAACA;wBAACA,MAAMA,CAACA,EAAEA,CAACA;oBAACA,CAACA;oBAEzDA,IAAIA,UAAUA,GAAGA,OAAOA,CAACA,IAAIA,CAACA;oBAC9BA,IAAIA,GAAGA,GAAGA,EAAEA,CAACA;oBACbA,gBAACA,CAACA,IAAIA,CAACA,UAAUA,EAAEA,UAAAA,CAACA;wBAChBA,EAAEA,CAACA,CAACA,CAACA,gBAACA,CAACA,QAAQA,CAACA,CAACA,CAACA,CAACA,CAACA,CAACA;4BACjBA,GAAGA,CAACA,IAAIA,CAACA,EAAEA,IAAIA,EAAEA,CAACA,EAAEA,CAACA,CAACA;4BACtBA,MAAMA,CAAAA;wBACVA,CAACA;wBACDA,IAAIA,IAAIA,GAAGA,MAAMA,CAACA,IAAIA,CAACA,CAACA,CAACA,CAACA;wBAC1BA,EAAEA,CAACA,CAACA,IAAIA,CAACA,MAAMA,GAAGA,CAACA,CAACA,CAACA,CAACA;4BAClBA,GAAGA,CAACA,IAAIA,CAACA,CAACA,CAACA,CAACA;wBAChBA,CAACA;wBAACA,IAAIA,CAACA,CAACA;4BACJA,GAAGA,CAACA,IAAIA,CAACA,EAAEA,IAAIA,EAAEA,CAACA,CAACA,IAAIA,CAACA,CAACA,CAACA,CAACA,EAACA,CAACA,CAACA;wBAClCA,CAACA;oBACLA,CAACA,CAACA,CAACA;oBAEHA,MAAMA,CAACA,GAAGA,CAAAA;gBACZA,CAACA;gBACHF,qBAACA;YAADA,CAACA,AArBD,IAqBC;YArBD,oCAqBC,CAAA"} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/response_parser.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/response_parser.ts deleted file mode 100644 index 5137d2f..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/response_parser.ts +++ /dev/null @@ -1,25 +0,0 @@ -/// -import _ from 'lodash'; - -export default class ResponseParser { - parse(query, results) { - if (!results || results.data.length === 0) { return []; } - - var sqlResults = results.data; - var res = []; - _.each(sqlResults, r => { - if (!_.isObject(r)) { - res.push({ text: r }); - return - } - let keys = Object.keys(r); - if (keys.length > 1) { - res.push(r); - } else { - res.push({ text: r[keys[0]]}); - } - }); - - return res - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/scanner.d.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/scanner.d.ts deleted file mode 100644 index 6df9029..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/scanner.d.ts +++ /dev/null @@ -1,24 +0,0 @@ -export default class Scanner { - tree: any; - rootToken: any; - token: any; - skipSpace: boolean; - re: any; - expectedNext: boolean; - _sOriginal: any; - _s: any; - /** @ngInject */ - constructor(s: any); - raw(): any; - expect(token: any): void; - isToken(token: any): boolean; - expectNext(): void; - next(): boolean; - _next(): boolean; - Format(): string; - Print(ast: any): string; - push(argument: any): void; - setRoot(token: any): void; - isExpectedNext(): boolean; - toAST(): any; -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/scanner.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/scanner.js deleted file mode 100644 index 2145273..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/scanner.js +++ /dev/null @@ -1,447 +0,0 @@ -System.register(['lodash'], function(exports_1) { - var lodash_1; - var Scanner, wsRe, commentRe, idRe, intRe, powerIntRe, floatRe, stringRe, binaryOpRe, statementRe, joinsRe, macroFuncRe, condRe, inRe, closureRe, specCharsRe, macroRe, skipSpaceRe, builtInFuncRe, operatorRe, dataTypeRe, wsOnlyRe, commentOnlyRe, idOnlyRe, closureOnlyRe, macroFuncOnlyRe, statementOnlyRe, joinsOnlyRe, operatorOnlyRe, dataTypeOnlyRe, builtInFuncOnlyRe, macroOnlyRe, inOnlyRe, condOnlyRe, numOnlyRe, stringOnlyRe, skipSpaceOnlyRe, binaryOnlyRe, tokenRe, tabSize, newLine; - function isSkipSpace(token) { - return skipSpaceOnlyRe.test(token); - } - function isCond(token) { - return condOnlyRe.test(token); - } - function isIn(token) { - return inOnlyRe.test(token); - } - function isJoin(token) { - return joinsOnlyRe.test(token); - } - function isWS(token) { - return wsOnlyRe.test(token); - } - function isMacroFunc(token) { - return macroFuncOnlyRe.test(token); - } - function isMacro(token) { - return macroOnlyRe.test(token); - } - function isComment(token) { - return commentOnlyRe.test(token); - } - function isID(token) { - return idOnlyRe.test(token); - } - function isStatement(token) { - return statementOnlyRe.test(token); - } - function isOperator(token) { - return operatorOnlyRe.test(token); - } - function isDataType(token) { - return dataTypeOnlyRe.test(token); - } - function isBuiltInFunc(token) { - return builtInFuncOnlyRe.test(token); - } - function isClosureChars(token) { - return closureOnlyRe.test(token); - } - function isNum(token) { - return numOnlyRe.test(token); - } - function isString(token) { - return stringOnlyRe.test(token); - } - function isBinary(token) { - return binaryOnlyRe.test(token); - } - function printItems(items, tab, separator) { - if (tab === void 0) { tab = ''; } - if (separator === void 0) { separator = ''; } - var result = ''; - if (lodash_1.default.isArray(items)) { - if (items.length === 1) { - result += ' ' + items[0]; - } - else { - result += newLine; - items.forEach(function (item, i) { - result += tab + tabSize + item; - if (i !== items.length - 1) { - result += separator; - result += newLine; - } - }); - } - } - else { - result = newLine + '(' + newLine + print(items, tab + tabSize) + newLine + ')'; - } - return result; - } - function toAST(s) { - var scanner = new Scanner(s); - return scanner.toAST(); - } - function isSet(obj, prop) { - return obj.hasOwnProperty(prop) && !lodash_1.default.isEmpty(obj[prop]); - } - function isClosured(argument) { - return (argument.match(/\(/g) || []).length === (argument.match(/\)/g) || []).length; - } - function betweenBraces(query) { - var openBraces = 1, subQuery = ''; - for (var i = 0; i < query.length; i++) { - if (query.charAt(i) === '(') { - openBraces++; - } - if (query.charAt(i) === ')') { - if (openBraces === 1) { - subQuery = query.substring(0, i); - break; - } - openBraces--; - } - } - return subQuery; - } - // see https://clickhouse.yandex/reference_ru.html#SELECT - function print(AST, tab) { - if (tab === void 0) { tab = ''; } - var result = ''; - if (isSet(AST, '$rate')) { - result += tab + '$rate('; - result += printItems(AST.$rate, tab, ',') + ')'; - } - if (isSet(AST, '$columns')) { - result += tab + '$columns('; - result += printItems(AST.$columns, tab, ',') + ')'; - } - if (isSet(AST, '$rateColumns')) { - result += tab + '$rateColumns('; - result += printItems(AST.$rateColumns, tab, ',') + ')'; - } - if (isSet(AST, 'select')) { - result += tab + 'SELECT'; - result += printItems(AST.select, tab, ','); - } - if (isSet(AST, 'from')) { - result += newLine + tab + 'FROM'; - result += printItems(AST.from, tab); - } - if (isSet(AST, 'join')) { - result += tab + newLine + AST.join.type.toUpperCase() + - printItems(AST.join.source, tab) + - ' USING ' + printItems(AST.join.using, tab, ','); - } - if (isSet(AST, 'prewhere')) { - result += newLine + tab + 'PREWHERE'; - result += printItems(AST.prewhere, tab); - } - if (isSet(AST, 'where')) { - result += newLine + tab + 'WHERE'; - result += printItems(AST.where, tab); - } - if (isSet(AST, 'group by')) { - result += newLine + tab + 'GROUP BY'; - result += printItems(AST['group by'], tab, ','); - } - if (isSet(AST, 'having')) { - result += newLine + tab + 'HAVING'; - result += printItems(AST.having, tab); - } - if (isSet(AST, 'order by')) { - result += newLine + tab + 'ORDER BY'; - result += printItems(AST['order by'], tab, ','); - } - if (isSet(AST, 'limit')) { - result += newLine + tab + 'LIMIT'; - result += printItems(AST.limit, tab, ','); - } - if (isSet(AST, 'union all')) { - AST['union all'].forEach(function (v) { - result += newLine + newLine + tab + 'UNION ALL' + newLine + newLine; - result += print(v, tab); - }); - } - if (isSet(AST, 'format')) { - result += newLine + tab + 'FORMAT'; - result += printItems(AST.format, tab); - } - return result; - } - return { - setters:[ - function (lodash_1_1) { - lodash_1 = lodash_1_1; - }], - execute: function() { - Scanner = (function () { - /** @ngInject */ - function Scanner(s) { - this._sOriginal = s; - this.token = null; - } - Scanner.prototype.raw = function () { - return this._sOriginal; - }; - ; - Scanner.prototype.expect = function (token) { - this.expectNext(); - if (!this.isToken(token)) { - throw ("expecting [" + token + "], but got [" + this.token + "] at [" + this._s + "]"); - } - }; - ; - Scanner.prototype.isToken = function (token) { - return lodash_1.default.toUpper(token) === lodash_1.default.toUpper(this.token); - }; - ; - Scanner.prototype.expectNext = function () { - if (!this.next()) { - throw ("expecting additional token at the end of query [" + this._sOriginal + "]"); - } - }; - ; - Scanner.prototype.next = function () { - while (this._next()) { - if (this.skipSpace && isWS(this.token)) { - // skip whitespace - continue; - } - if (isComment(this.token)) { - // skip comment - continue; - } - return true; - } - return false; - }; - ; - Scanner.prototype._next = function () { - if (this._s.length === 0) { - return false; - } - var r = this.re.exec(this._s); - if (r === null) { - throw ("cannot find next token in [" + this._s + "]"); - } - this.token = r[0]; - this._s = this._s.substring(this.token.length); - return true; - }; - ; - Scanner.prototype.Format = function () { - return print(this.toAST()); - }; - ; - Scanner.prototype.Print = function (ast) { - return print(ast); - }; - Scanner.prototype.push = function (argument) { - this.tree[this.rootToken].push(argument); - this.expectedNext = false; - }; - Scanner.prototype.setRoot = function (token) { - this.rootToken = token.toLowerCase(); - this.tree[this.rootToken] = []; - this.expectedNext = true; - }; - Scanner.prototype.isExpectedNext = function () { - var v = this.expectedNext; - this.expectedNext = false; - return v; - }; - Scanner.prototype.toAST = function () { - this._s = this._sOriginal; - this.tree = {}; - this.setRoot('root'); - this.expectedNext = false; - this.skipSpace = true; - this.re = new RegExp("^(?:" + tokenRe + ")", 'i'); - var subQuery = '', argument = ''; - while (this.next()) { - if (!this.isExpectedNext() && isStatement(this.token) && !this.tree.hasOwnProperty(lodash_1.default.toLower(this.token))) { - if (!isClosured(argument)) { - argument += this.token; - continue; - } - if (argument.length > 0) { - this.push(argument); - argument = ''; - } - this.setRoot(this.token); - } - else if (this.token === ',' && isClosured(argument)) { - this.push(argument); - argument = ''; - this.expectedNext = true; - } - else if (isClosureChars(this.token) && this.rootToken === 'from') { - subQuery = betweenBraces(this._s); - this.tree[this.rootToken] = toAST(subQuery); - this._s = this._s.substring(subQuery.length + 1); - } - else if (isMacroFunc(this.token)) { - var func = this.token; - if (!this.next()) { - throw ("wrong function signature for `" + func + "` at [" + this._s + "]"); - } - subQuery = betweenBraces(this._s); - var subAST = toAST(subQuery); - if (isSet(subAST, 'root')) { - this.tree[func] = subAST['root'].map(function (item) { - return item; - }); - } - else { - this.tree[func] = subAST; - } - this._s = this._s.substring(subQuery.length + 1); - // macro funcs are used instead of SELECT statement - this.tree['select'] = []; - } - else if (isIn(this.token)) { - argument += ' ' + this.token; - if (!this.next()) { - throw ("wrong in signature for `" + argument + "` at [" + this._s + "]"); - } - if (isClosureChars(this.token)) { - subQuery = betweenBraces(this._s); - var subAST = toAST(subQuery); - if (isSet(subAST, 'root')) { - argument += ' (' + subAST['root'].map(function (item) { - return item; - }); - argument = argument + ')'; - } - else { - argument += ' (' + newLine + print(subAST, tabSize) + ')'; - this.push(argument); - argument = ''; - } - this._s = this._s.substring(subQuery.length + 1); - } - else { - argument += ' ' + this.token; - } - } - else if (isCond(this.token) && (this.rootToken === 'where' || this.rootToken === 'prewhere')) { - if (isClosured(argument)) { - this.push(argument); - argument = this.token; - } - else { - argument += ' ' + this.token; - } - } - else if (isJoin(this.token)) { - var joinType = this.token, source = void 0; - if (!this.next()) { - throw ("wrong join signature for `" + joinType + "` at [" + this._s + "]"); - } - if (isClosureChars(this.token)) { - subQuery = betweenBraces(this._s); - source = toAST(subQuery); - this._s = this._s.substring(subQuery.length + 1); - } - else { - source = [this.token]; - } - this.expect('using'); - this.tree['join'] = { type: joinType, source: source, using: [] }; - while (this.next()) { - if (isStatement(this.token)) { - if (argument !== '') { - this.push(argument); - argument = ''; - } - this.setRoot(this.token); - break; - } - if (!isID(this.token)) { - continue; - } - this.tree['join'].using.push(this.token); - } - } - else if (this.rootToken === 'union all') { - var statement = 'union all'; - this._s = this.token + ' ' + this._s; - var subQueryPos = this._s.toLowerCase().indexOf(statement); - while (subQueryPos !== -1) { - var subQuery_1 = this._s.substring(0, subQueryPos); - var ast_1 = toAST(subQuery_1); - this.tree[statement].push(ast_1); - this._s = this._s.substring(subQueryPos + statement.length, this._s.length); - subQueryPos = this._s.toLowerCase().indexOf(statement); - } - var ast = toAST(this._s); - this._s = ''; - this.tree[statement].push(ast); - } - else if (isClosureChars(this.token) || this.token === '.') { - argument += this.token; - } - else if (this.token === ',') { - argument += this.token + ' '; - } - else { - argument += (argument === '' || isSkipSpace(argument[argument.length - 1])) - ? this.token - : ' ' + this.token; - } - } - if (argument !== '') { - this.push(argument); - } - return this.tree; - }; - ; - return Scanner; - })(); - exports_1("default", Scanner); - wsRe = "\\s+", commentRe = "--[^\n]*|/\\*(?:[^*]|\\*[^/])*\\*/", idRe = "[a-zA-Z_][a-zA-Z_0-9]*", intRe = "\\d+", powerIntRe = "\\d+e\\d+", floatRe = "\\d+\\.\\d*|\\d*\\.\\d+|\\d+[eE][-+]\\d+", stringRe = "('[^']*')|(`[^`]*`)", binaryOpRe = "=>|\\|\\||>=|<=|==|!=|<>|->|[-+/%*=<>\\.!]", statementRe = "(select|from|where|having|order by|group by|limit|format|prewhere|union all)", joinsRe = "(any inner join|any left join|all inner join|all left join" + - "|global any inner join|global any left join|global all inner join|global all left join)", macroFuncRe = "(\\$rateColumns|\\$rate|\\$columns)", condRe = "\\b(or|and)\\b", inRe = "\\b(global in|global not in|not in|in)\\b", closureRe = "[\\(\\)\\[\\]]", specCharsRe = "[,?:]", macroRe = "\\$[A-Za-z0-9_$]+", skipSpaceRe = "[\\(\\.! \\[]", builtInFuncRe = "\\b(avg|countIf|first|last|max|min|sum|sumIf|ucase|lcase|mid|round|rank|now|" + - "coalesce|ifnull|isnull|nvl|count|timeSlot|yesterday|today|now|toRelativeSecondNum|" + - "toRelativeMinuteNum|toRelativeHourNum|toRelativeDayNum|toRelativeWeekNum|toRelativeMonthNum|" + - "toRelativeYearNum|toTime|toStartOfHour|toStartOfFiveMinute|toStartOfMinute|toStartOfYear|" + - "toStartOfQuarter|toStartOfMonth|toMonday|toSecond|toMinute|toHour|toDayOfWeek|toDayOfMonth|" + - "toMonth|toYear|toFixedString|toStringCutToZero|reinterpretAsString|reinterpretAsDate|" + - "reinterpretAsDateTime|reinterpretAsFloat32|reinterpretAsFloat64|reinterpretAsInt8|" + - "reinterpretAsInt16|reinterpretAsInt32|reinterpretAsInt64|reinterpretAsUInt8|" + - "reinterpretAsUInt16|reinterpretAsUInt32|reinterpretAsUInt64|toUInt8|toUInt16|toUInt32|" + - "toUInt64|toInt8|toInt16|toInt32|toInt64|toFloat32|toFloat64|toDate|toDateTime|toString|" + - "bitAnd|bitOr|bitXor|bitNot|bitShiftLeft|bitShiftRight|abs|negate|modulo|intDivOrZero|" + - "intDiv|divide|multiply|minus|plus|empty|notEmpty|length|lengthUTF8|lower|upper|lowerUTF8|" + - "upperUTF8|reverse|reverseUTF8|concat|substring|substringUTF8|appendTrailingCharIfAbsent|" + - "position|positionUTF8|match|extract|extractAll|like|notLike|replaceOne|replaceAll|" + - "replaceRegexpOne|range|arrayElement|has|indexOf|countEqual|arrayEnumerate|arrayEnumerateUniq|" + - "arrayJoin|arrayMap|arrayFilter|arrayExists|arrayCount|arrayAll|arrayFirst|arraySum|splitByChar|" + - "splitByString|alphaTokens|domainWithoutWWW|topLevelDomain|firstSignificantSubdomain|" + - "cutToFirstSignificantSubdomain|queryString|URLPathHierarchy|URLHierarchy|extractURLParameterNames|" + - "extractURLParameters|extractURLParameter|queryStringAndFragment|cutWWW|cutQueryString|" + - "cutFragment|cutQueryStringAndFragment|cutURLParameter|IPv4NumToString|IPv4StringToNum|" + - "IPv4NumToStringClassC|IPv6NumToString|IPv6StringToNum|rand|rand64|halfMD5|MD5|sipHash64|" + - "sipHash128|cityHash64|intHash32|intHash64|SHA1|SHA224|SHA256|URLHash|hex|unhex|bitmaskToList|" + - "bitmaskToArray|floor|ceil|round|roundToExp2|roundDuration|roundAge|regionToCountry|" + - "regionToContinent|regionToPopulation|regionIn|regionHierarchy|regionToName|OSToRoot|OSIn|" + - "OSHierarchy|SEToRoot|SEIn|SEHierarchy|dictGetUInt8|dictGetUInt16|dictGetUInt32|" + - "dictGetUInt64|dictGetInt8|dictGetInt16|dictGetInt32|dictGetInt64|dictGetFloat32|" + - "dictGetFloat64|dictGetDate|dictGetDateTime|dictGetString|dictGetHierarchy|dictHas|dictIsIn|" + - "argMin|argMax|uniqCombined|uniqHLL12|uniqExact|uniqExactIf|groupArray|groupUniqArray|quantile|" + - "quantileDeterministic|quantileTiming|quantileTimingWeighted|quantileExact|" + - "quantileExactWeighted|quantileTDigest|median|quantiles|varSamp|varPop|stddevSamp|stddevPop|" + - "covarSamp|covarPop|corr|sequenceMatch|sequenceCount|uniqUpTo|avgIf|" + - "quantilesTimingIf|argMinIf|uniqArray|sumArray|quantilesTimingArrayIf|uniqArrayIf|medianIf|" + - "quantilesIf|varSampIf|varPopIf|stddevSampIf|stddevPopIf|covarSampIf|covarPopIf|corrIf|" + - "uniqArrayIf|sumArrayIf|uniq)\\b", operatorRe = "\\b(select|group by|order by|from|where|limit|offset|having|as|" + - "when|else|end|type|left|right|on|outer|desc|asc|primary|key|between|" + - "foreign|not|null|inner|cross|natural|database|prewhere|using|global|in)\\b", dataTypeRe = "\\b(int|numeric|decimal|date|varchar|char|bigint|float|double|bit|binary|text|set|timestamp|" + - "money|real|number|integer|" + - "uint8|uint16|uint32|uint64|int8|int16|int32|int64|float32|float64|datetime|enum8|enum16|" + - "array|tuple|string)\\b", wsOnlyRe = new RegExp("^(?:" + wsRe + ")$"), commentOnlyRe = new RegExp("^(?:" + commentRe + ")$"), idOnlyRe = new RegExp("^(?:" + idRe + ")$"), closureOnlyRe = new RegExp("^(?:" + closureRe + ")$"), 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sourceMappingURL=scanner.js.map \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/scanner.js.map b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/scanner.js.map deleted file mode 100644 index fac0df2..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/scanner.js.map +++ /dev/null @@ -1 +0,0 @@ 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\ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/scanner.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/scanner.ts deleted file mode 100644 index 6892d3d..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/scanner.ts +++ /dev/null @@ -1,524 +0,0 @@ -import _ from 'lodash'; - -export default class Scanner { - tree: any; - rootToken: any; - token: any; - skipSpace: boolean; - re: any; - expectedNext: boolean; - - _sOriginal: any; - _s: any; - - /** @ngInject */ - constructor(s) { - this._sOriginal = s; - this.token = null; - } - - raw() { - return this._sOriginal; - }; - - expect(token) { - this.expectNext(); - if (!this.isToken(token)) { - throw("expecting [" + token + "], but got [" + this.token + "] at [" + this._s + "]"); - } - }; - - isToken(token) { - return _.toUpper(token) === _.toUpper(this.token); - }; - - expectNext() { - if (!this.next()) { - throw("expecting additional token at the end of query [" + this._sOriginal + "]"); - } - }; - - next() { - while (this._next()) { - if (this.skipSpace && isWS(this.token)) { - // skip whitespace - continue; - } - if (isComment(this.token)) { - // skip comment - continue; - } - return true; - } - return false; - }; - - _next() { - if (this._s.length === 0) { - return false; - } - let r = this.re.exec(this._s); - if (r === null) { - throw("cannot find next token in [" + this._s + "]"); - } - - this.token = r[0]; - this._s = this._s.substring(this.token.length); - return true; - }; - - Format() { - return print(this.toAST()); - }; - - Print(ast) { - return print(ast) - } - - push(argument) { - this.tree[this.rootToken].push(argument); - this.expectedNext = false; - } - - setRoot(token) { - this.rootToken = token.toLowerCase(); - this.tree[this.rootToken] = []; - this.expectedNext = true; - } - - isExpectedNext(): boolean { - let v = this.expectedNext; - this.expectedNext = false; - return v - } - - toAST() { - this._s = this._sOriginal; - this.tree = {}; - this.setRoot('root'); - this.expectedNext = false; - this.skipSpace = true; - this.re = new RegExp("^(?:" + tokenRe + ")", 'i'); - let subQuery = '', - argument = ''; - - while (this.next()) { - if (!this.isExpectedNext() && isStatement(this.token) && !this.tree.hasOwnProperty(_.toLower(this.token))) { - if (!isClosured(argument)) { - argument += this.token; - continue - } - if (argument.length > 0) { - this.push(argument); - argument = ''; - } - this.setRoot(this.token); - } - else if (this.token === ',' && isClosured(argument)) { - this.push(argument); - argument = ''; - this.expectedNext = true; - } - else if (isClosureChars(this.token) && this.rootToken === 'from') { - subQuery = betweenBraces(this._s); - this.tree[this.rootToken] = toAST(subQuery); - this._s = this._s.substring(subQuery.length + 1); - } - else if (isMacroFunc(this.token)) { - let func = this.token; - if (!this.next()) { - throw("wrong function signature for `" + func + "` at [" + this._s + "]"); - } - - subQuery = betweenBraces(this._s); - let subAST = toAST(subQuery); - if (isSet(subAST, 'root')) { - this.tree[func] = subAST['root'].map(function (item) { - return item; - }); - } else { - this.tree[func] = subAST; - } - this._s = this._s.substring(subQuery.length + 1); - - // macro funcs are used instead of SELECT statement - this.tree['select'] = []; - } - else if (isIn(this.token)) { - argument += ' ' + this.token; - if (!this.next()) { - throw("wrong in signature for `" + argument + "` at [" + this._s + "]"); - } - - if (isClosureChars(this.token)) { - subQuery = betweenBraces(this._s); - let subAST = toAST(subQuery); - if (isSet(subAST, 'root')) { - argument += ' (' + subAST['root'].map(function (item) { - return item; - }); - argument = argument + ')'; - } else { - argument += ' (' + newLine + print(subAST, tabSize) + ')'; - this.push(argument); - argument = ''; - } - this._s = this._s.substring(subQuery.length + 1); - } else { - argument += ' ' + this.token; - } - } - else if (isCond(this.token) && (this.rootToken === 'where' || this.rootToken === 'prewhere')) { - if (isClosured(argument)) { - this.push(argument); - argument = this.token; - } else { - argument += ' ' + this.token; - } - } - else if (isJoin(this.token)) { - let joinType = this.token, source; - if (!this.next()) { - throw("wrong join signature for `" + joinType + "` at [" + this._s + "]"); - } - - if (isClosureChars(this.token)) { - subQuery = betweenBraces(this._s); - source = toAST(subQuery); - this._s = this._s.substring(subQuery.length + 1); - } else { - source = [this.token]; - } - - this.expect('using'); - this.tree['join'] = {type: joinType, source: source, using: []}; - while (this.next()) { - if (isStatement(this.token)) { - if (argument !== '') { - this.push(argument); - argument = ''; - } - this.setRoot(this.token); - break; - } - if (!isID(this.token)) { - continue; - } - this.tree['join'].using.push(this.token); - } - } else if (this.rootToken === 'union all') { - let statement = 'union all'; - this._s = this.token + ' ' + this._s; - let subQueryPos = this._s.toLowerCase().indexOf(statement); - while (subQueryPos !== -1) { - let subQuery = this._s.substring(0, subQueryPos); - let ast = toAST(subQuery); - this.tree[statement].push(ast); - this._s = this._s.substring(subQueryPos + statement.length, this._s.length); - subQueryPos = this._s.toLowerCase().indexOf(statement); - } - let ast = toAST(this._s); - this._s = ''; - this.tree[statement].push(ast); - } - else if (isClosureChars(this.token) || this.token === '.') { - argument += this.token; - } else if (this.token === ',') { - argument += this.token + ' '; - } else { - argument += (argument === '' || isSkipSpace(argument[argument.length - 1])) - ? this.token - : ' ' + this.token; - } - } - - if (argument !== '') { - this.push(argument); - } - return this.tree; - }; -} - -let wsRe = "\\s+", - commentRe = "--[^\n]*|/\\*(?:[^*]|\\*[^/])*\\*/", - idRe = "[a-zA-Z_][a-zA-Z_0-9]*", - intRe = "\\d+", - powerIntRe = "\\d+e\\d+", - floatRe = "\\d+\\.\\d*|\\d*\\.\\d+|\\d+[eE][-+]\\d+", - stringRe = "('[^']*')|(`[^`]*`)", - binaryOpRe = "=>|\\|\\||>=|<=|==|!=|<>|->|[-+/%*=<>\\.!]", - statementRe = "(select|from|where|having|order by|group by|limit|format|prewhere|union all)", - joinsRe = "(any inner join|any left join|all inner join|all left join" + - "|global any inner join|global any left join|global all inner join|global all left join)", - macroFuncRe = "(\\$rateColumns|\\$rate|\\$columns)", - condRe = "\\b(or|and)\\b", - inRe = "\\b(global in|global not in|not in|in)\\b", - closureRe = "[\\(\\)\\[\\]]", - specCharsRe = "[,?:]", - macroRe = "\\$[A-Za-z0-9_$]+", - skipSpaceRe = "[\\(\\.! \\[]", - - builtInFuncRe = "\\b(avg|countIf|first|last|max|min|sum|sumIf|ucase|lcase|mid|round|rank|now|" + - "coalesce|ifnull|isnull|nvl|count|timeSlot|yesterday|today|now|toRelativeSecondNum|" + - "toRelativeMinuteNum|toRelativeHourNum|toRelativeDayNum|toRelativeWeekNum|toRelativeMonthNum|" + - "toRelativeYearNum|toTime|toStartOfHour|toStartOfFiveMinute|toStartOfMinute|toStartOfYear|" + - "toStartOfQuarter|toStartOfMonth|toMonday|toSecond|toMinute|toHour|toDayOfWeek|toDayOfMonth|" + - "toMonth|toYear|toFixedString|toStringCutToZero|reinterpretAsString|reinterpretAsDate|" + - "reinterpretAsDateTime|reinterpretAsFloat32|reinterpretAsFloat64|reinterpretAsInt8|" + - "reinterpretAsInt16|reinterpretAsInt32|reinterpretAsInt64|reinterpretAsUInt8|" + - "reinterpretAsUInt16|reinterpretAsUInt32|reinterpretAsUInt64|toUInt8|toUInt16|toUInt32|" + - "toUInt64|toInt8|toInt16|toInt32|toInt64|toFloat32|toFloat64|toDate|toDateTime|toString|" + - "bitAnd|bitOr|bitXor|bitNot|bitShiftLeft|bitShiftRight|abs|negate|modulo|intDivOrZero|" + - "intDiv|divide|multiply|minus|plus|empty|notEmpty|length|lengthUTF8|lower|upper|lowerUTF8|" + - "upperUTF8|reverse|reverseUTF8|concat|substring|substringUTF8|appendTrailingCharIfAbsent|" + - "position|positionUTF8|match|extract|extractAll|like|notLike|replaceOne|replaceAll|" + - "replaceRegexpOne|range|arrayElement|has|indexOf|countEqual|arrayEnumerate|arrayEnumerateUniq|" + - "arrayJoin|arrayMap|arrayFilter|arrayExists|arrayCount|arrayAll|arrayFirst|arraySum|splitByChar|" + - "splitByString|alphaTokens|domainWithoutWWW|topLevelDomain|firstSignificantSubdomain|" + - "cutToFirstSignificantSubdomain|queryString|URLPathHierarchy|URLHierarchy|extractURLParameterNames|" + - "extractURLParameters|extractURLParameter|queryStringAndFragment|cutWWW|cutQueryString|" + - "cutFragment|cutQueryStringAndFragment|cutURLParameter|IPv4NumToString|IPv4StringToNum|" + - "IPv4NumToStringClassC|IPv6NumToString|IPv6StringToNum|rand|rand64|halfMD5|MD5|sipHash64|" + - "sipHash128|cityHash64|intHash32|intHash64|SHA1|SHA224|SHA256|URLHash|hex|unhex|bitmaskToList|" + - "bitmaskToArray|floor|ceil|round|roundToExp2|roundDuration|roundAge|regionToCountry|" + - "regionToContinent|regionToPopulation|regionIn|regionHierarchy|regionToName|OSToRoot|OSIn|" + - "OSHierarchy|SEToRoot|SEIn|SEHierarchy|dictGetUInt8|dictGetUInt16|dictGetUInt32|" + - "dictGetUInt64|dictGetInt8|dictGetInt16|dictGetInt32|dictGetInt64|dictGetFloat32|" + - "dictGetFloat64|dictGetDate|dictGetDateTime|dictGetString|dictGetHierarchy|dictHas|dictIsIn|" + - "argMin|argMax|uniqCombined|uniqHLL12|uniqExact|uniqExactIf|groupArray|groupUniqArray|quantile|" + - "quantileDeterministic|quantileTiming|quantileTimingWeighted|quantileExact|" + - "quantileExactWeighted|quantileTDigest|median|quantiles|varSamp|varPop|stddevSamp|stddevPop|" + - "covarSamp|covarPop|corr|sequenceMatch|sequenceCount|uniqUpTo|avgIf|" + - "quantilesTimingIf|argMinIf|uniqArray|sumArray|quantilesTimingArrayIf|uniqArrayIf|medianIf|" + - "quantilesIf|varSampIf|varPopIf|stddevSampIf|stddevPopIf|covarSampIf|covarPopIf|corrIf|" + - "uniqArrayIf|sumArrayIf|uniq)\\b", - operatorRe = "\\b(select|group by|order by|from|where|limit|offset|having|as|" + - "when|else|end|type|left|right|on|outer|desc|asc|primary|key|between|" + - "foreign|not|null|inner|cross|natural|database|prewhere|using|global|in)\\b", - dataTypeRe = "\\b(int|numeric|decimal|date|varchar|char|bigint|float|double|bit|binary|text|set|timestamp|" + - "money|real|number|integer|" + - "uint8|uint16|uint32|uint64|int8|int16|int32|int64|float32|float64|datetime|enum8|enum16|" + - "array|tuple|string)\\b", - - wsOnlyRe = new RegExp("^(?:" + wsRe + ")$"), - commentOnlyRe = new RegExp("^(?:" + commentRe + ")$"), - idOnlyRe = new RegExp("^(?:" + idRe + ")$"), - closureOnlyRe = new RegExp("^(?:" + closureRe + ")$"), - macroFuncOnlyRe = new RegExp("^(?:" + macroFuncRe + ")$"), - statementOnlyRe = new RegExp("^(?:" + statementRe + ")$", 'i'), - joinsOnlyRe = new RegExp("^(?:" + joinsRe + ")$", 'i'), - operatorOnlyRe = new RegExp("^(?:" + operatorRe + ")$", 'i'), - dataTypeOnlyRe = new RegExp("^(?:" + dataTypeRe + ")$"), - builtInFuncOnlyRe = new RegExp("^(?:" + builtInFuncRe + ")$"), - macroOnlyRe = new RegExp("^(?:" + macroRe + ")$", 'i'), - inOnlyRe = new RegExp("^(?:" + inRe + ")$", 'i'), - condOnlyRe = new RegExp("^(?:" + condRe + ")$", 'i'), - numOnlyRe = new RegExp("^(?:" + [powerIntRe, intRe, floatRe].join("|") + ")$"), - stringOnlyRe = new RegExp("^(?:" + stringRe + ")$"), - skipSpaceOnlyRe = new RegExp("^(?:" + skipSpaceRe + ")$"), - binaryOnlyRe = new RegExp("^(?:" + binaryOpRe + ")$"); - -var tokenRe = [statementRe, macroFuncRe, joinsRe, inRe, wsRe, commentRe, idRe, stringRe, powerIntRe, floatRe, intRe, - binaryOpRe, closureRe, specCharsRe, macroRe].join("|"); - -function isSkipSpace(token) { - return skipSpaceOnlyRe.test(token); -} - -function isCond(token) { - return condOnlyRe.test(token); -} - -function isIn(token) { - return inOnlyRe.test(token); -} - -function isJoin(token) { - return joinsOnlyRe.test(token); -} - -function isWS(token) { - return wsOnlyRe.test(token); -} - -function isMacroFunc(token) { - return macroFuncOnlyRe.test(token); -} - -function isMacro(token) { - return macroOnlyRe.test(token); -} - -function isComment(token) { - return commentOnlyRe.test(token); -} - -function isID(token) { - return idOnlyRe.test(token); -} - -function isStatement(token) { - return statementOnlyRe.test(token); -} - -function isOperator(token) { - return operatorOnlyRe.test(token); -} - -function isDataType(token) { - return dataTypeOnlyRe.test(token); -} - -function isBuiltInFunc(token) { - return builtInFuncOnlyRe.test(token); -} - -function isClosureChars(token) { - return closureOnlyRe.test(token); -} - -function isNum(token) { - return numOnlyRe.test(token); -} - -function isString(token) { - return stringOnlyRe.test(token); -} - -function isBinary(token) { - return binaryOnlyRe.test(token); -} - -var tabSize = ' ', // 4 spaces - newLine = '\n'; - -function printItems(items, tab = '', separator = '') { - var result = ''; - if (_.isArray(items)) { - if (items.length === 1) { - result += ' ' + items[0]; - } else { - result += newLine; - items.forEach(function (item, i) { - result += tab + tabSize + item; - if (i !== items.length - 1) { - result += separator; - result += newLine; - } - }); - } - } else { - result = newLine + '(' + newLine + print(items, tab + tabSize) + newLine + ')'; - } - - return result; -} - -function toAST(s) { - var scanner = new Scanner(s); - return scanner.toAST(); -} - -function isSet(obj, prop) { - return obj.hasOwnProperty(prop) && !_.isEmpty(obj[prop]); -} - -function isClosured(argument) { - return (argument.match(/\(/g) || []).length === (argument.match(/\)/g) || []).length; -} - -function betweenBraces(query) { - var openBraces = 1, subQuery = ''; - for (var i = 0; i < query.length; i++) { - if (query.charAt(i) === '(') { - openBraces++; - } - if (query.charAt(i) === ')') { - if (openBraces === 1) { - subQuery = query.substring(0, i); - break; - } - openBraces--; - } - } - return subQuery; -} - -// see https://clickhouse.yandex/reference_ru.html#SELECT -function print(AST, tab = '') { - var result = ''; - if (isSet(AST, '$rate')) { - result += tab + '$rate('; - result += printItems(AST.$rate, tab, ',') + ')'; - } - - if (isSet(AST, '$columns')) { - result += tab + '$columns('; - result += printItems(AST.$columns, tab, ',') + ')'; - } - - if (isSet(AST, '$rateColumns')) { - result += tab + '$rateColumns('; - result += printItems(AST.$rateColumns, tab, ',') + ')'; - } - - if (isSet(AST, 'select')) { - result += tab + 'SELECT'; - result += printItems(AST.select, tab, ','); - } - - if (isSet(AST, 'from')) { - result += newLine + tab + 'FROM'; - result += printItems(AST.from, tab); - } - - if (isSet(AST, 'join')) { - result += tab + newLine + AST.join.type.toUpperCase() + - printItems(AST.join.source, tab) + - ' USING ' + printItems(AST.join.using, tab, ','); - } - - if (isSet(AST, 'prewhere')) { - result += newLine + tab + 'PREWHERE'; - result += printItems(AST.prewhere, tab); - } - - if (isSet(AST, 'where')) { - result += newLine + tab + 'WHERE'; - result += printItems(AST.where, tab); - } - - if (isSet(AST, 'group by')) { - result += newLine + tab + 'GROUP BY'; - result += printItems(AST['group by'], tab, ','); - } - - if (isSet(AST, 'having')) { - result += newLine + tab + 'HAVING'; - result += printItems(AST.having, tab); - } - - if (isSet(AST, 'order by')) { - result += newLine + tab + 'ORDER BY'; - result += printItems(AST['order by'], tab, ','); - } - - if (isSet(AST, 'limit')) { - result += newLine + tab + 'LIMIT'; - result += printItems(AST.limit, tab, ','); - } - - if (isSet(AST, 'union all')) { - AST['union all'].forEach(function (v) { - result += newLine + newLine + tab + 'UNION ALL' + newLine + newLine; - result += print(v, tab) - }) - } - - if (isSet(AST, 'format')) { - result += newLine + tab + 'FORMAT'; - result += printItems(AST.format, tab); - } - - return result; -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/snippets/clickhouse.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/snippets/clickhouse.js deleted file mode 100644 index 84317d6..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/snippets/clickhouse.js +++ /dev/null @@ -1,21 +0,0 @@ -"use strict"; - -System.register([], function (_export, _context) { - "use strict"; - - return { - setters: [], - execute: function () { - // jshint ignore: start - // jscs: disable - ace.define("ace/snippets/clickhouse", ["require", "exports", "module"], function (require, exports, module) { - "use strict"; - - exports.snippets = []; - - exports.scope = "clickhouse"; - }); - } - }; -}); -//# sourceMappingURL=clickhouse.js.map diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/snippets/clickhouse.js.map b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/snippets/clickhouse.js.map deleted file mode 100644 index 1a0ab1e..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/snippets/clickhouse.js.map +++ /dev/null @@ -1 +0,0 @@ -{"version":3,"sources":["../../src/snippets/clickhouse.js"],"names":["ace","define","require","exports","module","snippets","scope"],"mappings":";;;;;;;;AAAA;AACA;AACAA,gBAAIC,MAAJ,CAAW,yBAAX,EAAqC,CAAC,SAAD,EAAW,SAAX,EAAqB,QAArB,CAArC,EAAqE,UAASC,OAAT,EAAkBC,OAAlB,EAA2BC,MAA3B,EAAmC;AACpG;;AAEAD,wBAAQE,QAAR,GAAmB,EAAnB;;AAGAF,wBAAQG,KAAR,GAAgB,YAAhB;AACH,aAPD","file":"clickhouse.js","sourcesContent":["// jshint ignore: start\r\n// jscs: disable\r\nace.define(\"ace/snippets/clickhouse\",[\"require\",\"exports\",\"module\"], function(require, exports, module) {\r\n \"use strict\";\r\n\r\n exports.snippets = [\r\n ];\r\n\r\n exports.scope = \"clickhouse\";\r\n});"]} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_query.d.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_query.d.ts deleted file mode 100644 index c36cfb6..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_query.d.ts +++ /dev/null @@ -1,25 +0,0 @@ -/// -export default class SqlQuery { - target: any; - templateSrv: any; - options: any; - /** @ngInject */ - constructor(target: any, templateSrv?: any, options?: any); - replace(options: any, adhocFilters: any): any; - static columns(query: string): string; - static _columns(key: string, value: string, fromQuery: string): string; - static rateColumns(query: string): string; - static rate(query: string, ast: any): string; - static _fromIndex(query: string): number; - static _rate(args: any, fromQuery: string): string; - static _applyTimeFilter(query: string): string; - static getTimeSeries(dateTimeType: string): string; - static getTimeFilter(isToNow: boolean, dateTimeType: string): string; - static convertTimestamp(date: any, dateTimeType: string): number; - static round(date: any, round: string): any; - static convertInterval(interval: any, intervalFactor: any, dateTimeType: string): number; - static interpolateQueryExpr(value: any, variable: any, defaultFormatFn: any): any; - static clickhouseOperator(value: any): any; - static clickhouseEscape(value: any, variable: any): any; - static unescape(query: any): any; -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_query.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_query.js deleted file mode 100644 index 17c74e4..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_query.js +++ /dev/null @@ -1,332 +0,0 @@ -/// -System.register(['lodash', 'app/core/utils/datemath', 'moment', './scanner'], function(exports_1) { - var lodash_1, dateMath, moment_1, scanner_1; - var durationSplitRegexp, SqlQuery; - return { - setters:[ - function (lodash_1_1) { - lodash_1 = lodash_1_1; - }, - function (dateMath_1) { - dateMath = dateMath_1; - }, - function (moment_1_1) { - moment_1 = moment_1_1; - }, - function (scanner_1_1) { - scanner_1 = scanner_1_1; - }], - execute: function() { - durationSplitRegexp = /(\d+)(ms|s|m|h|d|w|M|y)/; - SqlQuery = (function () { - /** @ngInject */ - function SqlQuery(target, templateSrv, options) { - this.target = target; - this.templateSrv = templateSrv; - this.options = options; - } - SqlQuery.prototype.replace = function (options, adhocFilters) { - var self = this, query = this.target.query, scanner = new scanner_1.default(query), dateTimeType = this.target.dateTimeType ? this.target.dateTimeType : 'DATETIME', from = SqlQuery.convertTimestamp(SqlQuery.round(this.options.range.from, this.target.round), dateTimeType), to = SqlQuery.convertTimestamp(SqlQuery.round(this.options.range.to, this.target.round), dateTimeType), timeSeries = SqlQuery.getTimeSeries(dateTimeType), timeFilter = SqlQuery.getTimeFilter(this.options.rangeRaw.to === 'now', dateTimeType), i = this.templateSrv.replace(this.target.interval, options.scopedVars) || options.interval, interval = SqlQuery.convertInterval(i, this.target.intervalFactor || 1, dateTimeType); - try { - var ast = scanner.toAST(); - if (adhocFilters.length > 0) { - if (!ast.hasOwnProperty('where')) { - ast.where = []; - } - adhocFilters.forEach(function (af) { - var parts = af.key.split('.'); - if (parts.length < 3) { - console.log("adhoc filters: filter " + af.key + "` has wrong format"); - return; - } - if (self.target.database != parts[0] || self.target.table != parts[1]) { - return; - } - var operator = SqlQuery.clickhouseOperator(af.operator); - var cond = parts[2] + " " + operator + " " + af.value; - if (ast.where.length > 0) { - // OR is not implemented - // @see https://github.com/grafana/grafana/issues/10918 - cond = "AND " + cond; - } - ast.where.push(cond); - }); - } - query = scanner.Print(ast); - if (ast.hasOwnProperty('$columns') && !lodash_1.default.isEmpty(ast['$columns'])) { - query = SqlQuery.columns(query); - } - else if (ast.hasOwnProperty('$rateColumns') && !lodash_1.default.isEmpty(ast['$rateColumns'])) { - query = SqlQuery.rateColumns(query); - } - else if (ast.hasOwnProperty('$rate') && !lodash_1.default.isEmpty(ast['$rate'])) { - query = SqlQuery.rate(query, ast); - } - } - catch (err) { - console.log('AST parser error: ', err.message); - } - query = this.templateSrv.replace(query, options.scopedVars, SqlQuery.interpolateQueryExpr); - query = SqlQuery.unescape(query); - this.target.rawQuery = query - .replace(/\$timeSeries/g, timeSeries) - .replace(/\$timeFilter/g, timeFilter) - .replace(/\$table/g, this.target.database + '.' + this.target.table) - .replace(/\$from/g, from) - .replace(/\$to/g, to) - .replace(/\$dateCol/g, this.target.dateColDataType) - .replace(/\$dateTimeCol/g, this.target.dateTimeColDataType) - .replace(/\$interval/g, interval) - .replace(/(?:\r\n|\r|\n)/g, ' '); - return this.target.rawQuery; - }; - // $columns(query) - SqlQuery.columns = function (query) { - if (query.slice(0, 9) === '$columns(') { - var fromIndex = SqlQuery._fromIndex(query); - var args = query.slice(9, fromIndex) - .trim() // rm spaces - .slice(0, -1), // cut ending brace - scanner = new scanner_1.default(args), ast = scanner.toAST(); - var root = ast['root']; - if (root.length !== 2) { - throw { message: 'Amount of arguments must equal 2 for $columns func. Parsed arguments are: ' + root.join(', ') }; - } - query = SqlQuery._columns(root[0], root[1], query.slice(fromIndex)); - } - return query; - }; - SqlQuery._columns = function (key, value, fromQuery) { - if (key.slice(-1) === ')' || value.slice(-1) === ')') { - throw { message: 'Some of passed arguments are without aliases: ' + key + ', ' + value }; - } - var keyAlias = key.trim().split(' ').pop(), valueAlias = value.trim().split(' ').pop(), havingIndex = fromQuery.toLowerCase().indexOf('having'), having = "", aliasRegex = /%(\w+)/g, concatExpr = ""; - if (key.match(/^'.*'$/) && key.match(aliasRegex)) { - concatExpr = 'concat(' + key.replace(aliasRegex, "', $1, '").replace(/, ''/g, '') + ')'; - key = keyAlias = lodash_1.default.uniq(key.match(aliasRegex).map(function (k) { return k.slice(1); })).join(', '); - } - if (havingIndex !== -1) { - having = fromQuery.slice(havingIndex, fromQuery.length); - fromQuery = fromQuery.slice(0, havingIndex); - } - fromQuery = SqlQuery._applyTimeFilter(fromQuery); - return 'SELECT ' + - 't' + - ', groupArray((' + (concatExpr || keyAlias) + ', ' + valueAlias + ')) as groupArr' + - ' FROM (' + - ' SELECT $timeSeries as t' + - ', ' + key + - ', ' + value + ' ' + - fromQuery + - ' GROUP BY t, ' + keyAlias + - ' ' + having + - ' ORDER BY t, ' + keyAlias + - ') ' + - 'GROUP BY t ' + - 'ORDER BY t'; - }; - // $rateColumns(query) - SqlQuery.rateColumns = function (query) { - if (query.slice(0, 13) === '$rateColumns(') { - var fromIndex = SqlQuery._fromIndex(query); - var args = query.slice(13, fromIndex) - .trim() // rm spaces - .slice(0, -1), // cut ending brace - scanner = new scanner_1.default(args), ast = scanner.toAST(); - var root = ast['root']; - if (root.length !== 2) { - throw { message: 'Amount of arguments must equal 2 for $columns func. Parsed arguments are: ' + root.join(', ') }; - } - query = SqlQuery._columns(root[0], root[1], query.slice(fromIndex)); - query = 'SELECT t' + - ', arrayMap(a -> (a.1, a.2/runningDifference( t/1000 )), groupArr)' + - ' FROM (' + - query + - ')'; - } - return query; - }; - // $rate(query) - SqlQuery.rate = function (query, ast) { - if (query.slice(0, 6) === '$rate(') { - var fromIndex = SqlQuery._fromIndex(query); - if (ast.$rate.length < 1) { - throw { message: 'Amount of arguments must be > 0 for $rate func. Parsed arguments are: ' + ast.$rate.join(', ') }; - } - query = SqlQuery._rate(ast['$rate'], query.slice(fromIndex)); - } - return query; - }; - SqlQuery._fromIndex = function (query) { - var fromIndex = query.toLowerCase().indexOf('from'); - if (fromIndex === -1) { - throw { message: 'Could not find FROM-statement at: ' + query }; - } - return fromIndex; - }; - SqlQuery._rate = function (args, fromQuery) { - var aliases = []; - lodash_1.default.each(args, function (arg) { - if (arg.slice(-1) === ')') { - throw { message: 'Argument "' + arg + '" cant be used without alias' }; - } - aliases.push(arg.trim().split(' ').pop()); - }); - var rateColums = []; - lodash_1.default.each(aliases, function (a) { - rateColums.push(a + '/runningDifference(t/1000) ' + a + 'Rate'); - }); - fromQuery = SqlQuery._applyTimeFilter(fromQuery); - return 'SELECT ' + '' + - 't' + - ', ' + rateColums.join(',') + - ' FROM (' + - ' SELECT $timeSeries as t' + - ', ' + args.join(',') + - ' ' + fromQuery + - ' GROUP BY t' + - ' ORDER BY t' + - ')'; - }; - SqlQuery._applyTimeFilter = function (query) { - if (query.toLowerCase().indexOf('where') !== -1) { - query = query.replace(/where/i, 'WHERE $timeFilter AND '); - } - else { - query += ' WHERE $timeFilter'; - } - return query; - }; - SqlQuery.getTimeSeries = function (dateTimeType) { - if (dateTimeType === 'DATETIME') { - return '(intDiv(toUInt32($dateTimeCol), $interval) * $interval) * 1000'; - } - if (dateTimeType === 'TIMESTAMPMS') { - return 'intDiv($dateTimeCol, $interval) * $interval'; - } - return '(intDiv($dateTimeCol, $interval) * $interval) * 1000'; - }; - SqlQuery.getTimeFilter = function (isToNow, dateTimeType) { - var dateCol = function (t) { - if (dateTimeType === 'TIMESTAMPMS') { - t = t + '/1000'; - } - return 'toDate(' + t + ')'; - }; - var dateTimeCol = function (t) { - if (dateTimeType === 'DATETIME') { - return 'toDateTime(' + t + ')'; - } - return t; - }; - if (isToNow) { - return '$dateCol >= ' + dateCol('$from') + ' AND $dateTimeCol >= ' + dateTimeCol('$from'); - } - return '$dateCol BETWEEN ' + dateCol('$from') + ' AND ' + dateCol('$to') + ' AND $dateTimeCol BETWEEN ' + dateTimeCol('$from') + ' AND ' + dateTimeCol('$to'); - }; - // date is a moment object - SqlQuery.convertTimestamp = function (date, dateTimeType) { - //return date.format("'Y-MM-DD HH:mm:ss'") - if (lodash_1.default.isString(date)) { - date = dateMath.parse(date, true); - } - return Math.floor(dateTimeType === 'TIMESTAMPMS' ? date.valueOf() : date.valueOf() / 1000); - }; - SqlQuery.round = function (date, round) { - if (round === "" || round === undefined || round === "0s") { - return date; - } - if (lodash_1.default.isString(date)) { - date = dateMath.parse(date, true); - } - var coeff = SqlQuery.convertInterval(round, 1, 'TIMESTAMPMS'); - var rounded = Math.floor(date.valueOf() / coeff) * coeff; - return moment_1.default(rounded); - }; - SqlQuery.convertInterval = function (interval, intervalFactor, dateTimeType) { - var m = interval.match(durationSplitRegexp); - if (m === null) { - throw { message: 'Received duration is invalid: ' + interval }; - } - var dur = moment_1.default.duration(parseInt(m[1]), m[2]); - var sec = dur.asSeconds(); - if (dateTimeType === 'TIMESTAMPMS') { - return Math.ceil((sec * 1000 || 1) * intervalFactor); - } - if (sec < 1) { - sec = 1; - } - return Math.ceil(sec * intervalFactor); - }; - SqlQuery.interpolateQueryExpr = function (value, variable, defaultFormatFn) { - // if no `multiselect` or `include all` - do not escape - if (!variable.multi && !variable.includeAll) { - return value; - } - if (typeof value === 'string') { - return SqlQuery.clickhouseEscape(value, variable); - } - var escapedValues = lodash_1.default.map(value, function (v) { - return SqlQuery.clickhouseEscape(v, variable); - }); - return escapedValues.join(','); - }; - SqlQuery.clickhouseOperator = function (value) { - switch (value) { - case "=": - case "!=": - case ">": - case "<": - return value; - case "=~": - return "LIKE"; - case "!~": - return "NOT LIKE"; - default: - console.log("adhoc filters: got unsupported operator `" + value + "`"); - return value; - } - }; - SqlQuery.clickhouseEscape = function (value, variable) { - var isDigit = true; - // if at least one of options is not digit - lodash_1.default.each(variable.options, function (opt) { - if (opt.value === '$__all') { - return true; - } - if (!opt.value.match(/^\d+$/)) { - isDigit = false; - return false; - } - return true; - }); - if (isDigit) { - return value; - } - else { - return "'" + value.replace(/[\\']/g, '\\$&') + "'"; - } - }; - SqlQuery.unescape = function (query) { - var macros = '$unescape('; - var openMacros = query.indexOf(macros); - while (openMacros !== -1) { - var closeMacros = query.indexOf(')', openMacros); - if (closeMacros === -1) { - throw { message: 'unable to find closing brace for $unescape macros: ' + query.substring(0, openMacros) }; - } - var arg = query.substring(openMacros + macros.length, closeMacros) - .trim(); - arg = arg.replace(/[']+/g, ''); - query = query.substring(0, openMacros) + arg + query.substring(closeMacros + 1, query.length); - openMacros = query.indexOf('$unescape('); - } - return query; - }; - return SqlQuery; - })(); - exports_1("default", SqlQuery); - } - } -}); -//# sourceMappingURL=sql_query.js.map \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_query.js.map b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_query.js.map deleted file mode 100644 index 17419ae..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_query.js.map +++ /dev/null @@ -1 +0,0 @@ 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\ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_query.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_query.ts deleted file mode 100644 index 5867d4a..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_query.ts +++ /dev/null @@ -1,377 +0,0 @@ -/// - -import _ from 'lodash'; -import * as dateMath from 'app/core/utils/datemath'; -import moment from 'moment'; -import Scanner from './scanner'; - -var durationSplitRegexp = /(\d+)(ms|s|m|h|d|w|M|y)/; - -export default class SqlQuery { - target: any; - templateSrv: any; - options: any; - - /** @ngInject */ - constructor(target, templateSrv?, options?) { - this.target = target; - this.templateSrv = templateSrv; - this.options = options; - } - - replace(options, adhocFilters) { - var self = this, - query = this.target.query, - scanner = new Scanner(query), - dateTimeType = this.target.dateTimeType ? this.target.dateTimeType : 'DATETIME', - from = SqlQuery.convertTimestamp(SqlQuery.round(this.options.range.from, this.target.round), dateTimeType), - to = SqlQuery.convertTimestamp(SqlQuery.round(this.options.range.to, this.target.round), dateTimeType), - timeSeries = SqlQuery.getTimeSeries(dateTimeType), - timeFilter = SqlQuery.getTimeFilter(this.options.rangeRaw.to === 'now', dateTimeType), - i = this.templateSrv.replace(this.target.interval, options.scopedVars) || options.interval, - interval = SqlQuery.convertInterval(i, this.target.intervalFactor || 1, dateTimeType); - try { - let ast = scanner.toAST(); - if (adhocFilters.length > 0) { - if (!ast.hasOwnProperty('where')) { - ast.where = []; - } - adhocFilters.forEach(function(af) { - let parts = af.key.split('.'); - if (parts.length < 3) { - console.log("adhoc filters: filter " + af.key + "` has wrong format"); - return - } - if (self.target.database != parts[0] || self.target.table != parts[1]) { - return - } - let operator = SqlQuery.clickhouseOperator(af.operator); - let cond = parts[2] + " " + operator + " " + af.value; - if (ast.where.length > 0) { - // OR is not implemented - // @see https://github.com/grafana/grafana/issues/10918 - cond = "AND " + cond - } - ast.where.push(cond) - }); - } - query = scanner.Print(ast); - if (ast.hasOwnProperty('$columns') && !_.isEmpty(ast['$columns'])) { - query = SqlQuery.columns(query); - } else if (ast.hasOwnProperty('$rateColumns') && !_.isEmpty(ast['$rateColumns'])) { - query = SqlQuery.rateColumns(query); - } else if (ast.hasOwnProperty('$rate') && !_.isEmpty(ast['$rate'])) { - query = SqlQuery.rate(query, ast); - } - } catch (err) { - console.log('AST parser error: ', err.message) - } - - query = this.templateSrv.replace(query, options.scopedVars, SqlQuery.interpolateQueryExpr); - query = SqlQuery.unescape(query); - this.target.rawQuery = query - .replace(/\$timeSeries/g, timeSeries) - .replace(/\$timeFilter/g, timeFilter) - .replace(/\$table/g, this.target.database + '.' + this.target.table) - .replace(/\$from/g, from) - .replace(/\$to/g, to) - .replace(/\$dateCol/g, this.target.dateColDataType) - .replace(/\$dateTimeCol/g, this.target.dateTimeColDataType) - .replace(/\$interval/g, interval) - .replace(/(?:\r\n|\r|\n)/g, ' '); - return this.target.rawQuery; - } - - // $columns(query) - static columns(query: string): string { - if (query.slice(0, 9) === '$columns(') { - var fromIndex = SqlQuery._fromIndex(query); - var args = query.slice(9,fromIndex) - .trim() // rm spaces - .slice(0, -1), // cut ending brace - scanner = new Scanner(args), - ast = scanner.toAST(); - var root = ast['root']; - - if (root.length !== 2) { - throw {message: 'Amount of arguments must equal 2 for $columns func. Parsed arguments are: ' + root.join(', ')}; - } - - query = SqlQuery._columns(root[0], root[1], query.slice(fromIndex)); - } - - return query; - } - - static _columns(key: string, value: string, fromQuery: string): string { - if (key.slice(-1) === ')' || value.slice(-1) === ')') { - throw {message: 'Some of passed arguments are without aliases: ' + key + ', ' + value}; - } - - var keyAlias = key.trim().split(' ').pop(), - valueAlias = value.trim().split(' ').pop(), - havingIndex = fromQuery.toLowerCase().indexOf('having'), - having = "", - aliasRegex = /%(\w+)/g, - concatExpr = ""; - - if (key.match(/^'.*'$/) && key.match(aliasRegex)) { - concatExpr = 'concat(' + key.replace(aliasRegex, "', $1, '").replace(/, ''/g, '') + ')'; - key = keyAlias = _.uniq(key.match(aliasRegex).map(k => k.slice(1))).join(', '); - } - - if (havingIndex !== -1) { - having = fromQuery.slice(havingIndex, fromQuery.length); - fromQuery = fromQuery.slice(0, havingIndex); - } - fromQuery = SqlQuery._applyTimeFilter(fromQuery); - - return 'SELECT ' + - 't' + - ', groupArray((' + (concatExpr || keyAlias) + ', ' + valueAlias + ')) as groupArr' + - ' FROM (' + - ' SELECT $timeSeries as t' + - ', ' + key + - ', ' + value + ' ' + - fromQuery + - ' GROUP BY t, ' + keyAlias + - ' ' + having + - ' ORDER BY t, ' + keyAlias + - ') ' + - 'GROUP BY t ' + - 'ORDER BY t'; - } - - // $rateColumns(query) - static rateColumns(query: string): string { - if (query.slice(0, 13) === '$rateColumns(') { - var fromIndex = SqlQuery._fromIndex(query); - var args = query.slice(13,fromIndex) - .trim() // rm spaces - .slice(0, -1), // cut ending brace - scanner = new Scanner(args), - ast = scanner.toAST(); - var root = ast['root']; - - if (root.length !== 2) { - throw {message: 'Amount of arguments must equal 2 for $columns func. Parsed arguments are: ' + root.join(', ')}; - } - - query = SqlQuery._columns(root[0], root[1], query.slice(fromIndex)); - query = 'SELECT t' + - ', arrayMap(a -> (a.1, a.2/runningDifference( t/1000 )), groupArr)' + - ' FROM (' + - query + - ')'; - } - - return query; - } - - // $rate(query) - static rate(query: string, ast: any): string { - if (query.slice(0, 6) === '$rate(') { - var fromIndex = SqlQuery._fromIndex(query); - if (ast.$rate.length < 1) { - throw {message: 'Amount of arguments must be > 0 for $rate func. Parsed arguments are: ' + ast.$rate.join(', ')}; - } - - query = SqlQuery._rate(ast['$rate'], query.slice(fromIndex)); - } - - return query; - } - - static _fromIndex(query: string): number { - var fromIndex = query.toLowerCase().indexOf('from'); - if (fromIndex === -1) { - throw {message: 'Could not find FROM-statement at: ' + query}; - } - return fromIndex; - } - - static _rate(args, fromQuery: string): string { - var aliases = []; - _.each(args, function(arg){ - if (arg.slice(-1) === ')') { - throw {message: 'Argument "' + arg + '" cant be used without alias'}; - } - aliases.push(arg.trim().split(' ').pop()); - }); - - var rateColums = []; - _.each(aliases, function(a){ - rateColums.push(a + '/runningDifference(t/1000) ' + a + 'Rate'); - }); - - fromQuery = SqlQuery._applyTimeFilter(fromQuery); - return 'SELECT ' + '' + - 't' + - ', ' + rateColums.join(',') + - ' FROM (' + - ' SELECT $timeSeries as t' + - ', ' + args.join(',') + - ' ' + fromQuery + - ' GROUP BY t' + - ' ORDER BY t' + - ')'; - } - - static _applyTimeFilter(query: string): string { - if ( query.toLowerCase().indexOf('where') !== -1 ) { - query = query.replace(/where/i, 'WHERE $timeFilter AND '); - } else { - query += ' WHERE $timeFilter'; - } - - return query; - } - - static getTimeSeries(dateTimeType: string): string { - if (dateTimeType === 'DATETIME') { - return '(intDiv(toUInt32($dateTimeCol), $interval) * $interval) * 1000'; - } - - if (dateTimeType === 'TIMESTAMPMS') { - return 'intDiv($dateTimeCol, $interval) * $interval'; - } - - return '(intDiv($dateTimeCol, $interval) * $interval) * 1000' - } - - static getTimeFilter(isToNow: boolean, dateTimeType: string): string { - var dateCol = function (t: string): string { - if (dateTimeType === 'TIMESTAMPMS') { - t = t + '/1000'; - } - - return 'toDate(' + t + ')'; - }; - - var dateTimeCol = function (t: string): string { - if (dateTimeType === 'DATETIME') { - return 'toDateTime('+ t +')'; - } - return t - }; - - if (isToNow) { - return '$dateCol >= ' + dateCol('$from') + ' AND $dateTimeCol >= ' + dateTimeCol('$from'); - } - - return '$dateCol BETWEEN ' + dateCol('$from') + ' AND ' + dateCol('$to') + ' AND $dateTimeCol BETWEEN ' + dateTimeCol('$from') + ' AND ' + dateTimeCol('$to'); - } - - // date is a moment object - static convertTimestamp(date: any, dateTimeType: string) { - //return date.format("'Y-MM-DD HH:mm:ss'") - if (_.isString(date)) { - date = dateMath.parse(date, true); - } - - return Math.floor(dateTimeType === 'TIMESTAMPMS' ? date.valueOf() : date.valueOf() / 1000); - } - - static round(date: any, round: string): any { - if (round === "" || round === undefined || round === "0s" ) { - return date; - } - - if (_.isString(date)) { - date = dateMath.parse(date, true); - } - - let coeff = SqlQuery.convertInterval(round, 1, 'TIMESTAMPMS'); - let rounded = Math.floor(date.valueOf() / coeff) * coeff; - return moment(rounded); - } - - static convertInterval(interval, intervalFactor, dateTimeType: string) { - var m = interval.match(durationSplitRegexp); - if (m === null) { - throw {message: 'Received duration is invalid: ' + interval}; - } - - var dur = moment.duration(parseInt(m[1]), m[2]); - var sec = dur.asSeconds(); - - if (dateTimeType === 'TIMESTAMPMS') { - return Math.ceil((sec * 1000 || 1) * intervalFactor); - } - - if (sec < 1) { - sec = 1; - } - - return Math.ceil(sec * intervalFactor); - } - - static interpolateQueryExpr (value, variable, defaultFormatFn) { - // if no `multiselect` or `include all` - do not escape - if (!variable.multi && !variable.includeAll) { - return value; - } - if (typeof value === 'string') { - return SqlQuery.clickhouseEscape(value, variable); - } - let escapedValues = _.map(value, function(v){ - return SqlQuery.clickhouseEscape(v, variable); - }); - return escapedValues.join(','); - } - - static clickhouseOperator(value) { - switch (value){ - case "=": - case "!=": - case ">": - case "<": - return value; - case "=~": - return "LIKE"; - case "!~": - return "NOT LIKE"; - default: - console.log("adhoc filters: got unsupported operator `" + value + "`"); - return value - } - } - - static clickhouseEscape(value, variable) { - var isDigit = true; - // if at least one of options is not digit - _.each(variable.options, function(opt): boolean{ - if (opt.value === '$__all') { - return true; - } - if (!opt.value.match(/^\d+$/)) { - isDigit = false; - return false; - } - return true - }); - - if (isDigit) { - return value; - } else { - return "'" + value.replace(/[\\']/g, '\\$&') + "'"; - } - } - - static unescape(query) { - let macros = '$unescape('; - let openMacros = query.indexOf(macros); - while (openMacros !== -1) { - let closeMacros = query.indexOf(')', openMacros); - if(closeMacros === -1) { - throw {message: 'unable to find closing brace for $unescape macros: ' + query.substring(0, openMacros)}; - } - let arg = query.substring(openMacros+macros.length, closeMacros) - .trim(); - arg = arg.replace(/[']+/g, ''); - query = query.substring(0, openMacros) + arg + query.substring(closeMacros+1, query.length); - openMacros = query.indexOf('$unescape('); - } - return query - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_series.d.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_series.d.ts deleted file mode 100644 index a98d2bf..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_series.d.ts +++ /dev/null @@ -1,14 +0,0 @@ -export default class SqlSeries { - series: any; - meta: any; - tillNow: any; - from: any; - to: any; - /** @ngInject */ - constructor(options: any); - toTable(): any; - toTimeSeries(): any; - extrapolate(datapoints: any): any; - static _toJSType(type: any): string; - static _formatValue(value: any): any; -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_series.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_series.js deleted file mode 100644 index a873865..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_series.js +++ /dev/null @@ -1,143 +0,0 @@ -System.register(['lodash'], function(exports_1) { - var lodash_1; - var SqlSeries; - return { - setters:[ - function (lodash_1_1) { - lodash_1 = lodash_1_1; - }], - execute: function() { - SqlSeries = (function () { - /** @ngInject */ - function SqlSeries(options) { - this.series = options.series; - this.meta = options.meta; - this.tillNow = options.tillNow; - this.from = options.from; - this.to = options.to; - } - SqlSeries.prototype.toTable = function () { - var self = this, data = []; - if (this.series.length === 0) { - return data; - } - var columns = []; - lodash_1.default.each(self.meta, function (col) { - columns.push({ "text": col.name, "type": SqlSeries._toJSType(col.type) }); - }); - var rows = []; - lodash_1.default.each(self.series, function (ser) { - var r = []; - lodash_1.default.each(ser, function (v) { - r.push(SqlSeries._formatValue(v)); - }); - rows.push(r); - }); - data.push({ - "columns": columns, - "rows": rows, - "type": "table" - }); - return data; - }; - SqlSeries.prototype.toTimeSeries = function () { - var self = this, timeSeries = []; - if (self.series.length === 0) { - return timeSeries; - } - // timeCol have to be the first column always - var timeCol = self.meta[0], metrics = {}, intervals = [], t; - lodash_1.default.each(self.series, function (series) { - t = SqlSeries._formatValue(series[timeCol.name]); - intervals.push(t); - // rm time value from series - delete series[timeCol.name]; - lodash_1.default.each(series, function (val, key) { - if (lodash_1.default.isArray(val)) { - lodash_1.default.each(val, function (arr) { - (metrics[arr[0]] = metrics[arr[0]] || {})[t] = arr[1]; - }); - } - else { - (metrics[key] = metrics[key] || {})[t] = val; - } - }); - }); - lodash_1.default.each(metrics, function (v, k) { - var datapoints = []; - lodash_1.default.each(intervals, function (interval) { - if (metrics[k][interval] === undefined) { - metrics[k][interval] = null; - } - datapoints.push([SqlSeries._formatValue(metrics[k][interval]), interval]); - }); - timeSeries.push({ target: k, datapoints: self.extrapolate(datapoints) }); - }); - return timeSeries; - }; - ; - SqlSeries.prototype.extrapolate = function (datapoints) { - if (datapoints.length < 10 || (!this.tillNow && datapoints[0][0] !== 0)) { - return datapoints; - } - // Duration between first/last samples and boundary of range. - var durationToStart = datapoints[0][1] / 1000 - this.from, durationToEnd = this.to - datapoints[datapoints.length - 1][1] / 1000; - // If the first/last samples are close to the boundaries of the range, - // extrapolate the result. - var sampledInterval = (datapoints[datapoints.length - 1][1] - datapoints[0][1]) / 1000, averageDurationBetweenSamples = sampledInterval / (datapoints.length - 1); - var diff; - // close to left border and value is 0 because of runningDifference function - if (durationToStart < averageDurationBetweenSamples && datapoints[0][0] === 0) { - diff = ((datapoints[1][0] - datapoints[2][0]) / datapoints[1][0]) * 0.1; - diff %= 1; - if (isNaN(diff)) { - diff = 0; - } - datapoints[0][0] = datapoints[1][0] * (1 + diff); - } - if (durationToEnd < averageDurationBetweenSamples) { - diff = ((datapoints[datapoints.length - 2][0] - datapoints[datapoints.length - 3][0]) / datapoints[datapoints.length - 2][0]) * 0.1; - diff %= 1; - if (isNaN(diff)) { - diff = 0; - } - datapoints[datapoints.length - 1][0] = datapoints[datapoints.length - 2][0] * (1 + diff); - } - return datapoints; - }; - ; - SqlSeries._toJSType = function (type) { - switch (type) { - case 'UInt8': - case 'UInt16': - case 'UInt32': - case 'UInt64': - case 'Int8': - case 'Int16': - case 'Int32': - case 'Int64': - return "number"; - default: - return "string"; - } - }; - SqlSeries._formatValue = function (value) { - if (value === null) { - return value; - } - var numeric = Number(value); - if (isNaN(numeric)) { - return value; - } - else { - return numeric; - } - }; - ; - return SqlSeries; - })(); - exports_1("default", SqlSeries); - } - } -}); -//# sourceMappingURL=sql_series.js.map \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_series.js.map b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_series.js.map deleted file mode 100644 index e6e234a..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_series.js.map +++ /dev/null @@ -1 +0,0 @@ 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\ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_series.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_series.ts deleted file mode 100644 index bb7487b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/dist/sql_series.ts +++ /dev/null @@ -1,150 +0,0 @@ -import _ from 'lodash'; - -export default class SqlSeries { - series: any; - meta: any; - tillNow: any; - from: any; - to: any; - - /** @ngInject */ - constructor(options) { - this.series = options.series; - this.meta = options.meta; - this.tillNow = options.tillNow; - this.from = options.from; - this.to = options.to; - } - - toTable():any { - let self = this, data = []; - if (this.series.length === 0) { - return data; - } - - let columns = []; - _.each(self.meta, function(col) { - columns.push({"text": col.name, "type": SqlSeries._toJSType(col.type)}) - }); - - let rows = []; - _.each(self.series, function (ser) { - let r = []; - _.each(ser, function (v) { - r.push(SqlSeries._formatValue(v)) - }); - rows.push(r) - }); - - data.push({ - "columns": columns, - "rows": rows, - "type": "table" - }); - - return data - } - - toTimeSeries():any { - let self = this, timeSeries = []; - if (self.series.length === 0) { - return timeSeries; - } - - // timeCol have to be the first column always - let timeCol = self.meta[0], metrics = {}, intervals = [], t; - _.each(self.series, function(series) { - t = SqlSeries._formatValue(series[timeCol.name]); - intervals.push(t); - // rm time value from series - delete series[timeCol.name]; - _.each(series, function(val, key) { - if (_.isArray(val)) { - _.each(val, function(arr) { - (metrics[arr[0]] = metrics[arr[0]] || {})[t] = arr[1]; - }); - } else { - (metrics[key] = metrics[key] || {})[t] = val; - } - }); - }); - - _.each(metrics, function(v, k) { - let datapoints = []; - _.each(intervals, function(interval) { - if (metrics[k][interval] === undefined) { - metrics[k][interval] = null; - } - datapoints.push([SqlSeries._formatValue(metrics[k][interval]), interval]); - }); - timeSeries.push({target: k, datapoints: self.extrapolate(datapoints)}); - }); - return timeSeries; - }; - - extrapolate(datapoints) { - if (datapoints.length < 10 || (!this.tillNow && datapoints[0][0] !== 0)) { - return datapoints; - } - - // Duration between first/last samples and boundary of range. - let durationToStart = datapoints[0][1]/1000 - this.from, - durationToEnd = this.to - datapoints[datapoints.length-1][1]/1000; - - // If the first/last samples are close to the boundaries of the range, - // extrapolate the result. - let sampledInterval = (datapoints[datapoints.length-1][1] - datapoints[0][1])/1000, - averageDurationBetweenSamples = sampledInterval / (datapoints.length-1); - - let diff; - // close to left border and value is 0 because of runningDifference function - if (durationToStart < averageDurationBetweenSamples && datapoints[0][0] === 0) { - diff = ((datapoints[1][0] - datapoints[2][0]) / datapoints[1][0]) * 0.1; - diff %= 1; - if (isNaN(diff)) { - diff = 0; - } - datapoints[0][0] = datapoints[1][0] * (1 + diff); - } - - if (durationToEnd < averageDurationBetweenSamples) { - diff = ((datapoints[datapoints.length-2][0] - datapoints[datapoints.length-3][0]) / datapoints[datapoints.length-2][0]) * 0.1; - diff %= 1; - if (isNaN(diff)) { - diff = 0; - } - datapoints[datapoints.length-1][0] = datapoints[datapoints.length-2][0] * (1 + diff); - } - - return datapoints; - }; - - static _toJSType(type:any):string { - switch (type) { - case 'UInt8': - case 'UInt16': - case 'UInt32': - case 'UInt64': - case 'Int8': - case 'Int16': - case 'Int32': - case 'Int64': - return "number"; - default: - return "string" - } - } - - static _formatValue(value:any) { - if (value === null) { - return value - } - - let numeric = Number(value); - if (isNaN(numeric)) { - return value - } else { - return numeric - } - }; -} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/karma.conf.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/karma.conf.js deleted file mode 100644 index 4cacfaa..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/karma.conf.js +++ /dev/null @@ -1,90 +0,0 @@ -'use strict'; -module.exports = function(config) { - config.set({ - frameworks: ['systemjs', 'mocha', 'expect', 'sinon'], - - files: [ - 'specs/*specs.ts', - 'specs/**/*specs.ts', - 'specs/lib/*.ts', - { pattern: 'src/**/*.ts', included: false }, - { pattern: 'node_modules/grafana-sdk-mocks/**/*.ts', included: false }, - { pattern: 'node_modules/grafana-sdk-mocks/**/*.js', included: false }, - { pattern: 'node_modules/typescript/lib/typescript.js', included: false }, - { pattern: 'node_modules/systemjs-plugin-css/css.js', included: false }, - { pattern: 'node_modules/lodash/lodash.js', included: false }, - { pattern: 'node_modules/moment/moment.js', included: false }, - { pattern: 'node_modules/q/q.js', included: false } - ], - - systemjs: { - // // SystemJS configuration specifically for tests, added after your config file. - // // Good for adding test libraries and mock modules - config: { - // Set path for third-party libraries as modules - paths: { - 'systemjs': 'node_modules/systemjs/dist/system.js', - 'system-polyfills': 'node_modules/systemjs/dist/system-polyfills.js', - 'lodash': 'node_modules/lodash/lodash.js', - 'moment': 'node_modules/moment/moment.js', - 'q': 'node_modules/q/q.js', - 'typescript': 'node_modules/typescript/lib/typescript.js', - 'plugin-typescript': 'node_modules/plugin-typescript/lib/plugin.js', - 'css': 'node_modules/systemjs-plugin-css/css.js', - 'app/': 'node_modules/grafana-sdk-mocks/app/' - }, - - map: { - 'plugin-typescript': 'node_modules/plugin-typescript/lib/', - css: 'node_modules/systemjs-plugin-css/css.js', - 'typescript': 'node_modules/typescript/', - 'app/core/utils/kbn': 'node_modules/grafana-sdk-mocks/app/core/utils/kbn.js' - }, - - packages: { - 'plugin-typescript': { - 'main': 'plugin.js' - }, - 'typescript': { - 'main': 'lib/typescript.js', - 'meta': { - 'lib/typescript.js': { - 'exports': 'ts' - } - } - }, - 'app': { - 'defaultExtension': 'ts', - 'meta': { - '*.js': { - 'loader': 'typescript' - } - } - }, - 'src': { - 'defaultExtension': 'ts', - meta: { - '*.css': { loader: 'css' } - } - }, - 'specs': { - 'defaultExtension': 'ts', - 'meta': { - '*.js': { - 'loader': 'typescript' - } - } - } - }, - - transpiler: 'plugin-typescript' - } - }, - - reporters: ['dots'], - - logLevel: config.LOG_INFO, - - browsers: ['PhantomJS'] - }); -}; \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/package.json b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/package.json deleted file mode 100644 index e86c32b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/package.json +++ /dev/null @@ -1,45 +0,0 @@ -{ - "name": "vertamedia-clickhouse", - "version": "1.4.2", - "description": "ClickHouse datasource for Grafana", - "scripts": { - "build": "grunt", - "test": "./node_modules/karma/bin/karma start" - }, - "author": "Vertamedia", - "license": "MIT", - "dependencies": { - "babel-plugin-transform-es2015-for-of": "^6.6.0", - "babel-plugin-transform-es2015-modules-systemjs": "^6.24.1", - "babel-preset-es2015": "^6.24.1", - "lodash": "^4.17.4", - "moment": "^2.18.1", - "q": "^1.5.1", - "brace": "^0.10.0", - "systemjs": "^0.19.47" - }, - "devDependencies": { - "@types/lodash": "^4.14.83", - "babel": "^6.23.0", - "grafana-sdk-mocks": "github:grafana/grafana-sdk-mocks", - "grunt": "^1.0.1", - "grunt-babel": "~6.0.0", - "grunt-contrib-clean": "^1.1.0", - "grunt-contrib-copy": "^1.0.0", - "grunt-contrib-watch": "^1.0.0", - "grunt-systemjs-builder": "^1.0.0", - "grunt-typescript": "^0.8.0", - "karma": "^1.7.1", - "karma-chrome-launcher": "^2.2.0", - "karma-expect": "^1.1.3", - "karma-mocha": "^1.3.0", - "karma-phantomjs-launcher": "^1.0.4", - "karma-sinon": "^1.0.5", - "karma-systemjs": "^0.16.0", - "load-grunt-tasks": "3.5.2", - "mocha": "^1.21.5", - "plugin-typescript": "^7.1.1", - "sinon": "^3.3.0", - "systemjs-plugin-css": "^0.1.35" - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/datasource_specs.d.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/datasource_specs.d.ts deleted file mode 100644 index e69de29..0000000 diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/datasource_specs.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/datasource_specs.js deleted file mode 100644 index 239d1cd..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/datasource_specs.js +++ /dev/null @@ -1,115 +0,0 @@ -System.register(["lodash", "test/lib/common", "./../sql_series"], function (exports_1, context_1) { - "use strict"; - var __moduleName = context_1 && context_1.id; - var lodash_1, common_1, sql_series_1; - return { - setters: [ - function (lodash_1_1) { - lodash_1 = lodash_1_1; - }, - function (common_1_1) { - common_1 = common_1_1; - }, - function (sql_series_1_1) { - sql_series_1 = sql_series_1_1; - } - ], - execute: function () { - common_1.describe("clickhouse sql series:", function () { - common_1.describe("SELECT $timeseries response", function () { - var response = { - "meta": [ - { - "name": "t" - }, - { - "name": "good" - }, - { - "name": "bad" - } - ], - "data": [ - { - "t": "1485443760000", - "good": 26070, - "bad": 17 - }, - { - "t": "1485443820000", - "good": 24824, - "bad": 12 - }, - { - "t": "1485443880000", - "good": 25268, - "bad": 17 - } - ], - }; - var sqlSeries = new sql_series_1.default({ - series: response.data, - meta: response.meta, - table: '', - }); - var timeSeries = sqlSeries.getTimeSeries(); - common_1.it("expects two results", function () { - common_1.expect(lodash_1.default.size(timeSeries)).to.be(2); - }); - common_1.it("should get three datapoints", function () { - common_1.expect(lodash_1.default.size(timeSeries[0].datapoints)).to.be(3); - common_1.expect(lodash_1.default.size(timeSeries[1].datapoints)).to.be(3); - }); - }); - common_1.describe("SELECT $columns response", function () { - var response = { - "meta": [ - { - "name": "t", - "type": "UInt64" - }, - { - "name": "requests", - "type": "Array(Tuple(String, Float64))" - } - ], - "data": [ - { - "t": "1485445140000", - "requests": [["Chrome", null], ["Edge", null], ["Firefox", null]] - }, - { - "t": "1485445200000", - "requests": [["Chrome", 1], ["Edge", 4], ["Firefox", 7]] - }, - { - "t": "1485445260000", - "requests": [["Chrome", 2], ["Chromium", 5], ["Edge", 8], ["Firefox", 11]] - }, - { - "t": "1485445320000", - "requests": [["Chrome", 3], ["Chromium", 6], ["Edge", 9], ["Firefox", 12]] - } - ] - }; - var sqlSeries = new sql_series_1.default({ - series: response.data, - meta: response.meta, - table: '', - }); - var timeSeries = sqlSeries.getTimeSeries(); - common_1.it("expects four results", function () { - common_1.expect(lodash_1.default.size(timeSeries)).to.be(4); - }); - common_1.it("should get three datapoints", function () { - common_1.expect(lodash_1.default.size(timeSeries[0].datapoints)).to.be(4); - common_1.expect(lodash_1.default.size(timeSeries[1].datapoints)).to.be(4); - common_1.expect(lodash_1.default.size(timeSeries[2].datapoints)).to.be(4); - common_1.expect(lodash_1.default.size(timeSeries[3].datapoints)).to.be(4); - }); - 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\ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/datasource_specs.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/datasource_specs.ts deleted file mode 100644 index 7f87705..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/datasource_specs.ts +++ /dev/null @@ -1,110 +0,0 @@ -import _ from 'lodash'; -import {describe, it, expect} from './lib/common'; -import SqlSeries from '../src/sql_series'; - -describe("clickhouse sql series:", () => { - describe("SELECT $timeseries response", () => { - var response = { - "meta": - [ - { - "name": "t" - }, - { - "name": "good" - }, - { - "name": "bad" - } - ], - "data": - [ - { - "t": "1485443760000", - "good": 26070, - "bad": 17 - }, - { - "t": "1485443820000", - "good": 24824, - "bad": 12 - }, - { - "t": "1485443880000", - "good": 25268, - "bad": 17 - } - ], - }; - - var sqlSeries = new SqlSeries({ - series: response.data, - meta: response.meta, - table: '', - }); - var timeSeries = sqlSeries.toTimeSeries(); - it("expects two results", () => { - expect(_.size(timeSeries)).to.be(2); - }); - - it("should get three datapoints", () => { - expect(_.size(timeSeries[0].datapoints)).to.be(3); - expect(_.size(timeSeries[1].datapoints)).to.be(3); - }); - }); - - describe("SELECT $columns response", () => { - var response = { - "meta": - [ - { - "name": "t", - "type": "UInt64" - }, - { - "name": "requests", - "type": "Array(Tuple(String, Float64))" - } - ], - - "data": - [ - { - "t": "1485445140000", - "requests": [["Chrome",null],["Edge",null],["Firefox",null]] - }, - { - "t": "1485445200000", - "requests": [["Chrome",1],["Edge",4],["Firefox",7]] - }, - { - "t": "1485445260000", - "requests": [["Chrome",2],["Chromium",5],["Edge",8],["Firefox",11]] - }, - { - "t": "1485445320000", - "requests": [["Chrome",3],["Chromium",6],["Edge",9],["Firefox",12]] - } - ] - }; - - var sqlSeries = new SqlSeries({ - series: response.data, - meta: response.meta, - table: '', - }); - var timeSeries = sqlSeries.toTimeSeries(); - - it("expects four results", () => { - expect(_.size(timeSeries)).to.be(4); - }); - - it("should get three datapoints", () => { - expect(_.size(timeSeries[0].datapoints)).to.be(4); - expect(_.size(timeSeries[1].datapoints)).to.be(4); - expect(_.size(timeSeries[2].datapoints)).to.be(4); - expect(_.size(timeSeries[3].datapoints)).to.be(4); - }); - }); - -}); diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/lib/common.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/lib/common.ts deleted file mode 100644 index 8bd214a..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/lib/common.ts +++ /dev/null @@ -1,24 +0,0 @@ -/// - -var _global = (window); -var beforeEach = _global.beforeEach; -var before = _global.before; -var describe = _global.describe; -var it = _global.it; -var sinon = _global.sinon; -var expect = _global.expect; - -var angularMocks = { - module: _global.module, - inject: _global.inject, -}; - -export { - beforeEach, - before, - describe, - it, - sinon, - expect, - angularMocks, -}; diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/lib/context_srv_stub.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/lib/context_srv_stub.ts deleted file mode 100644 index 949853e..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/lib/context_srv_stub.ts +++ /dev/null @@ -1,5 +0,0 @@ -export default class ContextSrvStub { - hasRole() { - return true; - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/lib/template_srv_stub.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/lib/template_srv_stub.ts deleted file mode 100644 index 9ca2249..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/lib/template_srv_stub.ts +++ /dev/null @@ -1,31 +0,0 @@ -import _ from 'lodash'; - -export default class TemplateSrvStub { - variables = []; - templateSettings = { interpolate : /\[\[([\s\S]+?)\]\]/g }; - data = {}; - - replace(text) { - return _.template(text, this.templateSettings)(this.data); - } - - getAdhocFilters() { - return []; - } - - variableExists() { - return false; - } - - highlightVariablesAsHtml(str) { - return str; - } - - setGrafanaVariable(name, value) { - this.data[name] = value; - } - - init() {} - fillVariableValuesForUrl() {} - updateTemplateData() {} -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/lib/time_srv_stub.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/lib/time_srv_stub.ts deleted file mode 100644 index 3af12b5..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/lib/time_srv_stub.ts +++ /dev/null @@ -1,26 +0,0 @@ -import {sinon} from '../lib/common'; -import dateMath from 'app/core/utils/datemath'; - -export default class TimeSrvStub { - init = sinon.spy(); - time = { from: 'now-1h', to: 'now'}; - - timeRange(parse) { - if (parse === false) { - return this.time; - } - - return { - from : dateMath.parse(this.time.from, false), - to : dateMath.parse(this.time.to, true) - }; - } - - replace(target) { - return target; - } - - setTime(time) { - this.time = time; - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/scanner_specs.d.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/scanner_specs.d.ts deleted file mode 100644 index e69de29..0000000 diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/scanner_specs.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/scanner_specs.js deleted file mode 100644 index bfd036b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/scanner_specs.js +++ /dev/null @@ -1,215 +0,0 @@ -System.register(["test/lib/common", "./../scanner"], function (exports_1, context_1) { - "use strict"; - var __moduleName = context_1 && context_1.id; - var common_1, scanner_1; - return { - setters: [ - function (common_1_1) { - common_1 = common_1_1; - }, - function (scanner_1_1) { - scanner_1 = scanner_1_1; - } - ], - execute: function () { - common_1.describe("scanner:", function () { - common_1.describe("highlight", function () { - var query = "SELECT $timeSeries as t, count() FROM $table WHERE $timeFilter GROUP BY t ORDER BY t", expectedHighlight = 'SELECT ' + - '$timeSeries as t, ' + - 'count() FROM $table' + - ' WHERE $timeFilter' + - ' GROUP BY t ORDER BY t'; - var scanner = new scanner_1.default(query); - common_1.it("expects equality", function () { - common_1.expect(scanner.Highlight()).to.be(expectedHighlight); - }); - }); - common_1.describe("AST case 1", function () { - var query = "SELECT EventDate, col1, col2, toUInt32(col1 > 0 ? col2/col1*10000 : 0)/100 AS percent " + - "FROM ( SELECT EventDate, col1, countIf(col2 GLOBAL IN some_table) AS col2_shared, " + - "count() AS col_count, uniqCombinedIf(col3, col3 GLOBAL IN some_table) AS col3_shared, " + - "uniqCombined(col3) AS unique_col3 FROM general_table_all PREWHERE Event IN ('type1') " + - "AND EventDate <= '2016-12-20' WHERE (EventDate, col1) GLOBAL IN some_table GROUP BY " + - "EventDate, col1) GLOBAL ANY LEFT JOIN ( SELECT EventDate, col1, countIf(col2 GLOBAL IN some_table) " + - "AS col2_shared, count() AS col_count, uniqCombinedIf(col3, col3 GLOBAL IN some_table) AS col3_shared, " + - "uniqCombined(col3) AS unique_col3 FROM general_table_all PREWHERE Event IN ('type2') " + - "AND EventDate <= '2016-12-20' WHERE (EventDate, col1) GLOBAL IN some_table " + - "AND col4 GLOBAL IN some_table GROUP BY EventDate, col1) USING EventDate, col1 " + - "ORDER BY EventDate, col1 FORMAT CSVWithNames", scanner = new scanner_1.default(query); - var expectedAST = { - "root": [], - "select": [ - "EventDate", - "col1", - "col2", - "toUInt32(col1 > 0 ? col2 / col1 * 10000 : 0) / 100 AS percent" - ], - "from": { - "root": [], - "select": [ - "EventDate", - "col1", - "countIf(col2 GLOBAL IN some_table) AS col2_shared", - "count() AS col_count", - "uniqCombinedIf(col3, col3 GLOBAL IN some_table) AS col3_shared", - "uniqCombined(col3) AS unique_col3" - ], - "from": [ - "general_table_all" - ], - "prewhere": [ - "Event IN ('type1')", - "AND EventDate <= '2016-12-20'" - ], - "where": [ - "(EventDate, col1) GLOBAL IN some_table" - ], - "group by": [ - "EventDate", - "col1" - ] - }, - "join": { - "type": "GLOBAL ANY LEFT JOIN", - "source": { - "root": [], - "select": [ - "EventDate", - "col1", - "countIf(col2 GLOBAL IN some_table) AS col2_shared", - "count() AS col_count", - "uniqCombinedIf(col3, col3 GLOBAL IN some_table) AS col3_shared", - "uniqCombined(col3) AS unique_col3" - ], - "from": [ - "general_table_all" - ], - "prewhere": [ - "Event IN ('type2')", - "AND EventDate <= '2016-12-20'" - ], - "where": [ - "(EventDate, col1) GLOBAL IN some_table", - "AND col4 GLOBAL IN some_table" - ], - "group by": [ - "EventDate", - "col1" - ] - }, - "using": [ - "EventDate", - "col1", - "EventDate", - "col1" - ] - }, - "format": [ - "CSVWithNames" - ] - }; - common_1.it("expects equality", function () { - common_1.expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - common_1.describe("AST case 2", function () { - var query = "$rateColumns((AppType = '' ? 'undefined' : AppType) type, sum(Hits) hits) " + - "FROM table_all WHERE Event = 'request' AND (-1 IN ($template) OR col IN ($template)) HAVING hits > $interval", scanner = new scanner_1.default(query); - var expectedAST = { - "root": [], - "$rateColumns": [ - "(AppType = '' ? 'undefined' : AppType) type", - "sum(Hits) hits" - ], - "select": [], - "from": [ - "table_all" - ], - "where": [ - "Event = 'request'", - "AND(- 1 IN ($template) OR col IN ($template))" - ], - "having": [ - "hits > $interval" - ] - }; - common_1.it("expects equality", function () { - common_1.expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - common_1.describe("AST case 3", function () { - var query = "SELECT $timeSeries as t, count() AS `SMALL` FROM db.table " + - "WHERE W0 <= 400 AND LastEvent>=1 AND $timeFilter GROUP BY t ORDER BY t", scanner = new scanner_1.default(query); - var expectedAST = { - "root": [], - "select": [ - "$timeSeries as t", - "count() AS `SMALL`" - ], - "from": [ - "db.table" - ], - "where": [ - "W0 <= 400", - "AND LastEvent >= 1", - "AND $timeFilter" - ], - "group by": [ - "t" - ], - "order by": [ - "t" - ] - }; - common_1.it("expects equality", function () { - common_1.expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - common_1.describe("AST case 4", function () { - var query = "SELECT LogTime, Entity, Message FROM $table " + - "ANY LEFT JOIN (SELECT * FROM default.log_events) USING EventCode " + - "WHERE $timeFilter ORDER BY LogTime DESC LIMIT $__limit", scanner = new scanner_1.default(query); - var expectedAST = { - "root": [], - "select": [ - "LogTime", - "Entity", - "Message" - ], - "from": [ - "$table" - ], - "join": { - "type": "ANY LEFT JOIN", - "source": { - "root": [], - "select": [ - "*" - ], - "from": [ - "default.log_events" - ] - }, - "using": [ - "EventCode" - ] - }, - "where": [ - "$timeFilter" - ], - "order by": [ - "LogTime DESC" - ], - "limit": [ - "$__limit" - ] - }; - common_1.it("expects equality", function () { - common_1.expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - }); - } - }; -}); -//# sourceMappingURL=scanner_specs.js.map \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/scanner_specs.js.map b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/scanner_specs.js.map deleted file mode 100644 index 2b34349..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/scanner_specs.js.map +++ /dev/null @@ -1 +0,0 @@ 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\ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/scanner_specs.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/scanner_specs.ts deleted file mode 100644 index 058f079..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/specs/scanner_specs.ts +++ /dev/null @@ -1,450 +0,0 @@ -import {describe, it, expect} from './lib/common'; -import Scanner from '../src/scanner'; - -describe("scanner:", () => { - describe("AST case 1", () => { - let query = "SELECT EventDate, col1, col2, toUInt32(col1 > 0 ? col2/col1*10000 : 0)/100 AS percent " + - "FROM ( SELECT EventDate, col1, countIf(col2 GLOBAL IN some_table) AS col2_shared, " + - "count() AS col_count, uniqCombinedIf(col3, col3 GLOBAL IN some_table) AS col3_shared, " + - "uniqCombined(col3) AS unique_col3 FROM general_table_all PREWHERE Event IN ('type1') " + - "AND EventDate <= '2016-12-20' WHERE (EventDate, col1) GLOBAL IN some_table GROUP BY " + - "EventDate, col1) GLOBAL ANY LEFT JOIN ( SELECT EventDate, col1, countIf(col2 GLOBAL IN some_table) " + - "AS col2_shared, count() AS col_count, uniqCombinedIf(col3, col3 GLOBAL IN some_table) AS col3_shared, " + - "uniqCombined(col3) AS unique_col3 FROM general_table_all PREWHERE Event IN ('type2') " + - "AND EventDate <= '2016-12-20' WHERE (EventDate, col1) GLOBAL IN some_table " + - "AND col4 GLOBAL IN some_table GROUP BY EventDate, col1) USING EventDate, col1 " + - "ORDER BY EventDate, col1 FORMAT CSVWithNames", - scanner = new Scanner(query); - - let expectedAST = { - "root": [], - "select": [ - "EventDate", - "col1", - "col2", - "toUInt32(col1 > 0 ? col2 / col1 * 10000 : 0) / 100 AS percent" - ], - "from": { - "root": [], - "select": [ - "EventDate", - "col1", - "countIf(col2 GLOBAL IN some_table) AS col2_shared", - "count() AS col_count", - "uniqCombinedIf(col3, col3 GLOBAL IN some_table) AS col3_shared", - "uniqCombined(col3) AS unique_col3" - ], - "from": [ - "general_table_all" - ], - "prewhere": [ - "Event IN ('type1')", - "AND EventDate <= '2016-12-20'" - ], - "where": [ - "(EventDate, col1) GLOBAL IN some_table" - ], - "group by": [ - "EventDate", - "col1" - ] - }, - "join": { - "type": "GLOBAL ANY LEFT JOIN", - "source": { - "root": [], - "select": [ - "EventDate", - "col1", - "countIf(col2 GLOBAL IN some_table) AS col2_shared", - "count() AS col_count", - "uniqCombinedIf(col3, col3 GLOBAL IN some_table) AS col3_shared", - "uniqCombined(col3) AS unique_col3" - ], - "from": [ - "general_table_all" - ], - "prewhere": [ - "Event IN ('type2')", - "AND EventDate <= '2016-12-20'" - ], - "where": [ - "(EventDate, col1) GLOBAL IN some_table", - "AND col4 GLOBAL IN some_table" - ], - "group by": [ - "EventDate", - "col1" - ] - }, - "using": [ - "EventDate", - "col1", - ] - }, - "order by": [ - "EventDate", - "col1" - ], - "format": [ - "CSVWithNames" - ] - }; - - it("expects equality", () => { - expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - - describe("AST case 2", () => { - let query = "$rateColumns((AppType = '' ? 'undefined' : AppType) type, sum(Hits) hits) " + - "FROM table_all WHERE Event = 'request' AND (-1 IN ($template) OR col IN ($template)) HAVING hits > $interval", - scanner = new Scanner(query); - - let expectedAST = { - "root": [], - "$rateColumns": [ - "(AppType = '' ? 'undefined' : AppType) type", - "sum(Hits) hits" - ], - "select": [], - "from": [ - "table_all" - ], - "where": [ - "Event = 'request'", - "AND(- 1 IN ($template) OR col IN ($template))" - ], - "having": [ - "hits > $interval" - ] - }; - - it("expects equality", () => { - expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - - describe("AST case 3", () => { - let query = "SELECT $timeSeries as t, count() AS `SMALL` FROM db.table " + - "WHERE W0 <= 400 AND LastEvent>=1 AND $timeFilter GROUP BY t ORDER BY t", - scanner = new Scanner(query); - - let expectedAST = { - "root": [], - "select": [ - "$timeSeries as t", - "count() AS `SMALL`" - ], - "from": [ - "db.table" - ], - "where": [ - "W0 <= 400", - "AND LastEvent >= 1", - "AND $timeFilter" - ], - "group by": [ - "t" - ], - "order by": [ - "t" - ] - }; - - it("expects equality", () => { - expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - - - describe("AST case 4", () => { - let query = "SELECT LogTime, Entity, Message FROM $table " + - "ANY LEFT JOIN (SELECT * FROM default.log_events) USING EventCode " + - "WHERE $timeFilter ORDER BY LogTime DESC LIMIT $__limit", - scanner = new Scanner(query); - - let expectedAST = { - "root": [], - "select": [ - "LogTime", - "Entity", - "Message" - ], - "from": [ - "$table" - ], - "join": { - "type": "ANY LEFT JOIN", - "source": { - "root": [], - "select": [ - "*" - ], - "from": [ - "default.log_events" - ] - }, - "using": [ - "EventCode" - ] - }, - "where": [ - "$timeFilter" - ], - "order by": [ - "LogTime DESC" - ], - "limit": [ - "$__limit" - ] - }; - - it("expects equality", () => { - expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - - describe("AST case 5", () => { - let query = "SELECT select FROM $table", - scanner = new Scanner(query); - - let expectedAST = { - "root": [], - "select": [ - "select" - ], - "from": [ - "$table" - ] - }; - - it("expects equality", () => { - expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - - describe("AST case 6", () => { - let query = "SELECT 1, select FROM $table", - scanner = new Scanner(query); - - let expectedAST = { - "root": [], - "select": [ - "1", - "select" - ], - "from": [ - "$table" - ] - }; - - it("expects equality", () => { - expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - - describe("AST case 7", () => { - let query = "SELECT t, countIf(Format='1') FROM $table", - scanner = new Scanner(query); - - let expectedAST = { - "root": [], - "select": [ - "t", - "countIf(Format = '1')" - ], - "from": [ - "$table" - ] - }; - - it("expects equality", () => { - expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - - describe("AST case 8", () => { - let query = "SELECT from FROM from", - scanner = new Scanner(query); - - let expectedAST = { - "root": [], - "select": [ - "from" - ], - "from": [ - "from" - ] - }; - - it("expects equality", () => { - expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - - describe("AST case 9", () => { - let query = "SELECT" + - " t, groupArray((process_name, duration)) as groupArr " + - " FROM (" + - " SELECT" + - " (intDiv(toUInt32(event_datetime), 5) * 5) * 1000 as t," + - " process_name," + - " quantile(0.95)(duration) duration" + - " FROM xx " + - " WHERE event_date >= toDate(1514966917) AND event_datetime >= toDateTime(1514966917)" + - " GROUP BY t, process_name ORDER BY t, process_name" + - ") GROUP BY t ORDER BY t FORMAT JSON", - scanner = new Scanner(query); - - let expectedAST = { - "root": [], - "select": [ - "t", - "groupArray((process_name, duration)) as groupArr" - ], - "from": { - "root": [], - "select": [ - "(intDiv(toUInt32(event_datetime), 5) * 5) * 1000 as t", - "process_name", - "quantile(0.95)(duration) duration" - ], - "from": [ - "xx" - ], - "where": [ - "event_date >= toDate(1514966917)", - "AND event_datetime >= toDateTime(1514966917)", - ], - "group by": [ - "t", - "process_name" - ], - "order by": [ - "t", - "process_name" - ] - }, - "group by": [ - "t" - ], - "order by": [ - "t" - ], - "format": [ - "JSON" - ] - }; - - it("expects equality", () => { - expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - - describe("AST case 10(array)", () => { - let query = "SELECT count() FROM $table WHERE type[1] = 'key' AND zone['City'] = 'Kyiv'", - scanner = new Scanner(query); - - let expectedAST = { - "root": [], - "select": [ - "count()" - ], - "from": [ - "$table" - ], - "where": [ - "type[1] = 'key'", - "AND zone['City'] = 'Kyiv'", - ], - }; - - it("expects equality", () => { - expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - - describe("AST case 11(union all)", () => { - let query = "SELECT a, b FROM table1 UNION ALL select c, d from table2 UNION ALL select e, f from table3", - scanner = new Scanner(query); - - let expectedAST = { - "root": [], - "select": [ - "a", - "b" - ], - "from": [ - "table1" - ], - "union all": [ - { - "root": [], - "select": [ - "c", - "d" - ], - "from": [ - "table2" - ], - }, - { - "root": [], - "select": [ - "e", - "f" - ], - "from": [ - "table3" - ], - } - ], - }; - - it("expects equality", () => { - expect(scanner.toAST()).to.eql(expectedAST); - }); - }); - - describe("AST case 12(union all closure)", () => { - let query = "SELECT * FROM (select c, d from table2 UNION ALL select e, f from table3) ORDER BY c", - scanner = new Scanner(query); - - let expectedAST = { - "root": [], - "select": [ - "*" - ], - "from": { - "root": [], - "select": [ - "c", - "d" - ], - "from": [ - "table2" - ], - "union all": [ - { - "root": [], - "select": [ - "e", - "f" - ], - "from": [ - "table3" - ], - } - ], - }, - "order by": [ - "c" - ], - }; - - it("expects equality", () => { - expect(scanner.toAST()).to.eql(expectedAST); - }); - }); -}); diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/adhoc.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/adhoc.ts deleted file mode 100644 index 803578d..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/adhoc.ts +++ /dev/null @@ -1,46 +0,0 @@ -const columnsQuery = "SELECT database, table, name, type FROM system.columns where database != 'system' ORDER BY database, table"; -const regexEnum = /'(?:[^']+|'')+'/gmi; - -export default class AdhocCtrl { - tagKeys: any[]; - tagValues: any[]; - - /** @ngInject */ - constructor() { - this.tagKeys = []; - this.tagValues = []; - } - - GetTagKeys(datasource: any){ - var self = this; - if (this.tagKeys.length > 0) { - return Promise.resolve(this.tagKeys); - } - return datasource.metricFindQuery(columnsQuery) - .then(function(response){ - response.forEach(function(item){ - let text = item.database + '.' + item.table + '.' + item.name; - let value = item.name; - self.tagKeys.push({text: text, value: value}); - if (item.type.slice(0, 4) === 'Enum') { - let options = item.type.match(regexEnum); - if (options.length > 0) { - self.tagValues[text] = []; - options.forEach(function(o) { - self.tagValues[text].push({text: o, value: o}) - }) - } - } - }); - return Promise.resolve(self.tagKeys); - }); - - } - - GetTagValues(options){ - if (this.tagValues.hasOwnProperty(options.key)) { - return Promise.resolve(this.tagValues[options.key]); - } - return Promise.resolve([]) - } -} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/clickhouse-info.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/clickhouse-info.js deleted file mode 100644 index 20342a9..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/clickhouse-info.js +++ /dev/null @@ -1,2132 +0,0 @@ -// jshint ignore: start -// jscs: disable - -ace.define("ace/mode/clickhouse_info", ["require", "exports", "module"], function (require, exports, module) { - "use strict"; - - var p = {}; - p.Keywords = [ - "SELECT", - "CASE", - "THEN", - "DISTINCT", - "INSERT", - "UPDATE", - "DELETE", - "WHERE", - "AND", - "OR", - "OFFSET", - "HAVING", - "AS", - "GLOBAL", - "FROM", - "WHEN", - "ELSE", - "END", - "TYPE", - "LEFT", - "RIGHT", - "USING", - "JOIN", - "ON", - "OUTER", - "DESC", - "ASC", - "UNION", - "CREATE", - "TABLE", - "PRIMARY", - "KEY", - "FOREIGN", - "NOT", - "REFERENCES", - "DEFAULT", - "INNER", - "CROSS", - "NATURAL", - "DATABASE", - "DROP", - "GRANT", - "ANY", - "BETWEEN", - "ATTACH", - "DETACH", - "CAST", - "WITH", - "BIT_AND", - "BIT_OR", - "BIT_XOR", - "DESCRIBE", - "OPTIMIZE", - "PREWHERE", - "TOTALS", - "DATABASES", - "PROCESSLIST", - "SHOW", - "LIMIT", - "IF", - "IF NOT EXISTS", - "IF EXISTS", - "GROUP", - "ORDER", - "BY", - "IN", - "FORMAT", - "GROUP BY", - "ORDER BY", - "UNION ALL" - ]; - p.DataTypes = [ - "int", - "numeric", - "decimal", - "date", - "varchar", - "char", - "bigint", - "float", - "double", - "bit", - "binary", - "text", - "set", - "timestamp", - "uint8", - "uint16", - "uint32", - "uint64", - "int8", - "int16", - "int32", - "int64", - "float32", - "float64", - "datetime", - "enum8", - "enum16", - "array", - "tuple", - "string" - ]; - p.Constants = [ - "true", - "false", - "NULL" - ]; - p.Funcs = [ - "IPv4NumToString", "IPv4NumToStringClassC", "IPv4StringToNum", "IPv6NumToString", "IPv6StringToNum", - "MD5", "OSHierarchy", "OSIn", "OSToRoot", "SEHierarchy", "SEIn", "SEToRoot", "SHA1", "SHA224", "SHA256", - "URLHash", "URLHierarchy", "URLPathHierarchy", "abs", "alphaTokens", "appendTrailingCharIfAbsent", - "argMax", "argMin", "argMinIf", "arrayAll", "arrayCount", "arrayElement", "arrayEnumerate", "arrayEnumerateUniq", - "arrayExists", "arrayFilter", "arrayFirst", "arrayJoin", "arrayMap", "arraySum", "avg", "avgIf", - "bitAnd", "bitNot", "bitOr", "bitShiftLeft", "bitShiftRight", "bitXor", "bitmaskToArray", "bitmaskToList", - "ceil", "cityHash64", "coalesce", "concat", "corr", "corrIf", "count", "countEqual", "countIf", "countIf", - "covarPop", "covarPopIf", "covarSamp", "covarSampIf", "cutFragment", "cutQueryString", "cutQueryStringAndFragment", - "cutToFirstSignificantSubdomain", "cutURLParameter", "cutWWW", "dictGetDate", "dictGetDateTime", "dictGetFloat32", - "dictGetFloat64", "dictGetHierarchy", "dictGetInt16", "dictGetInt32", "dictGetInt64", "dictGetInt8", "dictGetString", - "dictGetUInt16", "dictGetUInt32", "dictGetUInt64", "dictGetUInt8", "dictHas", "dictIsIn", "divide", "domainWithoutWWW", - "empty", "extract", "extractAll", "extractURLParameter", "extractURLParameterNames", "extractURLParameters", - "first", "firstSignificantSubdomain", "floor", "groupArray", "groupUniqArray", "halfMD5", "has", "hex", "ifnull", - "indexOf", "intDiv", "intDivOrZero", "intHash32", "intHash64", "isnull", "last", "lcase", "length", "lengthUTF8", - "like", "lower", "lowerUTF8", "match", "max", "median", "medianIf", "mid", "min", "minus", "modulo", "multiply", - "negate", "notEmpty", "notLike", "now", "now", "nvl", "plus", "position", "positionUTF8", "quantile", - "quantileDeterministic", "quantileExact", "quantileExactWeighted", "quantileTDigest", "quantileTiming", - "quantileTimingWeighted", "quantiles", "quantilesIf", "quantilesTimingArrayIf", "quantilesTimingIf", "queryString", - "queryStringAndFragment", "rand", "rand64", "range", "rank", "regionHierarchy", "regionIn", "regionToContinent", - "regionToCountry", "regionToName", "regionToPopulation", "reinterpretAsDate", "reinterpretAsDateTime", "reinterpretAsFloat32", - "reinterpretAsFloat64", "reinterpretAsInt16", "reinterpretAsInt32", "reinterpretAsInt64", "reinterpretAsInt8", "reinterpretAsString", - "reinterpretAsUInt16", "reinterpretAsUInt32", "reinterpretAsUInt64", "reinterpretAsUInt8", "replaceAll", "replaceOne", - "replaceRegexpOne", "reverse", "reverseUTF8", "round", "round", "roundAge", "roundDuration", "roundToExp2", "sequenceCount", - "sequenceMatch", "sipHash128", "sipHash64", "splitByChar", "splitByString", "stddevPop", "stddevPopIf", "stddevSamp", - "stddevSampIf", "substring", "substringUTF8", "sum", "sumArray", "sumArrayIf", "sumIf", "timeSlot", "toDate", "toDateTime", - "toDayOfMonth", "toDayOfWeek", "toFixedString", "toFloat32", "toFloat64", "toHour", "toInt16", "toInt32", "toInt64", "toInt8", - "toMinute", "toMonday", "toMonth", "toRelativeDayNum", "toRelativeHourNum", "toRelativeMinuteNum", "toRelativeMonthNum", - "toRelativeSecondNum", "toRelativeWeekNum", "toRelativeYearNum", "toSecond", "toStartOfFiveMinute", "toStartOfHour", - "toStartOfMinute", "toStartOfMonth", "toStartOfQuarter", "toStartOfYear", "toString", "toStringCutToZero", "toTime", "toUInt16", - "toUInt32", "toUInt64", "toUInt8", "toYear", "today", "topLevelDomain", "ucase", "unhex", "uniq", "uniqArray", "uniqArrayIf", - "uniqArrayIf", "uniqCombined", "uniqCombinedIf", "uniqExact", "uniqExactIf", "uniqHLL12", "uniqUpTo", "upper", "upperUTF8", "varPop", "varPopIf", - "varSamp", "varSampIf", "yesterday" - ]; - p.Macros = [ - "$table", - "$timeCol", - "$dateTimeCol", - "$from", - "$to", - "$interval", - "$timeFilter", - "$timeSeries", - "$rate", - "$columns", - "$rateColumns", - "$unescape" - ]; - p.KeywordsRe = function () { - return this.re(p.Keywords) - }; - p.ConstantsRe = function () { - return this.re(p.Constants) - }; - p.FunctionsRe = function () { - return this.re(p.Funcs).concat(this.re(p.Macros)) - }; - p.DataTypesRe = function () { - return this.re(p.DataTypes); - }; - p.FunctionsCompletions = function () { - return [ - { - "name": "corr", - "def": "corr(x, y)", - "docText": "Calculates the Pearson correlation coefficient: Σ((x - x̅)(y - y̅)) / sqrt(Σ((x - x̅)2) * Σ((y - y̅)2))." - }, - { - "name": "stddevPop", - "def": "stddevPop(x)", - "docText": "The result is equal to the square root of `varPop(x)`." - }, - { - "name": "varPop", - "def": "varPop(x, y)", - "docText": "Calculates the value of `Σ((x - x̅)(y - y̅)) / n`." - }, - { - "name": "argMin", - "def": "argMin(arg, val)", - "docText": "Calculates the `arg` value for a minimal `val` value. If there are several different values of `arg` for minimal values of `val`, the first of these values encountered is output." - }, - { - "name": "quantileExactWeighted", - "def": "quantileExactWeighted(level)(x, weight)", - "docText": "Computes the level quantile exactly. In this case, each value is taken into account with the weight weight - as if it is present weight once. The arguments of the function can be considered as histograms, where the value \"x\" corresponds to the \"column\" of the histogram of the height weight, and the function itself can be considered as the summation of histograms.\n" + - "\n" + - "The algorithm is a hash table. Because of this, in case the transmitted values ​​are often repeated, the function consumes less RAM than the quantileExact. You can use this function instead of quantileExact, specifying the number 1 as the weight." - }, - { - "name": "min", - "def": "min(v)", - "docText": "Minimal execution speed in rows per second. Checked on every data block when `timeout_before_checking_execution_speed` expires. If the execution speed is lower, an exception is thrown." - }, - { - "name": "varSamp", - "def": "varSamp(x)", - "docText": "Calculates the amount Σ((x - x̅)2) / (n - 1), where 'n' is the sample size and 'x̅' is the average value of 'x'.\n" + - "\n" + - "It represents an unbiased estimate of the variance of a random variable, if the values passed to the function are a sample of this random amount.\n" + - "\n" + - "Returns Float64. If n <= 1, it returns +∞." - }, - { - "name": "anyLast", - "def": "anyLast(x)", - "docText": "Selects the last value encountered. \n" + - "\n" + - " The result is just as indeterminate as for the `any` function." - }, - { - "name": "argMax", - "def": "argMax(arg, val)", - "docText": "Calculates the `arg` value for a maximum `val` value. If there are several different values of `arg` for maximum values of `val`, the first of these values encountered is output." - }, - { - "name": "count", - "def": "count()", - "docText": "Counts the number of rows. " - }, - { - "name": "any", - "def": "any(x)", - "docText": "Selects the last value encountered. \n" + - "\n" + - " The result is just as indeterminate as for the `any` function." - }, - { - "name": "avg", - "def": "avg(x)", - "docText": "Calculates the average. \n" + - "\n" + - " Only works for numbers. \n" + - "\n" + - " The result is always Float64." - }, - { - "name": "sequenceMatch", - "def": "sequenceMatch(pattern)(time, cond1, cond2, ...)", - "docText": "Pattern matching for event chains.\n" + - "\n" + - "'pattern' is a string containing a pattern to match. The pattern is similar to a regular expression. 'time' is the event time of the DateTime type. 'cond1, cond2 ...' are from one to 32 arguments of the UInt8 type that indicate whether an event condition was met.\n" + - "\n" + - "The function collects a sequence of events in RAM. Then it checks whether this sequence matches the pattern. It returns UInt8 - 0 if the pattern isn't matched, or 1 if it matches.\n" + - "\n" + - "Example: sequenceMatch('(?1).*(?2)')(EventTime, URL LIKE '%company%', URL LIKE '%cart%') - whether there was a chain of events in which pages with the address in company were visited earlier than pages with the address in cart." - }, - { - "name": "stddevSamp", - "def": "stddevSamp(x)", - "docText": "The result is equal to the square root of `varSamp(x)`." - }, - { - "name": "medianDeterministic", - "def": "medianDeterministic(x, determinator)", - "docText": "This function works similarly to the `median` function - it approximates the median." - }, - { - "name": "quantilesTimingWeighted", - "def": "quantilesTimingWeighted(level1, level2, ...)(x, weight)", - "docText": "Calculates the quantiles of all specified levels using the same algorithm as the `medianTimingWeighted` function." - }, - { - "name": "uniq", - "def": "uniq(N)(x)", - "docText": "Calculates the number of different argument values, if it is less than or equal to N. \n" + - "\n" + - " If the number of different argument values is greater than N, it returns N + 1." - }, - { - "name": "covarSamp", - "def": "covarSamp(x, y)", - "docText": "Calculates the value of `Σ((x - x̅)(y - y̅)) / (n - 1)`. " + - "Returns Float64. If n <= 1, it returns +∞." - }, - { - "name": "max", - "def": "max(v)", - "docText": "Maximum number of bytes (uncompressed data) that can be passed to a remote server or saved in a temporary table when using GLOBAL IN." - }, - { - "name": "quantileTDigest", - "def": "quantileTDigest(level)(x)", - "docText": "t-digest" - }, - { - "name": "quantilesTiming", - "def": "quantilesTiming(level1, level2, ...)(x, weight)", - "docText": "Calculates the quantiles of all specified levels using the same algorithm as the `medianTimingWeighted` function." - }, - { - "name": "quantiles", - "def": "quantiles(level1, level2, ...)(x, determinator)", - "docText": "Calculates the quantiles of all specified levels using the same algorithm as the `medianDeterministic` function." - }, - { - "name": "quantile", - "def": "quantile(level1, level2, ...)(x, determinator)", - "docText": "Calculates the quantiles of all specified levels using the same algorithm as the `medianDeterministic` function." - }, - { - "name": "groupArray", - "def": "groupArray(x)", - "docText": "Creates an array of argument values. \n" + - "\n" + - " Values can be added to the array in any (indeterminate) order. \n" + - "\n" + - " In some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY." - }, - { - "name": "sum", - "def": "sum(x)", - "docText": "Calculates the sum. \n" + - "\n" + - " Only works for numbers." - }, - { - "name": "median", - "def": "median(x, weight)", - "docText": "Differs from the `medianTiming` function in that it has a second argument - `weights`. Weight is a non-negative integer. \n" + - "\n" + - " The result is calculated as if the `x` value were passed `weight` number of times to the `medianTiming` function." - }, - { - "name": "quantileTiming", - "def": "quantileTiming(level)(x, weight)", - "docText": "Calculates the quantile of `level` using the same algorithm as the `medianTimingWeighted` function." - }, - { - "name": "quantileTimingWeighted", - "def": "quantileTimingWeighted(level)(x, weight)", - "docText": "Calculates the quantile of `level` using the same algorithm as the `medianTimingWeighted` function." - }, - { - "name": "groupUniqArray", - "def": "groupUniqArray(x)", - "docText": "Creates an array from different argument values. Memory consumption is the same as for the `uniqExact` function." - }, - { - "name": "uniqHLL12", - "def": "uniqHLL12(x)", - "docText": "Uses the HyperLogLog algorithm to approximate the number of different values of the argument. " - }, - { - "name": "covarPop", - "def": "covarPop(x, y)", - "docText": "Calculates the value of `Σ((x - x̅)(y - y̅)) / n`." - }, - { - "name": "sequenceCount", - "def": "sequenceCount(pattern)(time, cond1, cond2, ...)", - "docText": "sequenceMatch" - }, - { - "name": "quantileDeterministic", - "def": "quantileDeterministic(level)(x, determinator)", - "docText": "Calculates the quantile of `level` using the same algorithm as the `medianDeterministic` function." - }, - { - "name": "quantileExact", - "def": "quantileExact(level)(x, weight)", - "docText": "" - }, - { - "name": "quantilesDeterministic", - "def": "quantilesDeterministic(level1, level2, ...)(x, determinator)", - "docText": "Calculates the quantiles of all specified levels using the same algorithm as the `medianDeterministic` function." - }, - { - "name": "medianTiming", - "def": "medianTiming(x, weight)", - "docText": "Differs from the `medianTiming` function in that it has a second argument - `weights`." - }, - { - "name": "medianTimingWeighted", - "def": "medianTimingWeighted(x, weight)", - "docText": "Differs from the `medianTiming` function in that it has a second argument - `weights`." - }, - { - "name": "uniqExact", - "def": "uniqExact(x)", - "docText": "Calculates the number of different values of the argument, exactly. \n" + - "\n" + - " There is no reason to fear approximations, so it`s better to use the `uniq` function. \n" + - "\n" + - " You should use the `uniqExact` function if you definitely need an exact result. " - }, - { - "name": "uniqCombined", - "def": "uniqCombined(x)", - "docText": "" - }, - { - "name": "uniqCombinedIf", - "def": "uniqCombinedIf(x,v)", - "docText": "" - }, - { - "name": "uniqUpTo", - "def": "uniqUpTo(N)(x)", - "docText": "Calculates the number of different argument values, if it is less than or equal to N. \n" + - "\n" + - " If the number of different argument values is greater than N, it returns N + 1." - }, - { - "name": "substring", - "def": "substring(s, offset, length)", - "docText": "The same as `substring`, but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception)." - }, - { - "name": "notLike", - "def": "notLike(haystack, pattern), haystack NOT LIKE pattern operator", - "docText": "The same thing as `like`, but negative." - }, - { - "name": "hostName", - "def": "hostName()", - "docText": "Returns a string with the name of the host that this function was performed on. For distributed processing, this is the name of the remote server host, if the function is performed on a remote server." - }, - { - "name": "globalNotIn", - "def": "globalNotIn(v)", - "docText": "See the section `IN operators`." - }, - { - "name": "or", - "def": "or(v)", - "docText": "The same thing as `max_temporary_columns`, but without counting constant columns. \n" + - "\n" + - " Note that constant columns are formed fairly often when running a query, but they require approximately zero computing resources." - }, - { - "name": "extractAll", - "def": "extractAll(haystack, pattern)", - "docText": "Extracts all the fragments of a string using a regular expression. If `haystack` doesn`t match the `pattern` regex, an empty string is returned. Returns an array of strings consisting of all matches to the regex." - }, - { - "name": "arrayFirst", - "def": "arrayFirst(func, arr1, ...)", - "docText": "Returns the index of the first element in the `arr1` array for which `func` returns something other than 0." - }, - { - "name": "arrayExists", - "def": "arrayExists([func,] arr1, ...)", - "docText": "Returns 1 if there is at least one element in `arr` for which `func` returns something other than 0. Otherwise, it returns 0." - }, - { - "name": "arrayCount", - "def": "arrayCount([func,] arr1, ...)", - "docText": "Returns the number of elements in `arr` for which `func` returns something other than 0. If `func` is not specified, it returns the number of non-zero items in the array." - }, - { - "name": "arrayMap", - "def": "arrayMap(func, arr1, ...)", - "docText": "Returns an array obtained from the original application of the `func` function to each element in the `arr` array." - }, - { - "name": "now", - "def": "now(v)", - "docText": "If the parameter is true, INSERT operation will skip columns with unknown names from input. \n" + - "\n" + - " Otherwise, an exception will be generated, it is default behavior. \n" + - "\n" + - " The parameter works only for JSONEachRow and TSKV input formats." - }, - { - "name": "intDiv", - "def": "intDiv(a, b)", - "docText": "Differs from `intDiv` in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one." - }, - { - "name": "topLevelDomain", - "def": "topLevelDomain(v)", - "docText": "Selects the top-level domain. Example: .ru." - }, - { - "name": "intHash32", - "def": "intHash32(v)", - "docText": "Calculates a 32-bit hash code from any type of integer. \n" + - "\n" + - " This is a relatively fast non-cryptographic hash function of average quality for numbers." - }, - { - "name": "replaceOne", - "def": "replaceOne(haystack, pattern, replacement)", - "docText": "Replaces the first occurrence, if it exists, of the `pattern` substring in `haystack` with the `replacement` substring. \n" + - "\n" + - " Hereafter, `pattern` and `replacement` must be constants." - }, - { - "name": "cityHash64", - "def": "cityHash64(v)", - "docText": "Calculates CityHash64 from a string or a similar hash function for any number of any type of arguments. \n" + - "\n" + - " For String-type arguments, CityHash is used. This is a fast non-cryptographic hash function for strings with decent quality" - }, - { - "name": "OSToRoot", - "def": "OSToRoot(v)", - "docText": "Accepts a UInt8 number - the ID of the operating system from the Yandex.Metrica dictionary. If any OS matches the passed number, it returns a UInt8 number - the ID of the corresponding root OS (for example, it converts Windows Vista to Windows). Otherwise, returns 0." - }, - { - "name": "sipHash128", - "def": "sipHash128(v)", - "docText": "Calculates SipHash from a string. \n" + - "\n" + - " Accepts a String-type argument. Returns FixedString(16). \n" + - "\n" + - " Differs from sipHash64 in that the final xor-folding state is only done up to 128 bits." - }, - { - "name": "SHA1", - "def": "SHA1(v)", - "docText": "Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32)." - }, - { - "name": "asin", - "def": "asin(x)", - "docText": "The arc sine." - }, - { - "name": "SHA256", - "def": "SHA256(v)", - "docText": "Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32)." - }, - { - "name": "MD5", - "def": "MD5(v)", - "docText": "Calculates the MD5 from a string and returns the resulting set of bytes as FixedString(16)." - }, - { - "name": "bitmaskToList", - "def": "bitmaskToList(num)", - "docText": "Accepts an integer. Returns a string containing the list of powers of two that total the source number when summed. They are comma-separated without spaces in text format, in ascending order." - }, - { - "name": "array", - "def": "array(v)", - "docText": "The -`Array` suffix can be appended to any aggregate function. In this case, the aggregate function takes arguments of the `Array(T)` type (arrays) instead of `T` type arguments. If the aggregate function accepts multiple arguments, this must be arrays of equal lengths. " - }, - { - "name": "dictGetStringOrDefault", - "def": "dictGetStringOrDefault(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "greaterOrEquals", - "def": "greaterOrEquals(v)", - "docText": "" - }, - { - "name": "e", - "def": "e(v)", - "docText": "What to do when the amount of data exceeds one of the limits: `throw` or `break`. By default, throw." - }, - { - "name": "runningDifference", - "def": "runningDifference(x)", - "docText": "Calculates the difference between consecutive values in the data block. \n" + - "\n" + - " Result of the function depends on the order of the data in the blocks. \n" + - "\n" + - " It works only inside of the each processed block of data. Data splitting in the blocks is not explicitly controlled by the user. " - }, - { - "name": "not", - "def": "not(v)", - "docText": "See the section `IN operators`." - }, - { - "name": "intHash64", - "def": "intHash64(v)", - "docText": "Calculates a 64-bit hash code from any type of integer. \n" + - "\n" + - " It works faster than intHash32. Average quality." - }, - { - "name": "acos", - "def": "acos(x)", - "docText": "The arc cosine." - }, - { - "name": "dictGetString", - "def": "dictGetString(v)", - "docText": "" - }, - { - "name": "and", - "def": "and(x, determinator)", - "docText": "This function works similarly to the `median` function - it approximates the median. However, in contrast to `median`, the result is deterministic and does not depend on the order of query execution. \n" + - "\n" + - " To achieve this, the function takes a second argument - the `determinator`. " - }, - { - "name": "dictGetDate", - "def": "dictGetDate(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "dictGetFloat32", - "def": "dictGetFloat32(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "dictGetInt8", - "def": "dictGetInt8(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "dictGetUInt32", - "def": "dictGetUInt32(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "OSIn", - "def": "OSIn(lhs, rhs)", - "docText": "Checks whether the `lhs` operating system belongs to the `rhs` operating system." - }, - { - "name": "arrayFirstIndex", - "def": "arrayFirstIndex(func, arr1, ...)", - "docText": "Returns the index of the first element in the `arr1` array for which `func` returns something other than 0." - }, - { - "name": "ceil", - "def": "ceil(x[, N])", - "docText": "Returns the smallest round number that is greater than or equal to `x`. In every other way, it is the same as the `floor` function (see above)." - }, - { - "name": "fragment", - "def": "fragment(v)", - "docText": "Removes the query-string and fragment identifier. The question mark and number sign are also removed." - }, - { - "name": "dictGetUInt8", - "def": "dictGetUInt8(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "dictHas", - "def": "dictHas(v)", - "docText": "dictHas('dict_name', id) check the presence of a key in the dictionary. Returns a value of type UInt8, equal to 0, if there is no key and 1 if there is a key." - }, - { - "name": "arraySum", - "def": "arraySum([func,] arr1, ...)", - "docText": "Returns the sum of the `func` values. If the function is omitted, it just returns the sum of the array elements." - }, - { - "name": "emptyArrayDateTime", - "def": "emptyArrayDateTime(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "intDivOrZero", - "def": "intDivOrZero(a, b)", - "docText": "Differs from `intDiv` in that it returns zero when dividing by zero or when dividing a minimal negative number by minus one." - }, - { - "name": "SEHierarchy", - "def": "SEHierarchy(v)", - "docText": "Accepts a UInt8 number - the ID of the search engine from the Yandex.Metrica dictionary. Returns an array with a hierarchy of search engines. Similar to the `regionHierarchy` function." - }, - { - "name": "regionToContinent", - "def": "regionToContinent(id[, geobase])", - "docText": "Converts a region to a continent. In every other way, this function is the same as `regionToCity`. \n" + - "\n" + - " Example: `regionToContinent(toUInt32(213)) = 10001` converts Moscow (213) to Eurasia (10001)." - }, - { - "name": "dictGetInt32", - "def": "dictGetInt32(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "toInt8", - "def": "toInt8(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "materialize", - "def": "materialize(x)", - "docText": "Turns a constant into a full column containing just one value. \n" + - "\n" + - " In ClickHouse, full columns and constants are represented differently in memory. Functions work differently for constant arguments and normal arguments (different code is executed), although the result is almost always the same. This function is for debugging this behavior." - }, - { - "name": "regionToCountry", - "def": "regionToCountry(id[, geobase])", - "docText": "Converts a region to a country. In every other way, this function is the same as `regionToCity`. \n" + - "\n" + - " Example: `regionToCountry(toUInt32(213)) = 225` converts Moscow (213) to Russia (225)." - }, - { - "name": "dictGetDateTime", - "def": "dictGetDateTime(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "xor", - "def": "xor(v)", - "docText": "" - }, - { - "name": "regionToCity", - "def": "regionToCity(id[, geobase])", - "docText": "Accepts a UInt32 number - the region ID from the Yandex geobase. If this region is a city or part of a city, it returns the region ID for the appropriate city. Otherwise, returns 0." - }, - { - "name": "dictGetFloat64", - "def": "dictGetFloat64(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "timeSlot", - "def": "timeSlot(StartTime, Duration)", - "docText": "For a time interval starting at `StartTime` and continuing for `Duration` seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour." - }, - { - "name": "toTime", - "def": "toTime(v)", - "docText": "Converts a date with time to the date of the start of the Unix Epoch, while preserving the time." - }, - { - "name": "log2", - "def": "log2(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to the binary logarithm of the argument." - }, - { - "name": "toRelativeHourNum", - "def": "toRelativeHourNum(v)", - "docText": "Converts a date with time or date to the number of the hour, starting from a certain fixed point in the past." - }, - { - "name": "toRelativeDayNum", - "def": "toRelativeDayNum(v)", - "docText": "Converts a date with time or date to the number of the day, starting from a certain fixed point in the past." - }, - { - "name": "toRelativeWeekNum", - "def": "toRelativeWeekNum(v)", - "docText": "Converts a date with time or date to the number of the week, starting from a certain fixed point in the past." - }, - { - "name": "splitByString", - "def": "splitByString(separator, s)", - "docText": "The same as above, but it uses a string of multiple characters as the separator. The string must be non-empty." - }, - { - "name": "currentDatabase", - "def": "currentDatabase()", - "docText": "Returns the name of the current database. \n" + - "\n" + - " You can use this function in table engine parameters in a CREATE TABLE query where you need to specify the database." - }, - { - "name": "toRelativeMonthNum", - "def": "toRelativeMonthNum(v)", - "docText": "Converts a date with time or date to the number of the month, starting from a certain fixed point in the past." - }, - { - "name": "visibleWidth", - "def": "visibleWidth(x)", - "docText": "Calculates the approximate width when outputting values to the console in text format (tab-separated). This function is used by the system for implementing Pretty formats." - }, - { - "name": "bitShiftRight", - "def": "bitShiftRight(a, b)", - "docText": "" - }, - { - "name": "toRelativeYearNum", - "def": "toRelativeYearNum(v)", - "docText": "Converts a date with time or date to the number of the year, starting from a certain fixed point in the past." - }, - { - "name": "toStartOfHour", - "def": "toStartOfHour(v)", - "docText": "Rounds down a date with time to the start of the hour." - }, - { - "name": "halfMD5", - "def": "halfMD5(v)", - "docText": "Calculates the MD5 from a string. Then it takes the first 8 bytes of the hash and interprets them as UInt64 in big endian." - }, - { - "name": "toStartOfFiveMinute", - "def": "toStartOfFiveMinute(v)", - "docText": "Rounds down a date with time to the start of the 5 minute (00:00, 00:05, 00:10...)." - }, - { - "name": "toUInt16OrZero", - "def": "toUInt16OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument." - }, - { - "name": "toMonday", - "def": "toMonday(v)", - "docText": "Rounds down a date or date with time to the nearest Monday. \n" + - "\n" + - " Returns the date." - }, - { - "name": "IPv6NumToString", - "def": "IPv6NumToString(x)", - "docText": "Accepts a FixedString(16) value containing the IPv6 address in binary format. Returns a string containing this address in text format. " - }, - { - "name": "indexOf", - "def": "indexOf(arr, x)", - "docText": "Returns the index of the `x` element (starting from 1) if it is in the array, or 0 if it is not." - }, - { - "name": "today", - "def": "today(v)", - "docText": "Accepts zero arguments and returns the current date at one of the moments of request execution. \n" + - "\n" + - " The same as `toDate(now())`." - }, - { - "name": "emptyArrayToSingle", - "def": "emptyArrayToSingle(v)", - "docText": "Accepts an empty array as argument and returns an array of one element equal to the default value." - }, - { - "name": "sleep", - "def": "sleep(seconds)", - "docText": "Sleeps `seconds` seconds on each data block. You can specify an integer or a floating-point number." - }, - { - "name": "extract", - "def": "extract(params, name)", - "docText": "Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string. Examples: " - }, - { - "name": "emptyArrayInt8", - "def": "emptyArrayInt8(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "regionToName", - "def": "regionToName(id[, lang])", - "docText": "Accepts a UInt32 number - the region ID from the Yandex geobase. A string with the name of the language can be passed as a second argument. Supported languages are: ru, en, ua, uk, by, kz, tr. If the second argument is omitted, the language `ru` is used. " - }, - { - "name": "concat", - "def": "concat(arr[, separator])", - "docText": "Concatenates strings from the array elements, using `separator` as the separator. \n" + - "\n" + - " `separator` is a string constant, an optional parameter. By default it is an empty string. \n" + - "\n" + - " Returns a string." - }, - { - "name": "convertCharset", - "def": "convertCharset(s, from, to)", - "docText": "Returns a string with the data `s` (encoded as `from` charset) that was converted to the `to` charset." - }, - { - "name": "toMonth", - "def": "toMonth(v)", - "docText": "Converts a date or date with time to a UInt8 number containing the month number (1-12)." - }, - { - "name": "IPv6StringToNum", - "def": "IPv6StringToNum(s)", - "docText": "The reverse function of IPv6NumToString. If the IPv6 address has an invalid format, it returns a string of null bytes. \n" + - "\n" + - " HEX can be uppercase or lowercase." - }, - { - "name": "emptyArrayString", - "def": "emptyArrayString(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "uptime", - "def": "uptime()", - "docText": "Returns server's uptime in seconds." - }, - { - "name": "blockSize", - "def": "blockSize()", - "docText": "Gets the size of the block. \n" + - "\n" + - " In ClickHouse, queries are always run on blocks (sets of column parts). This function allows getting the size of the block that you called it for." - }, - { - "name": "toInt64OrZero", - "def": "toInt64OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "pathFull", - "def": "pathFull(v)", - "docText": "Selects the path, but including query-string and fragment. Example: /top/news.html?page=2#comments" - }, - { - "name": "emptyArrayDate", - "def": "emptyArrayDate(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "emptyArrayInt64", - "def": "emptyArrayInt64(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "toInt32OrZero", - "def": "toInt32OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "greater", - "def": "greater(v)", - "docText": "" - }, - { - "name": "emptyArrayInt32", - "def": "emptyArrayInt32(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "toString", - "def": "toString(str)", - "docText": "Accepts a FixedString(16) value containing the UUID in the binary format. Returns a readable string containing the UUID in the text format." - }, - { - "name": "greatest", - "def": "greatest(a, b)", - "docText": "Returns the greatest element of a and b." - }, - { - "name": "emptyArrayUInt64", - "def": "emptyArrayUInt64(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "emptyArrayUInt32", - "def": "emptyArrayUInt32(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "formatReadableSize", - "def": "formatReadableSize(x)", - "docText": "Gets a size (number of bytes). Returns a string that contains rounded size with the suffix (KiB, MiB etc.).\n" + - "\n" + - "Example:\n" + - "\n" + - "SELECT\n" + - " arrayJoin([1, 1024, 1024*1024, 192851925]) AS f_bytes,\n" + - " formatReadableSize(filesize_bytes) AS filesize\n" + - "┌─f_bytes-─┬─filesize───┐\n" + - "│ 1 │ 1.00 B │\n" + - "│ 1024 │ 1.00 KiB │\n" + - "│ 1048576 │ 1.00 MiB │\n" + - "│192851925 │ 183.92 MiB │\n" + - "└──────────┴────────────┘\n" - }, - { - "name": "toInt16OrZero", - "def": "toInt16OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "emptyArrayUInt8", - "def": "emptyArrayUInt8(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "protocol", - "def": "protocol(v)", - "docText": "Selects the protocol. Examples: http, ftp, mailto, magnet..." - }, - { - "name": "regionToPopulation", - "def": "regionToPopulation(id[, geobase])", - "docText": "Gets the population for a region. \n" + - "\n" + - " The population can be recorded in files with the geobase. See the section `External dictionaries`. \n" + - "\n" + - " If the population is not recorded for the region, it returns 0. \n" + - "\n" + - " In the Yandex geobase, the population might be recorded for child regions, but not for parent regions." - }, - { - "name": "notIn", - "def": "notIn(v)", - "docText": "See the section `IN operators`." - }, - { - "name": "position", - "def": "position(haystack, needle)", - "docText": "The same as `position`, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception). \n" + - "\n" + - " There's also positionCaseInsensitiveUTF8 function." - }, - { - "name": "arrayElement", - "def": "arrayElement(arr, n), arr[n] operator", - "docText": "Get the element with the index `n` from the array `arr`. \n" + - "\n" + - " `n` should be any integer type. \n" + - "\n" + - " Indexes in an array begin from one. \n" + - "\n" + - " Negative indexes are supported - in this case, it selects the corresponding element numbered from the end. " - }, - { - "name": "toStringCutToZero", - "def": "toStringCutToZero(s)", - "docText": "Accepts a String or FixedString argument. Returns a String that is cut to a first null byte occurrence." - }, - { - "name": "log", - "def": "log(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument." - }, - { - "name": "SEIn", - "def": "SEIn(lhs, rhs)", - "docText": "Checks whether the `lhs` search engine belongs to the `rhs` search engine." - }, - { - "name": "replicate", - "def": "replicate(v)", - "docText": "Create a MergeTree table with a different name. Move all the data from the directory with the ReplicatedMergeTree table data to the new table`s data directory." - }, - { - "name": "sipHash64", - "def": "sipHash64(v)", - "docText": "Calculates SipHash from a string. \n" + - "\n" + - " Accepts a String-type argument. Returns UInt64. \n" + - "\n" + - " SipHash is a cryptographic hash function. It works at least three times faster than MD5. For more information, see https://131002.net/siphash/" - }, - { - "name": "emptyArrayUInt16", - "def": "emptyArrayUInt16(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "hex", - "def": "hex(str)", - "docText": "Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits doesn`t have to be even." - }, - { - "name": "regionToDistrict", - "def": "regionToDistrict(id[, geobase])", - "docText": "Converts a region to a federal district (type 4 in the geobase). In every other way, this function is the same as `regionToCity`. " - }, - { - "name": "arrayFilter", - "def": "arrayFilter(func, arr1, ...)", - "docText": "Returns an array containing only the elements in `arr1` for which `func` returns something other than 0. \n" + - "\n" + - " Examples: \n" + - "\n" + - " SELECT arrayFilter(x -> x LIKE `%World%`, [`Hello`, `abc World`]) AS res " - }, - { - "name": "toStartOfQuarter", - "def": "toStartOfQuarter(v)", - "docText": "Rounds down a date or date with time to the first day of the quarter. \n" + - "\n" + - " The first day of the quarter is either 1 January, 1 April, 1 July, or 1 October. Returns the date." - }, - { - "name": "divide", - "def": "divide(a, b), a / b operator", - "docText": "Calculates the quotient of the numbers. The result type is always a floating-point type. \n" + - "\n" + - " It is not integer division. For integer division, use the `intDiv` function. \n" + - "\n" + - " When dividing by zero you get `inf`, `-inf`, or `nan`." - }, - { - "name": "reverseUTF8", - "def": "reverseUTF8(v)", - "docText": "Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn`t throw an exception)." - }, - { - "name": "toDate", - "def": "toDate(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "emptyArrayFloat64", - "def": "emptyArrayFloat64(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "abs", - "def": "abs(s, c)", - "docText": "If the `s` string is non-empty and does not contain the `c` character at the end, it appends the `c` character to the end." - }, - { - "name": "yesterday", - "def": "yesterday(v)", - "docText": "Accepts zero arguments and returns yesterday`s date at one of the moments of request execution. \n" + - "\n" + - " The same as `today() - 1`." - }, - { - "name": "toMinute", - "def": "toMinute(v)", - "docText": "Converts a date with time to a UInt8 number containing the number of the minute of the hour (0-59)." - }, - { - "name": "bitXor", - "def": "bitXor(a, b)", - "docText": "" - }, - { - "name": "minus", - "def": "minus(a, b), a - b operator", - "docText": "Calculates the difference. The result is always signed. \n" + - "\n" + - " You can also calculate whole numbers from a date or date with time. The idea is the same - see above for `plus`." - }, - { - "name": "toDateTime", - "def": "toDateTime(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "modulo", - "def": "modulo(a, b), a % b operator", - "docText": "Calculates the remainder after division. \n" + - "\n" + - " If arguments are floating-point numbers, they are pre-converted to integers by dropping the decimal portion. The remainder is taken in the same sense as in C++. Truncated division is used for negative numbers. \n" + - "\n" + - " An exception is thrown when dividing by zero or when dividing a minimal negative number by minus one." - }, - { - "name": "bitmaskToArray", - "def": "bitmaskToArray(num)", - "docText": "Accepts an integer. Returns an array of UInt64 numbers containing the list of powers of two that total the source number when summed. Numbers in the array are in ascending order." - }, - { - "name": "negate", - "def": "negate(a), -a operator", - "docText": "Calculates a number with the reverse sign. The result is always signed." - }, - { - "name": "emptyArrayFloat32", - "def": "emptyArrayFloat32(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "range", - "def": "range(N)", - "docText": "Returns an array of numbers from 0 to N-1. \n" + - "\n" + - " Just in case, an exception is thrown if arrays with a total length of more than 100,000,000 elements are created in a data block." - }, - { - "name": "arrayAll", - "def": "arrayAll([func,] arr1, ...)", - "docText": "Returns 1 if `func` returns something other than 0 for all the elements in `arr`. Otherwise, it returns 0." - }, - { - "name": "toInt32", - "def": "toInt32(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "unhex", - "def": "unhex(str)", - "docText": "Accepts a string containing any number of hexadecimal digits, and returns a string containing the corresponding bytes. Supports both uppercase and lowercase letters A-F. The number of hexadecimal digits doesn`t have to be even. If it is odd, the last digit is interpreted as the younger half of the 00-0F byte. If the argument string contains anything other than hexadecimal digits, some implementation-defined result is returned (an exception isn`t thrown). \n" + - "\n" + - " If you want to convert the result to a number, you can use the functions `reverse` and `reinterpretAsType`." - }, - { - "name": "toFloat64", - "def": "toFloat64(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "bitAnd", - "def": "bitAnd(a, b)", - "docText": "" - }, - { - "name": "toStartOfYear", - "def": "toStartOfYear(v)", - "docText": "Rounds down a date or date with time to the first day of the year. \n" + - "\n" + - " Returns the date." - }, - { - "name": "bitOr", - "def": "bitOr(a, b)", - "docText": "" - }, - { - "name": "SEToRoot", - "def": "SEToRoot(v)", - "docText": "Accepts a UInt8 number - the ID of the search engine from the Yandex.Metrica dictionary. If any search engine matches the passed number, it returns a UInt8 number - the ID of the corresponding root search engine (for example, it converts Yandex.Images to Yandex). Otherwise, returns 0." - }, - { - "name": "toRelativeMinuteNum", - "def": "toRelativeMinuteNum(v)", - "docText": "Converts a date with time or date to the number of the minute, starting from a certain fixed point in the past." - }, - { - "name": "visitParamExtractInt", - "def": "visitParamExtractInt(params, name)", - "docText": "The same as for Int64." - }, - { - "name": "emptyArrayInt16", - "def": "emptyArrayInt16(v)", - "docText": "Accepts zero arguments and returns an empty array of the appropriate type." - }, - { - "name": "visitParamExtractString", - "def": "visitParamExtractString(params, name)", - "docText": "Parses the string in double quotes. The value is unescaped. If unescaping failed, it returns an empty string. " - }, - { - "name": "arrayEnumerateUniq", - "def": "arrayEnumerateUniq(arr, ...)", - "docText": "Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value. " - }, - { - "name": "visitParamExtractUInt", - "def": "visitParamExtractUInt(params, name)", - "docText": "Parses UInt64 from the value of the field named `name`. If this is a string field, it tries to parse a number from the beginning of the string. If the field doesn`t exist, or it exists but doesn`t contain a number, it returns 0." - }, - { - "name": "toTypeName", - "def": "toTypeName(x)", - "docText": "Gets the type name. Returns a string containing the type name of the passed argument." - }, - { - "name": "empty", - "def": "empty(v)", - "docText": "Accepts an empty array as argument and returns an array of one element equal to the default value." - }, - { - "name": "multiply", - "def": "multiply(a, b), a * b operator", - "docText": "Calculates the product of the numbers." - }, - { - "name": "has", - "def": "has('database', 'table', 'column')", - "docText": "Accepts constant String columns - database name, table name and column name. Returns constant UInt8 value, equal to 1 if column exists, otherwise 0.\n" + - "\n" + - " If table doesn't exist than exception is thrown. \n" + - "\n" + - " For elements of nested data structure function checks existence of column. For nested data structure 0 is returned." - }, - { - "name": "bitNot", - "def": "bitNot(a)", - "docText": "" - }, - { - "name": "lessOrEquals", - "def": "lessOrEquals(v)", - "docText": "greaterOrEquals, >= operator" - }, - { - "name": "reinterpretAsInt64", - "def": "reinterpretAsInt64(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, - { - "name": "IPv4NumToString", - "def": "IPv4NumToString(num)", - "docText": "Similar to IPv4NumToString, but using `xxx` instead of the last octet. " - }, - { - "name": "bitShiftLeft", - "def": "bitShiftLeft(a, b)", - "docText": "" - }, - { - "name": "dictGetInt16", - "def": "dictGetInt16(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "toUInt32OrZero", - "def": "toUInt32OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "regionIn", - "def": "regionIn(lhs, rhs[, geobase])", - "docText": "Checks whether a `lhs` region belongs to a `rhs` region. Returns a UInt8 number equal to 1 if it belongs, or 0 if it doesn`t belong. \n" + - "\n" + - " The relationship is reflexive - any region also belongs to itself." - }, - { - "name": "reinterpretAsUInt8", - "def": "reinterpretAsUInt8(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, - { - "name": "dictIsIn", - "def": "dictIsIn(v)", - "docText": "`dictIsIn(`dict_name`, child_id, ancestor_id)` \n" + - "\n" + - " - For the `dict_name` hierarchical dictionary, finds out whether the `child_id` key is located inside `ancestor_id` (or matches `ancestor_id`). Returns UInt8." - }, - { - "name": "toSecond", - "def": "toSecond(v)", - "docText": "Converts a date with time to a UInt8 number containing the number of the second in the minute (0-59). \n" + - "\n" + - " Leap seconds are not accounted for." - }, - { - "name": "least", - "def": "least(a, b)", - "docText": "Returns the least element of a and b." - }, - { - "name": "countEqual", - "def": "countEqual(arr, x)", - "docText": "Returns the number of elements in the array equal to `x`. Equivalent to arrayCount(elem -> elem = x, arr)." - }, - { - "name": "IPv4StringToNum", - "def": "IPv4StringToNum(s)", - "docText": "The reverse function of IPv4NumToString. If the IPv4 address has an invalid format, it returns 0." - }, - { - "name": "replaceRegexpAll", - "def": "replaceRegexpAll(haystack, pattern, replacement)", - "docText": "This does the same thing, but replaces all the occurrences" - }, - { - "name": "SHA224", - "def": "SHA224(v)", - "docText": "Calculates SHA-1, SHA-224, or SHA-256 from a string and returns the resulting set of bytes as FixedString(20), FixedString(28), or FixedString(32). \n" + - "\n" + - " The function works fairly slowly (SHA-1 processes about 5 million short strings per second per processor core, while SHA-224 and SHA-256 process about 2.2 million). " - }, - { - "name": "URLHash", - "def": "URLHash(url[, N])", - "docText": "A fast, decent-quality non-cryptographic hash function for a string obtained from a URL using some type of normalization. \n" + - "\n" + - " URLHash(s) - Calculates a hash from a string without one of the trailing symbols /,? or # at the end, if present" - }, - { - "name": "equals", - "def": "equals(v)", - "docText": "

greaterOrEquals, >= operator

" - }, - { - "name": "plus", - "def": "plus(a, b), a + b operator", - "docText": "Calculates the sum of the numbers. \n" + - "\n" + - " You can also add whole numbers with a date or date and time. In the case of a date, adding a whole number means adding the corresponding number of days. For a date with time, it means adding the corresponding number of seconds." - }, - { - "name": "less", - "def": "less(v)", - "docText": "

greaterOrEquals, >= operator

" - }, - { - "name": "regionHierarchy", - "def": "regionHierarchy(id[, geobase])", - "docText": "Accepts a UInt32 number - the region ID from the Yandex geobase. Returns an array of region IDs consisting of the passed region and all parents along the chain. \n" + - "\n" + - " Example: `regionHierarchy(toUInt32(213)) = [213,1,3,225,10001,10000]`." - }, - { - "name": "toUInt64OrZero", - "def": "toUInt64OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "rowNumberInAllBlocks", - "def": "rowNumberInAllBlocks()", - "docText": "Returns an incremental row number within all blocks that were processed by this function." - }, - { - "name": "toDayOfWeek", - "def": "toDayOfWeek(v)", - "docText": "Converts a date or date with time to a UInt8 number containing the number of the day of the week (Monday is 1, and Sunday is 7)." - }, - { - "name": "bar", - "def": "bar(v)", - "docText": "Allows building a unicode-art diagram. \n" + - "\n" + - " bar(x, min, max, width) - Draws a band with a width proportional to (x - min) and equal to `width` characters when x" - }, - { - "name": "if", - "def": "if(v)", - "docText": "The suffix -`If` can be appended to the name of any aggregate function. In this case, the aggregate function accepts an extra argument - a condition (Uint8 type). " - }, - { - "name": "regionToArea", - "def": "regionToArea(id[, geobase])", - "docText": "Converts a region to an area (type 5 in the geobase). In every other way, this function is the same as `regionToCity`." - }, - { - "name": "dictGetUInt16", - "def": "dictGetUInt16(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "toUInt8", - "def": "toUInt8(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "toUInt16", - "def": "toUInt16(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "path", - "def": "path(URL)", - "docText": "- The same thing, but without the protocol and host in the result. The / element (root) is not included." - }, - { - "name": "extractURLParameters", - "def": "extractURLParameters(URL)", - "docText": "- Gets an array of name=value strings corresponding to the URL parameters. The values are not decoded in any way." - }, - { - "name": "OSHierarchy", - "def": "OSHierarchy(v)", - "docText": "Accepts a UInt8 number - the ID of the operating system from the Yandex.Metrica dictionary. Returns an array with a hierarchy of operating systems. Similar to the `regionHierarchy` function." - }, - { - "name": "cutQueryStringAndFragment", - "def": "cutQueryStringAndFragment(v)", - "docText": "Removes the query-string and fragment identifier. The question mark and number sign are also removed." - }, - { - "name": "timeSlots", - "def": "timeSlots(StartTime, Duration)", - "docText": "For a time interval starting at `StartTime` and continuing for `Duration` seconds, it returns an array of moments in time, consisting of points from this interval rounded down to the half hour. \n" + - "\n" + - " For example, `timeSlots(toDateTime(`2012-01-01 12:20:00`), toUInt32(600)) = [toDateTime(`2012-01-01 12:00:00`), toDateTime(`2012-01-01 12:30:00`)]`. \n" + - "\n" + - " This is necessary for searching for pageviews in the corresponding session." - }, - { - "name": "toUInt32", - "def": "toUInt32(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "toStartOfMinute", - "def": "toStartOfMinute(v)", - "docText": "Rounds down a date with time to the start of the minute." - }, - { - "name": "version", - "def": "version()", - "docText": "Returns server's version as a string." - }, - { - "name": "toUInt64", - "def": "toUInt64(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "toInt16", - "def": "toInt16(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "reinterpretAsInt16", - "def": "reinterpretAsInt16(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, - { - "name": "toInt64", - "def": "toInt64(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "toFixedString", - "def": "toFixedString(s, N)", - "docText": "Converts a String type argument to a FixedString(N) type (a string with fixed length N). N must be a constant. If the string has fewer bytes than N, it is passed with null bytes to the right. If the string has more bytes than N, an exception is thrown." - }, - { - "name": "toFloat32", - "def": "toFloat32(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "dictGetHierarchy", - "def": "dictGetHierarchy(v)", - "docText": "`dictGetHierarchy(`dict_name`, id)` \n" + - "\n" + - " - For the `dict_name` hierarchical dictionary, returns an array of dictionary keys starting from `id` and continuing along the chain of parent elements. Returns Array(UInt64)." - }, - { - "name": "dictGetInt64", - "def": "dictGetInt64(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "CAST", - "def": "CAST(x, t)", - "docText": "Casts `x` to the `t` data type. \n" + - "\n" + - " The syntax `CAST(x AS t)` is also supported. \n" - }, - { - "name": "toRelativeSecondNum", - "def": "toRelativeSecondNum(v)", - "docText": "Converts a date with time or date to the number of the second, starting from a certain fixed point in the past." - }, - { - "name": "toUInt8OrZero", - "def": "toUInt8OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "toStartOfMonth", - "def": "toStartOfMonth(v)", - "docText": "Rounds down a date or date with time to the first day of the month. \n" + - "\n" + - " Returns the date." - }, - { - "name": "rand64", - "def": "rand64(v)", - "docText": "Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers. \n" + - "\n" + - " Uses a linear congruential generator." - }, - { - "name": "toInt8OrZero", - "def": "toInt8OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "toFloat32OrZero", - "def": "toFloat32OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "toYear", - "def": "toYear(v)", - "docText": "Converts a date or date with time to a UInt16 number containing the year number (AD)." - }, - { - "name": "atan", - "def": "atan(x)", - "docText": "The arc tangent." - }, - { - "name": "toFloat64OrZero", - "def": "toFloat64OrZero(v)", - "docText": "Functions for converting between numbers, strings (but not fixed strings), dates, and dates with times. All these functions accept one argument. " - }, - { - "name": "arrayEnumerate", - "def": "arrayEnumerate(arr, ...)", - "docText": "Returns an array the same size as the source array, indicating for each element what its position is among elements with the same value." - }, - { - "name": "toDayOfMonth", - "def": "toDayOfMonth(v)", - "docText": "Converts a date or date with time to a UInt8 number containing the number of the day of the month (1-31)." - }, - { - "name": "dictGetUInt64", - "def": "dictGetUInt64(v)", - "docText": "dictGetT('dict_name', 'attr_name', id) Gets the value of the 'attr_name' attribute from the 'dict_name' dictionary by the 'id' key. 'dict_name' and 'attr_name' are constant strings. 'id' must be UInt64. If the 'id' key is not in the dictionary, it returns the default value set in the dictionary definition." - }, - { - "name": "IPv4NumToStringClassC", - "def": "IPv4NumToStringClassC(num)", - "docText": "Similar to IPv4NumToString, but using `xxx` instead of the last octet. " - }, - { - "name": "reinterpretAsString", - "def": "reinterpretAsString(v)", - "docText": "This function accepts a number or date or date with time, and returns a string containing bytes representing the corresponding value in host order (little endian). Null bytes are dropped from the end. For example, a UInt32 type value of 255 is a string that is one byte long." - }, - { - "name": "toHour", - "def": "toHour(v)", - "docText": "Converts a date with time to a UInt8 number containing the number of the hour in 24-hour time (0-23). \n" + - "\n" + - " This function assumes that if clocks are moved ahead, it is by one hour and occurs at 2 a.m., and if clocks are moved back, it is by one hour and occurs at 3 a.m. (which is not always true - even in Moscow the clocks were once changed at a different time)." - }, - { - "name": "ignore", - "def": "ignore(...)", - "docText": "A function that accepts any arguments and always returns 0. \n" + - "\n" + - " However, the argument is still calculated. This can be used for benchmarks." - }, - { - "name": "arrayJoin", - "def": "arrayJoin(v)", - "docText": "This is a very unusual function.\n" + - "\n" + - "Normal functions don't change a set of rows, but just change the values in each row (map). Aggregate functions compress a set of rows (fold or reduce). The 'arrayJoin' function takes each row and generates a set of rows (unfold).\n" + - "\n" + - "This function takes an array as an argument, and propagates the source row to multiple rows for the number of elements in the array. All the values in columns are simply copied, except the values in the column where this function is applied - it is replaced with the corresponding array value.\n" + - "\n" + - "A query can use multiple 'arrayJoin' functions. In this case, the transformation is performed multiple times.\n" + - "\n" + - "Note the ARRAY JOIN syntax in the SELECT query, which provides broader possibilities." - }, - { - "name": "length", - "def": "length(v)", - "docText": "Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception). \n" + - "\n" + - " The result type is UInt64." - }, - { - "name": "tuple", - "def": "tuple(tuple, n), operator x.N", - "docText": "A function that allows getting columns from a tuple. \n" + - "\n" + - " `N` is the column index, starting from 1. `N` must be a constant. `N` must be a strict postive integer no greater than the size of the tuple. \n" + - "\n" + - " There is no cost to execute the function." - }, - { - "name": "extractURLParameterNames", - "def": "extractURLParameterNames(URL)", - "docText": "- Gets an array of name=value strings corresponding to the names of URL parameters. The values are not decoded in any way." - }, - { - "name": "tupleElement", - "def": "tupleElement(tuple, n), operator x.N", - "docText": "A function that allows getting columns from a tuple. \n" + - "\n" + - " `N` is the column index, starting from 1. `N` must be a constant. `N` must be a strict postive integer no greater than the size of the tuple. \n" + - "\n" + - " There is no cost to execute the function." - }, - { - "name": "in", - "def": "in(v)", - "docText": "What to do when the amount of data exceeds one of the limits: `throw` or `break`. By default, throw." - }, - { - "name": "globalIn", - "def": "globalIn(v)", - "docText": "See the section `IN operators`." - }, - { - "name": "isFinite", - "def": "isFinite(x)", - "docText": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is not infinite and not a NaN, otherwise 0." - }, - { - "name": "isNaN", - "def": "isNaN(x)", - "docText": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is a NaN, otherwise 0." - }, - { - "name": "isInfinite", - "def": "isInfinite(x)", - "docText": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. \n" + - "\n" + - " Note that 0 is returned for a NaN." - }, - { - "name": "transform", - "def": "transform(v)", - "docText": "Transforms a value according to the explicitly defined mapping of some elements to other ones. \n" + - "\n" + - " There are two variations of this function: \n" + - "\n" + - " 1. `transform(x, array_from, array_to, default)` " - }, - { - "name": "rand", - "def": "rand(v)", - "docText": "Returns a pseudo-random UInt64 number, evenly distributed among all UInt64-type numbers. \n" + - "\n" + - " Uses a linear congruential generator." - }, - { - "name": "reinterpretAsUInt16", - "def": "reinterpretAsUInt16(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, - { - "name": "pi", - "def": "pi(v)", - "docText": "Maximum pipeline depth. Corresponds to the number of transformations that each data block goes through during query processing. Counted within the limits of a single server. If the pipeline depth is greater, an exception is thrown. By default, 1000." - }, - { - "name": "reinterpretAsUInt32", - "def": "reinterpretAsUInt32(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, - { - "name": "reinterpretAsUInt64", - "def": "reinterpretAsUInt64(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, - { - "name": "reinterpretAsInt8", - "def": "reinterpretAsInt8(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, - { - "name": "upperUTF8", - "def": "upperUTF8(v)", - "docText": "Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. It doesn`t detect the language. So for Turkish the result might not be exactly correct. " - }, - { - "name": "reinterpretAsInt32", - "def": "reinterpretAsInt32(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, - { - "name": "reinterpretAsFloat32", - "def": "reinterpretAsFloat32(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, - { - "name": "reinterpretAsFloat64", - "def": "reinterpretAsFloat64(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, - { - "name": "reinterpretAsDate", - "def": "reinterpretAsDate(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, - { - "name": "reinterpretAsDateTime", - "def": "reinterpretAsDateTime(v)", - "docText": "Functions accept a string and interpret the bytes placed at the beginning of the string as a number in host order (little endian). If the string isn't long enough, the functions work as if the string is padded with the necessary number of null bytes. If the string is longer than needed, the extra bytes are ignored. A date is interpreted as the number of days since the beginning of the Unix Epoch, and a date with time is interpreted as the number of seconds since the beginning of the Unix Epoch." - }, - { - "name": "roundToExp2", - "def": "roundToExp2(num)", - "docText": "Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to the nearest (whole non-negative) degree of two." - }, - { - "name": "upper", - "def": "upper(v)", - "docText": "Converts a string to uppercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. It doesn`t detect the language. So for Turkish the result might not be exactly correct." - }, - { - "name": "positionUTF8", - "def": "positionUTF8(haystack, needle)", - "docText": "The same as `position`, but the position is returned in Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception). \n" + - "\n" + - " There's also positionCaseInsensitiveUTF8 function." - }, - { - "name": "roundDuration", - "def": "roundDuration(num)", - "docText": "Accepts a number. If the number is less than one, it returns 0. Otherwise, it rounds the number down to numbers from the set: 1, 10, 30, 60, 120, 180, 240, 300, 600, 1200, 1800, 3600, 7200, 18000, 36000. This function is specific to Yandex.Metrica and used for implementing the report on session length." - }, - { - "name": "roundAge", - "def": "roundAge(num)", - "docText": "Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to numbers from the set: 18, 25, 35, 45. This function is specific to Yandex.Metrica and used for implementing the report on user age." - }, - { - "name": "round", - "def": "round(num)", - "docText": "Accepts a number. If the number is less than 18, it returns 0. Otherwise, it rounds the number down to numbers from the set: 18, 25, 35, 45. This function is specific to Yandex.Metrica and used for implementing the report on user age." - }, - { - "name": "floor", - "def": "floor(x[, N])", - "docText": "Returns a rounder number that is less than or equal to `x`." - }, - { - "name": "notEmpty", - "def": "notEmpty(v)", - "docText": "Returns 0 for an empty array, or 1 for a non-empty array. \n" + - "\n" + - " The result type is UInt8. \n" + - "\n" + - " The function also works for strings." - }, - { - "name": "lengthUTF8", - "def": "lengthUTF8(v)", - "docText": "Returns the length of a string in Unicode code points (not in characters), assuming that the string contains a set of bytes that make up UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception). \n" + - "\n" + - " The result type is UInt64." - }, - { - "name": "lower", - "def": "lower(v)", - "docText": "Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. It doesn`t detect the language. " - }, - { - "name": "lowerUTF8", - "def": "lowerUTF8(v)", - "docText": "Converts a string to lowercase, assuming the string contains a set of bytes that make up a UTF-8 encoded text. It doesn`t detect the language. " - }, - { - "name": "reverse", - "def": "reverse(v)", - "docText": "Reverses a sequence of Unicode code points, assuming that the string contains a set of bytes representing a UTF-8 text. Otherwise, it does something else (it doesn`t throw an exception)." - }, - { - "name": "URLPathHierarchy", - "def": "URLPathHierarchy(URL)", - "docText": "- The same thing, but without the protocol and host in the result. The / element (root) is not included. " - }, - { - "name": "substringUTF8", - "def": "substringUTF8(s, offset, length)", - "docText": "The same as `substring`, but for Unicode code points. Works under the assumption that the string contains a set of bytes representing a UTF-8 encoded text. If this assumption is not met, it returns some result (it doesn`t throw an exception)." - }, - { - "name": "appendTrailingCharIfAbsent", - "def": "appendTrailingCharIfAbsent(s, c)", - "docText": "If the `s` string is non-empty and does not contain the `c` character at the end, it appends the `c` character to the end." - }, - { - "name": "alphaTokens", - "def": "alphaTokens(s)", - "docText": "Selects substrings of consecutive bytes from the range a-z and A-Z. \n" + - "\n" + - " Returns an array of selected substrings." - }, - { - "name": "splitByChar", - "def": "splitByChar(separator, s)", - "docText": "Splits a string into substrings, using `separator` as the separator. \n" + - "\n" + - " `separator` must be a string constant consisting of exactly one character. \n" + - "\n" + - " Returns an array of selected substrings" - }, - { - "name": "arrayStringConcat", - "def": "arrayStringConcat(arr[, separator])", - "docText": "Concatenates strings from the array elements, using `separator` as the separator. \n" + - "\n" + - " `separator` is a string constant, an optional parameter. By default it is an empty string. \n" + - "\n" + - " Returns a string." - }, - { - "name": "replaceAll", - "def": "replaceAll(haystack, pattern, replacement)", - "docText": "Replaces all occurrences of the `pattern` substring in `haystack` with the `replacement` substring." - }, - { - "name": "replaceRegexpOne", - "def": "replaceRegexpOne(haystack, pattern, replacement)", - "docText": "Replacement using the `pattern` regular expression. A re2 regular expression. Replaces only the first occurrence, if it exists. \n" + - "\n" + - " A pattern can be specified as `replacement`. " - }, - { - "name": "cbrt", - "def": "cbrt(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to the cubic root of the argument." - }, - { - "name": "match", - "def": "match(pattern)(time, cond1, cond2, ...)", - "docText": "Pattern matching for event chains. \n" + - "\n" + - " `pattern` is a string containing a pattern to match. The pattern is similar to a regular expression." - }, - { - "name": "cutURLParameter", - "def": "cutURLParameter(URL, name)", - "docText": "Removes the URL parameter named `name`, if present. This function works under the assumption that the parameter name is encoded in the URL exactly the same way as in the passed argument." - }, - { - "name": "like", - "def": "like(haystack, pattern), haystack NOT LIKE pattern operator", - "docText": "The same thing as `like`, but negative." - }, - { - "name": "domain", - "def": "domain(v)", - "docText": "- Selects the part of the domain that includes top-level subdomains up to the `first significant subdomain` (see the explanation above). \n" + - "\n" + - " For example, cutToFirstSignificantSubdomain(`https://news.yandex.com.tr/`) = `yandex.com.tr`." - }, - { - "name": "domainWithoutWWW", - "def": "domainWithoutWWW(v)", - "docText": "- Selects the domain and removes no more than one `www.` from the beginning of it, if present." - }, - { - "name": "firstSignificantSubdomain", - "def": "firstSignificantSubdomain(v)", - "docText": "- Selects the part of the domain that includes top-level subdomains up to the `first significant subdomain` (see the explanation above). \n" + - "\n" + - " For example, cutToFirstSignificantSubdomain(`https://news.yandex.com.tr/`) = `yandex.com.tr`." - }, - { - "name": "queryString", - "def": "queryString(v)", - "docText": "Removes the query-string and fragment identifier. The question mark and number sign are also removed." - }, - { - "name": "queryStringAndFragment", - "def": "queryStringAndFragment(v)", - "docText": "Removes the query-string and fragment identifier. The question mark and number sign are also removed." - }, - { - "name": "extractURLParameter", - "def": "extractURLParameter(URL)", - "docText": "- Gets an array of name=value strings corresponding to the names of URL parameters. The values are not decoded in any way." - }, - { - "name": "URLHierarchy", - "def": "URLHierarchy(URL)", - "docText": "- Gets an array containing the URL trimmed to the `/`, `?` characters in the path and query-string. Consecutive separator characters are counted as one. The cut is made in the position after all the consecutive separator characters. Example:" - }, - { - "name": "cutToFirstSignificantSubdomain", - "def": "cutToFirstSignificantSubdomain(v)", - "docText": "- Selects the part of the domain that includes top-level subdomains up to the `first significant subdomain` (see the explanation above). \n" + - "\n" + - " For example, cutToFirstSignificantSubdomain(`https://news.yandex.com.tr/`) = `yandex.com.tr`." - }, - { - "name": "cutWWW", - "def": "cutWWW(v)", - "docText": "Removes no more than one `www.` from the beginning of the URL`s domain, if present." - }, - { - "name": "cutQueryString", - "def": "cutQueryString(v)", - "docText": "Removes the query-string and fragment identifier. The question mark and number sign are also removed." - }, - { - "name": "cutFragment", - "def": "cutFragment(v)", - "docText": "Removes the fragment identifier. The number sign is also removed." - }, - { - "name": "visitParamHas", - "def": "visitParamHas(params, name)", - "docText": "Checks whether there is a field with the `name` name." - }, - { - "name": "visitParamExtractFloat", - "def": "visitParamExtractFloat(params, name)", - "docText": "The same as for Float64." - }, - { - "name": "visitParamExtractBool", - "def": "visitParamExtractBool(params, name)", - "docText": "Parses a true/false value. The result is UInt8." - }, - { - "name": "visitParamExtractRaw", - "def": "visitParamExtractRaw(params, name)", - "docText": "Returns the value of a field, including separators." - }, - { - "name": "exp", - "def": "exp(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to 10 in power of x." - }, - { - "name": "exp2", - "def": "exp2(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to 2 in power of x." - }, - { - "name": "exp10", - "def": "exp10(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to 10 in power of x." - }, - { - "name": "tgamma", - "def": "tgamma(x)", - "docText": "Gamma function." - }, - { - "name": "log10", - "def": "log10(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to the decimal logarithm of the argument." - }, - { - "name": "sqrt", - "def": "sqrt(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to the square root of the argument." - }, - { - "name": "erf", - "def": "erf(v)", - "docText": "What to do when the amount of data exceeds one of the limits: `throw` or `break`. By default, throw." - }, - { - "name": "erfc", - "def": "erfc(x)", - "docText": "Accepts a numeric argument and returns a Float64 number close to 1 - erf(x), but without loss of precision for large `x` values." - }, - { - "name": "lgamma", - "def": "lgamma(x)", - "docText": "The logarithm of the gamma function." - }, - { - "name": "sin", - "def": "sin(x)", - "docText": "Accepts Float32 and Float64 and returns UInt8 equal to 1 if the argument is infinite, otherwise 0. \n" + - "\n" + - " Note that 0 is returned for a NaN." - }, - { - "name": "cos", - "def": "cos(x)", - "docText": "The arc cosine." - }, - { - "name": "tan", - "def": "tan(x)", - "docText": "The arc tangent." - }, - { - "name": "pow", - "def": "pow(x, y)", - "docText": "x in power of y." - } - ] - }; - - p.MacrosCompletions = function () { - return [ - { - "name": "$table", - "def": "$table", - "docText": "Replaced with selected table name from Query Builder" - }, - { - "name": "$timeCol", - "def": "$timeCol", - "docText": "Replaced with `Date:Col` value from Query Builder" - }, - { - "name": "$dateTimeCol", - "def": "$dateTimeCol", - "docText": "Replaced with `Column:DateTime` or `Column:TimeStamp` value from Query Builder" - }, - { - "name": "$from", - "def": "$from", - "docText": "Replaced with timestamp/1000 value of selected `Time Range:From`" - }, - { - "name": "$to", - "def": "$to", - "docText": "Replaced with timestamp/1000 value of selected `Time Range:To`" - }, - { - "name": "$interval", - "def": "$interval", - "docText": "Replaced with selected `Group by time interval` value (as a number of seconds)" - }, - { - "name": "$timeFilter", - "def": "$timeFilter", - "docText": "Replaced with currently selected `Time Range`. Requires `Column:Date` and `Column:DateTime` or `Column:TimeStamp` to be selected" - }, - { - "name": "$timeSeries", - "def": "$timeSeries", - "docText": "Replaced with special ClickHouse construction to convert results as time-series data. Use it as `SELECT $timeSeries...`. Require `Column:DateTime` or `Column:TimeStamp` to be selected" - }, - { - "name": "$rate", - "def": "$rate(cols...)", - "docText": "Converts query results as `change rate per interval`. Can be used to display changes-per-second." + - "\n" + - "Example:\n $rate(countIf(Type = 200) AS good, countIf(Type != 200) AS bad) FROM requests" - }, - { - "name": "$columns", - "def": "$columns(key, value)", - "docText": "Query values as array of [key, value], where key will be used as label. Can be used to display multiple lines at graph" + - "\n" + - "Example:\n $columns(OSName, count(*) c) FROM requests" - }, - { - "name": "$rateColumns", - "def": "$rateColumns(key, value)", - "docText": "Is a combination of `$columns` and `$rate` ." + - "\n" + - "Example:\n $rateColumns(OS, count(*) c) FROM requests" - }, - { - "name": "$unescape", - "def": "$unescape($variable)", - "docText": "Unescapes variable value by removing single quotes" + - "\n" + - "Example:\n SELECT $unescape($column) FROM requests WHERE $unescape($column) = 5" - } - ]; - }; - - p.re = function (list) { - return list.join("|") - }; - - exports.ClickhouseInfo = p; -}); \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/datasource.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/datasource.ts deleted file mode 100644 index 9989b6f..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/datasource.ts +++ /dev/null @@ -1,182 +0,0 @@ -/// - -import _ from 'lodash'; - -import SqlSeries from './sql_series'; -import SqlQuery from './sql_query'; -import ResponseParser from './response_parser'; -import AdhocCtrl from './adhoc'; - -export class ClickHouseDatasource { - type: string; - name: string; - supportMetrics: boolean; - url: string; - directUrl: string; - basicAuth: any; - withCredentials: any; - usePOST: boolean; - addCorsHeader: boolean; - responseParser: any; - adhocCtrl: AdhocCtrl; - - /** @ngInject */ - constructor(instanceSettings, - private $q, - private backendSrv, - private templateSrv) { - this.type = 'clickhouse'; - this.name = instanceSettings.name; - this.supportMetrics = true; - this.responseParser = new ResponseParser(); - this.url = instanceSettings.url; - this.directUrl = instanceSettings.directUrl; - this.basicAuth = instanceSettings.basicAuth; - this.withCredentials = instanceSettings.withCredentials; - this.addCorsHeader = instanceSettings.jsonData.addCorsHeader; - this.usePOST = instanceSettings.jsonData.usePOST; - this.adhocCtrl = new AdhocCtrl(); - } - - _request(query) { - let options: any = { - url: this.url - }; - - if (this.usePOST) { - options.method = 'POST'; - options.data = query; - } else { - options.method = 'GET'; - options.url += '/?query=' + encodeURIComponent(query); - } - - if (this.basicAuth || this.withCredentials) { - options.withCredentials = true; - } - - options.headers = options.headers || {}; - if (this.basicAuth) { - options.headers.Authorization = this.basicAuth; - } - - if (this.addCorsHeader) { - if (this.usePOST) { - options.url += "?add_http_cors_header=1"; - } else { - options.url += "&add_http_cors_header=1"; - } - } - - return this.backendSrv.datasourceRequest(options).then(result => { - return result.data; - }); - }; - - query(options) { - var queries = [], q, - adhocFilters = this.templateSrv.getAdhocFilters(this.name); - - _.map(options.targets, (target) => { - if (!target.hide && target.query) { - var queryModel = new SqlQuery(target, this.templateSrv, options); - q = queryModel.replace(options, adhocFilters); - queries.push(q); - } - }); - - // No valid targets, return the empty result to save a round trip. - if (_.isEmpty(queries)) { - var d = this.$q.defer(); - d.resolve({data: []}); - return d.promise; - } - - var allQueryPromise = _.map(queries, query => { - return this._seriesQuery(query); - }); - - - return this.$q.all(allQueryPromise).then((responses): any => { - var result = [], i = 0; - _.each(responses, (response) => { - var target = options.targets[i]; - i++; - if (!response || !response.rows) { - return; - } - - var sqlSeries = new SqlSeries({ - series: response.data, - meta: response.meta, - tillNow: options.rangeRaw.to === 'now', - from: SqlQuery.convertTimestamp(options.range.from, target.dateTimeType), - to: SqlQuery.convertTimestamp(options.range.to, target.dateTimeType) - }); - if (target.format === 'table') { - _.each(sqlSeries.toTable(), (data) => { - result.push(data); - }); - } else { - _.each(sqlSeries.toTimeSeries(), (data) => { - result.push(data); - }); - } - }); - return {data: result}; - }); - }; - - metricFindQuery(query, options?: any) { - var interpolated; - try { - var scopedVars = {}; - - if (options && options.range) { - scopedVars['from'] = { - text: options.range.from.valueOf(), - value: options.range.from.valueOf() - } - - scopedVars['to'] = { - text: options.range.to.valueOf(), - value: options.range.to.valueOf() - } - } - - interpolated = this.templateSrv.replace(query, scopedVars, SqlQuery.interpolateQueryExpr); - } catch (err) { - return this.$q.reject(err); - } - - return this._seriesQuery(interpolated) - .then(_.curry(this.responseParser.parse)(query)); - }; - - testDatasource() { - return this.metricFindQuery('SELECT 1').then( - () => { - return {status: "success", message: "Data source is working", title: "Success"}; - }); - }; - - _seriesQuery(query) { - query = query.replace(/(?:\r\n|\r|\n)/g, ' '); - query += ' FORMAT JSON'; - return this._request(query); - }; - - - targetContainsTemplate(target) { - return this.templateSrv.variableExists(target.expr); - }; - - getTagKeys() { - return this.adhocCtrl.GetTagKeys(this); - - } - - getTagValues(options) { - return this.adhocCtrl.GetTagValues(options); - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/img/clickhouse_logo.svg b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/img/clickhouse_logo.svg deleted file mode 100644 index 3ab95fe..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/img/clickhouse_logo.svg +++ /dev/null @@ -1 +0,0 @@ -Clickhouse \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/mode-clickhouse.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/mode-clickhouse.js deleted file mode 100644 index 79f7873..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/mode-clickhouse.js +++ /dev/null @@ -1,188 +0,0 @@ -// jshint ignore: start -// jscs: disable - -ace.define("ace/mode/clickhouse_highlight_rules", ["require", "exports", "module", "ace/lib/oop", "ace/mode/text_highlight_rules"], function (require, exports, module) { - "use strict"; - - var oop = require("../lib/oop"); - var TextHighlightRules = require("./text_highlight_rules").TextHighlightRules; - var ClickhouseInfo = require("./clickhouse_info").ClickhouseInfo; - - var ClickHouseHighlightRules = function () { - var keywords = ClickhouseInfo.KeywordsRe(), - builtinConstants = ClickhouseInfo.ConstantsRe(), - builtinFunctions = ClickhouseInfo.FunctionsRe(), - dataTypes = ClickhouseInfo.DataTypesRe(); - - var keywordMapper = this.createKeywordMapper({ - "support.function": builtinFunctions, - "keyword": keywords, - "constant.language": builtinConstants, - "storage.type": dataTypes - }, "identifier", true); - - this.$rules = { - "start": [{ - token: "comment", - regex: "--.*$" - }, { - token: "comment.block", - start: "```", - end: "```" - }, { - token: "string", // ' string - regex: "'.*?'" - }, { - token: "variable", - regex: "\\$\\w+" - }, { - token: "keyword.operator", - regex: "\\+|\\-|\\/|\\/\\/|%|<@>|@>|<@|&|\\^|~|<|>|<=|=>|==|!=|<>|=|\\?|:" - }, { - token: "constant.numeric", // float - regex: "[+-]?\\d+(?:(?:\\.\\d*)?(?:[eE][+-]?\\d+)?)?\\b" - }, { - token: keywordMapper, - regex: "[a-zA-Z_$][a-zA-Z0-9_$]*\\b" - }, { - token: "paren.lparen", - regex: "[\\(]" - }, { - token: "paren.rparen", - regex: "[\\)]" - }, { - token: "text", - regex: "\\s+" - }] - }; - this.normalizeRules(); - }; - - oop.inherits(ClickHouseHighlightRules, TextHighlightRules); - - exports.ClickHouseHighlightRules = ClickHouseHighlightRules; -}); - -ace.define("ace/mode/clickhouse_completions", ["require", "exports", "module", "ace/token_iterator", "ace/lib/lang"], function (require, exports, module) { - "use strict"; - - var lang = require("../lib/lang"), - ClickhouseInfo = require("./clickhouse_info").ClickhouseInfo; - - var keyWordsCompletions = ClickhouseInfo.Keywords.map(function (word) { - return { - caption: word, - value: word, - meta: "keyword", - score: Number.MAX_VALUE - } - }); - - var constantCompletions = ClickhouseInfo.Constants.map(function (word) { - return { - caption: word, - value: word, - meta: "constant", - score: Number.MAX_VALUE - }; - }); - - var macrosCompletions = ClickhouseInfo.MacrosCompletions().map(function (item) { - return { - caption: item.name, - value: item.name, - docHTML: convertToHTML(item), - meta: "macros", - score: Number.MAX_VALUE - }; - }); - - - var functionsCompletions = ClickhouseInfo.FunctionsCompletions().map(function (item) { - return { - caption: item.name, - value: item.name+"()", - docHTML: convertToHTML(item), - meta: "function", - score: Number.MAX_VALUE - }; - }); - - function wrapText(str, len) { - len = len || 90; - var lines = []; - var space_index = 0; - var line_start = 0; - var next_line_end = len; - var line = ""; - for (var i = 0; i < str.length; i++) { - if (str[i] === ' ') { - space_index = i; - } else if (i >= next_line_end && space_index !== 0) { - line = str.slice(line_start, space_index); - lines.push(line); - line_start = space_index + 1; - next_line_end = i + len; - space_index = 0; - } - } - line = str.slice(line_start); - lines.push(line); - return lines.join(" 
"); - } - - function convertMarkDownTags(text) { - text = text.replace(/```(.+)```/, "
$1
"); - text = text.replace(/`([^`]+)`/, "$1"); - return text; - } - - function convertToHTML(item) { - var docText = lang.escapeHTML(item.docText); - docText = convertMarkDownTags(wrapText(docText, 90)); - return [ - "", lang.escapeHTML(item.def), "", "
", docText, "
 " - ].join(""); - } - - var ClickhouseCompletions = function () { - }; - - (function () { - this.getCompletions = function (state, session, pos, prefix, callback) { - var completions = keyWordsCompletions.concat(functionsCompletions).concat(constantCompletions); - completions = completions.concat(macrosCompletions); - callback(null, completions); - }; - - }).call(ClickhouseCompletions.prototype); - - exports.ClickhouseCompletions = ClickhouseCompletions; -}); - - -ace.define("ace/mode/clickhouse", ["require", "exports", "module", "ace/lib/oop", "ace/mode/text", "ace/mode/clickhouse_highlight_rules"], function (require, exports, module) { - "use strict"; - - var oop = require("../lib/oop"); - var TextMode = require("./text").Mode; - var ClickHouseHighlightRules = require("./clickhouse_highlight_rules").ClickHouseHighlightRules; - var ClickhouseCompletions = require("./clickhouse_completions").ClickhouseCompletions; - - - var Mode = function () { - this.HighlightRules = ClickHouseHighlightRules; - this.$completer = new ClickhouseCompletions(); - // replace keyWordCompleter - this.completer = this.$completer; - }; - - oop.inherits(Mode, TextMode); - - (function () { - this.$id = "ace/mode/clickhouse"; - }).call(Mode.prototype); - - exports.Mode = Mode; -}); - diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/module.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/module.ts deleted file mode 100644 index 17708c1..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/module.ts +++ /dev/null @@ -1,12 +0,0 @@ -import {ClickHouseDatasource} from './datasource'; -import {SqlQueryCtrl} from './query_ctrl'; - -class SqlConfigCtrl { - static templateUrl = 'partials/config.html'; -} - -export { - ClickHouseDatasource as Datasource, - SqlQueryCtrl as QueryCtrl, - SqlConfigCtrl as ConfigCtrl, -}; diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/partials/config.html b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/partials/config.html deleted file mode 100644 index 47d03ab..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/partials/config.html +++ /dev/null @@ -1,21 +0,0 @@ - - - -

Additional

- -
- - - -
- -
- - -
diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/partials/query.editor.html b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/partials/query.editor.html deleted file mode 100644 index b66c237..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/partials/query.editor.html +++ /dev/null @@ -1,209 +0,0 @@ - - -
- - -
- -
-
-
- - - - -
- -
-
-
-
- -
-
- -
- -
- -
- -
- - - - Select - DateTime - or UInt32 column for binding with Grafana's range selector - -
- -
- - -
- -
-
-
-
- -
- -
- -
- -
-
- - - - Leave blank for auto handling based on time range and panel width - -
-
- -
- -
-
- -
- - - - Set rounding for `$from` and `$to` timestamps.
- For example, if set `1m` - both `$from` and `$to` will be rounded to beginning of minute.
- It will make all requests similar during one minute which is good for caching. - -
- -
-
-
-
- -
-
- -
- -
-
- -
- -
- -
- -
- -
- -
-
- -
-
- 
{{ctrl.target.rawQuery}}
-
- -
- 
-Macros
-
-$table - replaced with selected table name from Query Builder
-$timeCol - replaced with Date:Col value from Query Builder
-$dateTimeCol - replaced with Column:DateTime or Column:TimeStamp value from Query Builder
-$from - replaced with timestamp/1000 value of selected "Time Range:From"
-$to - replaced with timestamp/1000 value of selected "Time Range:To"
-$interval - replaced with selected "Group by time interval" value (as a number of seconds)
-$timeFilter - replaced with currently selected "Time Range".
-            Require Column:Date and Column:DateTime or Column:TimeStamp to be selected
-$timeSeries - replaced with special ClickHouse construction to convert results as time-series data.
-            Use it as "SELECT $timeSeries...". Require Column:DateTime or Column:TimeStamp to be selected
-$unescape - unescapes variable value by removing single quotes.
-            Used for multiple-value string variables: "SELECT $unescape($column) FROM requests WHERE $unescape($column) = 5"
-A description of macros is available by typing their names in Raw Editor
-
-
-Functions (Only one function per query allowed)
-
-$rate(cols...) - function to convert query results as "change rate per interval".
-Example usage: $rate(countIf(Type = 200) * 60 AS good, countIf(Type != 200) * 60 AS bad) FROM requests
-
-$columns(key, value) - function to query values as array of [key, value], where key would be used as label.
-Example usage: $columns(Type, count() c) FROM requests
-
-$rateColumns(key, value) - is an combination of $columns and $rate.
-Example usage: $rateColumns(Type, count() c) FROM requests
-
-
-
- - diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/plugin.json b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/plugin.json deleted file mode 100644 index 8d6ce7b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/plugin.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "id": "vertamedia-clickhouse-datasource", - "type": "datasource", - "name": "ClickHouse", - - "metrics": true, - "annotations": false, - "alerting": false, - - "info": { - "description": "ClickHouse datasource for Grafana", - "author": { - "name": "Vertamedia", - "url": "https://vertamedia.com/" - }, - "keywords": ["clickhouse"], - "logos": { - "small": "img/clickhouse_logo.svg", - "large": "img/clickhouse_logo.svg" - }, - "links": [ - { - "name": "GitHub", - "url": "https://github.com/Vertamedia/clickhouse-grafana" - } - ], - "version": "1.4.2" - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/query_ctrl.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/query_ctrl.ts deleted file mode 100644 index 9ebf95e..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/query_ctrl.ts +++ /dev/null @@ -1,357 +0,0 @@ -/// - -import $ from 'jquery'; -import _ from 'lodash'; -import './clickhouse-info'; -import './mode-clickhouse'; -import './snippets/clickhouse'; -import SqlQuery from './sql_query'; -import {QueryCtrl} from 'app/plugins/sdk'; -import Scanner from './scanner'; - -const defaultQuery = "SELECT $timeSeries as t, count() FROM $table WHERE $timeFilter GROUP BY t ORDER BY t"; - -class SqlQueryCtrl extends QueryCtrl { - static templateUrl = 'partials/query.editor.html'; - - queryModel: SqlQuery; - databaseSegment: any; - - dateTimeType: any; - dateColDataTypeSegment: any; - dateTimeColDataTypeSegment: any; - tableSegment: any; - formats: any[]; - - panel: any; - datasource: any; - target: any; - resolutions: any; - scanner: any; - editMode: boolean; - textareaHeight: any; - dateTimeTypeOptions: any; - - completerCache: any[]; - - tableLoading: boolean; - datetimeLoading: boolean; - dateLoading: boolean; - - showLastQuerySQL: boolean; - showHelp: boolean; - - /** @ngInject **/ - constructor($scope, $injector, templateSrv, private uiSegmentSrv) { - super($scope, $injector); - - this.queryModel = new SqlQuery(this.target, templateSrv, this.panel.scopedVars); - - this.databaseSegment = uiSegmentSrv.newSegment( - this.target.database || {fake: true, value: '-- database --'} - ); - - this.tableSegment = uiSegmentSrv.newSegment( - this.target.table || {fake: true, value: '-- table --'} - ); - - this.dateColDataTypeSegment = uiSegmentSrv.newSegment( - this.target.dateColDataType || {fake: true, value: '-- date : col --'} - ); - - this.dateTimeColDataTypeSegment = uiSegmentSrv.newSegment( - this.target.dateTimeColDataType || {fake: true, value: '-- dateTime : col --'} - ); - - this.resolutions = _.map([1,2,3,4,5,10], function(f) { - return {factor: f, label: '1/' + f}; - }); - - this.completerCache = []; - - this.dateTimeTypeOptions = [ - {text: 'Column:DateTime', value: 'DATETIME'}, - {text: 'Column:TimeStamp', value: 'TIMESTAMP'}, - {text: 'Column:TimeStampMs', value: 'TIMESTAMPMS'}, - ]; - - this.formats = [ - {text: 'Time series', value: 'time_series'}, - {text: 'Table', value: 'table'}, - ]; - - this.target.format = this.target.format || 'time_series'; - this.target.dateTimeType = this.target.dateTimeType || this.dateTimeTypeOptions[0].value; - this.target.round = this.target.round || "0s"; - this.target.intervalFactor = this.target.intervalFactor || 1; - this.target.query = this.target.query || defaultQuery; - this.target.formattedQuery = this.target.formattedQuery || this.target.query; - this.scanner = new Scanner(this.target.query); - if (this.target.query === defaultQuery) { - this.target.query = this.format(); - } - } - - getCollapsedText() { - return this.target.query; - } - - fakeSegment(value) { - return this.uiSegmentSrv.newSegment({fake: true, value: value}); - } - - getDateColDataTypeSegments() { - var target = this.target; - target.dateLoading = true; - return this.querySegment('DATE').then(function(response){ - target.dateLoading = false; - return response; - }); - } - - dateColDataTypeChanged() { - this.target.dateColDataType = this.dateColDataTypeSegment.value; - } - - dateTimeTypeChanged() { - var self = this; - this.getDateTimeColDataTypeSegments().then(function(segments) { - if (segments.length === 0) { - return; - } - self.applySegment(self.dateTimeColDataTypeSegment, segments[0]); - self.dateTimeColDataTypeChanged(); - }); - } - - getDateTimeColDataTypeSegments() { - var target = this.target; - target.datetimeLoading = true; - return this.querySegment(target.dateTimeType).then(function(response){ - target.datetimeLoading = false; - return response; - }); - } - - dateTimeColDataTypeChanged() { - this.target.dateTimeColDataType = this.dateTimeColDataTypeSegment.value; - } - - toggleEditorMode() { - this.target.rawQuery = !this.target.rawQuery; - } - - toggleEdit(e: any, editMode: boolean) { - if (editMode) { - this.editMode = true; - this.textareaHeight = "height: " + $(e.currentTarget).outerHeight() + "px;"; - return; - } - - if ( this.editMode === true ) { - this.editMode = false; - this.refresh(); - } - } - - getCompleter() { - return this; - } - - getCompletions(editor, session, pos, prefix, callback) { - if (this.target.database === undefined || this.target.table === undefined) { - callback(null, []); - return; - } - - let self = this; - let key = self.target.database + '.' + self.target.table; - if (self.completerCache[key]) { - callback(null, self.completerCache[key]); - return; - } - - self.queryColumns().then(function(response){ - self.completerCache[key] = response.map(function (item) { - return { - caption: item.text, - value: item.text, - meta: key, - docHTML: SqlQueryCtrl._convertToHTML(item), - }; - }); - callback(null, self.completerCache[key]); - }); - } - - static _convertToHTML(item: any) { - var desc = item.value, - space_index = 0, - start = 0, - line = "", - next_line_end = 60, - lines = []; - for (var i = 0; i < desc.length; i++) { - if (desc[i] === ' ') { - space_index = i; - } else if (i >= next_line_end && space_index !== 0) { - line = desc.slice(start, space_index); - lines.push(line); - start = space_index + 1; - next_line_end = i + 60; - space_index = 0; - } - } - line = desc.slice(start); - lines.push(line); - return ["", item.text, "", "
", lines.join(" 
")].join(""); - } - - getDatabaseSegments() { - return this.querySegment('DATABASES'); - } - - databaseChanged() { - this.target.database = this.databaseSegment.value; - this.applySegment(this.tableSegment, this.fakeSegment('-- table : col --')); - this.applySegment(this.dateColDataTypeSegment, this.fakeSegment('-- date : col --')); - this.applySegment(this.dateTimeColDataTypeSegment, this.fakeSegment('-- dateTime : col --')); - } - - getTableSegments() { - var target = this.target; - target.tableLoading = true; - return this.querySegment('TABLES').then(function(response){ - target.tableLoading = false; - return response; - }); - } - - tableChanged() { - this.target.table = this.tableSegment.value; - this.applySegment(this.dateColDataTypeSegment, this.fakeSegment('-- date : col --')); - this.applySegment(this.dateTimeColDataTypeSegment, this.fakeSegment('-- dateTime : col --')); - - var self = this; - this.getDateColDataTypeSegments().then(function(segments) { - if (segments.length === 0) { - return; - } - self.applySegment(self.dateColDataTypeSegment, segments[0]); - self.dateColDataTypeChanged(); - }); - this.getDateTimeColDataTypeSegments().then(function(segments) { - if (segments.length === 0) { - return; - } - self.applySegment(self.dateTimeColDataTypeSegment, segments[0]); - self.dateTimeColDataTypeChanged(); - }); - } - - formatQuery() { - this.target.query = this.format(); - this.toggleEdit({}, false); - } - - toQueryMode() { - this.toggleEditorMode(); - this.refresh(); - } - - format() { - try { - return this.getScanner().Format(); - } catch (err) { - console.log("Parse error: ", err); - return this.getScanner().raw(); - } - } - - getScanner() { - if (this.scanner.raw() !== this.target.query) { - this.scanner = new Scanner(this.target.query); - } - return this.scanner; - } - - handleQueryError(err) { - this.error = err.message || 'Failed to issue metric query'; - return []; - } - - queryColumns() { - var query = this.buildExploreQuery('COLUMNS'); - return this.datasource.metricFindQuery(query) - } - - querySegment(type: string) { - var query = this.buildExploreQuery(type); - return this.datasource.metricFindQuery(query) - .then(this.uiSegmentSrv.transformToSegments(false)) - .catch(this.handleQueryError.bind(this)); - } - - applySegment(dst, src) { - dst.value = src.value; - dst.html = src.html || src.value; - dst.fake = src.fake === undefined ? false : src.fake; - } - - buildExploreQuery(type) { - var query; - switch (type){ - case 'TABLES': - query = 'SELECT name ' + - 'FROM system.tables ' + - 'WHERE database = \'' + this.target.database + '\' ' + - 'ORDER BY name'; - break; - case 'DATE': - query = 'SELECT name ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\' AND ' + - 'type = \'Date\' ' + - 'ORDER BY name'; - break; - case 'DATETIME': - query = 'SELECT name ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\' AND ' + - 'type LIKE \'DateTime%\' ' + - 'ORDER BY name'; - break; - case 'TIMESTAMP': - query = 'SELECT name ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\' AND ' + - 'type = \'UInt32\' ' + - 'ORDER BY name'; - break; - case 'TIMESTAMPMS': - query = 'SELECT name ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\' AND ' + - 'type = \'UInt64\' ' + - 'ORDER BY name'; - break; - case 'DATABASES': - query = 'SELECT name ' + - 'FROM system.databases ' + - 'ORDER BY name'; - break; - case 'COLUMNS': - query = 'SELECT name text, type value ' + - 'FROM system.columns ' + - 'WHERE database = \'' + this.target.database + '\' AND ' + - 'table = \'' + this.target.table + '\''; - break; - } - return query; - }; -} -export {SqlQueryCtrl}; diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/response_parser.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/response_parser.ts deleted file mode 100644 index 5137d2f..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/response_parser.ts +++ /dev/null @@ -1,25 +0,0 @@ -/// -import _ from 'lodash'; - -export default class ResponseParser { - parse(query, results) { - if (!results || results.data.length === 0) { return []; } - - var sqlResults = results.data; - var res = []; - _.each(sqlResults, r => { - if (!_.isObject(r)) { - res.push({ text: r }); - return - } - let keys = Object.keys(r); - if (keys.length > 1) { - res.push(r); - } else { - res.push({ text: r[keys[0]]}); - } - }); - - return res - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/scanner.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/scanner.ts deleted file mode 100644 index 6892d3d..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/scanner.ts +++ /dev/null @@ -1,524 +0,0 @@ -import _ from 'lodash'; - -export default class Scanner { - tree: any; - rootToken: any; - token: any; - skipSpace: boolean; - re: any; - expectedNext: boolean; - - _sOriginal: any; - _s: any; - - /** @ngInject */ - constructor(s) { - this._sOriginal = s; - this.token = null; - } - - raw() { - return this._sOriginal; - }; - - expect(token) { - this.expectNext(); - if (!this.isToken(token)) { - throw("expecting [" + token + "], but got [" + this.token + "] at [" + this._s + "]"); - } - }; - - isToken(token) { - return _.toUpper(token) === _.toUpper(this.token); - }; - - expectNext() { - if (!this.next()) { - throw("expecting additional token at the end of query [" + this._sOriginal + "]"); - } - }; - - next() { - while (this._next()) { - if (this.skipSpace && isWS(this.token)) { - // skip whitespace - continue; - } - if (isComment(this.token)) { - // skip comment - continue; - } - return true; - } - return false; - }; - - _next() { - if (this._s.length === 0) { - return false; - } - let r = this.re.exec(this._s); - if (r === null) { - throw("cannot find next token in [" + this._s + "]"); - } - - this.token = r[0]; - this._s = this._s.substring(this.token.length); - return true; - }; - - Format() { - return print(this.toAST()); - }; - - Print(ast) { - return print(ast) - } - - push(argument) { - this.tree[this.rootToken].push(argument); - this.expectedNext = false; - } - - setRoot(token) { - this.rootToken = token.toLowerCase(); - this.tree[this.rootToken] = []; - this.expectedNext = true; - } - - isExpectedNext(): boolean { - let v = this.expectedNext; - this.expectedNext = false; - return v - } - - toAST() { - this._s = this._sOriginal; - this.tree = {}; - this.setRoot('root'); - this.expectedNext = false; - this.skipSpace = true; - this.re = new RegExp("^(?:" + tokenRe + ")", 'i'); - let subQuery = '', - argument = ''; - - while (this.next()) { - if (!this.isExpectedNext() && isStatement(this.token) && !this.tree.hasOwnProperty(_.toLower(this.token))) { - if (!isClosured(argument)) { - argument += this.token; - continue - } - if (argument.length > 0) { - this.push(argument); - argument = ''; - } - this.setRoot(this.token); - } - else if (this.token === ',' && isClosured(argument)) { - this.push(argument); - argument = ''; - this.expectedNext = true; - } - else if (isClosureChars(this.token) && this.rootToken === 'from') { - subQuery = betweenBraces(this._s); - this.tree[this.rootToken] = toAST(subQuery); - this._s = this._s.substring(subQuery.length + 1); - } - else if (isMacroFunc(this.token)) { - let func = this.token; - if (!this.next()) { - throw("wrong function signature for `" + func + "` at [" + this._s + "]"); - } - - subQuery = betweenBraces(this._s); - let subAST = toAST(subQuery); - if (isSet(subAST, 'root')) { - this.tree[func] = subAST['root'].map(function (item) { - return item; - }); - } else { - this.tree[func] = subAST; - } - this._s = this._s.substring(subQuery.length + 1); - - // macro funcs are used instead of SELECT statement - this.tree['select'] = []; - } - else if (isIn(this.token)) { - argument += ' ' + this.token; - if (!this.next()) { - throw("wrong in signature for `" + argument + "` at [" + this._s + "]"); - } - - if (isClosureChars(this.token)) { - subQuery = betweenBraces(this._s); - let subAST = toAST(subQuery); - if (isSet(subAST, 'root')) { - argument += ' (' + subAST['root'].map(function (item) { - return item; - }); - argument = argument + ')'; - } else { - argument += ' (' + newLine + print(subAST, tabSize) + ')'; - this.push(argument); - argument = ''; - } - this._s = this._s.substring(subQuery.length + 1); - } else { - argument += ' ' + this.token; - } - } - else if (isCond(this.token) && (this.rootToken === 'where' || this.rootToken === 'prewhere')) { - if (isClosured(argument)) { - this.push(argument); - argument = this.token; - } else { - argument += ' ' + this.token; - } - } - else if (isJoin(this.token)) { - let joinType = this.token, source; - if (!this.next()) { - throw("wrong join signature for `" + joinType + "` at [" + this._s + "]"); - } - - if (isClosureChars(this.token)) { - subQuery = betweenBraces(this._s); - source = toAST(subQuery); - this._s = this._s.substring(subQuery.length + 1); - } else { - source = [this.token]; - } - - this.expect('using'); - this.tree['join'] = {type: joinType, source: source, using: []}; - while (this.next()) { - if (isStatement(this.token)) { - if (argument !== '') { - this.push(argument); - argument = ''; - } - this.setRoot(this.token); - break; - } - if (!isID(this.token)) { - continue; - } - this.tree['join'].using.push(this.token); - } - } else if (this.rootToken === 'union all') { - let statement = 'union all'; - this._s = this.token + ' ' + this._s; - let subQueryPos = this._s.toLowerCase().indexOf(statement); - while (subQueryPos !== -1) { - let subQuery = this._s.substring(0, subQueryPos); - let ast = toAST(subQuery); - this.tree[statement].push(ast); - this._s = this._s.substring(subQueryPos + statement.length, this._s.length); - subQueryPos = this._s.toLowerCase().indexOf(statement); - } - let ast = toAST(this._s); - this._s = ''; - this.tree[statement].push(ast); - } - else if (isClosureChars(this.token) || this.token === '.') { - argument += this.token; - } else if (this.token === ',') { - argument += this.token + ' '; - } else { - argument += (argument === '' || isSkipSpace(argument[argument.length - 1])) - ? this.token - : ' ' + this.token; - } - } - - if (argument !== '') { - this.push(argument); - } - return this.tree; - }; -} - -let wsRe = "\\s+", - commentRe = "--[^\n]*|/\\*(?:[^*]|\\*[^/])*\\*/", - idRe = "[a-zA-Z_][a-zA-Z_0-9]*", - intRe = "\\d+", - powerIntRe = "\\d+e\\d+", - floatRe = "\\d+\\.\\d*|\\d*\\.\\d+|\\d+[eE][-+]\\d+", - stringRe = "('[^']*')|(`[^`]*`)", - binaryOpRe = "=>|\\|\\||>=|<=|==|!=|<>|->|[-+/%*=<>\\.!]", - statementRe = "(select|from|where|having|order by|group by|limit|format|prewhere|union all)", - joinsRe = "(any inner join|any left join|all inner join|all left join" + - "|global any inner join|global any left join|global all inner join|global all left join)", - macroFuncRe = "(\\$rateColumns|\\$rate|\\$columns)", - condRe = "\\b(or|and)\\b", - inRe = "\\b(global in|global not in|not in|in)\\b", - closureRe = "[\\(\\)\\[\\]]", - specCharsRe = "[,?:]", - macroRe = "\\$[A-Za-z0-9_$]+", - skipSpaceRe = "[\\(\\.! \\[]", - - builtInFuncRe = "\\b(avg|countIf|first|last|max|min|sum|sumIf|ucase|lcase|mid|round|rank|now|" + - "coalesce|ifnull|isnull|nvl|count|timeSlot|yesterday|today|now|toRelativeSecondNum|" + - "toRelativeMinuteNum|toRelativeHourNum|toRelativeDayNum|toRelativeWeekNum|toRelativeMonthNum|" + - "toRelativeYearNum|toTime|toStartOfHour|toStartOfFiveMinute|toStartOfMinute|toStartOfYear|" + - "toStartOfQuarter|toStartOfMonth|toMonday|toSecond|toMinute|toHour|toDayOfWeek|toDayOfMonth|" + - "toMonth|toYear|toFixedString|toStringCutToZero|reinterpretAsString|reinterpretAsDate|" + - "reinterpretAsDateTime|reinterpretAsFloat32|reinterpretAsFloat64|reinterpretAsInt8|" + - "reinterpretAsInt16|reinterpretAsInt32|reinterpretAsInt64|reinterpretAsUInt8|" + - "reinterpretAsUInt16|reinterpretAsUInt32|reinterpretAsUInt64|toUInt8|toUInt16|toUInt32|" + - "toUInt64|toInt8|toInt16|toInt32|toInt64|toFloat32|toFloat64|toDate|toDateTime|toString|" + - "bitAnd|bitOr|bitXor|bitNot|bitShiftLeft|bitShiftRight|abs|negate|modulo|intDivOrZero|" + - "intDiv|divide|multiply|minus|plus|empty|notEmpty|length|lengthUTF8|lower|upper|lowerUTF8|" + - "upperUTF8|reverse|reverseUTF8|concat|substring|substringUTF8|appendTrailingCharIfAbsent|" + - "position|positionUTF8|match|extract|extractAll|like|notLike|replaceOne|replaceAll|" + - "replaceRegexpOne|range|arrayElement|has|indexOf|countEqual|arrayEnumerate|arrayEnumerateUniq|" + - "arrayJoin|arrayMap|arrayFilter|arrayExists|arrayCount|arrayAll|arrayFirst|arraySum|splitByChar|" + - "splitByString|alphaTokens|domainWithoutWWW|topLevelDomain|firstSignificantSubdomain|" + - "cutToFirstSignificantSubdomain|queryString|URLPathHierarchy|URLHierarchy|extractURLParameterNames|" + - "extractURLParameters|extractURLParameter|queryStringAndFragment|cutWWW|cutQueryString|" + - "cutFragment|cutQueryStringAndFragment|cutURLParameter|IPv4NumToString|IPv4StringToNum|" + - "IPv4NumToStringClassC|IPv6NumToString|IPv6StringToNum|rand|rand64|halfMD5|MD5|sipHash64|" + - "sipHash128|cityHash64|intHash32|intHash64|SHA1|SHA224|SHA256|URLHash|hex|unhex|bitmaskToList|" + - "bitmaskToArray|floor|ceil|round|roundToExp2|roundDuration|roundAge|regionToCountry|" + - "regionToContinent|regionToPopulation|regionIn|regionHierarchy|regionToName|OSToRoot|OSIn|" + - "OSHierarchy|SEToRoot|SEIn|SEHierarchy|dictGetUInt8|dictGetUInt16|dictGetUInt32|" + - "dictGetUInt64|dictGetInt8|dictGetInt16|dictGetInt32|dictGetInt64|dictGetFloat32|" + - "dictGetFloat64|dictGetDate|dictGetDateTime|dictGetString|dictGetHierarchy|dictHas|dictIsIn|" + - "argMin|argMax|uniqCombined|uniqHLL12|uniqExact|uniqExactIf|groupArray|groupUniqArray|quantile|" + - "quantileDeterministic|quantileTiming|quantileTimingWeighted|quantileExact|" + - "quantileExactWeighted|quantileTDigest|median|quantiles|varSamp|varPop|stddevSamp|stddevPop|" + - "covarSamp|covarPop|corr|sequenceMatch|sequenceCount|uniqUpTo|avgIf|" + - "quantilesTimingIf|argMinIf|uniqArray|sumArray|quantilesTimingArrayIf|uniqArrayIf|medianIf|" + - "quantilesIf|varSampIf|varPopIf|stddevSampIf|stddevPopIf|covarSampIf|covarPopIf|corrIf|" + - "uniqArrayIf|sumArrayIf|uniq)\\b", - operatorRe = "\\b(select|group by|order by|from|where|limit|offset|having|as|" + - "when|else|end|type|left|right|on|outer|desc|asc|primary|key|between|" + - "foreign|not|null|inner|cross|natural|database|prewhere|using|global|in)\\b", - dataTypeRe = "\\b(int|numeric|decimal|date|varchar|char|bigint|float|double|bit|binary|text|set|timestamp|" + - "money|real|number|integer|" + - "uint8|uint16|uint32|uint64|int8|int16|int32|int64|float32|float64|datetime|enum8|enum16|" + - "array|tuple|string)\\b", - - wsOnlyRe = new RegExp("^(?:" + wsRe + ")$"), - commentOnlyRe = new RegExp("^(?:" + commentRe + ")$"), - idOnlyRe = new RegExp("^(?:" + idRe + ")$"), - closureOnlyRe = new RegExp("^(?:" + closureRe + ")$"), - macroFuncOnlyRe = new RegExp("^(?:" + macroFuncRe + ")$"), - statementOnlyRe = new RegExp("^(?:" + statementRe + ")$", 'i'), - joinsOnlyRe = new RegExp("^(?:" + joinsRe + ")$", 'i'), - operatorOnlyRe = new RegExp("^(?:" + operatorRe + ")$", 'i'), - dataTypeOnlyRe = new RegExp("^(?:" + dataTypeRe + ")$"), - builtInFuncOnlyRe = new RegExp("^(?:" + builtInFuncRe + ")$"), - macroOnlyRe = new RegExp("^(?:" + macroRe + ")$", 'i'), - inOnlyRe = new RegExp("^(?:" + inRe + ")$", 'i'), - condOnlyRe = new RegExp("^(?:" + condRe + ")$", 'i'), - numOnlyRe = new RegExp("^(?:" + [powerIntRe, intRe, floatRe].join("|") + ")$"), - stringOnlyRe = new RegExp("^(?:" + stringRe + ")$"), - skipSpaceOnlyRe = new RegExp("^(?:" + skipSpaceRe + ")$"), - binaryOnlyRe = new RegExp("^(?:" + binaryOpRe + ")$"); - -var tokenRe = [statementRe, macroFuncRe, joinsRe, inRe, wsRe, commentRe, idRe, stringRe, powerIntRe, floatRe, intRe, - binaryOpRe, closureRe, specCharsRe, macroRe].join("|"); - -function isSkipSpace(token) { - return skipSpaceOnlyRe.test(token); -} - -function isCond(token) { - return condOnlyRe.test(token); -} - -function isIn(token) { - return inOnlyRe.test(token); -} - -function isJoin(token) { - return joinsOnlyRe.test(token); -} - -function isWS(token) { - return wsOnlyRe.test(token); -} - -function isMacroFunc(token) { - return macroFuncOnlyRe.test(token); -} - -function isMacro(token) { - return macroOnlyRe.test(token); -} - -function isComment(token) { - return commentOnlyRe.test(token); -} - -function isID(token) { - return idOnlyRe.test(token); -} - -function isStatement(token) { - return statementOnlyRe.test(token); -} - -function isOperator(token) { - return operatorOnlyRe.test(token); -} - -function isDataType(token) { - return dataTypeOnlyRe.test(token); -} - -function isBuiltInFunc(token) { - return builtInFuncOnlyRe.test(token); -} - -function isClosureChars(token) { - return closureOnlyRe.test(token); -} - -function isNum(token) { - return numOnlyRe.test(token); -} - -function isString(token) { - return stringOnlyRe.test(token); -} - -function isBinary(token) { - return binaryOnlyRe.test(token); -} - -var tabSize = ' ', // 4 spaces - newLine = '\n'; - -function printItems(items, tab = '', separator = '') { - var result = ''; - if (_.isArray(items)) { - if (items.length === 1) { - result += ' ' + items[0]; - } else { - result += newLine; - items.forEach(function (item, i) { - result += tab + tabSize + item; - if (i !== items.length - 1) { - result += separator; - result += newLine; - } - }); - } - } else { - result = newLine + '(' + newLine + print(items, tab + tabSize) + newLine + ')'; - } - - return result; -} - -function toAST(s) { - var scanner = new Scanner(s); - return scanner.toAST(); -} - -function isSet(obj, prop) { - return obj.hasOwnProperty(prop) && !_.isEmpty(obj[prop]); -} - -function isClosured(argument) { - return (argument.match(/\(/g) || []).length === (argument.match(/\)/g) || []).length; -} - -function betweenBraces(query) { - var openBraces = 1, subQuery = ''; - for (var i = 0; i < query.length; i++) { - if (query.charAt(i) === '(') { - openBraces++; - } - if (query.charAt(i) === ')') { - if (openBraces === 1) { - subQuery = query.substring(0, i); - break; - } - openBraces--; - } - } - return subQuery; -} - -// see https://clickhouse.yandex/reference_ru.html#SELECT -function print(AST, tab = '') { - var result = ''; - if (isSet(AST, '$rate')) { - result += tab + '$rate('; - result += printItems(AST.$rate, tab, ',') + ')'; - } - - if (isSet(AST, '$columns')) { - result += tab + '$columns('; - result += printItems(AST.$columns, tab, ',') + ')'; - } - - if (isSet(AST, '$rateColumns')) { - result += tab + '$rateColumns('; - result += printItems(AST.$rateColumns, tab, ',') + ')'; - } - - if (isSet(AST, 'select')) { - result += tab + 'SELECT'; - result += printItems(AST.select, tab, ','); - } - - if (isSet(AST, 'from')) { - result += newLine + tab + 'FROM'; - result += printItems(AST.from, tab); - } - - if (isSet(AST, 'join')) { - result += tab + newLine + AST.join.type.toUpperCase() + - printItems(AST.join.source, tab) + - ' USING ' + printItems(AST.join.using, tab, ','); - } - - if (isSet(AST, 'prewhere')) { - result += newLine + tab + 'PREWHERE'; - result += printItems(AST.prewhere, tab); - } - - if (isSet(AST, 'where')) { - result += newLine + tab + 'WHERE'; - result += printItems(AST.where, tab); - } - - if (isSet(AST, 'group by')) { - result += newLine + tab + 'GROUP BY'; - result += printItems(AST['group by'], tab, ','); - } - - if (isSet(AST, 'having')) { - result += newLine + tab + 'HAVING'; - result += printItems(AST.having, tab); - } - - if (isSet(AST, 'order by')) { - result += newLine + tab + 'ORDER BY'; - result += printItems(AST['order by'], tab, ','); - } - - if (isSet(AST, 'limit')) { - result += newLine + tab + 'LIMIT'; - result += printItems(AST.limit, tab, ','); - } - - if (isSet(AST, 'union all')) { - AST['union all'].forEach(function (v) { - result += newLine + newLine + tab + 'UNION ALL' + newLine + newLine; - result += print(v, tab) - }) - } - - if (isSet(AST, 'format')) { - result += newLine + tab + 'FORMAT'; - result += printItems(AST.format, tab); - } - - return result; -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/snippets/clickhouse.js b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/snippets/clickhouse.js deleted file mode 100644 index 153c8ff..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/snippets/clickhouse.js +++ /dev/null @@ -1,10 +0,0 @@ -// jshint ignore: start -// jscs: disable -ace.define("ace/snippets/clickhouse",["require","exports","module"], function(require, exports, module) { - "use strict"; - - exports.snippets = [ - ]; - - exports.scope = "clickhouse"; -}); \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/sql_query.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/sql_query.ts deleted file mode 100644 index 5867d4a..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/sql_query.ts +++ /dev/null @@ -1,377 +0,0 @@ -/// - -import _ from 'lodash'; -import * as dateMath from 'app/core/utils/datemath'; -import moment from 'moment'; -import Scanner from './scanner'; - -var durationSplitRegexp = /(\d+)(ms|s|m|h|d|w|M|y)/; - -export default class SqlQuery { - target: any; - templateSrv: any; - options: any; - - /** @ngInject */ - constructor(target, templateSrv?, options?) { - this.target = target; - this.templateSrv = templateSrv; - this.options = options; - } - - replace(options, adhocFilters) { - var self = this, - query = this.target.query, - scanner = new Scanner(query), - dateTimeType = this.target.dateTimeType ? this.target.dateTimeType : 'DATETIME', - from = SqlQuery.convertTimestamp(SqlQuery.round(this.options.range.from, this.target.round), dateTimeType), - to = SqlQuery.convertTimestamp(SqlQuery.round(this.options.range.to, this.target.round), dateTimeType), - timeSeries = SqlQuery.getTimeSeries(dateTimeType), - timeFilter = SqlQuery.getTimeFilter(this.options.rangeRaw.to === 'now', dateTimeType), - i = this.templateSrv.replace(this.target.interval, options.scopedVars) || options.interval, - interval = SqlQuery.convertInterval(i, this.target.intervalFactor || 1, dateTimeType); - try { - let ast = scanner.toAST(); - if (adhocFilters.length > 0) { - if (!ast.hasOwnProperty('where')) { - ast.where = []; - } - adhocFilters.forEach(function(af) { - let parts = af.key.split('.'); - if (parts.length < 3) { - console.log("adhoc filters: filter " + af.key + "` has wrong format"); - return - } - if (self.target.database != parts[0] || self.target.table != parts[1]) { - return - } - let operator = SqlQuery.clickhouseOperator(af.operator); - let cond = parts[2] + " " + operator + " " + af.value; - if (ast.where.length > 0) { - // OR is not implemented - // @see https://github.com/grafana/grafana/issues/10918 - cond = "AND " + cond - } - ast.where.push(cond) - }); - } - query = scanner.Print(ast); - if (ast.hasOwnProperty('$columns') && !_.isEmpty(ast['$columns'])) { - query = SqlQuery.columns(query); - } else if (ast.hasOwnProperty('$rateColumns') && !_.isEmpty(ast['$rateColumns'])) { - query = SqlQuery.rateColumns(query); - } else if (ast.hasOwnProperty('$rate') && !_.isEmpty(ast['$rate'])) { - query = SqlQuery.rate(query, ast); - } - } catch (err) { - console.log('AST parser error: ', err.message) - } - - query = this.templateSrv.replace(query, options.scopedVars, SqlQuery.interpolateQueryExpr); - query = SqlQuery.unescape(query); - this.target.rawQuery = query - .replace(/\$timeSeries/g, timeSeries) - .replace(/\$timeFilter/g, timeFilter) - .replace(/\$table/g, this.target.database + '.' + this.target.table) - .replace(/\$from/g, from) - .replace(/\$to/g, to) - .replace(/\$dateCol/g, this.target.dateColDataType) - .replace(/\$dateTimeCol/g, this.target.dateTimeColDataType) - .replace(/\$interval/g, interval) - .replace(/(?:\r\n|\r|\n)/g, ' '); - return this.target.rawQuery; - } - - // $columns(query) - static columns(query: string): string { - if (query.slice(0, 9) === '$columns(') { - var fromIndex = SqlQuery._fromIndex(query); - var args = query.slice(9,fromIndex) - .trim() // rm spaces - .slice(0, -1), // cut ending brace - scanner = new Scanner(args), - ast = scanner.toAST(); - var root = ast['root']; - - if (root.length !== 2) { - throw {message: 'Amount of arguments must equal 2 for $columns func. Parsed arguments are: ' + root.join(', ')}; - } - - query = SqlQuery._columns(root[0], root[1], query.slice(fromIndex)); - } - - return query; - } - - static _columns(key: string, value: string, fromQuery: string): string { - if (key.slice(-1) === ')' || value.slice(-1) === ')') { - throw {message: 'Some of passed arguments are without aliases: ' + key + ', ' + value}; - } - - var keyAlias = key.trim().split(' ').pop(), - valueAlias = value.trim().split(' ').pop(), - havingIndex = fromQuery.toLowerCase().indexOf('having'), - having = "", - aliasRegex = /%(\w+)/g, - concatExpr = ""; - - if (key.match(/^'.*'$/) && key.match(aliasRegex)) { - concatExpr = 'concat(' + key.replace(aliasRegex, "', $1, '").replace(/, ''/g, '') + ')'; - key = keyAlias = _.uniq(key.match(aliasRegex).map(k => k.slice(1))).join(', '); - } - - if (havingIndex !== -1) { - having = fromQuery.slice(havingIndex, fromQuery.length); - fromQuery = fromQuery.slice(0, havingIndex); - } - fromQuery = SqlQuery._applyTimeFilter(fromQuery); - - return 'SELECT ' + - 't' + - ', groupArray((' + (concatExpr || keyAlias) + ', ' + valueAlias + ')) as groupArr' + - ' FROM (' + - ' SELECT $timeSeries as t' + - ', ' + key + - ', ' + value + ' ' + - fromQuery + - ' GROUP BY t, ' + keyAlias + - ' ' + having + - ' ORDER BY t, ' + keyAlias + - ') ' + - 'GROUP BY t ' + - 'ORDER BY t'; - } - - // $rateColumns(query) - static rateColumns(query: string): string { - if (query.slice(0, 13) === '$rateColumns(') { - var fromIndex = SqlQuery._fromIndex(query); - var args = query.slice(13,fromIndex) - .trim() // rm spaces - .slice(0, -1), // cut ending brace - scanner = new Scanner(args), - ast = scanner.toAST(); - var root = ast['root']; - - if (root.length !== 2) { - throw {message: 'Amount of arguments must equal 2 for $columns func. Parsed arguments are: ' + root.join(', ')}; - } - - query = SqlQuery._columns(root[0], root[1], query.slice(fromIndex)); - query = 'SELECT t' + - ', arrayMap(a -> (a.1, a.2/runningDifference( t/1000 )), groupArr)' + - ' FROM (' + - query + - ')'; - } - - return query; - } - - // $rate(query) - static rate(query: string, ast: any): string { - if (query.slice(0, 6) === '$rate(') { - var fromIndex = SqlQuery._fromIndex(query); - if (ast.$rate.length < 1) { - throw {message: 'Amount of arguments must be > 0 for $rate func. Parsed arguments are: ' + ast.$rate.join(', ')}; - } - - query = SqlQuery._rate(ast['$rate'], query.slice(fromIndex)); - } - - return query; - } - - static _fromIndex(query: string): number { - var fromIndex = query.toLowerCase().indexOf('from'); - if (fromIndex === -1) { - throw {message: 'Could not find FROM-statement at: ' + query}; - } - return fromIndex; - } - - static _rate(args, fromQuery: string): string { - var aliases = []; - _.each(args, function(arg){ - if (arg.slice(-1) === ')') { - throw {message: 'Argument "' + arg + '" cant be used without alias'}; - } - aliases.push(arg.trim().split(' ').pop()); - }); - - var rateColums = []; - _.each(aliases, function(a){ - rateColums.push(a + '/runningDifference(t/1000) ' + a + 'Rate'); - }); - - fromQuery = SqlQuery._applyTimeFilter(fromQuery); - return 'SELECT ' + '' + - 't' + - ', ' + rateColums.join(',') + - ' FROM (' + - ' SELECT $timeSeries as t' + - ', ' + args.join(',') + - ' ' + fromQuery + - ' GROUP BY t' + - ' ORDER BY t' + - ')'; - } - - static _applyTimeFilter(query: string): string { - if ( query.toLowerCase().indexOf('where') !== -1 ) { - query = query.replace(/where/i, 'WHERE $timeFilter AND '); - } else { - query += ' WHERE $timeFilter'; - } - - return query; - } - - static getTimeSeries(dateTimeType: string): string { - if (dateTimeType === 'DATETIME') { - return '(intDiv(toUInt32($dateTimeCol), $interval) * $interval) * 1000'; - } - - if (dateTimeType === 'TIMESTAMPMS') { - return 'intDiv($dateTimeCol, $interval) * $interval'; - } - - return '(intDiv($dateTimeCol, $interval) * $interval) * 1000' - } - - static getTimeFilter(isToNow: boolean, dateTimeType: string): string { - var dateCol = function (t: string): string { - if (dateTimeType === 'TIMESTAMPMS') { - t = t + '/1000'; - } - - return 'toDate(' + t + ')'; - }; - - var dateTimeCol = function (t: string): string { - if (dateTimeType === 'DATETIME') { - return 'toDateTime('+ t +')'; - } - return t - }; - - if (isToNow) { - return '$dateCol >= ' + dateCol('$from') + ' AND $dateTimeCol >= ' + dateTimeCol('$from'); - } - - return '$dateCol BETWEEN ' + dateCol('$from') + ' AND ' + dateCol('$to') + ' AND $dateTimeCol BETWEEN ' + dateTimeCol('$from') + ' AND ' + dateTimeCol('$to'); - } - - // date is a moment object - static convertTimestamp(date: any, dateTimeType: string) { - //return date.format("'Y-MM-DD HH:mm:ss'") - if (_.isString(date)) { - date = dateMath.parse(date, true); - } - - return Math.floor(dateTimeType === 'TIMESTAMPMS' ? date.valueOf() : date.valueOf() / 1000); - } - - static round(date: any, round: string): any { - if (round === "" || round === undefined || round === "0s" ) { - return date; - } - - if (_.isString(date)) { - date = dateMath.parse(date, true); - } - - let coeff = SqlQuery.convertInterval(round, 1, 'TIMESTAMPMS'); - let rounded = Math.floor(date.valueOf() / coeff) * coeff; - return moment(rounded); - } - - static convertInterval(interval, intervalFactor, dateTimeType: string) { - var m = interval.match(durationSplitRegexp); - if (m === null) { - throw {message: 'Received duration is invalid: ' + interval}; - } - - var dur = moment.duration(parseInt(m[1]), m[2]); - var sec = dur.asSeconds(); - - if (dateTimeType === 'TIMESTAMPMS') { - return Math.ceil((sec * 1000 || 1) * intervalFactor); - } - - if (sec < 1) { - sec = 1; - } - - return Math.ceil(sec * intervalFactor); - } - - static interpolateQueryExpr (value, variable, defaultFormatFn) { - // if no `multiselect` or `include all` - do not escape - if (!variable.multi && !variable.includeAll) { - return value; - } - if (typeof value === 'string') { - return SqlQuery.clickhouseEscape(value, variable); - } - let escapedValues = _.map(value, function(v){ - return SqlQuery.clickhouseEscape(v, variable); - }); - return escapedValues.join(','); - } - - static clickhouseOperator(value) { - switch (value){ - case "=": - case "!=": - case ">": - case "<": - return value; - case "=~": - return "LIKE"; - case "!~": - return "NOT LIKE"; - default: - console.log("adhoc filters: got unsupported operator `" + value + "`"); - return value - } - } - - static clickhouseEscape(value, variable) { - var isDigit = true; - // if at least one of options is not digit - _.each(variable.options, function(opt): boolean{ - if (opt.value === '$__all') { - return true; - } - if (!opt.value.match(/^\d+$/)) { - isDigit = false; - return false; - } - return true - }); - - if (isDigit) { - return value; - } else { - return "'" + value.replace(/[\\']/g, '\\$&') + "'"; - } - } - - static unescape(query) { - let macros = '$unescape('; - let openMacros = query.indexOf(macros); - while (openMacros !== -1) { - let closeMacros = query.indexOf(')', openMacros); - if(closeMacros === -1) { - throw {message: 'unable to find closing brace for $unescape macros: ' + query.substring(0, openMacros)}; - } - let arg = query.substring(openMacros+macros.length, closeMacros) - .trim(); - arg = arg.replace(/[']+/g, ''); - query = query.substring(0, openMacros) + arg + query.substring(closeMacros+1, query.length); - openMacros = query.indexOf('$unescape('); - } - return query - } -} diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/sql_series.ts b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/sql_series.ts deleted file mode 100644 index bb7487b..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/src/sql_series.ts +++ /dev/null @@ -1,150 +0,0 @@ -import _ from 'lodash'; - -export default class SqlSeries { - series: any; - meta: any; - tillNow: any; - from: any; - to: any; - - /** @ngInject */ - constructor(options) { - this.series = options.series; - this.meta = options.meta; - this.tillNow = options.tillNow; - this.from = options.from; - this.to = options.to; - } - - toTable():any { - let self = this, data = []; - if (this.series.length === 0) { - return data; - } - - let columns = []; - _.each(self.meta, function(col) { - columns.push({"text": col.name, "type": SqlSeries._toJSType(col.type)}) - }); - - let rows = []; - _.each(self.series, function (ser) { - let r = []; - _.each(ser, function (v) { - r.push(SqlSeries._formatValue(v)) - }); - rows.push(r) - }); - - data.push({ - "columns": columns, - "rows": rows, - "type": "table" - }); - - return data - } - - toTimeSeries():any { - let self = this, timeSeries = []; - if (self.series.length === 0) { - return timeSeries; - } - - // timeCol have to be the first column always - let timeCol = self.meta[0], metrics = {}, intervals = [], t; - _.each(self.series, function(series) { - t = SqlSeries._formatValue(series[timeCol.name]); - intervals.push(t); - // rm time value from series - delete series[timeCol.name]; - _.each(series, function(val, key) { - if (_.isArray(val)) { - _.each(val, function(arr) { - (metrics[arr[0]] = metrics[arr[0]] || {})[t] = arr[1]; - }); - } else { - (metrics[key] = metrics[key] || {})[t] = val; - } - }); - }); - - _.each(metrics, function(v, k) { - let datapoints = []; - _.each(intervals, function(interval) { - if (metrics[k][interval] === undefined) { - metrics[k][interval] = null; - } - datapoints.push([SqlSeries._formatValue(metrics[k][interval]), interval]); - }); - timeSeries.push({target: k, datapoints: self.extrapolate(datapoints)}); - }); - return timeSeries; - }; - - extrapolate(datapoints) { - if (datapoints.length < 10 || (!this.tillNow && datapoints[0][0] !== 0)) { - return datapoints; - } - - // Duration between first/last samples and boundary of range. - let durationToStart = datapoints[0][1]/1000 - this.from, - durationToEnd = this.to - datapoints[datapoints.length-1][1]/1000; - - // If the first/last samples are close to the boundaries of the range, - // extrapolate the result. - let sampledInterval = (datapoints[datapoints.length-1][1] - datapoints[0][1])/1000, - averageDurationBetweenSamples = sampledInterval / (datapoints.length-1); - - let diff; - // close to left border and value is 0 because of runningDifference function - if (durationToStart < averageDurationBetweenSamples && datapoints[0][0] === 0) { - diff = ((datapoints[1][0] - datapoints[2][0]) / datapoints[1][0]) * 0.1; - diff %= 1; - if (isNaN(diff)) { - diff = 0; - } - datapoints[0][0] = datapoints[1][0] * (1 + diff); - } - - if (durationToEnd < averageDurationBetweenSamples) { - diff = ((datapoints[datapoints.length-2][0] - datapoints[datapoints.length-3][0]) / datapoints[datapoints.length-2][0]) * 0.1; - diff %= 1; - if (isNaN(diff)) { - diff = 0; - } - datapoints[datapoints.length-1][0] = datapoints[datapoints.length-2][0] * (1 + diff); - } - - return datapoints; - }; - - static _toJSType(type:any):string { - switch (type) { - case 'UInt8': - case 'UInt16': - case 'UInt32': - case 'UInt64': - case 'Int8': - case 'Int16': - case 'Int32': - case 'Int64': - return "number"; - default: - return "string" - } - } - - static _formatValue(value:any) { - if (value === null) { - return value - } - - let numeric = Number(value); - if (isNaN(numeric)) { - return value - } else { - return numeric - } - }; -} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/tsconfig.json b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/tsconfig.json deleted file mode 100644 index 491451f..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/tsconfig.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "compileOnSave": false, - "compilerOptions": { - "target": "ES5", - "module": "system", - "sourceMap": true, - "declaration": true, - "emitDecoratorMetadata": true, - "experimentalDecorators": true, - "noImplicitAny": false - }, - "exclude": [ - "node_modules" - ] -} \ No newline at end of file diff --git a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/tslint.json b/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/tslint.json deleted file mode 100644 index 260e820..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/vertamedia-clickhouse-datasource/tslint.json +++ /dev/null @@ -1,61 +0,0 @@ -{ - "rules": { - "class-name": true, - "comment-format": [false, "check-space"], - "curly": true, - "eofline": true, - "forin": false, - "indent": [true, "spaces"], - "label-position": true, - "label-undefined": true, - "max-line-length": [true, 140], - "member-access": false, - "no-arg": true, - "no-bitwise": true, - "no-console": [true, - "debug", - "info", - "time", - "timeEnd", - "trace" - ], - "no-construct": true, - "no-debugger": true, - "no-duplicate-key": true, - "no-duplicate-variable": true, - "no-empty": false, - "no-eval": true, - "no-inferrable-types": true, - "no-shadowed-variable": false, - "no-string-literal": false, - "no-switch-case-fall-through": false, - "no-trailing-whitespace": true, - "no-unused-expression": false, - "no-unused-variable": false, - "no-unreachable": true, - "no-use-before-declare": true, - "no-var-keyword": false, - "object-literal-sort-keys": false, - "one-line": [true, - "check-open-brace", - "check-catch", - "check-else" - ], - "radix": false, - "semicolon": true, - "triple-equals": [true, "allow-null-check"], - "typedef-whitespace": [true, { - "call-signature": "nospace", - "index-signature": "nospace", - "parameter": "nospace", - "property-declaration": "nospace", - "variable-declaration": "nospace" - }], - "variable-name": [true, "ban-keywords"], - "whitespace": [true, - "check-branch", - "check-decl", - "check-type" - ] - } -} \ No newline at end of file diff --git a/image_content/config/kafka/etc/kafka/conf/connect-console-sink.properties b/image_content/config/kafka/etc/kafka/conf/connect-console-sink.properties deleted file mode 100644 index e240a8f..0000000 --- a/image_content/config/kafka/etc/kafka/conf/connect-console-sink.properties +++ /dev/null @@ -1,19 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -name=local-console-sink -connector.class=org.apache.kafka.connect.file.FileStreamSinkConnector -tasks.max=1 -topics=connect-test \ No newline at end of file diff --git a/image_content/config/kafka/etc/kafka/conf/connect-console-source.properties b/image_content/config/kafka/etc/kafka/conf/connect-console-source.properties deleted file mode 100644 index d0e2069..0000000 --- a/image_content/config/kafka/etc/kafka/conf/connect-console-source.properties +++ /dev/null @@ -1,19 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -name=local-console-source -connector.class=org.apache.kafka.connect.file.FileStreamSourceConnector -tasks.max=1 -topic=connect-test \ No newline at end of file diff --git a/image_content/config/kafka/etc/kafka/conf/connect-distributed.properties b/image_content/config/kafka/etc/kafka/conf/connect-distributed.properties deleted file mode 100644 index 931b853..0000000 --- a/image_content/config/kafka/etc/kafka/conf/connect-distributed.properties +++ /dev/null @@ -1,51 +0,0 @@ -## -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -## - -# These are defaults. This file just demonstrates how to override some settings. -bootstrap.servers=localhost:9092 - -# unique name for the cluster, used in forming the Connect cluster group. Note that this must not conflict with consumer group IDs -group.id=connect-cluster - -# The converters specify the format of data in Kafka and how to translate it into Connect data. Every Connect user will -# need to configure these based on the format they want their data in when loaded from or stored into Kafka -key.converter=org.apache.kafka.connect.json.JsonConverter -value.converter=org.apache.kafka.connect.json.JsonConverter -# Converter-specific settings can be passed in by prefixing the Converter's setting with the converter we want to apply -# it to -key.converter.schemas.enable=true -value.converter.schemas.enable=true - -# The internal converter used for offsets and config data is configurable and must be specified, but most users will -# always want to use the built-in default. Offset and config data is never visible outside of Kafka Connect in this format. -internal.key.converter=org.apache.kafka.connect.json.JsonConverter -internal.value.converter=org.apache.kafka.connect.json.JsonConverter -internal.key.converter.schemas.enable=false -internal.value.converter.schemas.enable=false - -# Topic to use for storing offsets. This topic should have many partitions and be replicated. -offset.storage.topic=connect-offsets - -# Topic to use for storing connector and task configurations; note that this should be a single partition, highly replicated topic. -# You may need to manually create the topic to ensure single partition for the config topic as auto created topics may have multiple partitions. -config.storage.topic=connect-configs - -# Topic to use for storing statuses. This topic can have multiple partitions and should be replicated. -status.storage.topic=connect-status - -# Flush much faster than normal, which is useful for testing/debugging -offset.flush.interval.ms=10000 \ No newline at end of file diff --git a/image_content/config/kafka/etc/kafka/conf/connect-file-sink.properties b/image_content/config/kafka/etc/kafka/conf/connect-file-sink.properties deleted file mode 100644 index 594ccc6..0000000 --- a/image_content/config/kafka/etc/kafka/conf/connect-file-sink.properties +++ /dev/null @@ -1,20 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -name=local-file-sink -connector.class=FileStreamSink -tasks.max=1 -file=test.sink.txt -topics=connect-test \ No newline at end of file diff --git a/image_content/config/kafka/etc/kafka/conf/connect-file-source.properties b/image_content/config/kafka/etc/kafka/conf/connect-file-source.properties deleted file mode 100644 index 599cf4c..0000000 --- a/image_content/config/kafka/etc/kafka/conf/connect-file-source.properties +++ /dev/null @@ -1,20 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -name=local-file-source -connector.class=FileStreamSource -tasks.max=1 -file=test.txt -topic=connect-test \ No newline at end of file diff --git a/image_content/config/kafka/etc/kafka/conf/connect-log4j.properties b/image_content/config/kafka/etc/kafka/conf/connect-log4j.properties deleted file mode 100644 index 158daed..0000000 --- a/image_content/config/kafka/etc/kafka/conf/connect-log4j.properties +++ /dev/null @@ -1,23 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -log4j.rootLogger=INFO, stdout - -log4j.appender.stdout=org.apache.log4j.ConsoleAppender -log4j.appender.stdout.layout=org.apache.log4j.PatternLayout -log4j.appender.stdout.layout.ConversionPattern=[%d] %p %m (%c:%L)%n - -log4j.logger.org.apache.zookeeper=ERROR -log4j.logger.org.I0Itec.zkclient=ERROR \ No newline at end of file diff --git a/image_content/config/kafka/etc/kafka/conf/connect-standalone.properties b/image_content/config/kafka/etc/kafka/conf/connect-standalone.properties deleted file mode 100644 index 8760590..0000000 --- a/image_content/config/kafka/etc/kafka/conf/connect-standalone.properties +++ /dev/null @@ -1,37 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# These are defaults. This file just demonstrates how to override some settings. -bootstrap.servers=localhost:9092 - -# The converters specify the format of data in Kafka and how to translate it into Connect data. Every Connect user will -# need to configure these based on the format they want their data in when loaded from or stored into Kafka -key.converter=org.apache.kafka.connect.json.JsonConverter -value.converter=org.apache.kafka.connect.json.JsonConverter -# Converter-specific settings can be passed in by prefixing the Converter's setting with the converter we want to apply -# it to -key.converter.schemas.enable=true -value.converter.schemas.enable=true - -# The internal converter used for offsets and config data is configurable and must be specified, but most users will -# always want to use the built-in default. Offset and config data is never visible outside of Kafka Connect in this format. -internal.key.converter=org.apache.kafka.connect.json.JsonConverter -internal.value.converter=org.apache.kafka.connect.json.JsonConverter -internal.key.converter.schemas.enable=false -internal.value.converter.schemas.enable=false - -offset.storage.file.filename=/tmp/connect.offsets -# Flush much faster than normal, which is useful for testing/debugging -offset.flush.interval.ms=10000 diff --git a/image_content/config/kafka/etc/kafka/conf/log4j.properties b/image_content/config/kafka/etc/kafka/conf/log4j.properties deleted file mode 100644 index 4d2dec5..0000000 --- a/image_content/config/kafka/etc/kafka/conf/log4j.properties +++ /dev/null @@ -1,87 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -log4j.rootLogger=ERROR, stdout - -log4j.appender.stdout=org.apache.log4j.ConsoleAppender -log4j.appender.stdout.layout=org.apache.log4j.PatternLayout -log4j.appender.stdout.layout.ConversionPattern=[%d] %p %m (%c)%n - -log4j.appender.kafkaAppender=org.apache.log4j.DailyRollingFileAppender -log4j.appender.kafkaAppender.DatePattern='.'yyyy-MM-dd-HH -log4j.appender.kafkaAppender.File=${kafka.logs.dir}/server.log -log4j.appender.kafkaAppender.layout=org.apache.log4j.PatternLayout -log4j.appender.kafkaAppender.layout.ConversionPattern=[%d] %p %m (%c)%n - -log4j.appender.stateChangeAppender=org.apache.log4j.DailyRollingFileAppender -log4j.appender.stateChangeAppender.DatePattern='.'yyyy-MM-dd-HH -log4j.appender.stateChangeAppender.File=${kafka.logs.dir}/state-change.log -log4j.appender.stateChangeAppender.layout=org.apache.log4j.PatternLayout -log4j.appender.stateChangeAppender.layout.ConversionPattern=[%d] %p %m (%c)%n - -log4j.appender.requestAppender=org.apache.log4j.DailyRollingFileAppender -log4j.appender.requestAppender.DatePattern='.'yyyy-MM-dd-HH -log4j.appender.requestAppender.File=${kafka.logs.dir}/kafka-request.log -log4j.appender.requestAppender.layout=org.apache.log4j.PatternLayout -log4j.appender.requestAppender.layout.ConversionPattern=[%d] %p %m (%c)%n - -log4j.appender.cleanerAppender=org.apache.log4j.DailyRollingFileAppender -log4j.appender.cleanerAppender.DatePattern='.'yyyy-MM-dd-HH -log4j.appender.cleanerAppender.File=${kafka.logs.dir}/log-cleaner.log -log4j.appender.cleanerAppender.layout=org.apache.log4j.PatternLayout -log4j.appender.cleanerAppender.layout.ConversionPattern=[%d] %p %m (%c)%n - -log4j.appender.controllerAppender=org.apache.log4j.DailyRollingFileAppender -log4j.appender.controllerAppender.DatePattern='.'yyyy-MM-dd-HH -log4j.appender.controllerAppender.File=${kafka.logs.dir}/controller.log -log4j.appender.controllerAppender.layout=org.apache.log4j.PatternLayout -log4j.appender.controllerAppender.layout.ConversionPattern=[%d] %p %m (%c)%n - -log4j.appender.authorizerAppender=org.apache.log4j.DailyRollingFileAppender -log4j.appender.authorizerAppender.DatePattern='.'yyyy-MM-dd-HH -log4j.appender.authorizerAppender.File=${kafka.logs.dir}/kafka-authorizer.log -log4j.appender.authorizerAppender.layout=org.apache.log4j.PatternLayout -log4j.appender.authorizerAppender.layout.ConversionPattern=[%d] %p %m (%c)%n - -# Turn on all our debugging info -#log4j.logger.kafka.producer.async.DefaultEventHandler=DEBUG, kafkaAppender -#log4j.logger.kafka.client.ClientUtils=DEBUG, kafkaAppender -#log4j.logger.kafka.perf=DEBUG, kafkaAppender -#log4j.logger.kafka.perf.ProducerPerformance$ProducerThread=DEBUG, kafkaAppender -#log4j.logger.org.I0Itec.zkclient.ZkClient=DEBUG -log4j.logger.kafka=ERROR, kafkaAppender - -log4j.logger.kafka.network.RequestChannel$=WARN, requestAppender -log4j.additivity.kafka.network.RequestChannel$=false - -#log4j.logger.kafka.network.Processor=TRACE, requestAppender -#log4j.logger.kafka.server.KafkaApis=TRACE, requestAppender -#log4j.additivity.kafka.server.KafkaApis=false -log4j.logger.kafka.request.logger=WARN, requestAppender -log4j.additivity.kafka.request.logger=false - -log4j.logger.kafka.controller=TRACE, controllerAppender -log4j.additivity.kafka.controller=false - -log4j.logger.kafka.log.LogCleaner=ERROR, cleanerAppender -log4j.additivity.kafka.log.LogCleaner=false - -log4j.logger.state.change.logger=TRACE, stateChangeAppender -log4j.additivity.state.change.logger=false - -#Change this to debug to get the actual audit log for authorizer. -log4j.logger.kafka.authorizer.logger=WARN, authorizerAppender -log4j.additivity.kafka.authorizer.logger=false - diff --git a/image_content/config/kafka/etc/kafka/conf/server.properties b/image_content/config/kafka/etc/kafka/conf/server.properties deleted file mode 100644 index 4c53973..0000000 --- a/image_content/config/kafka/etc/kafka/conf/server.properties +++ /dev/null @@ -1,129 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# see kafka.server.KafkaConfig for additional details and defaults - -############################# Server Basics ############################# - -# The id of the broker. This must be set to a unique integer for each broker. -broker.id=0 - -############################# Socket Server Settings ############################# - -# The address the socket server listens on. It will get the value returned from -# java.net.InetAddress.getCanonicalHostName() if not configured. -# FORMAT: -# listeners = security_protocol://host_name:port -# EXAMPLE: -# listeners = PLAINTEXT://your.host.name:9092 -#listeners=PLAINTEXT://:9092 - -# Hostname and port the broker will advertise to producers and consumers. If not set, -# it uses the value for "listeners" if configured. Otherwise, it will use the value -# returned from java.net.InetAddress.getCanonicalHostName(). -#advertised.listeners=PLAINTEXT://your.host.name:9092 - -# The number of threads handling network requests -num.network.threads=3 - -# The number of threads doing disk I/O -num.io.threads=8 - -# The send buffer (SO_SNDBUF) used by the socket server -socket.send.buffer.bytes=102400 - -# The receive buffer (SO_RCVBUF) used by the socket server -socket.receive.buffer.bytes=102400 - -# The maximum size of a request that the socket server will accept (protection against OOM) -socket.request.max.bytes=104857600 - - -############################# Log Basics ############################# - -# A comma seperated list of directories under which to store log files -log.dirs=/data/kafka-logs - -# The default number of log partitions per topic. More partitions allow greater -# parallelism for consumption, but this will also result in more files across -# the brokers. -num.partitions=1 - -# The number of threads per data directory to be used for log recovery at startup and flushing at shutdown. -# This value is recommended to be increased for installations with data dirs located in RAID array. -num.recovery.threads.per.data.dir=1 - -############################# Internal Topic Settings ############################# -# The replication factor for the group metadata internal topics "__consumer_offsets" and "__transaction_state" -# For anything other than development testing, a value greater than 1 is recommended to ensure availability such as 3. -offsets.topic.replication.factor=1 -transaction.state.log.replication.factor=1 -transaction.state.log.min.isr=1 - -############################# Log Flush Policy ############################# - -# Messages are immediately written to the filesystem but by default we only fsync() to sync -# the OS cache lazily. The following configurations control the flush of data to disk. -# There are a few important trade-offs here: -# 1. Durability: Unflushed data may be lost if you are not using replication. -# 2. Latency: Very large flush intervals may lead to latency spikes when the flush does occur as there will be a lot of data to flush. -# 3. Throughput: The flush is generally the most expensive operation, and a small flush interval may lead to exceessive seeks. -# The settings below allow one to configure the flush policy to flush data after a period of time or -# every N messages (or both). This can be done globally and overridden on a per-topic basis. - -# The number of messages to accept before forcing a flush of data to disk -#log.flush.interval.messages=10000 - -# The maximum amount of time a message can sit in a log before we force a flush -#log.flush.interval.ms=1000 - -############################# Log Retention Policy ############################# - -# The following configurations control the disposal of log segments. The policy can -# be set to delete segments after a period of time, or after a given size has accumulated. -# A segment will be deleted whenever *either* of these criteria are met. Deletion always happens -# from the end of the log. - -log.cleaner.enable=true - -log.cleanup.policy=delete - -# The minimum age of a log file to be eligible for deletion -log.retention.hours=24 - -# A size-based retention policy for logs. Segments are pruned from the log as long as the remaining -# segments don't drop below log.retention.bytes. -log.retention.bytes=1073741824 - -# The maximum size of a log segment file. When this size is reached a new log segment will be created. -log.segment.bytes=1073741824 - -# The interval at which log segments are checked to see if they can be deleted according -# to the retention policies -log.retention.check.interval.ms=300000 - -############################# Zookeeper ############################# - -# Zookeeper connection string (see zookeeper docs for details). -# This is a comma separated host:port pairs, each corresponding to a zk -# server. e.g. "127.0.0.1:3000,127.0.0.1:3001,127.0.0.1:3002". -# You can also append an optional chroot string to the urls to specify the -# root directory for all kafka znodes. -zookeeper.connect=localhost:2181 - -# Timeout in ms for connecting to zookeeper -zookeeper.connection.timeout.ms=6000 - -delete.topic.enable=true - diff --git a/image_content/config/kafka/etc/kafka/conf/tools-log4j.properties b/image_content/config/kafka/etc/kafka/conf/tools-log4j.properties deleted file mode 100644 index b19e343..0000000 --- a/image_content/config/kafka/etc/kafka/conf/tools-log4j.properties +++ /dev/null @@ -1,21 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -log4j.rootLogger=WARN, stderr - -log4j.appender.stderr=org.apache.log4j.ConsoleAppender -log4j.appender.stderr.layout=org.apache.log4j.PatternLayout -log4j.appender.stderr.layout.ConversionPattern=[%d] %p %m (%c)%n -log4j.appender.stderr.Target=System.err diff --git a/image_content/config/kafka/usr/local/lib/systemd/system/kafka.service b/image_content/config/kafka/usr/local/lib/systemd/system/kafka.service deleted file mode 100644 index 73d721b..0000000 --- a/image_content/config/kafka/usr/local/lib/systemd/system/kafka.service +++ /dev/null @@ -1,13 +0,0 @@ -[Unit] -Requires=zookeeper.service -After=zookeeper.service - -[Service] -Type=simple -User=kafka -ExecStart=/opt/kafka/bin/kafka-server-start.sh /etc/kafka/conf/server.properties -ExecStop=/opt/kafka/bin/kafka-server-stop.sh -Restart=on-abnormal - -[Install] -WantedBy=multi-user.target diff --git a/image_content/config/novatellogreader/usr/local/lib/systemd/system/novatellogreader.service b/image_content/config/novatellogreader/usr/local/lib/systemd/system/novatellogreader.service deleted file mode 100644 index ebd35c8..0000000 --- a/image_content/config/novatellogreader/usr/local/lib/systemd/system/novatellogreader.service +++ /dev/null @@ -1,11 +0,0 @@ -[Unit] -Description=NovAtelLogReader -Requires=kafka.service -After=kafka.service - -[Service] -Type=simple -ExecStart=/opt/NovAtelLogReader/NovAtelLogReader - -[Install] -WantedBy=multi-user.target diff --git a/image_content/config/spark/opt/spark/LICENSE b/image_content/config/spark/opt/spark/LICENSE deleted file mode 100644 index 66a2e8f..0000000 --- a/image_content/config/spark/opt/spark/LICENSE +++ /dev/null @@ -1,300 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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The scikit-learn project -is distributed under the 3-Clause BSD license. -=============================================================================== - -For CSV functionality: - -/* - * Copyright 2014 Databricks - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright 2015 Ayasdi Inc - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the 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the License for the specific language governing permissions and -limitations under the License. diff --git a/image_content/config/spark/opt/spark/R/lib/SparkR/DESCRIPTION b/image_content/config/spark/opt/spark/R/lib/SparkR/DESCRIPTION deleted file mode 100644 index 5553751..0000000 --- a/image_content/config/spark/opt/spark/R/lib/SparkR/DESCRIPTION +++ /dev/null @@ -1,33 +0,0 @@ -Package: SparkR -Type: Package -Version: 2.2.1 -Title: R Frontend for Apache Spark -Description: Provides an R Frontend for Apache Spark. -Authors@R: c(person("Shivaram", "Venkataraman", role = c("aut", "cre"), - email = "shivaram@cs.berkeley.edu"), - person("Xiangrui", "Meng", role = "aut", - email = "meng@databricks.com"), - person("Felix", "Cheung", role = "aut", - email = "felixcheung@apache.org"), - person(family = "The Apache Software Foundation", role = c("aut", "cph"))) -License: Apache License (== 2.0) -URL: http://www.apache.org/ http://spark.apache.org/ -BugReports: http://spark.apache.org/contributing.html -Depends: R (>= 3.0), methods -Suggests: knitr, rmarkdown, testthat, e1071, survival -Collate: 'schema.R' 'generics.R' 'jobj.R' 'column.R' 'group.R' 'RDD.R' - 'pairRDD.R' 'DataFrame.R' 'SQLContext.R' 'WindowSpec.R' - 'backend.R' 'broadcast.R' 'catalog.R' 'client.R' 'context.R' - 'deserialize.R' 'functions.R' 'install.R' 'jvm.R' - 'mllib_classification.R' 'mllib_clustering.R' 'mllib_fpm.R' - 'mllib_recommendation.R' 'mllib_regression.R' 'mllib_stat.R' - 'mllib_tree.R' 'mllib_utils.R' 'serialize.R' 'sparkR.R' - 'stats.R' 'streaming.R' 'types.R' 'utils.R' 'window.R' -RoxygenNote: 6.0.1 -VignetteBuilder: knitr -Author: Shivaram Venkataraman [aut, cre], - Xiangrui Meng [aut], - Felix Cheung [aut], - The Apache Software Foundation [aut, cph] -Maintainer: Shivaram Venkataraman -Built: R 3.4.2; ; 2017-11-25 00:01:26 UTC; unix diff --git a/image_content/config/spark/opt/spark/R/lib/SparkR/INDEX b/image_content/config/spark/opt/spark/R/lib/SparkR/INDEX deleted file mode 100644 index 0713479..0000000 --- a/image_content/config/spark/opt/spark/R/lib/SparkR/INDEX +++ /dev/null @@ -1,416 +0,0 @@ -%in% Match a column with given values. -AFTSurvivalRegressionModel-class - S4 class that represents a - AFTSurvivalRegressionModel -ALSModel-class S4 class that represents an ALSModel -BisectingKMeansModel-class - S4 class that represents a BisectingKMeansModel -FPGrowthModel-class S4 class that represents a FPGrowthModel -GBTClassificationModel-class - S4 class that represents a - GBTClassificationModel -GBTRegressionModel-class - S4 class that represents a GBTRegressionModel -GaussianMixtureModel-class - S4 class that represents a GaussianMixtureModel -GeneralizedLinearRegressionModel-class - S4 class that represents a generalized linear - model -GroupedData-class S4 class that represents a GroupedData -IsotonicRegressionModel-class - S4 class that represents an - IsotonicRegressionModel -KMeansModel-class S4 class that represents a KMeansModel -KSTest-class S4 class that represents an KSTest -LDAModel-class S4 class that represents an LDAModel -LinearSVCModel-class S4 class that represents an LinearSVCModel -LogisticRegressionModel-class - S4 class that represents an - LogisticRegressionModel -MultilayerPerceptronClassificationModel-class - S4 class that represents a - MultilayerPerceptronClassificationModel -NaiveBayesModel-class S4 class that represents a NaiveBayesModel -RandomForestClassificationModel-class - S4 class that represents a - RandomForestClassificationModel -RandomForestRegressionModel-class - S4 class that represents a - RandomForestRegressionModel -SparkDataFrame-class S4 class that represents a SparkDataFrame -StreamingQuery-class S4 class that represents a StreamingQuery -WindowSpec-class S4 class that represents a WindowSpec -abs abs -acos acos -add_months add_months -agg summarize -alias alias -approxCountDistinct Returns the approximate number of distinct - items in a group -approxQuantile Calculates the approximate quantiles of - numerical columns of a SparkDataFrame -arrange Arrange Rows by Variables -array_contains array_contains -as.data.frame Download data from a SparkDataFrame into a R - data.frame -asc A set of operations working with SparkDataFrame - columns -ascii ascii -asin asin -atan atan -atan2 atan2 -attach,SparkDataFrame-method - Attach SparkDataFrame to R search path -avg avg -awaitTermination awaitTermination -base64 base64 -between between -bin bin -bitwiseNOT bitwiseNOT -bround bround -cache Cache -cacheTable Cache Table -cancelJobGroup Cancel active jobs for the specified group -cast Casts the column to a different data type. -cbrt cbrt -ceil Computes the ceiling of the given value -checkpoint checkpoint -clearCache Clear Cache -clearJobGroup Clear current job group ID and its description -coalesce Coalesce -collect Collects all the elements of a SparkDataFrame - and coerces them into an R data.frame. -colnames Column Names of SparkDataFrame -coltypes coltypes -column S4 class that represents a SparkDataFrame - column -concat concat -concat_ws concat_ws -conv conv -corr corr -cos cos -cosh cosh -count Count -countDistinct Count Distinct Values -cov cov -covar_pop covar_pop -crc32 crc32 -createDataFrame Create a SparkDataFrame -createExternalTable (Deprecated) Create an external table -createOrReplaceTempView - Creates a temporary view using the given name. -createTable Creates a table based on the dataset in a data - source -crossJoin CrossJoin -crosstab Computes a pair-wise frequency table of the - given columns -cume_dist cume_dist -currentDatabase Returns the current default database -dapply dapply -dapplyCollect dapplyCollect -date_add date_add -date_format date_format -date_sub date_sub -datediff datediff -dayofmonth dayofmonth -dayofyear dayofyear -decode decode -dense_rank dense_rank -describe summary -dim Returns the dimensions of SparkDataFrame -distinct Distinct -drop drop -dropDuplicates dropDuplicates -dropTempTable (Deprecated) Drop Temporary Table -dropTempView Drops the temporary view with the given view - name in the catalog. -dropna A set of SparkDataFrame functions working with - NA values -dtypes DataTypes -encode encode -endsWith endsWith -except except -exp exp -explain Explain -explode explode -expm1 expm1 -expr expr -factorial factorial -filter Filter -first Return the first row of a SparkDataFrame -fitted Get fitted result from a k-means model -floor floor -format_number format_number -format_string format_string -freqItems Finding frequent items for columns, possibly - with false positives -from_json from_json -from_unixtime from_unixtime -from_utc_timestamp from_utc_timestamp -gapply gapply -gapplyCollect gapplyCollect -generateAliasesForIntersectedCols - Creates a list of columns by replacing the - intersected ones with aliases -getNumPartitions getNumPartitions -glm,formula,ANY,SparkDataFrame-method - Generalized Linear Models (R-compliant) -greatest greatest -group_by GroupBy -hash hash -hashCode Compute the hashCode of an object -head Head -hex hex -hint hint -histogram Compute histogram statistics for given column -hour hour -hypot hypot -ifelse ifelse -initcap initcap -insertInto insertInto -install.spark Download and Install Apache Spark to a Local - Directory -instr instr -intersect Intersect -isActive isActive -isLocal isLocal -isStreaming isStreaming -isnan is.nan -join Join -kurtosis kurtosis -lag lag -last last -lastProgress lastProgress -last_day last_day -lead lead -least least -length length -levenshtein levenshtein -limit Limit -listColumns Returns a list of columns for the given - table/view in the specified database -listDatabases Returns a list of databases available -listFunctions Returns a list of functions registered in the - specified database -listTables Returns a list of tables or views in the - specified database -lit lit -locate locate -log log -log10 log10 -log1p log1p -log2 log2 -lower lower -lpad lpad -ltrim ltrim -max max -md5 md5 -mean mean -merge Merges two data frames -min min -minute minute -monotonically_increasing_id - monotonically_increasing_id -month month -months_between months_between -mutate Mutate -nanvl nanvl -ncol Returns the number of columns in a - SparkDataFrame -negate negate -next_day next_day -nrow Returns the number of rows in a SparkDataFrame -ntile ntile -orderBy Ordering Columns in a WindowSpec -otherwise otherwise -over over -partitionBy partitionBy -percent_rank percent_rank -persist Persist -pivot Pivot a column of the GroupedData and perform - the specified aggregation. -pmod pmod -posexplode posexplode -predict Makes predictions from a MLlib model -print.jobj Print a JVM object reference. -print.structField Print a Spark StructField. -print.structType Print a Spark StructType. -printSchema Print Schema of a SparkDataFrame -quarter quarter -queryName queryName -rand rand -randn randn -randomSplit randomSplit -rangeBetween rangeBetween -rank rank -rbind Union two or more SparkDataFrames -read.df Load a SparkDataFrame -read.jdbc Create a SparkDataFrame representing the - database table accessible via JDBC URL -read.json Create a SparkDataFrame from a JSON file. -read.ml Load a fitted MLlib model from the input path. -read.orc Create a SparkDataFrame from an ORC file. -read.parquet Create a SparkDataFrame from a Parquet file. -read.stream Load a streaming SparkDataFrame -read.text Create a SparkDataFrame from a text file. -recoverPartitions Recovers all the partitions in the directory of - a table and update the catalog -refreshByPath Invalidates and refreshes all the cached data - and metadata for SparkDataFrame containing path -refreshTable Invalidates and refreshes all the cached data - and metadata of the given table -regexp_extract regexp_extract -regexp_replace regexp_replace -registerTempTable (Deprecated) Register Temporary Table -rename rename -repartition Repartition -reverse reverse -rint rint -round round -row_number row_number -rowsBetween rowsBetween -rpad rpad -rtrim rtrim -sample Sample -sampleBy Returns a stratified sample without replacement -saveAsTable Save the contents of the SparkDataFrame to a - data source as a table -schema Get schema object -sd sd -second second -select Select -selectExpr SelectExpr -setCheckpointDir Set checkpoint directory -setCurrentDatabase Sets the current default database -setJobGroup Assigns a group ID to all the jobs started by - this thread until the group ID is set to a - different value or cleared. -setLogLevel Set new log level -sha1 sha1 -sha2 sha2 -shiftLeft shiftLeft -shiftRight shiftRight -shiftRightUnsigned shiftRightUnsigned -show show -showDF showDF -signum signum -sin sin -sinh sinh -size size -skewness skewness -sort_array sort_array -soundex soundex -spark.addFile Add a file or directory to be downloaded with - this Spark job on every node. -spark.als Alternating Least Squares (ALS) for - Collaborative Filtering -spark.bisectingKmeans Bisecting K-Means Clustering Model -spark.fpGrowth FP-growth -spark.gaussianMixture Multivariate Gaussian Mixture Model (GMM) -spark.gbt Gradient Boosted Tree Model for Regression and - Classification -spark.getSparkFiles Get the absolute path of a file added through - spark.addFile. -spark.getSparkFilesRootDirectory - Get the root directory that contains files - added through spark.addFile. -spark.glm Generalized Linear Models -spark.isoreg Isotonic Regression Model -spark.kmeans K-Means Clustering Model -spark.kstest (One-Sample) Kolmogorov-Smirnov Test -spark.lapply Run a function over a list of elements, - distributing the computations with Spark -spark.lda Latent Dirichlet Allocation -spark.logit Logistic Regression Model -spark.mlp Multilayer Perceptron Classification Model -spark.naiveBayes Naive Bayes Models -spark.randomForest Random Forest Model for Regression and - Classification -spark.survreg Accelerated Failure Time (AFT) Survival - Regression Model -spark.svmLinear Linear SVM Model -sparkR.callJMethod Call Java Methods -sparkR.callJStatic Call Static Java Methods -sparkR.conf Get Runtime Config from the current active - SparkSession -sparkR.init (Deprecated) Initialize a new Spark Context -sparkR.newJObject Create Java Objects -sparkR.session Get the existing SparkSession or initialize a - new SparkSession. -sparkR.session.stop Stop the Spark Session and Spark Context -sparkR.uiWebUrl Get the URL of the SparkUI instance for the - current active SparkSession -sparkR.version Get version of Spark on which this application - is running -sparkRHive.init (Deprecated) Initialize a new HiveContext -sparkRSQL.init (Deprecated) Initialize a new SQLContext -spark_partition_id Return the partition ID as a column -sql SQL Query -sqrt sqrt -startsWith startsWith -status status -stddev_pop stddev_pop -stddev_samp stddev_samp -stopQuery stopQuery -storageLevel StorageLevel -str Compactly display the structure of a dataset -struct struct -structField structField -structType structType -subset Subset -substr substr -substring_index substring_index -sum sum -sumDistinct sumDistinct -tableNames Table Names -tableToDF Create a SparkDataFrame from a SparkSQL table - or view -tables Tables -take Take the first NUM rows of a SparkDataFrame and - return the results as a R data.frame -tan tan -tanh tanh -toDegrees toDegrees -toJSON toJSON -toRadians toRadians -to_date to_date -to_json to_json -to_timestamp to_timestamp -to_utc_timestamp to_utc_timestamp -translate translate -trim trim -unbase64 unbase64 -uncacheTable Uncache Table -unhex unhex -union Return a new SparkDataFrame containing the - union of rows -unix_timestamp unix_timestamp -unpersist Unpersist -upper upper -var var -var_pop var_pop -var_samp var_samp -weekofyear weekofyear -when when -window window -windowOrderBy windowOrderBy -windowPartitionBy windowPartitionBy -with Evaluate a R expression in an environment - constructed from a SparkDataFrame -withColumn WithColumn -write.df Save the contents of SparkDataFrame to a data 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a/image_content/config/spark/opt/spark/R/lib/SparkR/NAMESPACE +++ /dev/null @@ -1,446 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Imports from base R -# Do not include stats:: "rpois", "runif" - causes error at runtime -importFrom("methods", "setGeneric", "setMethod", "setOldClass") -importFrom("methods", "is", "new", "signature", "show") -importFrom("stats", "gaussian", "setNames") -importFrom("utils", "download.file", "object.size", "packageVersion", "tail", "untar") - -# Disable native libraries till we figure out how to package it -# See SPARKR-7839 -#useDynLib(SparkR, stringHashCode) - -# S3 methods exported -export("sparkR.session") -export("sparkR.init") -export("sparkR.stop") -export("sparkR.session.stop") -export("sparkR.conf") -export("sparkR.version") -export("sparkR.uiWebUrl") -export("print.jobj") - -export("sparkR.newJObject") -export("sparkR.callJMethod") -export("sparkR.callJStatic") - -export("install.spark") - -export("sparkRSQL.init", - "sparkRHive.init") - -# MLlib integration -exportMethods("glm", - "spark.glm", - "predict", - "summary", - "spark.kmeans", - "fitted", - "spark.mlp", - "spark.naiveBayes", - "spark.survreg", - "spark.lda", - "spark.posterior", - "spark.perplexity", - "spark.isoreg", - "spark.gaussianMixture", - "spark.als", - "spark.kstest", - "spark.logit", - "spark.randomForest", - "spark.gbt", - "spark.bisectingKmeans", - "spark.svmLinear", - "spark.fpGrowth", - "spark.freqItemsets", - "spark.associationRules") - -# Job group lifecycle management methods -export("setJobGroup", - "clearJobGroup", - "cancelJobGroup") - -# Export Utility methods -export("setLogLevel") - -exportClasses("SparkDataFrame") - -exportMethods("arrange", - "as.data.frame", - "attach", - "cache", - "checkpoint", - "coalesce", - "collect", - "colnames", - "colnames<-", - "coltypes", - "coltypes<-", - "columns", - "count", - "cov", - "corr", - "covar_samp", - "covar_pop", - "createOrReplaceTempView", - "crossJoin", - "crosstab", - "dapply", - "dapplyCollect", - "describe", - "dim", - "distinct", - "drop", - "dropDuplicates", - "dropna", - "dtypes", - "except", - "explain", - "fillna", - "filter", - "first", - "freqItems", - "gapply", - "gapplyCollect", - "getNumPartitions", - "group_by", - "groupBy", - "head", - "hint", - "insertInto", - "intersect", - "isLocal", - "isStreaming", - "join", - "limit", - "merge", - "mutate", - "na.omit", - "names", - "names<-", - "ncol", - "nrow", - "orderBy", - "persist", - "printSchema", - "randomSplit", - "rbind", - "registerTempTable", - "rename", - "repartition", - "sample", - "sample_frac", - "sampleBy", - "saveAsParquetFile", - "saveAsTable", - "saveDF", - "schema", - "select", - "selectExpr", - "show", - "showDF", - "storageLevel", - "subset", - "summarize", - "summary", - "take", - "toJSON", - "transform", - "union", - "unionAll", - "unique", - "unpersist", - "where", - "with", - "withColumn", - "withColumnRenamed", - "write.df", - "write.jdbc", - "write.json", - "write.orc", - "write.parquet", - "write.stream", - "write.text", - "write.ml") - -exportClasses("Column") - -exportMethods("%in%", - "abs", - "acos", - "add_months", - "alias", - "approxCountDistinct", - "approxQuantile", - "array_contains", - "asc", - "ascii", - "asin", - "atan", - "atan2", - "avg", - "base64", - "between", - "bin", - "bitwiseNOT", - "bround", - "cast", - "cbrt", - "ceil", - "ceiling", - "column", - "concat", - "concat_ws", - "contains", - "conv", - "cos", - "cosh", - "count", - "countDistinct", - "crc32", - "hash", - "cume_dist", - "date_add", - "date_format", - "date_sub", - "datediff", - "dayofmonth", - "dayofyear", - "decode", - "dense_rank", - "desc", - "encode", - "endsWith", - "exp", - "explode", - "expm1", - "expr", - "factorial", - "first", - "floor", - "format_number", - "format_string", - "from_json", - "from_unixtime", - "from_utc_timestamp", - "getField", - "getItem", - "greatest", - "hex", - "histogram", - "hour", - "hypot", - "ifelse", - "initcap", - "instr", - "isNaN", - "isNotNull", - "isNull", - "is.nan", - "isnan", - "kurtosis", - "lag", - "last", - "last_day", - "lead", - "least", - "length", - "levenshtein", - "like", - "lit", - "locate", - "log", - "log10", - "log1p", - "log2", - "lower", - "lpad", - "ltrim", - "max", - "md5", - "mean", - "min", - "minute", - "monotonically_increasing_id", - "month", - "months_between", - "n", - "n_distinct", - "nanvl", - "negate", - "next_day", - "ntile", - "otherwise", - "over", - "percent_rank", - "pmod", - "posexplode", - "quarter", - "rand", - "randn", - "rank", - "regexp_extract", - "regexp_replace", - "reverse", - "rint", - "rlike", - "round", - "row_number", - "rpad", - "rtrim", - "second", - "sha1", - "sha2", - "shiftLeft", - "shiftRight", - "shiftRightUnsigned", - "sd", - "sign", - "signum", - "sin", - "sinh", - "size", - "skewness", - "sort_array", - "soundex", - "spark_partition_id", - "stddev", - "stddev_pop", - "stddev_samp", - "struct", - "sqrt", - "startsWith", - "substr", - "substring_index", - "sum", - "sumDistinct", - "tan", - "tanh", - "toDegrees", - "toRadians", - "to_date", - "to_json", - "to_timestamp", - "to_utc_timestamp", - "translate", - "trim", - "unbase64", - "unhex", - "unix_timestamp", - "upper", - "var", - "variance", - "var_pop", - "var_samp", - "weekofyear", - "when", - "window", - "year") - -exportClasses("GroupedData") -exportMethods("agg") -exportMethods("pivot") - -export("as.DataFrame", - "cacheTable", - "clearCache", - "createDataFrame", - "createExternalTable", - "createTable", - "currentDatabase", - "dropTempTable", - "dropTempView", - "jsonFile", - "listColumns", - "listDatabases", - "listFunctions", - "listTables", - "loadDF", - "parquetFile", - "read.df", - "read.jdbc", - "read.json", - "read.orc", - "read.parquet", - "read.stream", - "read.text", - "recoverPartitions", - "refreshByPath", - "refreshTable", - "setCheckpointDir", - "setCurrentDatabase", - "spark.lapply", - "spark.addFile", - "spark.getSparkFilesRootDirectory", - "spark.getSparkFiles", - "sql", - "str", - "tableToDF", - "tableNames", - "tables", - "uncacheTable", - "print.summary.GeneralizedLinearRegressionModel", - "read.ml", - "print.summary.KSTest", - "print.summary.RandomForestRegressionModel", - "print.summary.RandomForestClassificationModel", - "print.summary.GBTRegressionModel", - "print.summary.GBTClassificationModel") - -export("structField", - "structField.jobj", - "structField.character", - "print.structField", - "structType", - "structType.jobj", - "structType.structField", - "print.structType") - -exportClasses("WindowSpec") - -export("partitionBy", - "rowsBetween", - "rangeBetween") - -export("windowPartitionBy", - "windowOrderBy") - -exportClasses("StreamingQuery") - -export("awaitTermination", - "isActive", - "lastProgress", - "queryName", - "status", - "stopQuery") - - -S3method(print, jobj) -S3method(print, structField) -S3method(print, structType) -S3method(print, summary.GeneralizedLinearRegressionModel) -S3method(print, summary.KSTest) -S3method(print, summary.RandomForestRegressionModel) -S3method(print, summary.RandomForestClassificationModel) -S3method(print, summary.GBTRegressionModel) -S3method(print, summary.GBTClassificationModel) -S3method(structField, character) -S3method(structField, jobj) -S3method(structType, jobj) -S3method(structType, structField) diff --git a/image_content/config/spark/opt/spark/R/lib/SparkR/R/SparkR b/image_content/config/spark/opt/spark/R/lib/SparkR/R/SparkR deleted file mode 100644 index 3b65e3c..0000000 --- a/image_content/config/spark/opt/spark/R/lib/SparkR/R/SparkR +++ /dev/null @@ -1,27 +0,0 @@ -# File share/R/nspackloader.R -# Part of the R package, http://www.R-project.org -# -# Copyright (C) 1995-2012 The R Core Team -# -# This program is free software; you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation; either version 2 of the License, or -# (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# A copy of the GNU General Public License is available at -# http://www.r-project.org/Licenses/ - -local({ - info <- loadingNamespaceInfo() - pkg <- info$pkgname - ns <- .getNamespace(as.name(pkg)) - if (is.null(ns)) - stop("cannot find namespace environment for ", pkg, domain = NA); - dbbase <- file.path(info$libname, pkg, "R", pkg) - lazyLoad(dbbase, ns, filter = function(n) n != ".__NAMESPACE__.") -}) diff --git a/image_content/config/spark/opt/spark/R/lib/SparkR/R/SparkR.rdb b/image_content/config/spark/opt/spark/R/lib/SparkR/R/SparkR.rdb deleted file mode 120000 index c23f080..0000000 --- a/image_content/config/spark/opt/spark/R/lib/SparkR/R/SparkR.rdb +++ /dev/null @@ -1 +0,0 @@ -../../../../../../../../../.git/annex/objects/M9/mV/SHA256E-s1106995--5f8cb41049eceb00429c84946d7494a9f707aee22cb009f56474358935dc7363.rdb/SHA256E-s1106995--5f8cb41049eceb00429c84946d7494a9f707aee22cb009f56474358935dc7363.rdb \ No newline at 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100644 index aea7686..0000000 --- a/image_content/config/spark/opt/spark/R/lib/SparkR/html/00Index.html +++ /dev/null @@ -1,1715 +0,0 @@ - - -R: R Frontend for Apache Spark - - - -

R Frontend for Apache Spark - -

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Documentation for package ‘SparkR’ version 2.2.1

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Help Pages

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-A -B -C -D -E -F -G -H -I -J -K -L -M -N -O -P -Q -R -S -T -U -V -W -Y -misc -

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-- A --

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absabs
abs-methodabs
acosacos
acos-methodacos
add_monthsadd_months
add_months-methodadd_months
AFTSurvivalRegressionModel-classS4 class that represents a AFTSurvivalRegressionModel
aggsummarize
agg-methodsummarize
aliasalias
alias-methodalias
ALSModel-classS4 class that represents an ALSModel
approxCountDistinctReturns the approximate number of distinct items in a group
approxCountDistinct-methodReturns the approximate number of distinct items in a group
approxQuantileCalculates the approximate quantiles of numerical columns of a SparkDataFrame
approxQuantile-methodCalculates the approximate quantiles of numerical columns of a SparkDataFrame
arrangeArrange Rows by Variables
arrange-methodArrange Rows by Variables
array_containsarray_contains
array_contains-methodarray_contains
as.data.frameDownload data from a SparkDataFrame into a R data.frame
as.data.frame-methodDownload data from a SparkDataFrame into a R data.frame
as.DataFrameCreate a SparkDataFrame
as.DataFrame.defaultCreate a SparkDataFrame
ascA set of operations working with SparkDataFrame columns
asciiascii
ascii-methodascii
asinasin
asin-methodasin
associationRules-methodFP-growth
atanatan
atan-methodatan
atan2atan2
atan2-methodatan2
attachAttach SparkDataFrame to R search path
attach-methodAttach SparkDataFrame to R search path
avgavg
avg-methodavg
awaitTerminationawaitTermination
awaitTermination-methodawaitTermination
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-- B --

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base64base64
base64-methodbase64
betweenbetween
between-methodbetween
binbin
bin-methodbin
BisectingKMeansModel-classS4 class that represents a BisectingKMeansModel
bitwiseNOTbitwiseNOT
bitwiseNOT-methodbitwiseNOT
broundbround
bround-methodbround
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-- C --

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cacheCache
cache-methodCache
cacheTableCache Table
cacheTable.defaultCache Table
cancelJobGroupCancel active jobs for the specified group
cancelJobGroup.defaultCancel active jobs for the specified group
castCasts the column to a different data type.
cast-methodCasts the column to a different data type.
cbrtcbrt
cbrt-methodcbrt
ceilComputes the ceiling of the given value
ceil-methodComputes the ceiling of the given value
ceilingComputes the ceiling of the given value
ceiling-methodComputes the ceiling of the given value
checkpointcheckpoint
checkpoint-methodcheckpoint
clearCacheClear Cache
clearCache.defaultClear Cache
clearJobGroupClear current job group ID and its description
clearJobGroup.defaultClear current job group ID and its description
coalesceCoalesce
coalesce-methodCoalesce
collectCollects all the elements of a SparkDataFrame and coerces them into an R data.frame.
collect-methodCollects all the elements of a SparkDataFrame and coerces them into an R data.frame.
colnamesColumn Names of SparkDataFrame
colnames-methodColumn Names of SparkDataFrame
colnames<-Column Names of SparkDataFrame
colnames<--methodColumn Names of SparkDataFrame
coltypescoltypes
coltypes-methodcoltypes
coltypes<-coltypes
coltypes<--methodcoltypes
columnS4 class that represents a SparkDataFrame column
Column-classS4 class that represents a SparkDataFrame column
column-methodS4 class that represents a SparkDataFrame column
columnfunctionsA set of operations working with SparkDataFrame columns
columnsColumn Names of SparkDataFrame
columns-methodColumn Names of SparkDataFrame
concatconcat
concat-methodconcat
concat_wsconcat_ws
concat_ws-methodconcat_ws
containsA set of operations working with SparkDataFrame columns
convconv
conv-methodconv
corrcorr
corr-methodcorr
coscos
cos-methodcos
coshcosh
cosh-methodcosh
countCount
count-methodCount
count-methodReturns the number of rows in a SparkDataFrame
countDistinctCount Distinct Values
countDistinct-methodCount Distinct Values
covcov
cov-methodcov
covar_popcovar_pop
covar_pop-methodcovar_pop
covar_sampcov
covar_samp-methodcov
crc32crc32
crc32-methodcrc32
createDataFrameCreate a SparkDataFrame
createDataFrame.defaultCreate a SparkDataFrame
createExternalTable(Deprecated) Create an external table
createExternalTable.default(Deprecated) Create an external table
createOrReplaceTempViewCreates a temporary view using the given name.
createOrReplaceTempView-methodCreates a temporary view using the given name.
createTableCreates a table based on the dataset in a data source
crossJoinCrossJoin
crossJoin-methodCrossJoin
crosstabComputes a pair-wise frequency table of the given columns
crosstab-methodComputes a pair-wise frequency table of the given columns
cume_distcume_dist
cume_dist-methodcume_dist
currentDatabaseReturns the current default database
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-- D --

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dapplydapply
dapply-methoddapply
dapplyCollectdapplyCollect
dapplyCollect-methoddapplyCollect
datediffdatediff
datediff-methoddatediff
date_adddate_add
date_add-methoddate_add
date_formatdate_format
date_format-methoddate_format
date_subdate_sub
date_sub-methoddate_sub
dayofmonthdayofmonth
dayofmonth-methoddayofmonth
dayofyeardayofyear
dayofyear-methoddayofyear
decodedecode
decode-methoddecode
dense_rankdense_rank
dense_rank-methoddense_rank
descA set of operations working with SparkDataFrame columns
describesummary
describe-methodsummary
dimReturns the dimensions of SparkDataFrame
dim-methodReturns the dimensions of SparkDataFrame
distinctDistinct
distinct-methodDistinct
dropdrop
drop-methoddrop
dropDuplicatesdropDuplicates
dropDuplicates-methoddropDuplicates
dropnaA set of SparkDataFrame functions working with NA values
dropna-methodA set of SparkDataFrame functions working with NA values
dropTempTable(Deprecated) Drop Temporary Table
dropTempTable.default(Deprecated) Drop Temporary Table
dropTempViewDrops the temporary view with the given view name in the catalog.
dtypesDataTypes
dtypes-methodDataTypes
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-- E --

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encodeencode
encode-methodencode
endsWithendsWith
endsWith-methodendsWith
exceptexcept
except-methodexcept
expexp
exp-methodexp
explainExplain
explain-methodExplain
explodeexplode
explode-methodexplode
expm1expm1
expm1-methodexpm1
exprexpr
expr-methodexpr
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-- F --

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factorialfactorial
factorial-methodfactorial
fillnaA set of SparkDataFrame functions working with NA values
fillna-methodA set of SparkDataFrame functions working with NA values
filterFilter
filter-methodFilter
firstReturn the first row of a SparkDataFrame
first-methodReturn the first row of a SparkDataFrame
fittedGet fitted result from a k-means model
fitted-methodGet fitted result from a k-means model
fitted-methodBisecting K-Means Clustering Model
floorfloor
floor-methodfloor
format_numberformat_number
format_number-methodformat_number
format_stringformat_string
format_string-methodformat_string
FPGrowthModel-classS4 class that represents a FPGrowthModel
freqItemsFinding frequent items for columns, possibly with false positives
freqItems-methodFinding frequent items for columns, possibly with false positives
freqItemsets-methodFP-growth
from_jsonfrom_json
from_json-methodfrom_json
from_unixtimefrom_unixtime
from_unixtime-methodfrom_unixtime
from_utc_timestampfrom_utc_timestamp
from_utc_timestamp-methodfrom_utc_timestamp
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-- G --

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gapplygapply
gapply-methodgapply
gapplyCollectgapplyCollect
gapplyCollect-methodgapplyCollect
GaussianMixtureModel-classS4 class that represents a GaussianMixtureModel
GBTClassificationModel-classS4 class that represents a GBTClassificationModel
GBTRegressionModel-classS4 class that represents a GBTRegressionModel
GeneralizedLinearRegressionModel-classS4 class that represents a generalized linear model
generateAliasesForIntersectedColsCreates a list of columns by replacing the intersected ones with aliases
getFieldA set of operations working with SparkDataFrame columns
getItemA set of operations working with SparkDataFrame columns
getNumPartitionsgetNumPartitions
getNumPartitions-methodgetNumPartitions
glmGeneralized Linear Models (R-compliant)
glm-methodGeneralized Linear Models (R-compliant)
greatestgreatest
greatest-methodgreatest
groupByGroupBy
groupBy-methodGroupBy
groupedDataS4 class that represents a GroupedData
GroupedData-classS4 class that represents a GroupedData
group_byGroupBy
group_by-methodGroupBy
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-- H --

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hashhash
hash-methodhash
hashCodeCompute the hashCode of an object
headHead
head-methodHead
hexhex
hex-methodhex
hinthint
hint-methodhint
histogramCompute histogram statistics for given column
histogram-methodCompute histogram statistics for given column
hourhour
hour-methodhour
hypothypot
hypot-methodhypot
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-- I --

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ifelseifelse
ifelse-methodifelse
initcapinitcap
initcap-methodinitcap
insertIntoinsertInto
insertInto-methodinsertInto
install.sparkDownload and Install Apache Spark to a Local Directory
instrinstr
instr-methodinstr
intersectIntersect
intersect-methodIntersect
is.nanis.nan
is.nan-methodis.nan
isActiveisActive
isActive-methodisActive
isLocalisLocal
isLocal-methodisLocal
isNaNA set of operations working with SparkDataFrame columns
isnanis.nan
isnan-methodis.nan
isNotNullA set of operations working with SparkDataFrame columns
isNullA set of operations working with SparkDataFrame columns
IsotonicRegressionModel-classS4 class that represents an IsotonicRegressionModel
isStreamingisStreaming
isStreaming-methodisStreaming
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-- J --

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joinJoin
join-methodJoin
jsonFileCreate a SparkDataFrame from a JSON file.
jsonFile.defaultCreate a SparkDataFrame from a JSON file.
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-- K --

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KMeansModel-classS4 class that represents a KMeansModel
KSTest-classS4 class that represents an KSTest
kurtosiskurtosis
kurtosis-methodkurtosis
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-- L --

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laglag
lag-methodlag
lastlast
last-methodlast
lastProgresslastProgress
lastProgress-methodlastProgress
last_daylast_day
last_day-methodlast_day
LDAModel-classS4 class that represents an LDAModel
leadlead
lead-methodlead
leastleast
least-methodleast
lengthlength
length-methodlength
levenshteinlevenshtein
levenshtein-methodlevenshtein
likeA set of operations working with SparkDataFrame columns
limitLimit
limit-methodLimit
LinearSVCModel-classS4 class that represents an LinearSVCModel
listColumnsReturns a list of columns for the given table/view in the specified database
listDatabasesReturns a list of databases available
listFunctionsReturns a list of functions registered in the specified database
listTablesReturns a list of tables or views in the specified database
litlit
lit-methodlit
loadDFLoad a SparkDataFrame
loadDF.defaultLoad a SparkDataFrame
locatelocate
locate-methodlocate
loglog
log-methodlog
log10log10
log10-methodlog10
log1plog1p
log1p-methodlog1p
log2log2
log2-methodlog2
LogisticRegressionModel-classS4 class that represents an LogisticRegressionModel
lowerlower
lower-methodlower
lpadlpad
lpad-methodlpad
ltrimltrim
ltrim-methodltrim
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-- M --

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maxmax
max-methodmax
md5md5
md5-methodmd5
meanmean
mean-methodmean
mergeMerges two data frames
merge-methodMerges two data frames
minmin
min-methodmin
minuteminute
minute-methodminute
monotonically_increasing_idmonotonically_increasing_id
monotonically_increasing_id-methodmonotonically_increasing_id
monthmonth
month-methodmonth
months_betweenmonths_between
months_between-methodmonths_between
MultilayerPerceptronClassificationModel-classS4 class that represents a MultilayerPerceptronClassificationModel
mutateMutate
mutate-methodMutate
- -

-- N --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
nCount
n-methodCount
na.omitA set of SparkDataFrame functions working with NA values
na.omit-methodA set of SparkDataFrame functions working with NA values
NaiveBayesModel-classS4 class that represents a NaiveBayesModel
namesColumn Names of SparkDataFrame
names-methodColumn Names of SparkDataFrame
names<-Column Names of SparkDataFrame
names<--methodColumn Names of SparkDataFrame
nanvlnanvl
nanvl-methodnanvl
ncolReturns the number of columns in a SparkDataFrame
ncol-methodReturns the number of columns in a SparkDataFrame
negatenegate
negate-methodnegate
next_daynext_day
next_day-methodnext_day
nrowReturns the number of rows in a SparkDataFrame
nrow-methodReturns the number of rows in a SparkDataFrame
ntilentile
ntile-methodntile
n_distinctCount Distinct Values
n_distinct-methodCount Distinct Values
- -

-- O --

- - - - - - - - - - - - - - - - -
orderByOrdering Columns in a WindowSpec
orderBy-methodArrange Rows by Variables
orderBy-methodOrdering Columns in a WindowSpec
otherwiseotherwise
otherwise-methodotherwise
overover
over-methodover
- -

-- P --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
parquetFileCreate a SparkDataFrame from a Parquet file.
parquetFile.defaultCreate a SparkDataFrame from a Parquet file.
partitionBypartitionBy
partitionBy-methodpartitionBy
percent_rankpercent_rank
percent_rank-methodpercent_rank
persistPersist
persist-methodPersist
pivotPivot a column of the GroupedData and perform the specified aggregation.
pivot-methodPivot a column of the GroupedData and perform the specified aggregation.
pmodpmod
pmod-methodpmod
posexplodeposexplode
posexplode-methodposexplode
predictMakes predictions from a MLlib model
predict-methodAlternating Least Squares (ALS) for Collaborative Filtering
predict-methodBisecting K-Means Clustering Model
predict-methodFP-growth
predict-methodMultivariate Gaussian Mixture Model (GMM)
predict-methodGradient Boosted Tree Model for Regression and Classification
predict-methodGeneralized Linear Models
predict-methodIsotonic Regression Model
predict-methodK-Means Clustering Model
predict-methodLogistic Regression Model
predict-methodMultilayer Perceptron Classification Model
predict-methodNaive Bayes Models
predict-methodRandom Forest Model for Regression and Classification
predict-methodAccelerated Failure Time (AFT) Survival Regression Model
predict-methodLinear SVM Model
print.jobjPrint a JVM object reference.
print.structFieldPrint a Spark StructField.
print.structTypePrint a Spark StructType.
print.summary.GBTClassificationModelGradient Boosted Tree Model for Regression and Classification
print.summary.GBTRegressionModelGradient Boosted Tree Model for Regression and Classification
print.summary.GeneralizedLinearRegressionModelGeneralized Linear Models
print.summary.KSTest(One-Sample) Kolmogorov-Smirnov Test
print.summary.RandomForestClassificationModelRandom Forest Model for Regression and Classification
print.summary.RandomForestRegressionModelRandom Forest Model for Regression and Classification
printSchemaPrint Schema of a SparkDataFrame
printSchema-methodPrint Schema of a SparkDataFrame
- -

-- Q --

- - - - - - - - - - -
quarterquarter
quarter-methodquarter
queryNamequeryName
queryName-methodqueryName
- -

-- R --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
randrand
rand-methodrand
randnrandn
randn-methodrandn
RandomForestClassificationModel-classS4 class that represents a RandomForestClassificationModel
RandomForestRegressionModel-classS4 class that represents a RandomForestRegressionModel
randomSplitrandomSplit
randomSplit-methodrandomSplit
rangeBetweenrangeBetween
rangeBetween-methodrangeBetween
rankrank
rank-methodrank
rbindUnion two or more SparkDataFrames
rbind-methodUnion two or more SparkDataFrames
read.dfLoad a SparkDataFrame
read.df.defaultLoad a SparkDataFrame
read.jdbcCreate a SparkDataFrame representing the database table accessible via JDBC URL
read.jsonCreate a SparkDataFrame from a JSON file.
read.json.defaultCreate a SparkDataFrame from a JSON file.
read.mlLoad a fitted MLlib model from the input path.
read.orcCreate a SparkDataFrame from an ORC file.
read.parquetCreate a SparkDataFrame from a Parquet file.
read.parquet.defaultCreate a SparkDataFrame from a Parquet file.
read.streamLoad a streaming SparkDataFrame
read.textCreate a SparkDataFrame from a text file.
read.text.defaultCreate a SparkDataFrame from a text file.
recoverPartitionsRecovers all the partitions in the directory of a table and update the catalog
refreshByPathInvalidates and refreshes all the cached data and metadata for SparkDataFrame containing path
refreshTableInvalidates and refreshes all the cached data and metadata of the given table
regexp_extractregexp_extract
regexp_extract-methodregexp_extract
regexp_replaceregexp_replace
regexp_replace-methodregexp_replace
registerTempTable(Deprecated) Register Temporary Table
registerTempTable-method(Deprecated) Register Temporary Table
renamerename
rename-methodrename
repartitionRepartition
repartition-methodRepartition
reversereverse
reverse-methodreverse
rintrint
rint-methodrint
rlikeA set of operations working with SparkDataFrame columns
roundround
round-methodround
rowsBetweenrowsBetween
rowsBetween-methodrowsBetween
row_numberrow_number
row_number-methodrow_number
rpadrpad
rpad-methodrpad
rtrimrtrim
rtrim-methodrtrim
- -

-- S --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
sampleSample
sample-methodSample
sampleByReturns a stratified sample without replacement
sampleBy-methodReturns a stratified sample without replacement
sample_fracSample
sample_frac-methodSample
saveAsParquetFileSave the contents of SparkDataFrame as a Parquet file, preserving the schema.
saveAsParquetFile-methodSave the contents of SparkDataFrame as a Parquet file, preserving the schema.
saveAsTableSave the contents of the SparkDataFrame to a data source as a table
saveAsTable-methodSave the contents of the SparkDataFrame to a data source as a table
saveDFSave the contents of SparkDataFrame to a data source.
saveDF-methodSave the contents of SparkDataFrame to a data source.
schemaGet schema object
schema-methodGet schema object
sdsd
sd-methodsd
secondsecond
second-methodsecond
selectSelect
select-methodSelect
selectExprSelectExpr
selectExpr-methodSelectExpr
setCheckpointDirSet checkpoint directory
setCurrentDatabaseSets the current default database
setJobGroupAssigns a group ID to all the jobs started by this thread until the group ID is set to a different value or cleared.
setJobGroup.defaultAssigns a group ID to all the jobs started by this thread until the group ID is set to a different value or cleared.
setLogLevelSet new log level
sha1sha1
sha1-methodsha1
sha2sha2
sha2-methodsha2
shiftLeftshiftLeft
shiftLeft-methodshiftLeft
shiftRightshiftRight
shiftRight-methodshiftRight
shiftRightUnsignedshiftRightUnsigned
shiftRightUnsigned-methodshiftRightUnsigned
showshow
show-methodshow
showDFshowDF
showDF-methodshowDF
signsignum
sign-methodsignum
signumsignum
signum-methodsignum
sinsin
sin-methodsin
sinhsinh
sinh-methodsinh
sizesize
size-methodsize
skewnessskewness
skewness-methodskewness
sort_arraysort_array
sort_array-methodsort_array
soundexsoundex
soundex-methodsoundex
spark.addFileAdd a file or directory to be downloaded with this Spark job on every node.
spark.alsAlternating Least Squares (ALS) for Collaborative Filtering
spark.als-methodAlternating Least Squares (ALS) for Collaborative Filtering
spark.associationRulesFP-growth
spark.associationRules-methodFP-growth
spark.bisectingKmeansBisecting K-Means Clustering Model
spark.bisectingKmeans-methodBisecting K-Means Clustering Model
spark.fpGrowthFP-growth
spark.fpGrowth-methodFP-growth
spark.freqItemsetsFP-growth
spark.freqItemsets-methodFP-growth
spark.gaussianMixtureMultivariate Gaussian Mixture Model (GMM)
spark.gaussianMixture-methodMultivariate Gaussian Mixture Model (GMM)
spark.gbtGradient Boosted Tree Model for Regression and Classification
spark.gbt-methodGradient Boosted Tree Model for Regression and Classification
spark.getSparkFilesGet the absolute path of a file added through spark.addFile.
spark.getSparkFilesRootDirectoryGet the root directory that contains files added through spark.addFile.
spark.glmGeneralized Linear Models
spark.glm-methodGeneralized Linear Models
spark.isoregIsotonic Regression Model
spark.isoreg-methodIsotonic Regression Model
spark.kmeansK-Means Clustering Model
spark.kmeans-methodK-Means Clustering Model
spark.kstest(One-Sample) Kolmogorov-Smirnov Test
spark.kstest-method(One-Sample) Kolmogorov-Smirnov Test
spark.lapplyRun a function over a list of elements, distributing the computations with Spark
spark.ldaLatent Dirichlet Allocation
spark.lda-methodLatent Dirichlet Allocation
spark.logitLogistic Regression Model
spark.logit-methodLogistic Regression Model
spark.mlpMultilayer Perceptron Classification Model
spark.mlp-methodMultilayer Perceptron Classification Model
spark.naiveBayesNaive Bayes Models
spark.naiveBayes-methodNaive Bayes Models
spark.perplexityLatent Dirichlet Allocation
spark.perplexity-methodLatent Dirichlet Allocation
spark.posteriorLatent Dirichlet Allocation
spark.posterior-methodLatent Dirichlet Allocation
spark.randomForestRandom Forest Model for Regression and Classification
spark.randomForest-methodRandom Forest Model for Regression and Classification
spark.survregAccelerated Failure Time (AFT) Survival Regression Model
spark.survreg-methodAccelerated Failure Time (AFT) Survival Regression Model
spark.svmLinearLinear SVM Model
spark.svmLinear-methodLinear SVM Model
SparkDataFrame-classS4 class that represents a SparkDataFrame
sparkR.callJMethodCall Java Methods
sparkR.callJStaticCall Static Java Methods
sparkR.confGet Runtime Config from the current active SparkSession
sparkR.init(Deprecated) Initialize a new Spark Context
sparkR.newJObjectCreate Java Objects
sparkR.sessionGet the existing SparkSession or initialize a new SparkSession.
sparkR.session.stopStop the Spark Session and Spark Context
sparkR.stopStop the Spark Session and Spark Context
sparkR.uiWebUrlGet the URL of the SparkUI instance for the current active SparkSession
sparkR.versionGet version of Spark on which this application is running
sparkRHive.init(Deprecated) Initialize a new HiveContext
sparkRSQL.init(Deprecated) Initialize a new SQLContext
spark_partition_idReturn the partition ID as a column
spark_partition_id-methodReturn the partition ID as a column
sqlSQL Query
sql.defaultSQL Query
sqrtsqrt
sqrt-methodsqrt
startsWithstartsWith
startsWith-methodstartsWith
statusstatus
status-methodstatus
stddevsd
stddev-methodsd
stddev_popstddev_pop
stddev_pop-methodstddev_pop
stddev_sampstddev_samp
stddev_samp-methodstddev_samp
stopQuerystopQuery
stopQuery-methodstopQuery
storageLevelStorageLevel
storageLevel-methodStorageLevel
strCompactly display the structure of a dataset
str-methodCompactly display the structure of a dataset
StreamingQuery-classS4 class that represents a StreamingQuery
structstruct
struct-methodstruct
structFieldstructField
structField.characterstructField
structField.jobjstructField
structTypestructType
structType.jobjstructType
structType.structFieldstructType
subsetSubset
subset-methodSubset
substrsubstr
substr-methodsubstr
substring_indexsubstring_index
substring_index-methodsubstring_index
sumsum
sum-methodsum
sumDistinctsumDistinct
sumDistinct-methodsumDistinct
summarizesummarize
summarize-methodsummarize
summarysummary
summary-methodAlternating Least Squares (ALS) for Collaborative Filtering
summary-methodBisecting K-Means Clustering Model
summary-methodMultivariate Gaussian Mixture Model (GMM)
summary-methodGradient Boosted Tree Model for Regression and Classification
summary-methodGeneralized Linear Models
summary-methodIsotonic Regression Model
summary-methodK-Means Clustering Model
summary-method(One-Sample) Kolmogorov-Smirnov Test
summary-methodLatent Dirichlet Allocation
summary-methodLogistic Regression Model
summary-methodMultilayer Perceptron Classification Model
summary-methodNaive Bayes Models
summary-methodRandom Forest Model for Regression and Classification
summary-methodAccelerated Failure Time (AFT) Survival Regression Model
summary-methodLinear SVM Model
summary-methodsummary
- -

-- T --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
tableNamesTable Names
tableNames.defaultTable Names
tablesTables
tables.defaultTables
tableToDFCreate a SparkDataFrame from a SparkSQL table or view
takeTake the first NUM rows of a SparkDataFrame and return the results as a R data.frame
take-methodTake the first NUM rows of a SparkDataFrame and return the results as a R data.frame
tantan
tan-methodtan
tanhtanh
tanh-methodtanh
toDegreestoDegrees
toDegrees-methodtoDegrees
toJSONtoJSON
toJSON-methodtoJSON
toRadianstoRadians
toRadians-methodtoRadians
to_dateto_date
to_date-methodto_date
to_jsonto_json
to_json-methodto_json
to_timestampto_timestamp
to_timestamp-methodto_timestamp
to_utc_timestampto_utc_timestamp
to_utc_timestamp-methodto_utc_timestamp
transformMutate
transform-methodMutate
translatetranslate
translate-methodtranslate
trimtrim
trim-methodtrim
- -

-- U --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
unbase64unbase64
unbase64-methodunbase64
uncacheTableUncache Table
uncacheTable.defaultUncache Table
unhexunhex
unhex-methodunhex
unionReturn a new SparkDataFrame containing the union of rows
union-methodReturn a new SparkDataFrame containing the union of rows
unionAllReturn a new SparkDataFrame containing the union of rows
unionAll-methodReturn a new SparkDataFrame containing the union of rows
uniqueDistinct
unique-methodDistinct
unix_timestampunix_timestamp
unix_timestamp-methodunix_timestamp
unpersistUnpersist
unpersist-methodUnpersist
upperupper
upper-methodupper
- -

-- V --

- - - - - - - - - - - - - - - - - - -
varvar
var-methodvar
variancevar
variance-methodvar
var_popvar_pop
var_pop-methodvar_pop
var_sampvar_samp
var_samp-methodvar_samp
- -

-- W --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
weekofyearweekofyear
weekofyear-methodweekofyear
whenwhen
when-methodwhen
whereFilter
where-methodFilter
windowwindow
window-methodwindow
windowOrderBywindowOrderBy
windowOrderBy-methodwindowOrderBy
windowPartitionBywindowPartitionBy
windowPartitionBy-methodwindowPartitionBy
WindowSpec-classS4 class that represents a WindowSpec
withEvaluate a R expression in an environment constructed from a SparkDataFrame
with-methodEvaluate a R expression in an environment constructed from a SparkDataFrame
withColumnWithColumn
withColumn-methodWithColumn
withColumnRenamedrename
withColumnRenamed-methodrename
write.dfSave the contents of SparkDataFrame to a data source.
write.df-methodSave the contents of SparkDataFrame to a data source.
write.jdbcSave the content of SparkDataFrame to an external database table via JDBC.
write.jdbc-methodSave the content of SparkDataFrame to an external database table via JDBC.
write.jsonSave the contents of SparkDataFrame as a JSON file
write.json-methodSave the contents of SparkDataFrame as a JSON file
write.mlSaves the MLlib model to the input path
write.ml-methodAlternating Least Squares (ALS) for Collaborative Filtering
write.ml-methodBisecting K-Means Clustering Model
write.ml-methodFP-growth
write.ml-methodMultivariate Gaussian Mixture Model (GMM)
write.ml-methodGradient Boosted Tree Model for Regression and Classification
write.ml-methodGeneralized Linear Models
write.ml-methodIsotonic Regression Model
write.ml-methodK-Means Clustering Model
write.ml-methodLatent Dirichlet Allocation
write.ml-methodLogistic Regression Model
write.ml-methodMultilayer Perceptron Classification Model
write.ml-methodNaive Bayes Models
write.ml-methodRandom Forest Model for Regression and Classification
write.ml-methodAccelerated Failure Time (AFT) Survival Regression Model
write.ml-methodLinear SVM Model
write.orcSave the contents of SparkDataFrame as an ORC file, preserving the schema.
write.orc-methodSave the contents of SparkDataFrame as an ORC file, preserving the schema.
write.parquetSave the contents of SparkDataFrame as a Parquet file, preserving the schema.
write.parquet-methodSave the contents of SparkDataFrame as a Parquet file, preserving the schema.
write.streamWrite the streaming SparkDataFrame to a data source.
write.stream-methodWrite the streaming SparkDataFrame to a data source.
write.textSave the content of SparkDataFrame in a text file at the specified path.
write.text-methodSave the content of SparkDataFrame in a text file at the specified path.
- -

-- Y --

- - - - - - -
yearyear
year-methodyear
- -

-- misc --

- - - - - - - - - - - - - - - - - - - - - - - - - - -
$Select
$-methodSelect
$<-Select
$<--methodSelect
%in%Match a column with given values.
%in%-methodMatch a column with given values.
[Subset
[-methodSubset
[[Subset
[[-methodSubset
[[<-Subset
[[<--methodSubset
- diff --git a/image_content/config/spark/opt/spark/R/lib/SparkR/html/R.css b/image_content/config/spark/opt/spark/R/lib/SparkR/html/R.css deleted file mode 100644 index f10f5ea..0000000 --- a/image_content/config/spark/opt/spark/R/lib/SparkR/html/R.css +++ /dev/null @@ -1,97 +0,0 @@ -body { - background: white; - color: black; -} - -a:link { - background: white; - color: blue; -} - -a:visited { - background: white; - color: rgb(50%, 0%, 50%); -} - -h1 { - background: white; - color: rgb(55%, 55%, 55%); - font-family: monospace; - font-size: x-large; - text-align: center; -} - -h2 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-size: large; - text-align: center; -} - -h3 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-size: large; -} - -h4 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-style: italic; - font-size: large; -} - -h5 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; -} - -h6 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-style: italic; -} - -img.toplogo { - width: 4em; - vertical-align: middle; -} - -img.arrow { - width: 30px; - height: 30px; - border: 0; -} - -span.acronym { - font-size: small; -} - -span.env { - font-family: monospace; -} - -span.file { - font-family: monospace; -} - -span.option{ - font-family: monospace; -} - -span.pkg { - font-weight: bold; -} - -span.samp{ - font-family: monospace; -} - -div.vignettes a:hover { - background: rgb(85%, 85%, 85%); -} diff --git a/image_content/config/spark/opt/spark/R/lib/SparkR/profile/general.R b/image_content/config/spark/opt/spark/R/lib/SparkR/profile/general.R deleted file mode 100644 index 8c75c19..0000000 --- a/image_content/config/spark/opt/spark/R/lib/SparkR/profile/general.R +++ /dev/null @@ -1,23 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -.First <- function() { - packageDir <- Sys.getenv("SPARKR_PACKAGE_DIR") - dirs <- strsplit(packageDir, ",")[[1]] - .libPaths(c(dirs, .libPaths())) - Sys.setenv(NOAWT = 1) -} diff --git a/image_content/config/spark/opt/spark/R/lib/SparkR/profile/shell.R b/image_content/config/spark/opt/spark/R/lib/SparkR/profile/shell.R deleted file mode 100644 index 8a8111a..0000000 --- a/image_content/config/spark/opt/spark/R/lib/SparkR/profile/shell.R +++ /dev/null @@ -1,47 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -.First <- function() { - home <- Sys.getenv("SPARK_HOME") - .libPaths(c(file.path(home, "R", "lib"), .libPaths())) - Sys.setenv(NOAWT = 1) - - # Make sure SparkR package is the last loaded one - old <- getOption("defaultPackages") - options(defaultPackages = c(old, "SparkR")) - - spark <- SparkR::sparkR.session() - assign("spark", spark, envir = .GlobalEnv) - sc <- SparkR:::callJStatic("org.apache.spark.sql.api.r.SQLUtils", "getJavaSparkContext", spark) - assign("sc", sc, envir = .GlobalEnv) - sparkVer <- SparkR:::callJMethod(sc, "version") - cat("\n Welcome to") - cat("\n") - cat(" ____ __", "\n") - cat(" / __/__ ___ _____/ /__", "\n") - cat(" _\\ \\/ _ \\/ _ `/ __/ '_/", "\n") - cat(" /___/ .__/\\_,_/_/ /_/\\_\\") - if (nchar(sparkVer) == 0) { - cat("\n") - } else { - cat(" version ", sparkVer, "\n") - } - cat(" /_/", "\n") - cat("\n") - - cat("\n SparkSession available as 'spark'.\n") -} diff --git a/image_content/config/spark/opt/spark/R/lib/SparkR/tests/testthat/test_basic.R b/image_content/config/spark/opt/spark/R/lib/SparkR/tests/testthat/test_basic.R deleted file mode 100644 index 823d26f..0000000 --- a/image_content/config/spark/opt/spark/R/lib/SparkR/tests/testthat/test_basic.R +++ /dev/null @@ -1,92 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -context("basic tests for CRAN") - -test_that("create DataFrame from list or data.frame", { - sparkR.session(master = sparkRTestMaster, enableHiveSupport = FALSE, - sparkConfig = sparkRTestConfig) - - i <- 4 - df <- createDataFrame(data.frame(dummy = 1:i)) - expect_equal(count(df), i) - - l <- list(list(a = 1, b = 2), list(a = 3, b = 4)) - df <- createDataFrame(l) - expect_equal(columns(df), c("a", "b")) - - a <- 1:3 - b <- c("a", "b", "c") - ldf <- data.frame(a, b) - df <- createDataFrame(ldf) - expect_equal(columns(df), c("a", "b")) - expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) - expect_equal(count(df), 3) - ldf2 <- collect(df) - expect_equal(ldf$a, ldf2$a) - - mtcarsdf <- createDataFrame(mtcars) - expect_equivalent(collect(mtcarsdf), mtcars) - - bytes <- as.raw(c(1, 2, 3)) - df <- createDataFrame(list(list(bytes))) - expect_equal(collect(df)[[1]][[1]], bytes) - - sparkR.session.stop() -}) - -test_that("spark.glm and predict", { - sparkR.session(master = sparkRTestMaster, enableHiveSupport = FALSE, - sparkConfig = sparkRTestConfig) - - training <- suppressWarnings(createDataFrame(iris)) - # gaussian family - model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species) - prediction <- predict(model, training) - expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double") - vals <- collect(select(prediction, "prediction")) - rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris) - expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals) - - # Gamma family - x <- runif(100, -1, 1) - y <- rgamma(100, rate = 10 / exp(0.5 + 1.2 * x), shape = 10) - df <- as.DataFrame(as.data.frame(list(x = x, y = y))) - model <- glm(y ~ x, family = Gamma, df) - out <- capture.output(print(summary(model))) - expect_true(any(grepl("Dispersion parameter for gamma family", out))) - - # tweedie family - model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species, - family = "tweedie", var.power = 1.2, link.power = 0.0) - prediction <- predict(model, training) - expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double") - vals <- collect(select(prediction, "prediction")) - - # manual calculation of the R predicted values to avoid dependence on statmod - #' library(statmod) - #' rModel <- glm(Sepal.Width ~ Sepal.Length + Species, data = iris, - #' family = tweedie(var.power = 1.2, link.power = 0.0)) - #' print(coef(rModel)) - - rCoef <- c(0.6455409, 0.1169143, -0.3224752, -0.3282174) - rVals <- exp(as.numeric(model.matrix(Sepal.Width ~ Sepal.Length + Species, - data = iris) %*% rCoef)) - expect_true(all(abs(rVals - vals) < 1e-5), rVals - vals) - - sparkR.session.stop() -}) diff --git a/image_content/config/spark/opt/spark/R/lib/SparkR/worker/daemon.R b/image_content/config/spark/opt/spark/R/lib/SparkR/worker/daemon.R deleted file mode 100644 index 3a318b7..0000000 --- a/image_content/config/spark/opt/spark/R/lib/SparkR/worker/daemon.R +++ /dev/null @@ -1,55 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Worker daemon - -rLibDir <- Sys.getenv("SPARKR_RLIBDIR") -connectionTimeout <- as.integer(Sys.getenv("SPARKR_BACKEND_CONNECTION_TIMEOUT", "6000")) -dirs <- strsplit(rLibDir, ",")[[1]] -script <- file.path(dirs[[1]], "SparkR", "worker", "worker.R") - -# preload SparkR package, speedup worker -.libPaths(c(dirs, .libPaths())) -suppressPackageStartupMessages(library(SparkR)) - -port <- as.integer(Sys.getenv("SPARKR_WORKER_PORT")) -inputCon <- socketConnection( - port = port, open = "rb", blocking = TRUE, timeout = connectionTimeout) - -while (TRUE) { - ready <- socketSelect(list(inputCon)) - if (ready) { - port <- SparkR:::readInt(inputCon) - # There is a small chance that it could be interrupted by signal, retry one time - if (length(port) == 0) { - port <- SparkR:::readInt(inputCon) - if (length(port) == 0) { - cat("quitting daemon\n") - quit(save = "no") - } - } - p <- parallel:::mcfork() - if (inherits(p, "masterProcess")) { - close(inputCon) - Sys.setenv(SPARKR_WORKER_PORT = port) - try(source(script)) - # Set SIGUSR1 so that child can exit - tools::pskill(Sys.getpid(), tools::SIGUSR1) - parallel:::mcexit(0L) - } - } -} diff --git a/image_content/config/spark/opt/spark/R/lib/SparkR/worker/worker.R b/image_content/config/spark/opt/spark/R/lib/SparkR/worker/worker.R deleted file mode 100644 index 03e7450..0000000 --- a/image_content/config/spark/opt/spark/R/lib/SparkR/worker/worker.R +++ /dev/null @@ -1,264 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Worker class - -# Get current system time -currentTimeSecs <- function() { - as.numeric(Sys.time()) -} - -# Get elapsed time -elapsedSecs <- function() { - proc.time()[3] -} - -compute <- function(mode, partition, serializer, deserializer, key, - colNames, computeFunc, inputData) { - if (mode > 0) { - if (deserializer == "row") { - # Transform the list of rows into a data.frame - # Note that the optional argument stringsAsFactors for rbind is - # available since R 3.2.4. So we set the global option here. - oldOpt <- getOption("stringsAsFactors") - options(stringsAsFactors = FALSE) - - # Handle binary data types - if ("raw" %in% sapply(inputData[[1]], class)) { - inputData <- SparkR:::rbindRaws(inputData) - } else { - inputData <- do.call(rbind.data.frame, inputData) - } - - options(stringsAsFactors = oldOpt) - - names(inputData) <- colNames - } else { - # Check to see if inputData is a valid data.frame - stopifnot(deserializer == "byte") - stopifnot(class(inputData) == "data.frame") - } - - if (mode == 2) { - output <- computeFunc(key, inputData) - } else { - output <- computeFunc(inputData) - } - if (serializer == "row") { - # Transform the result data.frame back to a list of rows - output <- split(output, seq(nrow(output))) - } else { - # Serialize the ouput to a byte array - stopifnot(serializer == "byte") - } - } else { - output <- computeFunc(partition, inputData) - } - return (output) -} - -outputResult <- function(serializer, output, outputCon) { - if (serializer == "byte") { - SparkR:::writeRawSerialize(outputCon, output) - } else if (serializer == "row") { - SparkR:::writeRowSerialize(outputCon, output) - } else { - # write lines one-by-one with flag - lapply(output, function(line) SparkR:::writeString(outputCon, line)) - } -} - -# Constants -specialLengths <- list(END_OF_STERAM = 0L, TIMING_DATA = -1L) - -# Timing R process boot -bootTime <- currentTimeSecs() -bootElap <- elapsedSecs() - -rLibDir <- Sys.getenv("SPARKR_RLIBDIR") -connectionTimeout <- as.integer(Sys.getenv("SPARKR_BACKEND_CONNECTION_TIMEOUT", "6000")) -dirs <- strsplit(rLibDir, ",")[[1]] -# Set libPaths to include SparkR package as loadNamespace needs this -# TODO: Figure out if we can avoid this by not loading any objects that require -# SparkR namespace -.libPaths(c(dirs, .libPaths())) -suppressPackageStartupMessages(library(SparkR)) - -port <- as.integer(Sys.getenv("SPARKR_WORKER_PORT")) -inputCon <- socketConnection( - port = port, blocking = TRUE, open = "rb", timeout = connectionTimeout) -outputCon <- socketConnection( - port = port, blocking = TRUE, open = "wb", timeout = connectionTimeout) - -# read the index of the current partition inside the RDD -partition <- SparkR:::readInt(inputCon) - -deserializer <- SparkR:::readString(inputCon) -serializer <- SparkR:::readString(inputCon) - -# Include packages as required -packageNames <- unserialize(SparkR:::readRaw(inputCon)) -for (pkg in packageNames) { - suppressPackageStartupMessages(library(as.character(pkg), character.only = TRUE)) -} - -# read function dependencies -funcLen <- SparkR:::readInt(inputCon) -computeFunc <- unserialize(SparkR:::readRawLen(inputCon, funcLen)) -env <- environment(computeFunc) -parent.env(env) <- .GlobalEnv # Attach under global environment. - -# Timing init envs for computing -initElap <- elapsedSecs() - -# Read and set broadcast variables -numBroadcastVars <- SparkR:::readInt(inputCon) -if (numBroadcastVars > 0) { - for (bcast in seq(1:numBroadcastVars)) { - bcastId <- SparkR:::readInt(inputCon) - value <- unserialize(SparkR:::readRaw(inputCon)) - SparkR:::setBroadcastValue(bcastId, value) - } -} - -# Timing broadcast -broadcastElap <- elapsedSecs() -# Initial input timing -inputElap <- broadcastElap - -# If -1: read as normal RDD; if >= 0, treat as pairwise RDD and treat the int -# as number of partitions to create. -numPartitions <- SparkR:::readInt(inputCon) - -# 0 - RDD mode, 1 - dapply mode, 2 - gapply mode -mode <- SparkR:::readInt(inputCon) - -if (mode > 0) { - colNames <- SparkR:::readObject(inputCon) -} - -isEmpty <- SparkR:::readInt(inputCon) -computeInputElapsDiff <- 0 -outputComputeElapsDiff <- 0 - -if (isEmpty != 0) { - if (numPartitions == -1) { - if (deserializer == "byte") { - # Now read as many characters as described in funcLen - data <- SparkR:::readDeserialize(inputCon) - } else if (deserializer == "string") { - data <- as.list(readLines(inputCon)) - } else if (deserializer == "row" && mode == 2) { - dataWithKeys <- SparkR:::readMultipleObjectsWithKeys(inputCon) - keys <- dataWithKeys$keys - data <- dataWithKeys$data - } else if (deserializer == "row") { - data <- SparkR:::readMultipleObjects(inputCon) - } - - # Timing reading input data for execution - inputElap <- elapsedSecs() - if (mode > 0) { - if (mode == 1) { - output <- compute(mode, partition, serializer, deserializer, NULL, - colNames, computeFunc, data) - } else { - # gapply mode - for (i in 1:length(data)) { - # Timing reading input data for execution - inputElap <- elapsedSecs() - output <- compute(mode, partition, serializer, deserializer, keys[[i]], - colNames, computeFunc, data[[i]]) - computeElap <- elapsedSecs() - outputResult(serializer, output, outputCon) - outputElap <- elapsedSecs() - computeInputElapsDiff <- computeInputElapsDiff + (computeElap - inputElap) - outputComputeElapsDiff <- outputComputeElapsDiff + (outputElap - computeElap) - } - } - } else { - output <- compute(mode, partition, serializer, deserializer, NULL, - colNames, computeFunc, data) - } - if (mode != 2) { - # Not a gapply mode - computeElap <- elapsedSecs() - outputResult(serializer, output, outputCon) - outputElap <- elapsedSecs() - computeInputElapsDiff <- computeElap - inputElap - outputComputeElapsDiff <- outputElap - computeElap - } - } else { - if (deserializer == "byte") { - # Now read as many characters as described in funcLen - data <- SparkR:::readDeserialize(inputCon) - } else if (deserializer == "string") { - data <- readLines(inputCon) - } else if (deserializer == "row") { - data <- SparkR:::readMultipleObjects(inputCon) - } - # Timing reading input data for execution - inputElap <- elapsedSecs() - - res <- new.env() - - # Step 1: hash the data to an environment - hashTupleToEnvir <- function(tuple) { - # NOTE: execFunction is the hash function here - hashVal <- computeFunc(tuple[[1]]) - bucket <- as.character(hashVal %% numPartitions) - acc <- res[[bucket]] - # Create a new accumulator - if (is.null(acc)) { - acc <- SparkR:::initAccumulator() - } - SparkR:::addItemToAccumulator(acc, tuple) - res[[bucket]] <- acc - } - invisible(lapply(data, hashTupleToEnvir)) - # Timing computing - computeElap <- elapsedSecs() - - # Step 2: write out all of the environment as key-value pairs. - for (name in ls(res)) { - SparkR:::writeInt(outputCon, 2L) - SparkR:::writeInt(outputCon, as.integer(name)) - # Truncate the accumulator list to the number of elements we have - length(res[[name]]$data) <- res[[name]]$counter - SparkR:::writeRawSerialize(outputCon, res[[name]]$data) - } - # Timing output - outputElap <- elapsedSecs() - computeInputElapsDiff <- computeElap - inputElap - outputComputeElapsDiff <- outputElap - computeElap - } -} - -# Report timing -SparkR:::writeInt(outputCon, specialLengths$TIMING_DATA) -SparkR:::writeDouble(outputCon, bootTime) -SparkR:::writeDouble(outputCon, initElap - bootElap) # init -SparkR:::writeDouble(outputCon, broadcastElap - initElap) # broadcast -SparkR:::writeDouble(outputCon, inputElap - broadcastElap) # input -SparkR:::writeDouble(outputCon, computeInputElapsDiff) # compute -SparkR:::writeDouble(outputCon, outputComputeElapsDiff) # output - -# End of output -SparkR:::writeInt(outputCon, specialLengths$END_OF_STERAM) - -close(outputCon) -close(inputCon) diff --git a/image_content/config/spark/opt/spark/R/lib/sparkr.zip b/image_content/config/spark/opt/spark/R/lib/sparkr.zip deleted file mode 120000 index 32fb01d..0000000 --- a/image_content/config/spark/opt/spark/R/lib/sparkr.zip +++ /dev/null @@ -1 +0,0 @@ -../../../../../../../.git/annex/objects/f2/8g/SHA256E-s1716041--3411b956bc65451250bec11aac696a273a953d29552b9e23c6e329076c8f28a5.zip/SHA256E-s1716041--3411b956bc65451250bec11aac696a273a953d29552b9e23c6e329076c8f28a5.zip \ No newline at end of file diff --git a/image_content/config/spark/opt/spark/README.md b/image_content/config/spark/opt/spark/README.md deleted file mode 100644 index 1e521a7..0000000 --- a/image_content/config/spark/opt/spark/README.md +++ /dev/null @@ -1,103 +0,0 @@ -# Apache Spark - -Spark is a fast and general cluster computing system for Big Data. It provides -high-level APIs in Scala, Java, Python, and R, and an optimized engine that -supports general computation graphs for data analysis. It also supports a -rich set of higher-level tools including Spark SQL for SQL and DataFrames, -MLlib for machine learning, GraphX for graph processing, -and Spark Streaming for stream processing. - - - - -## Online Documentation - -You can find the latest Spark documentation, including a programming -guide, on the [project web page](http://spark.apache.org/documentation.html). -This README file only contains basic setup instructions. - -## Building Spark - -Spark is built using [Apache Maven](http://maven.apache.org/). -To build Spark and its example programs, run: - - build/mvn -DskipTests clean package - -(You do not need to do this if you downloaded a pre-built package.) - -You can build Spark using more than one thread by using the -T option with Maven, see ["Parallel builds in Maven 3"](https://cwiki.apache.org/confluence/display/MAVEN/Parallel+builds+in+Maven+3). -More detailed documentation is available from the project site, at -["Building Spark"](http://spark.apache.org/docs/latest/building-spark.html). - -For general development tips, including info on developing Spark using an IDE, see ["Useful Developer Tools"](http://spark.apache.org/developer-tools.html). - -## Interactive Scala Shell - -The easiest way to start using Spark is through the Scala shell: - - ./bin/spark-shell - -Try the following command, which should return 1000: - - scala> sc.parallelize(1 to 1000).count() - -## Interactive Python Shell - -Alternatively, if you prefer Python, you can use the Python shell: - - ./bin/pyspark - -And run the following command, which should also return 1000: - - >>> sc.parallelize(range(1000)).count() - -## Example Programs - -Spark also comes with several sample programs in the `examples` directory. -To run one of them, use `./bin/run-example [params]`. For example: - - ./bin/run-example SparkPi - -will run the Pi example locally. - -You can set the MASTER environment variable when running examples to submit -examples to a cluster. This can be a mesos:// or spark:// URL, -"yarn" to run on YARN, and "local" to run -locally with one thread, or "local[N]" to run locally with N threads. You -can also use an abbreviated class name if the class is in the `examples` -package. For instance: - - MASTER=spark://host:7077 ./bin/run-example SparkPi - -Many of the example programs print usage help if no params are given. - -## Running Tests - -Testing first requires [building Spark](#building-spark). Once Spark is built, tests -can be run using: - - ./dev/run-tests - -Please see the guidance on how to -[run tests for a module, or individual tests](http://spark.apache.org/developer-tools.html#individual-tests). - -## A Note About Hadoop Versions - -Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported -storage systems. Because the protocols have changed in different versions of -Hadoop, you must build Spark against the same version that your cluster runs. - -Please refer to the build documentation at -["Specifying the Hadoop Version"](http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version) -for detailed guidance on building for a particular distribution of Hadoop, including -building for particular Hive and Hive Thriftserver distributions. - -## Configuration - -Please refer to the [Configuration Guide](http://spark.apache.org/docs/latest/configuration.html) -in the online documentation for an overview on how to configure Spark. - -## Contributing - -Please review the [Contribution to Spark guide](http://spark.apache.org/contributing.html) -for information on how to get started contributing to the project. diff --git a/image_content/config/spark/opt/spark/RELEASE b/image_content/config/spark/opt/spark/RELEASE deleted file mode 100644 index 1e5b9dc..0000000 --- a/image_content/config/spark/opt/spark/RELEASE +++ /dev/null @@ -1,2 +0,0 @@ -Spark 2.2.1 built for Hadoop 2.6.5 -Build flags: -Psparkr -Phadoop-provided -Pyarn -Pmesos -DzincPort=3038 diff --git a/image_content/config/spark/opt/spark/bin/beeline b/image_content/config/spark/opt/spark/bin/beeline deleted file mode 100755 index 0585346..0000000 --- a/image_content/config/spark/opt/spark/bin/beeline +++ /dev/null @@ -1,32 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# -# Shell script for starting BeeLine - -# Enter posix mode for bash -set -o posix - -# Figure out if SPARK_HOME is set -if [ -z "${SPARK_HOME}" ]; then - source "$(dirname "$0")"/find-spark-home -fi - -CLASS="org.apache.hive.beeline.BeeLine" -exec "${SPARK_HOME}/bin/spark-class" $CLASS "$@" diff --git a/image_content/config/spark/opt/spark/bin/beeline.cmd b/image_content/config/spark/opt/spark/bin/beeline.cmd deleted file mode 100644 index 2d39369..0000000 --- a/image_content/config/spark/opt/spark/bin/beeline.cmd +++ /dev/null @@ -1,20 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -cmd /V /E /C "%~dp0spark-class.cmd" org.apache.hive.beeline.BeeLine %* diff --git a/image_content/config/spark/opt/spark/bin/find-spark-home b/image_content/config/spark/opt/spark/bin/find-spark-home deleted file mode 100755 index fa78407..0000000 --- a/image_content/config/spark/opt/spark/bin/find-spark-home +++ /dev/null @@ -1,41 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Attempts to find a proper value for SPARK_HOME. Should be included using "source" directive. - -FIND_SPARK_HOME_PYTHON_SCRIPT="$(cd "$(dirname "$0")"; pwd)/find_spark_home.py" - -# Short cirtuit if the user already has this set. -if [ ! -z "${SPARK_HOME}" ]; then - exit 0 -elif [ ! -f "$FIND_SPARK_HOME_PYTHON_SCRIPT" ]; then - # If we are not in the same directory as find_spark_home.py we are not pip installed so we don't - # need to search the different Python directories for a Spark installation. - # Note only that, if the user has pip installed PySpark but is directly calling pyspark-shell or - # spark-submit in another directory we want to use that version of PySpark rather than the - # pip installed version of PySpark. - export SPARK_HOME="$(cd "$(dirname "$0")"/..; pwd)" -else - # We are pip installed, use the Python script to resolve a reasonable SPARK_HOME - # Default to standard python interpreter unless told otherwise - if [[ -z "$PYSPARK_DRIVER_PYTHON" ]]; then - PYSPARK_DRIVER_PYTHON="${PYSPARK_PYTHON:-"python"}" - fi - export SPARK_HOME=$($PYSPARK_DRIVER_PYTHON "$FIND_SPARK_HOME_PYTHON_SCRIPT") -fi diff --git a/image_content/config/spark/opt/spark/bin/find-spark-home.cmd b/image_content/config/spark/opt/spark/bin/find-spark-home.cmd deleted file mode 100644 index 6f5009c..0000000 --- a/image_content/config/spark/opt/spark/bin/find-spark-home.cmd +++ /dev/null @@ -1,60 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem Path to Python script finding SPARK_HOME -set FIND_SPARK_HOME_PYTHON_SCRIPT=%~dp0find_spark_home.py - -rem Default to standard python interpreter unless told otherwise -set PYTHON_RUNNER=python -rem If PYSPARK_DRIVER_PYTHON is set, it overwrites the python version -if not "x%PYSPARK_DRIVER_PYTHON%"=="x" ( - set PYTHON_RUNNER=%PYSPARK_DRIVER_PYTHON% -) -rem If PYSPARK_PYTHON is set, it overwrites the python version -if not "x%PYSPARK_PYTHON%"=="x" ( - set PYTHON_RUNNER=%PYSPARK_PYTHON% -) - -rem If there is python installed, trying to use the root dir as SPARK_HOME -where %PYTHON_RUNNER% > nul 2>&1 -if %ERRORLEVEL% neq 0 ( - if not exist %PYTHON_RUNNER% ( - if "x%SPARK_HOME%"=="x" ( - echo Missing Python executable '%PYTHON_RUNNER%', defaulting to '%~dp0..' for SPARK_HOME ^ -environment variable. Please install Python or specify the correct Python executable in ^ -PYSPARK_DRIVER_PYTHON or PYSPARK_PYTHON environment variable to detect SPARK_HOME safely. - set SPARK_HOME=%~dp0.. - ) - ) -) - -rem Only attempt to find SPARK_HOME if it is not set. -if "x%SPARK_HOME%"=="x" ( - if not exist "%FIND_SPARK_HOME_PYTHON_SCRIPT%" ( - rem If we are not in the same directory as find_spark_home.py we are not pip installed so we don't - rem need to search the different Python directories for a Spark installation. - rem Note only that, if the user has pip installed PySpark but is directly calling pyspark-shell or - rem spark-submit in another directory we want to use that version of PySpark rather than the - rem pip installed version of PySpark. - set SPARK_HOME=%~dp0.. - ) else ( - rem We are pip installed, use the Python script to resolve a reasonable SPARK_HOME - for /f "delims=" %%i in ('%PYTHON_RUNNER% %FIND_SPARK_HOME_PYTHON_SCRIPT%') do set SPARK_HOME=%%i - ) -) diff --git a/image_content/config/spark/opt/spark/bin/load-spark-env.cmd b/image_content/config/spark/opt/spark/bin/load-spark-env.cmd deleted file mode 100644 index da28f15..0000000 --- a/image_content/config/spark/opt/spark/bin/load-spark-env.cmd +++ /dev/null @@ -1,59 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem This script loads spark-env.cmd if it exists, and ensures it is only loaded once. -rem spark-env.cmd is loaded from SPARK_CONF_DIR if set, or within the current directory's -rem conf/ subdirectory. - -if [%SPARK_ENV_LOADED%] == [] ( - set SPARK_ENV_LOADED=1 - - if not [%SPARK_CONF_DIR%] == [] ( - set user_conf_dir=%SPARK_CONF_DIR% - ) else ( - set user_conf_dir=..\conf - ) - - call :LoadSparkEnv -) - -rem Setting SPARK_SCALA_VERSION if not already set. - -set ASSEMBLY_DIR2="%SPARK_HOME%\assembly\target\scala-2.11" -set ASSEMBLY_DIR1="%SPARK_HOME%\assembly\target\scala-2.10" - -if [%SPARK_SCALA_VERSION%] == [] ( - - if exist %ASSEMBLY_DIR2% if exist %ASSEMBLY_DIR1% ( - echo "Presence of build for both scala versions(SCALA 2.10 and SCALA 2.11) detected." - echo "Either clean one of them or, set SPARK_SCALA_VERSION=2.11 in spark-env.cmd." - exit 1 - ) - if exist %ASSEMBLY_DIR2% ( - set SPARK_SCALA_VERSION=2.11 - ) else ( - set SPARK_SCALA_VERSION=2.10 - ) -) -exit /b 0 - -:LoadSparkEnv -if exist "%user_conf_dir%\spark-env.cmd" ( - call "%user_conf_dir%\spark-env.cmd" -) diff --git a/image_content/config/spark/opt/spark/bin/load-spark-env.sh b/image_content/config/spark/opt/spark/bin/load-spark-env.sh deleted file mode 100644 index 8a2f709..0000000 --- a/image_content/config/spark/opt/spark/bin/load-spark-env.sh +++ /dev/null @@ -1,63 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# This script loads spark-env.sh if it exists, and ensures it is only loaded once. -# spark-env.sh is loaded from SPARK_CONF_DIR if set, or within the current directory's -# conf/ subdirectory. - -# Figure out where Spark is installed -if [ -z "${SPARK_HOME}" ]; then - source "$(dirname "$0")"/find-spark-home -fi - -if [ -z "$SPARK_ENV_LOADED" ]; then - export SPARK_ENV_LOADED=1 - - # Returns the parent of the directory this script lives in. - parent_dir="${SPARK_HOME}" - - user_conf_dir="${SPARK_CONF_DIR:-"$parent_dir"/conf}" - - if [ -f "${user_conf_dir}/spark-env.sh" ]; then - # Promote all variable declarations to environment (exported) variables - set -a - . "${user_conf_dir}/spark-env.sh" - set +a - fi -fi - -# Setting SPARK_SCALA_VERSION if not already set. - -if [ -z "$SPARK_SCALA_VERSION" ]; then - - ASSEMBLY_DIR2="${SPARK_HOME}/assembly/target/scala-2.11" - ASSEMBLY_DIR1="${SPARK_HOME}/assembly/target/scala-2.10" - - if [[ -d "$ASSEMBLY_DIR2" && -d "$ASSEMBLY_DIR1" ]]; then - echo -e "Presence of build for both scala versions(SCALA 2.10 and SCALA 2.11) detected." 1>&2 - echo -e 'Either clean one of them or, export SPARK_SCALA_VERSION=2.11 in spark-env.sh.' 1>&2 - exit 1 - fi - - if [ -d "$ASSEMBLY_DIR2" ]; then - export SPARK_SCALA_VERSION="2.11" - else - export SPARK_SCALA_VERSION="2.10" - fi -fi diff --git a/image_content/config/spark/opt/spark/bin/pyspark b/image_content/config/spark/opt/spark/bin/pyspark deleted file mode 100755 index 98387c2..0000000 --- a/image_content/config/spark/opt/spark/bin/pyspark +++ /dev/null @@ -1,77 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -if [ -z "${SPARK_HOME}" ]; then - source "$(dirname "$0")"/find-spark-home -fi - -source "${SPARK_HOME}"/bin/load-spark-env.sh -export _SPARK_CMD_USAGE="Usage: ./bin/pyspark [options]" - -# In Spark 2.0, IPYTHON and IPYTHON_OPTS are removed and pyspark fails to launch if either option -# is set in the user's environment. Instead, users should set PYSPARK_DRIVER_PYTHON=ipython -# to use IPython and set PYSPARK_DRIVER_PYTHON_OPTS to pass options when starting the Python driver -# (e.g. PYSPARK_DRIVER_PYTHON_OPTS='notebook'). This supports full customization of the IPython -# and executor Python executables. - -# Fail noisily if removed options are set -if [[ -n "$IPYTHON" || -n "$IPYTHON_OPTS" ]]; then - echo "Error in pyspark startup:" - echo "IPYTHON and IPYTHON_OPTS are removed in Spark 2.0+. Remove these from the environment and set PYSPARK_DRIVER_PYTHON and PYSPARK_DRIVER_PYTHON_OPTS instead." - exit 1 -fi - -# Default to standard python interpreter unless told otherwise -if [[ -z "$PYSPARK_DRIVER_PYTHON" ]]; then - PYSPARK_DRIVER_PYTHON="${PYSPARK_PYTHON:-"python"}" -fi - -WORKS_WITH_IPYTHON=$(python -c 'import sys; print(sys.version_info >= (2, 7, 0))') - -# Determine the Python executable to use for the executors: -if [[ -z "$PYSPARK_PYTHON" ]]; then - if [[ $PYSPARK_DRIVER_PYTHON == *ipython* && ! $WORKS_WITH_IPYTHON ]]; then - echo "IPython requires Python 2.7+; please install python2.7 or set PYSPARK_PYTHON" 1>&2 - exit 1 - else - PYSPARK_PYTHON=python - fi -fi -export PYSPARK_PYTHON - -# Add the PySpark classes to the Python path: -export PYTHONPATH="${SPARK_HOME}/python/:$PYTHONPATH" -export PYTHONPATH="${SPARK_HOME}/python/lib/py4j-0.10.4-src.zip:$PYTHONPATH" - -# Load the PySpark shell.py script when ./pyspark is used interactively: -export OLD_PYTHONSTARTUP="$PYTHONSTARTUP" -export PYTHONSTARTUP="${SPARK_HOME}/python/pyspark/shell.py" - -# For pyspark tests -if [[ -n "$SPARK_TESTING" ]]; then - unset YARN_CONF_DIR - unset HADOOP_CONF_DIR - export PYTHONHASHSEED=0 - exec "$PYSPARK_DRIVER_PYTHON" -m "$1" - exit -fi - -export PYSPARK_DRIVER_PYTHON -export PYSPARK_DRIVER_PYTHON_OPTS -exec "${SPARK_HOME}"/bin/spark-submit pyspark-shell-main --name "PySparkShell" "$@" diff --git a/image_content/config/spark/opt/spark/bin/pyspark.cmd b/image_content/config/spark/opt/spark/bin/pyspark.cmd deleted file mode 100644 index 36f28be..0000000 --- a/image_content/config/spark/opt/spark/bin/pyspark.cmd +++ /dev/null @@ -1,23 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem This is the entry point for running PySpark. To avoid polluting the -rem environment, it just launches a new cmd to do the real work. - -cmd /V /E /C "%~dp0pyspark2.cmd" %* diff --git a/image_content/config/spark/opt/spark/bin/pyspark2.cmd b/image_content/config/spark/opt/spark/bin/pyspark2.cmd deleted file mode 100644 index 9d01da1..0000000 --- a/image_content/config/spark/opt/spark/bin/pyspark2.cmd +++ /dev/null @@ -1,38 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem Figure out where the Spark framework is installed -call "%~dp0find-spark-home.cmd" - -call "%SPARK_HOME%\bin\load-spark-env.cmd" -set _SPARK_CMD_USAGE=Usage: bin\pyspark.cmd [options] - -rem Figure out which Python to use. -if "x%PYSPARK_DRIVER_PYTHON%"=="x" ( - set PYSPARK_DRIVER_PYTHON=python - if not [%PYSPARK_PYTHON%] == [] set PYSPARK_DRIVER_PYTHON=%PYSPARK_PYTHON% -) - -set PYTHONPATH=%SPARK_HOME%\python;%PYTHONPATH% -set PYTHONPATH=%SPARK_HOME%\python\lib\py4j-0.10.4-src.zip;%PYTHONPATH% - -set OLD_PYTHONSTARTUP=%PYTHONSTARTUP% -set PYTHONSTARTUP=%SPARK_HOME%\python\pyspark\shell.py - -call "%SPARK_HOME%\bin\spark-submit2.cmd" pyspark-shell-main --name "PySparkShell" %* diff --git a/image_content/config/spark/opt/spark/bin/run-example b/image_content/config/spark/opt/spark/bin/run-example deleted file mode 100755 index 4ba5399..0000000 --- a/image_content/config/spark/opt/spark/bin/run-example +++ /dev/null @@ -1,25 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -if [ -z "${SPARK_HOME}" ]; then - source "$(dirname "$0")"/find-spark-home -fi - -export _SPARK_CMD_USAGE="Usage: ./bin/run-example [options] example-class [example args]" -exec "${SPARK_HOME}"/bin/spark-submit run-example "$@" diff --git a/image_content/config/spark/opt/spark/bin/run-example.cmd b/image_content/config/spark/opt/spark/bin/run-example.cmd deleted file mode 100644 index 4a919f8..0000000 --- a/image_content/config/spark/opt/spark/bin/run-example.cmd +++ /dev/null @@ -1,24 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem Figure out where the Spark framework is installed -call "%~dp0find-spark-home.cmd" - -set _SPARK_CMD_USAGE=Usage: ./bin/run-example [options] example-class [example args] -cmd /V /E /C "%~dp0spark-submit.cmd" run-example %* diff --git a/image_content/config/spark/opt/spark/bin/spark-class b/image_content/config/spark/opt/spark/bin/spark-class deleted file mode 100755 index 65d3b96..0000000 --- a/image_content/config/spark/opt/spark/bin/spark-class +++ /dev/null @@ -1,99 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -if [ -z "${SPARK_HOME}" ]; then - source "$(dirname "$0")"/find-spark-home -fi - -. "${SPARK_HOME}"/bin/load-spark-env.sh - -# Find the java binary -if [ -n "${JAVA_HOME}" ]; then - RUNNER="${JAVA_HOME}/bin/java" -else - if [ "$(command -v java)" ]; then - RUNNER="java" - else - echo "JAVA_HOME is not set" >&2 - exit 1 - fi -fi - -# Find Spark jars. -if [ -d "${SPARK_HOME}/jars" ]; then - SPARK_JARS_DIR="${SPARK_HOME}/jars" -else - SPARK_JARS_DIR="${SPARK_HOME}/assembly/target/scala-$SPARK_SCALA_VERSION/jars" -fi - -if [ ! -d "$SPARK_JARS_DIR" ] && [ -z "$SPARK_TESTING$SPARK_SQL_TESTING" ]; then - echo "Failed to find Spark jars directory ($SPARK_JARS_DIR)." 1>&2 - echo "You need to build Spark with the target \"package\" before running this program." 1>&2 - exit 1 -else - LAUNCH_CLASSPATH="$SPARK_JARS_DIR/*" -fi - -# Add the launcher build dir to the classpath if requested. -if [ -n "$SPARK_PREPEND_CLASSES" ]; then - LAUNCH_CLASSPATH="${SPARK_HOME}/launcher/target/scala-$SPARK_SCALA_VERSION/classes:$LAUNCH_CLASSPATH" -fi - -# For tests -if [[ -n "$SPARK_TESTING" ]]; then - unset YARN_CONF_DIR - unset HADOOP_CONF_DIR -fi - -# The launcher library will print arguments separated by a NULL character, to allow arguments with -# characters that would be otherwise interpreted by the shell. Read that in a while loop, populating -# an array that will be used to exec the final command. -# -# The exit code of the launcher is appended to the output, so the parent shell removes it from the -# command array and checks the value to see if the launcher succeeded. -build_command() { - "$RUNNER" -Xmx128m -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@" - printf "%d\0" $? -} - -# Turn off posix mode since it does not allow process substitution -set +o posix -CMD=() -while IFS= read -d '' -r ARG; do - CMD+=("$ARG") -done < <(build_command "$@") - -COUNT=${#CMD[@]} -LAST=$((COUNT - 1)) -LAUNCHER_EXIT_CODE=${CMD[$LAST]} - -# Certain JVM failures result in errors being printed to stdout (instead of stderr), which causes -# the code that parses the output of the launcher to get confused. In those cases, check if the -# exit code is an integer, and if it's not, handle it as a special error case. -if ! [[ $LAUNCHER_EXIT_CODE =~ ^[0-9]+$ ]]; then - echo "${CMD[@]}" | head -n-1 1>&2 - exit 1 -fi - -if [ $LAUNCHER_EXIT_CODE != 0 ]; then - exit $LAUNCHER_EXIT_CODE -fi - -CMD=("${CMD[@]:0:$LAST}") -exec "${CMD[@]}" diff --git a/image_content/config/spark/opt/spark/bin/spark-class.cmd b/image_content/config/spark/opt/spark/bin/spark-class.cmd deleted file mode 100644 index 96d0cda..0000000 --- a/image_content/config/spark/opt/spark/bin/spark-class.cmd +++ /dev/null @@ -1,23 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem This is the entry point for running a Spark class. To avoid polluting -rem the environment, it just launches a new cmd to do the real work. - -cmd /V /E /C "%~dp0spark-class2.cmd" %* diff --git a/image_content/config/spark/opt/spark/bin/spark-class2.cmd b/image_content/config/spark/opt/spark/bin/spark-class2.cmd deleted file mode 100644 index 400fbd7..0000000 --- a/image_content/config/spark/opt/spark/bin/spark-class2.cmd +++ /dev/null @@ -1,72 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem Figure out where the Spark framework is installed -call "%~dp0find-spark-home.cmd" - -call "%SPARK_HOME%\bin\load-spark-env.cmd" - -rem Test that an argument was given -if "x%1"=="x" ( - echo Usage: spark-class ^ [^] - exit /b 1 -) - -rem Find Spark jars. -if exist "%SPARK_HOME%\jars" ( - set SPARK_JARS_DIR="%SPARK_HOME%\jars" -) else ( - set SPARK_JARS_DIR="%SPARK_HOME%\assembly\target\scala-%SPARK_SCALA_VERSION%\jars" -) - -if not exist "%SPARK_JARS_DIR%"\ ( - echo Failed to find Spark jars directory. - echo You need to build Spark before running this program. - exit /b 1 -) - -set LAUNCH_CLASSPATH=%SPARK_JARS_DIR%\* - -rem Add the launcher build dir to the classpath if requested. -if not "x%SPARK_PREPEND_CLASSES%"=="x" ( - set LAUNCH_CLASSPATH="%SPARK_HOME%\launcher\target\scala-%SPARK_SCALA_VERSION%\classes;%LAUNCH_CLASSPATH%" -) - -rem Figure out where java is. -set RUNNER=java -if not "x%JAVA_HOME%"=="x" ( - set RUNNER=%JAVA_HOME%\bin\java -) else ( - where /q "%RUNNER%" - if ERRORLEVEL 1 ( - echo Java not found and JAVA_HOME environment variable is not set. - echo Install Java and set JAVA_HOME to point to the Java installation directory. - exit /b 1 - ) -) - -rem The launcher library prints the command to be executed in a single line suitable for being -rem executed by the batch interpreter. So read all the output of the launcher into a variable. -set LAUNCHER_OUTPUT=%temp%\spark-class-launcher-output-%RANDOM%.txt -"%RUNNER%" -Xmx128m -cp "%LAUNCH_CLASSPATH%" org.apache.spark.launcher.Main %* > %LAUNCHER_OUTPUT% -for /f "tokens=*" %%i in (%LAUNCHER_OUTPUT%) do ( - set SPARK_CMD=%%i -) -del %LAUNCHER_OUTPUT% -%SPARK_CMD% diff --git a/image_content/config/spark/opt/spark/bin/spark-shell b/image_content/config/spark/opt/spark/bin/spark-shell deleted file mode 100755 index 421f36c..0000000 --- a/image_content/config/spark/opt/spark/bin/spark-shell +++ /dev/null @@ -1,95 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# -# Shell script for starting the Spark Shell REPL - -cygwin=false -case "$(uname)" in - CYGWIN*) cygwin=true;; -esac - -# Enter posix mode for bash -set -o posix - -if [ -z "${SPARK_HOME}" ]; then - source "$(dirname "$0")"/find-spark-home -fi - -export _SPARK_CMD_USAGE="Usage: ./bin/spark-shell [options]" - -# SPARK-4161: scala does not assume use of the java classpath, -# so we need to add the "-Dscala.usejavacp=true" flag manually. We -# do this specifically for the Spark shell because the scala REPL -# has its own class loader, and any additional classpath specified -# through spark.driver.extraClassPath is not automatically propagated. -SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Dscala.usejavacp=true" - -function main() { - if $cygwin; then - # Workaround for issue involving JLine and Cygwin - # (see http://sourceforge.net/p/jline/bugs/40/). - # If you're using the Mintty terminal emulator in Cygwin, may need to set the - # "Backspace sends ^H" setting in "Keys" section of the Mintty options - # (see https://github.com/sbt/sbt/issues/562). - stty -icanon min 1 -echo > /dev/null 2>&1 - export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix" - "${SPARK_HOME}"/bin/spark-submit --class org.apache.spark.repl.Main --name "Spark shell" "$@" - stty icanon echo > /dev/null 2>&1 - else - export SPARK_SUBMIT_OPTS - "${SPARK_HOME}"/bin/spark-submit --class org.apache.spark.repl.Main --name "Spark shell" "$@" - fi -} - -# Copy restore-TTY-on-exit functions from Scala script so spark-shell exits properly even in -# binary distribution of Spark where Scala is not installed -exit_status=127 -saved_stty="" - -# restore stty settings (echo in particular) -function restoreSttySettings() { - stty $saved_stty - saved_stty="" -} - -function onExit() { - if [[ "$saved_stty" != "" ]]; then - restoreSttySettings - fi - exit $exit_status -} - -# to reenable echo if we are interrupted before completing. -trap onExit INT - -# save terminal settings -saved_stty=$(stty -g 2>/dev/null) -# clear on error so we don't later try to restore them -if [[ ! $? ]]; then - saved_stty="" -fi - -main "$@" - -# record the exit status lest it be overwritten: -# then reenable echo and propagate the code. -exit_status=$? -onExit - diff --git a/image_content/config/spark/opt/spark/bin/spark-shell.cmd b/image_content/config/spark/opt/spark/bin/spark-shell.cmd deleted file mode 100644 index 8e670f0..0000000 --- a/image_content/config/spark/opt/spark/bin/spark-shell.cmd +++ /dev/null @@ -1,23 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem This is the entry point for running Spark shell. To avoid polluting the -rem environment, it just launches a new cmd to do the real work. - -cmd /V /E /C "%~dp0spark-shell2.cmd" %* diff --git a/image_content/config/spark/opt/spark/bin/spark-shell2.cmd b/image_content/config/spark/opt/spark/bin/spark-shell2.cmd deleted file mode 100644 index 7ebfe58..0000000 --- a/image_content/config/spark/opt/spark/bin/spark-shell2.cmd +++ /dev/null @@ -1,37 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem Figure out where the Spark framework is installed -call "%~dp0find-spark-home.cmd" - -set _SPARK_CMD_USAGE=Usage: .\bin\spark-shell.cmd [options] - -rem SPARK-4161: scala does not assume use of the java classpath, -rem so we need to add the "-Dscala.usejavacp=true" flag manually. We -rem do this specifically for the Spark shell because the scala REPL -rem has its own class loader, and any additional classpath specified -rem through spark.driver.extraClassPath is not automatically propagated. -if "x%SPARK_SUBMIT_OPTS%"=="x" ( - set SPARK_SUBMIT_OPTS=-Dscala.usejavacp=true - goto run_shell -) -set SPARK_SUBMIT_OPTS="%SPARK_SUBMIT_OPTS% -Dscala.usejavacp=true" - -:run_shell -"%SPARK_HOME%\bin\spark-submit2.cmd" --class org.apache.spark.repl.Main --name "Spark shell" %* diff --git a/image_content/config/spark/opt/spark/bin/spark-sql b/image_content/config/spark/opt/spark/bin/spark-sql deleted file mode 100755 index b08b944..0000000 --- a/image_content/config/spark/opt/spark/bin/spark-sql +++ /dev/null @@ -1,25 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -if [ -z "${SPARK_HOME}" ]; then - source "$(dirname "$0")"/find-spark-home -fi - -export _SPARK_CMD_USAGE="Usage: ./bin/spark-sql [options] [cli option]" -exec "${SPARK_HOME}"/bin/spark-submit --class org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver "$@" diff --git a/image_content/config/spark/opt/spark/bin/spark-submit b/image_content/config/spark/opt/spark/bin/spark-submit deleted file mode 100755 index 4e9d361..0000000 --- a/image_content/config/spark/opt/spark/bin/spark-submit +++ /dev/null @@ -1,27 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -if [ -z "${SPARK_HOME}" ]; then - source "$(dirname "$0")"/find-spark-home -fi - -# disable randomized hash for string in Python 3.3+ -export PYTHONHASHSEED=0 - -exec "${SPARK_HOME}"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@" diff --git a/image_content/config/spark/opt/spark/bin/spark-submit.cmd b/image_content/config/spark/opt/spark/bin/spark-submit.cmd deleted file mode 100644 index 0a06736..0000000 --- a/image_content/config/spark/opt/spark/bin/spark-submit.cmd +++ /dev/null @@ -1,23 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem This is the entry point for running Spark submit. To avoid polluting the -rem environment, it just launches a new cmd to do the real work. - -cmd /V /E /C "%~dp0spark-submit2.cmd" %* diff --git a/image_content/config/spark/opt/spark/bin/spark-submit2.cmd b/image_content/config/spark/opt/spark/bin/spark-submit2.cmd deleted file mode 100644 index 58d6ed3..0000000 --- a/image_content/config/spark/opt/spark/bin/spark-submit2.cmd +++ /dev/null @@ -1,27 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem This is the entry point for running Spark submit. To avoid polluting the -rem environment, it just launches a new cmd to do the real work. - -rem disable randomized hash for string in Python 3.3+ -set PYTHONHASHSEED=0 - -set CLASS=org.apache.spark.deploy.SparkSubmit -"%~dp0spark-class2.cmd" %CLASS% %* diff --git a/image_content/config/spark/opt/spark/bin/sparkR b/image_content/config/spark/opt/spark/bin/sparkR deleted file mode 100755 index 29ab10d..0000000 --- a/image_content/config/spark/opt/spark/bin/sparkR +++ /dev/null @@ -1,26 +0,0 @@ -#!/bin/bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -if [ -z "${SPARK_HOME}" ]; then - source "$(dirname "$0")"/find-spark-home -fi - -source "${SPARK_HOME}"/bin/load-spark-env.sh -export _SPARK_CMD_USAGE="Usage: ./bin/sparkR [options]" -exec "${SPARK_HOME}"/bin/spark-submit sparkr-shell-main "$@" diff --git a/image_content/config/spark/opt/spark/bin/sparkR.cmd b/image_content/config/spark/opt/spark/bin/sparkR.cmd deleted file mode 100644 index a96a714..0000000 --- a/image_content/config/spark/opt/spark/bin/sparkR.cmd +++ /dev/null @@ -1,23 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem This is the entry point for running SparkR. To avoid polluting the -rem environment, it just launches a new cmd to do the real work. - -cmd /V /E /C "%~dp0sparkR2.cmd" %* diff --git a/image_content/config/spark/opt/spark/bin/sparkR2.cmd b/image_content/config/spark/opt/spark/bin/sparkR2.cmd deleted file mode 100644 index fd7187c..0000000 --- a/image_content/config/spark/opt/spark/bin/sparkR2.cmd +++ /dev/null @@ -1,26 +0,0 @@ -@echo off - -rem -rem Licensed to the Apache Software Foundation (ASF) under one or more -rem contributor license agreements. See the NOTICE file distributed with -rem this work for additional information regarding copyright ownership. -rem The ASF licenses this file to You under the Apache License, Version 2.0 -rem (the "License"); you may not use this file except in compliance with -rem the License. You may obtain a copy of the License at -rem -rem http://www.apache.org/licenses/LICENSE-2.0 -rem -rem Unless required by applicable law or agreed to in writing, software -rem distributed under the License is distributed on an "AS IS" BASIS, -rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -rem See the License for the specific language governing permissions and -rem limitations under the License. -rem - -rem Figure out where the Spark framework is installed -call "%~dp0find-spark-home.cmd" - -call "%SPARK_HOME%\bin\load-spark-env.cmd" - - -call "%SPARK_HOME%\bin\spark-submit2.cmd" sparkr-shell-main %* diff --git a/image_content/config/spark/opt/spark/conf/docker.properties.template b/image_content/config/spark/opt/spark/conf/docker.properties.template deleted file mode 100644 index 55cb094..0000000 --- a/image_content/config/spark/opt/spark/conf/docker.properties.template +++ /dev/null @@ -1,20 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -spark.mesos.executor.docker.image: -spark.mesos.executor.docker.volumes: /usr/local/lib:/host/usr/local/lib:ro -spark.mesos.executor.home: /opt/spark diff --git a/image_content/config/spark/opt/spark/conf/fairscheduler.xml.template b/image_content/config/spark/opt/spark/conf/fairscheduler.xml.template deleted file mode 100644 index 385b2e7..0000000 --- a/image_content/config/spark/opt/spark/conf/fairscheduler.xml.template +++ /dev/null @@ -1,31 +0,0 @@ - - - - - - - FAIR - 1 - 2 - - - FIFO - 2 - 3 - - diff --git a/image_content/config/spark/opt/spark/conf/log4j.properties b/image_content/config/spark/opt/spark/conf/log4j.properties deleted file mode 100644 index 2039ec3..0000000 --- a/image_content/config/spark/opt/spark/conf/log4j.properties +++ /dev/null @@ -1,40 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Set everything to be logged to the console -log4j.rootCategory=WARN, console -log4j.appender.console=org.apache.log4j.ConsoleAppender -log4j.appender.console.target=System.err -log4j.appender.console.layout=org.apache.log4j.PatternLayout -log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n - -# Set the default spark-shell log level to WARN. When running the spark-shell, the -# log level for this class is used to overwrite the root logger's log level, so that -# the user can have different defaults for the shell and regular Spark apps. -log4j.logger.org.apache.spark.repl.Main=WARN - -# Settings to quiet third party logs that are too verbose -log4j.logger.org.spark_project.jetty=WARN -log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR -log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=WARN -log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=WARN -log4j.logger.org.apache.parquet=ERROR -log4j.logger.parquet=ERROR - -# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support -log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL -log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR diff --git a/image_content/config/spark/opt/spark/conf/log4j.properties.template b/image_content/config/spark/opt/spark/conf/log4j.properties.template deleted file mode 100644 index ec1aa18..0000000 --- a/image_content/config/spark/opt/spark/conf/log4j.properties.template +++ /dev/null @@ -1,40 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Set everything to be logged to the console -log4j.rootCategory=INFO, console -log4j.appender.console=org.apache.log4j.ConsoleAppender -log4j.appender.console.target=System.err -log4j.appender.console.layout=org.apache.log4j.PatternLayout -log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n - -# Set the default spark-shell log level to WARN. When running the spark-shell, the -# log level for this class is used to overwrite the root logger's log level, so that -# the user can have different defaults for the shell and regular Spark apps. -log4j.logger.org.apache.spark.repl.Main=WARN - -# Settings to quiet third party logs that are too verbose -log4j.logger.org.spark_project.jetty=WARN -log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR -log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO -log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO -log4j.logger.org.apache.parquet=ERROR -log4j.logger.parquet=ERROR - -# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support -log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL -log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR diff --git a/image_content/config/spark/opt/spark/conf/metrics.properties.template b/image_content/config/spark/opt/spark/conf/metrics.properties.template deleted file mode 100644 index aeb76c9..0000000 --- a/image_content/config/spark/opt/spark/conf/metrics.properties.template +++ /dev/null @@ -1,170 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# syntax: [instance].sink|source.[name].[options]=[value] - -# This file configures Spark's internal metrics system. The metrics system is -# divided into instances which correspond to internal components. -# Each instance can be configured to report its metrics to one or more sinks. -# Accepted values for [instance] are "master", "worker", "executor", "driver", -# and "applications". A wildcard "*" can be used as an instance name, in -# which case all instances will inherit the supplied property. -# -# Within an instance, a "source" specifies a particular set of grouped metrics. -# there are two kinds of sources: -# 1. Spark internal sources, like MasterSource, WorkerSource, etc, which will -# collect a Spark component's internal state. Each instance is paired with a -# Spark source that is added automatically. -# 2. Common sources, like JvmSource, which will collect low level state. -# These can be added through configuration options and are then loaded -# using reflection. -# -# A "sink" specifies where metrics are delivered to. Each instance can be -# assigned one or more sinks. -# -# The sink|source field specifies whether the property relates to a sink or -# source. -# -# The [name] field specifies the name of source or sink. -# -# The [options] field is the specific property of this source or sink. The -# source or sink is responsible for parsing this property. -# -# Notes: -# 1. To add a new sink, set the "class" option to a fully qualified class -# name (see examples below). -# 2. Some sinks involve a polling period. The minimum allowed polling period -# is 1 second. -# 3. Wildcard properties can be overridden by more specific properties. -# For example, master.sink.console.period takes precedence over -# *.sink.console.period. -# 4. A metrics specific configuration -# "spark.metrics.conf=${SPARK_HOME}/conf/metrics.properties" should be -# added to Java properties using -Dspark.metrics.conf=xxx if you want to -# customize metrics system. You can also put the file in ${SPARK_HOME}/conf -# and it will be loaded automatically. -# 5. The MetricsServlet sink is added by default as a sink in the master, -# worker and driver, and you can send HTTP requests to the "/metrics/json" -# endpoint to get a snapshot of all the registered metrics in JSON format. -# For master, requests to the "/metrics/master/json" and -# "/metrics/applications/json" endpoints can be sent separately to get -# metrics snapshots of the master instance and applications. This -# MetricsServlet does not have to be configured. - -## List of available common sources and their properties. - -# org.apache.spark.metrics.source.JvmSource -# Note: Currently, JvmSource is the only available common source. -# It can be added to an instance by setting the "class" option to its -# fully qualified class name (see examples below). - -## List of available sinks and their properties. - -# org.apache.spark.metrics.sink.ConsoleSink -# Name: Default: Description: -# period 10 Poll period -# unit seconds Unit of the poll period - -# org.apache.spark.metrics.sink.CSVSink -# Name: Default: Description: -# period 10 Poll period -# unit seconds Unit of the poll period -# directory /tmp Where to store CSV files - -# org.apache.spark.metrics.sink.GangliaSink -# Name: Default: Description: -# host NONE Hostname or multicast group of the Ganglia server, -# must be set -# port NONE Port of the Ganglia server(s), must be set -# period 10 Poll period -# unit seconds Unit of the poll period -# ttl 1 TTL of messages sent by Ganglia -# dmax 0 Lifetime in seconds of metrics (0 never expired) -# mode multicast Ganglia network mode ('unicast' or 'multicast') - -# org.apache.spark.metrics.sink.JmxSink - -# org.apache.spark.metrics.sink.MetricsServlet -# Name: Default: Description: -# path VARIES* Path prefix from the web server root -# sample false Whether to show entire set of samples for histograms -# ('false' or 'true') -# -# * Default path is /metrics/json for all instances except the master. The -# master has two paths: -# /metrics/applications/json # App information -# /metrics/master/json # Master information - -# org.apache.spark.metrics.sink.GraphiteSink -# Name: Default: Description: -# host NONE Hostname of the Graphite server, must be set -# port NONE Port of the Graphite server, must be set -# period 10 Poll period -# unit seconds Unit of the poll period -# prefix EMPTY STRING Prefix to prepend to every metric's name -# protocol tcp Protocol ("tcp" or "udp") to use - -## Examples -# Enable JmxSink for all instances by class name -#*.sink.jmx.class=org.apache.spark.metrics.sink.JmxSink - -# Enable ConsoleSink for all instances by class name -#*.sink.console.class=org.apache.spark.metrics.sink.ConsoleSink - -# Polling period for the ConsoleSink -#*.sink.console.period=10 -# Unit of the polling period for the ConsoleSink -#*.sink.console.unit=seconds - -# Polling period for the ConsoleSink specific for the master instance -#master.sink.console.period=15 -# Unit of the polling period for the ConsoleSink specific for the master -# instance -#master.sink.console.unit=seconds - -# Enable CsvSink for all instances by class name -#*.sink.csv.class=org.apache.spark.metrics.sink.CsvSink - -# Polling period for the CsvSink -#*.sink.csv.period=1 -# Unit of the polling period for the CsvSink -#*.sink.csv.unit=minutes - -# Polling directory for CsvSink -#*.sink.csv.directory=/tmp/ - -# Polling period for the CsvSink specific for the worker instance -#worker.sink.csv.period=10 -# Unit of the polling period for the CsvSink specific for the worker instance -#worker.sink.csv.unit=minutes - -# Enable Slf4jSink for all instances by class name -#*.sink.slf4j.class=org.apache.spark.metrics.sink.Slf4jSink - -# Polling period for the Slf4JSink -#*.sink.slf4j.period=1 -# Unit of the polling period for the Slf4jSink -#*.sink.slf4j.unit=minutes - -# Enable JvmSource for instance master, worker, driver and executor -#master.source.jvm.class=org.apache.spark.metrics.source.JvmSource - -#worker.source.jvm.class=org.apache.spark.metrics.source.JvmSource - -#driver.source.jvm.class=org.apache.spark.metrics.source.JvmSource - -#executor.source.jvm.class=org.apache.spark.metrics.source.JvmSource diff --git a/image_content/config/spark/opt/spark/conf/slaves b/image_content/config/spark/opt/spark/conf/slaves deleted file mode 100644 index 60982dc..0000000 --- a/image_content/config/spark/opt/spark/conf/slaves +++ /dev/null @@ -1,19 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# A Spark Worker will be started on each of the machines listed below. -st9-ape-ionosphere2c-2 diff --git a/image_content/config/spark/opt/spark/conf/slaves.template b/image_content/config/spark/opt/spark/conf/slaves.template deleted file mode 100644 index be42a63..0000000 --- a/image_content/config/spark/opt/spark/conf/slaves.template +++ /dev/null @@ -1,19 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# A Spark Worker will be started on each of the machines listed below. -localhost \ No newline at end of file diff --git a/image_content/config/spark/opt/spark/conf/spark-defaults.conf b/image_content/config/spark/opt/spark/conf/spark-defaults.conf deleted file mode 100644 index 251ea92..0000000 --- a/image_content/config/spark/opt/spark/conf/spark-defaults.conf +++ /dev/null @@ -1,65 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Default system properties included when running spark-submit. -# This is useful for setting default environmental settings. -# Example: -# spark.master spark://master:7077 -# spark.eventLog.enabled true -# spark.eventLog.dir hdfs://namenode:8021/directory -# spark.serializer org.apache.spark.serializer.KryoSerializer -# spark.driver.memory 5g -# spark.executor.extraJavaOptions -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three" -spark.serializer org.apache.spark.serializer.KryoSerializer -spark.driver.memory 2g -spark.executor.memory 2g -spark.executor.cores 4 -spark.task.cpus 2 -spark.task.maxFailures 2 -spark.cores.max 12 -spark.local.dir /data/spark -spark.dynamicAllocation.maxExecutors 3 -spark.dynamicAllocation.executorIdleTimeout 240s -spark.dynamicAllocation.cachedExecutorIdleTimeout 3600s -spark.io.compression.codec lzf -# spark.speculation true -spark.shuffle.manager sort -spark.dynamicAllocation.enabled true -spark.shuffle.service.enabled true -spark.shuffle.compress true -spark.shuffle.spill.compress true -# spark.executor.extraClassPath /usr/lib/hadoop/lib/hadoop-lzo.jar:/etc/hbase/conf #:/opt/hdfs/jars/* -# spark.driver.extraClassPath /usr/lib/hadoop/lib/hadoop-lzo.jar:/etc/hbase/conf #:/opt/hdfs/jars/* -spark.ui.showConsoleProgress false -spark.streaming.receiver.writeAheadLog.enable false -spark.streaming.unpersist true -spark.streaming.stopGracefullyOnShutdown false -spark.streaming.ui.retainedBatches 10 -spark.ui.retainedJobs 10 -spark.ui.retainedStages 10 -spark.worker.ui.retainedExecutors 10 -spark.worker.ui.retainedDrivers 10 -spark.sql.ui.retainedExecutions 10 -spark.worker.cleanup.enabled=true -spark.worker.cleanup.appDataTtl=43200 -spark.cleaner.ttl=43200 -spark.eventLog.enabled false -# spark.eventLog.dir hdfs://st9-ape-ionosphere2m.ncfu.net:8020/spark -# spark.history.fs.logDirectory hdfs://st9-ape-ionosphere2m.ncfu.net:8020/spark -# spark.jars /usr/lib/hbase/hbase-client.jar,/usr/lib/hbase/hbase-server.jar,/usr/lib/hbase/hbase-common.jar,/usr/lib/hbase/hbase-protocol.jar,/usr/lib/hbase/hbase-hadoop-compat.jar,/usr/lib/hadoop/hadoop-annotations.jar,/usr/lib/hbase/lib/htrace-core.jar,/etc/hbase/conf/hbase-site.xml -spark.executor.extraJavaOptions=-Dlog4j.configuration=file:///opt/spark/conf/log4j.properties -spark.driver.extraJavaOptions=-Dlog4j.configuration=file:///opt/spark/conf/log4j.properties - diff --git a/image_content/config/spark/opt/spark/conf/spark-defaults.conf.template b/image_content/config/spark/opt/spark/conf/spark-defaults.conf.template deleted file mode 100644 index 19cba6e..0000000 --- a/image_content/config/spark/opt/spark/conf/spark-defaults.conf.template +++ /dev/null @@ -1,27 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Default system properties included when running spark-submit. -# This is useful for setting default environmental settings. - -# Example: -# spark.master spark://master:7077 -# spark.eventLog.enabled true -# spark.eventLog.dir hdfs://namenode:8021/directory -# spark.serializer org.apache.spark.serializer.KryoSerializer -# spark.driver.memory 5g -# spark.executor.extraJavaOptions -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three" diff --git a/image_content/config/spark/opt/spark/conf/spark-env.sh b/image_content/config/spark/opt/spark/conf/spark-env.sh deleted file mode 100755 index 541d14a..0000000 --- a/image_content/config/spark/opt/spark/conf/spark-env.sh +++ /dev/null @@ -1,72 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# This file is sourced when running various Spark programs. -# Copy it as spark-env.sh and edit that to configure Spark for your site. - -# Options read when launching programs locally with -# ./bin/run-example or ./bin/spark-submit -# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files -# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node -# - SPARK_PUBLIC_DNS, to set the public dns name of the driver program -# - SPARK_CLASSPATH, default classpath entries to append - -# Options read by executors and drivers running inside the cluster -# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node -# - SPARK_PUBLIC_DNS, to set the public DNS name of the driver program -# - SPARK_CLASSPATH, default classpath entries to append -# - SPARK_LOCAL_DIRS, storage directories to use on this node for shuffle and RDD data -# - MESOS_NATIVE_JAVA_LIBRARY, to point to your libmesos.so if you use Mesos - -# Options read in YARN client mode -# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files -# - SPARK_EXECUTOR_INSTANCES, Number of executors to start (Default: 2) -# - SPARK_EXECUTOR_CORES, Number of cores for the executors (Default: 1). -# - SPARK_EXECUTOR_MEMORY, Memory per Executor (e.g. 1000M, 2G) (Default: 1G) -# - SPARK_DRIVER_MEMORY, Memory for Driver (e.g. 1000M, 2G) (Default: 1G) - -# Options for the daemons used in the standalone deploy mode -# - SPARK_MASTER_HOST, to bind the master to a different IP address or hostname -# - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports for the master -# - SPARK_MASTER_OPTS, to set config properties only for the master (e.g. "-Dx=y") -# - SPARK_WORKER_CORES, to set the number of cores to use on this machine -# - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (e.g. 1000m, 2g) -# - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT, to use non-default ports for the worker -# - SPARK_WORKER_INSTANCES, to set the number of worker processes per node -# - SPARK_WORKER_DIR, to set the working directory of worker processes -# - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y") -# - SPARK_DAEMON_MEMORY, to allocate to the master, worker and history server themselves (default: 1g). -# - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y") -# - SPARK_SHUFFLE_OPTS, to set config properties only for the external shuffle service (e.g. "-Dx=y") -# - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y") -# - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers - -# SPARK_MASTER_HOST=st9-ape-ionosphere2c-1.ncfu.net - -# Generic options for the daemons used in the standalone deploy mode -# - SPARK_CONF_DIR Alternate conf dir. (Default: ${SPARK_HOME}/conf) -# - SPARK_LOG_DIR Where log files are stored. (Default: ${SPARK_HOME}/logs) -# - SPARK_PID_DIR Where the pid file is stored. (Default: /tmp) -# - SPARK_IDENT_STRING A string representing this instance of spark. (Default: $USER) -# - SPARK_NICENESS The scheduling priority for daemons. (Default: 0) -# - SPARK_NO_DAEMONIZE Run the proposed command in the foreground. It will not output a PID file. - -# HADOOP_CONF_DIR=/etc/hadoop/conf.ion -# LD_LIBRARY_PATH=/usr/lib/hadoop/lib/native -SPARK_DIST_CLASSPATH=$(hadoop classpath):/opt/spark/jars/* #:/usr/share/java/* diff --git a/image_content/config/spark/opt/spark/conf/spark-env.sh.template b/image_content/config/spark/opt/spark/conf/spark-env.sh.template deleted file mode 100755 index 5c1e876..0000000 --- a/image_content/config/spark/opt/spark/conf/spark-env.sh.template +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env bash - -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# This file is sourced when running various Spark programs. -# Copy it as spark-env.sh and edit that to configure Spark for your site. - -# Options read when launching programs locally with -# ./bin/run-example or ./bin/spark-submit -# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files -# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node -# - SPARK_PUBLIC_DNS, to set the public dns name of the driver program -# - SPARK_CLASSPATH, default classpath entries to append - -# Options read by executors and drivers running inside the cluster -# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node -# - SPARK_PUBLIC_DNS, to set the public DNS name of the driver program -# - SPARK_CLASSPATH, default classpath entries to append -# - SPARK_LOCAL_DIRS, storage directories to use on this node for shuffle and RDD data -# - MESOS_NATIVE_JAVA_LIBRARY, to point to your libmesos.so if you use Mesos - -# Options read in YARN client mode -# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files -# - SPARK_EXECUTOR_INSTANCES, Number of executors to start (Default: 2) -# - SPARK_EXECUTOR_CORES, Number of cores for the executors (Default: 1). -# - SPARK_EXECUTOR_MEMORY, Memory per Executor (e.g. 1000M, 2G) (Default: 1G) -# - SPARK_DRIVER_MEMORY, Memory for Driver (e.g. 1000M, 2G) (Default: 1G) - -# Options for the daemons used in the standalone deploy mode -# - SPARK_MASTER_HOST, to bind the master to a different IP address or hostname -# - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports for the master -# - SPARK_MASTER_OPTS, to set config properties only for the master (e.g. "-Dx=y") -# - SPARK_WORKER_CORES, to set the number of cores to use on this machine -# - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (e.g. 1000m, 2g) -# - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT, to use non-default ports for the worker -# - SPARK_WORKER_INSTANCES, to set the number of worker processes per node -# - SPARK_WORKER_DIR, to set the working directory of worker processes -# - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y") -# - SPARK_DAEMON_MEMORY, to allocate to the master, worker and history server themselves (default: 1g). -# - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y") -# - SPARK_SHUFFLE_OPTS, to set config properties only for the external shuffle service (e.g. "-Dx=y") -# - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y") -# - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers - -# Generic options for the daemons used in the standalone deploy mode -# - SPARK_CONF_DIR Alternate conf dir. (Default: ${SPARK_HOME}/conf) -# - SPARK_LOG_DIR Where log files are stored. (Default: ${SPARK_HOME}/logs) -# - SPARK_PID_DIR Where the pid file is stored. (Default: /tmp) -# - SPARK_IDENT_STRING A string representing this instance of spark. (Default: $USER) -# - SPARK_NICENESS The scheduling priority for daemons. (Default: 0) -# - SPARK_NO_DAEMONIZE Run the proposed command in the foreground. It will not output a PID file. diff --git a/image_content/config/spark/opt/spark/data/graphx/followers.txt b/image_content/config/spark/opt/spark/data/graphx/followers.txt deleted file mode 100644 index 7bb8e90..0000000 --- a/image_content/config/spark/opt/spark/data/graphx/followers.txt +++ /dev/null @@ -1,8 +0,0 @@ -2 1 -4 1 -1 2 -6 3 -7 3 -7 6 -6 7 -3 7 diff --git a/image_content/config/spark/opt/spark/data/graphx/users.txt b/image_content/config/spark/opt/spark/data/graphx/users.txt deleted file mode 100644 index 982d19d..0000000 --- a/image_content/config/spark/opt/spark/data/graphx/users.txt +++ /dev/null @@ -1,7 +0,0 @@ -1,BarackObama,Barack Obama -2,ladygaga,Goddess of Love -3,jeresig,John Resig -4,justinbieber,Justin Bieber -6,matei_zaharia,Matei Zaharia -7,odersky,Martin Odersky -8,anonsys diff --git a/image_content/config/spark/opt/spark/data/mllib/als/sample_movielens_ratings.txt b/image_content/config/spark/opt/spark/data/mllib/als/sample_movielens_ratings.txt deleted file mode 100644 index 0889142..0000000 --- a/image_content/config/spark/opt/spark/data/mllib/als/sample_movielens_ratings.txt +++ /dev/null @@ -1,1501 +0,0 @@ -0::2::3::1424380312 -0::3::1::1424380312 -0::5::2::1424380312 -0::9::4::1424380312 -0::11::1::1424380312 -0::12::2::1424380312 -0::15::1::1424380312 -0::17::1::1424380312 -0::19::1::1424380312 -0::21::1::1424380312 -0::23::1::1424380312 -0::26::3::1424380312 -0::27::1::1424380312 -0::28::1::1424380312 -0::29::1::1424380312 -0::30::1::1424380312 -0::31::1::1424380312 -0::34::1::1424380312 -0::37::1::1424380312 -0::41::2::1424380312 -0::44::1::1424380312 -0::45::2::1424380312 -0::46::1::1424380312 -0::47::1::1424380312 -0::48::1::1424380312 -0::50::1::1424380312 -0::51::1::1424380312 -0::54::1::1424380312 -0::55::1::1424380312 -0::59::2::1424380312 -0::61::2::1424380312 -0::64::1::1424380312 -0::67::1::1424380312 -0::68::1::1424380312 -0::69::1::1424380312 -0::71::1::1424380312 -0::72::1::1424380312 -0::77::2::1424380312 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a/image_content/config/spark/opt/spark/data/mllib/kmeans_data.txt +++ /dev/null @@ -1,6 +0,0 @@ -0.0 0.0 0.0 -0.1 0.1 0.1 -0.2 0.2 0.2 -9.0 9.0 9.0 -9.1 9.1 9.1 -9.2 9.2 9.2 diff --git a/image_content/config/spark/opt/spark/data/mllib/pagerank_data.txt b/image_content/config/spark/opt/spark/data/mllib/pagerank_data.txt deleted file mode 100644 index 95755ab..0000000 --- a/image_content/config/spark/opt/spark/data/mllib/pagerank_data.txt +++ /dev/null @@ -1,6 +0,0 @@ -1 2 -1 3 -1 4 -2 1 -3 1 -4 1 diff --git a/image_content/config/spark/opt/spark/data/mllib/pic_data.txt b/image_content/config/spark/opt/spark/data/mllib/pic_data.txt deleted file mode 100644 index fcfef8c..0000000 --- a/image_content/config/spark/opt/spark/data/mllib/pic_data.txt +++ /dev/null @@ -1,19 +0,0 @@ -0 1 1.0 -0 2 1.0 -0 3 1.0 -1 2 1.0 -1 3 1.0 -2 3 1.0 -3 4 0.1 -4 5 1.0 -4 15 1.0 -5 6 1.0 -6 7 1.0 -7 8 1.0 -8 9 1.0 -9 10 1.0 -10 11 1.0 -11 12 1.0 -12 13 1.0 -13 14 1.0 -14 15 1.0 diff --git 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a/image_content/config/spark/opt/spark/data/mllib/sample_libsvm_data.txt b/image_content/config/spark/opt/spark/data/mllib/sample_libsvm_data.txt deleted file mode 100644 index 861c70c..0000000 --- a/image_content/config/spark/opt/spark/data/mllib/sample_libsvm_data.txt +++ /dev/null @@ -1,100 +0,0 @@ -0 128:51 129:159 130:253 131:159 132:50 155:48 156:238 157:252 158:252 159:252 160:237 182:54 183:227 184:253 185:252 186:239 187:233 188:252 189:57 190:6 208:10 209:60 210:224 211:252 212:253 213:252 214:202 215:84 216:252 217:253 218:122 236:163 237:252 238:252 239:252 240:253 241:252 242:252 243:96 244:189 245:253 246:167 263:51 264:238 265:253 266:253 267:190 268:114 269:253 270:228 271:47 272:79 273:255 274:168 290:48 291:238 292:252 293:252 294:179 295:12 296:75 297:121 298:21 301:253 302:243 303:50 317:38 318:165 319:253 320:233 321:208 322:84 329:253 330:252 331:165 344:7 345:178 346:252 347:240 348:71 349:19 350:28 357:253 358:252 359:195 372:57 373:252 374:252 375:63 385:253 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2.055002875864414 0 0 0 0 -1 2.857738033247042 0 0 2.619965104088255 0 0 0 2.122974378789621 2.228387042742021 2.228387042742023 0 2.055002875864414 0 0 0 0 diff --git a/image_content/config/spark/opt/spark/data/mllib/streaming_kmeans_data_test.txt b/image_content/config/spark/opt/spark/data/mllib/streaming_kmeans_data_test.txt deleted file mode 100644 index 649a0d6..0000000 --- a/image_content/config/spark/opt/spark/data/mllib/streaming_kmeans_data_test.txt +++ /dev/null @@ -1,2 +0,0 @@ -(1.0), [1.7, 0.4, 0.9] -(2.0), [2.2, 1.8, 0.0] diff --git a/image_content/config/spark/opt/spark/data/streaming/AFINN-111.txt b/image_content/config/spark/opt/spark/data/streaming/AFINN-111.txt deleted file mode 100644 index 0f6fb8e..0000000 --- a/image_content/config/spark/opt/spark/data/streaming/AFINN-111.txt +++ /dev/null @@ -1,2477 +0,0 @@ -abandon -2 -abandoned -2 -abandons -2 -abducted -2 -abduction -2 -abductions -2 -abhor -3 -abhorred -3 -abhorrent -3 -abhors -3 -abilities 2 -ability 2 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-wins 4 -winwin 3 -wish 1 -wishes 1 -wishing 1 -withdrawal -3 -woebegone -2 -woeful -3 -won 3 -wonderful 4 -woo 3 -woohoo 3 -wooo 4 -woow 4 -worn -1 -worried -3 -worry -3 -worrying -3 -worse -3 -worsen -3 -worsened -3 -worsening -3 -worsens -3 -worshiped 3 -worst -3 -worth 2 -worthless -2 -worthy 2 -wow 4 -wowow 4 -wowww 4 -wrathful -3 -wreck -2 -wrong -2 -wronged -2 -wtf -4 -yeah 1 -yearning 1 -yeees 2 -yes 1 -youthful 2 -yucky -2 -yummy 3 -zealot -2 -zealots -2 -zealous 2 \ No newline at end of file diff --git a/image_content/config/spark/opt/spark/examples/jars/scopt_2.11-3.3.0.jar b/image_content/config/spark/opt/spark/examples/jars/scopt_2.11-3.3.0.jar deleted file mode 120000 index 2907845..0000000 --- a/image_content/config/spark/opt/spark/examples/jars/scopt_2.11-3.3.0.jar +++ /dev/null @@ -1 +0,0 @@ -../../../../../../../.git/annex/objects/GX/mQ/SHA256E-s121970--fbcbf3d313fe85f0bbd85f8d8456b54d419bc89f8797782d1aa66f49ca0f59e2.0.jar/SHA256E-s121970--fbcbf3d313fe85f0bbd85f8d8456b54d419bc89f8797782d1aa66f49ca0f59e2.0.jar \ No newline at end of file diff --git a/image_content/config/spark/opt/spark/examples/jars/spark-examples_2.11-2.2.1.jar b/image_content/config/spark/opt/spark/examples/jars/spark-examples_2.11-2.2.1.jar deleted file mode 120000 index e2457cf..0000000 --- a/image_content/config/spark/opt/spark/examples/jars/spark-examples_2.11-2.2.1.jar +++ /dev/null @@ -1 +0,0 @@ -../../../../../../../.git/annex/objects/Fm/p8/SHA256E-s1991400--4135107255472d18acb87cc3f9c1a074b64c6d581dc7151856200c41ce357409.1.jar/SHA256E-s1991400--4135107255472d18acb87cc3f9c1a074b64c6d581dc7151856200c41ce357409.1.jar \ No newline at end of file diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java deleted file mode 100644 index 362bd44..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java +++ /dev/null @@ -1,159 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples; - -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.function.Function; -import org.apache.spark.api.java.function.Function2; -import org.apache.spark.sql.SparkSession; - -import java.io.Serializable; -import java.util.Arrays; -import java.util.Random; -import java.util.regex.Pattern; - -/** - * Logistic regression based classification. - * - * This is an example implementation for learning how to use Spark. For more conventional use, - * please refer to org.apache.spark.ml.classification.LogisticRegression. - */ -public final class JavaHdfsLR { - - private static final int D = 10; // Number of dimensions - private static final Random rand = new Random(42); - - static void showWarning() { - String warning = "WARN: This is a naive implementation of Logistic Regression " + - "and is given as an example!\n" + - "Please use org.apache.spark.ml.classification.LogisticRegression " + - "for more conventional use."; - System.err.println(warning); - } - - static class DataPoint implements Serializable { - DataPoint(double[] x, double y) { - this.x = x; - this.y = y; - } - - double[] x; - double y; - } - - static class ParsePoint implements Function { - private static final Pattern SPACE = Pattern.compile(" "); - - @Override - public DataPoint call(String line) { - String[] tok = SPACE.split(line); - double y = Double.parseDouble(tok[0]); - double[] x = new double[D]; - for (int i = 0; i < D; i++) { - x[i] = Double.parseDouble(tok[i + 1]); - } - return new DataPoint(x, y); - } - } - - static class VectorSum implements Function2 { - @Override - public double[] call(double[] a, double[] b) { - double[] result = new double[D]; - for (int j = 0; j < D; j++) { - result[j] = a[j] + b[j]; - } - return result; - } - } - - static class ComputeGradient implements Function { - private final double[] weights; - - ComputeGradient(double[] weights) { - this.weights = weights; - } - - @Override - public double[] call(DataPoint p) { - double[] gradient = new double[D]; - for (int i = 0; i < D; i++) { - double dot = dot(weights, p.x); - gradient[i] = (1 / (1 + Math.exp(-p.y * dot)) - 1) * p.y * p.x[i]; - } - return gradient; - } - } - - public static double dot(double[] a, double[] b) { - double x = 0; - for (int i = 0; i < D; i++) { - x += a[i] * b[i]; - } - return x; - } - - public static void printWeights(double[] a) { - System.out.println(Arrays.toString(a)); - } - - public static void main(String[] args) { - - if (args.length < 2) { - System.err.println("Usage: JavaHdfsLR "); - System.exit(1); - } - - showWarning(); - - SparkSession spark = SparkSession - .builder() - .appName("JavaHdfsLR") - .getOrCreate(); - - JavaRDD lines = spark.read().textFile(args[0]).javaRDD(); - JavaRDD points = lines.map(new ParsePoint()).cache(); - int ITERATIONS = Integer.parseInt(args[1]); - - // Initialize w to a random value - double[] w = new double[D]; - for (int i = 0; i < D; i++) { - w[i] = 2 * rand.nextDouble() - 1; - } - - System.out.print("Initial w: "); - printWeights(w); - - for (int i = 1; i <= ITERATIONS; i++) { - System.out.println("On iteration " + i); - - double[] gradient = points.map( - new ComputeGradient(w) - ).reduce(new VectorSum()); - - for (int j = 0; j < D; j++) { - w[j] -= gradient[j]; - } - - } - - System.out.print("Final w: "); - printWeights(w); - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java deleted file mode 100644 index cf12de3..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java +++ /dev/null @@ -1,121 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples; - -import scala.Tuple2; -import scala.Tuple3; - -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.sql.SparkSession; - -import java.io.Serializable; -import java.util.Arrays; -import java.util.List; -import java.util.regex.Matcher; -import java.util.regex.Pattern; - -/** - * Executes a roll up-style query against Apache logs. - * - * Usage: JavaLogQuery [logFile] - */ -public final class JavaLogQuery { - - public static final List exampleApacheLogs = Arrays.asList( - "10.10.10.10 - \"FRED\" [18/Jan/2013:17:56:07 +1100] \"GET http://images.com/2013/Generic.jpg " + - "HTTP/1.1\" 304 315 \"http://referall.com/\" \"Mozilla/4.0 (compatible; MSIE 7.0; " + - "Windows NT 5.1; GTB7.4; .NET CLR 2.0.50727; .NET CLR 3.0.04506.30; .NET CLR 3.0.04506.648; " + - ".NET CLR 3.5.21022; .NET CLR 3.0.4506.2152; .NET CLR 1.0.3705; .NET CLR 1.1.4322; .NET CLR " + - "3.5.30729; Release=ARP)\" \"UD-1\" - \"image/jpeg\" \"whatever\" 0.350 \"-\" - \"\" 265 923 934 \"\" " + - "62.24.11.25 images.com 1358492167 - Whatup", - "10.10.10.10 - \"FRED\" [18/Jan/2013:18:02:37 +1100] \"GET http://images.com/2013/Generic.jpg " + - "HTTP/1.1\" 304 306 \"http:/referall.com\" \"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; " + - "GTB7.4; .NET CLR 2.0.50727; .NET CLR 3.0.04506.30; .NET CLR 3.0.04506.648; .NET CLR " + - "3.5.21022; .NET CLR 3.0.4506.2152; .NET CLR 1.0.3705; .NET CLR 1.1.4322; .NET CLR " + - "3.5.30729; Release=ARP)\" \"UD-1\" - \"image/jpeg\" \"whatever\" 0.352 \"-\" - \"\" 256 977 988 \"\" " + - "0 73.23.2.15 images.com 1358492557 - Whatup"); - - public static final Pattern apacheLogRegex = Pattern.compile( - "^([\\d.]+) (\\S+) (\\S+) \\[([\\w\\d:/]+\\s[+\\-]\\d{4})\\] \"(.+?)\" (\\d{3}) ([\\d\\-]+) \"([^\"]+)\" \"([^\"]+)\".*"); - - /** Tracks the total query count and number of aggregate bytes for a particular group. */ - public static class Stats implements Serializable { - - private final int count; - private final int numBytes; - - public Stats(int count, int numBytes) { - this.count = count; - this.numBytes = numBytes; - } - public Stats merge(Stats other) { - return new Stats(count + other.count, numBytes + other.numBytes); - } - - public String toString() { - return String.format("bytes=%s\tn=%s", numBytes, count); - } - } - - public static Tuple3 extractKey(String line) { - Matcher m = apacheLogRegex.matcher(line); - if (m.find()) { - String ip = m.group(1); - String user = m.group(3); - String query = m.group(5); - if (!user.equalsIgnoreCase("-")) { - return new Tuple3<>(ip, user, query); - } - } - return new Tuple3<>(null, null, null); - } - - public static Stats extractStats(String line) { - Matcher m = apacheLogRegex.matcher(line); - if (m.find()) { - int bytes = Integer.parseInt(m.group(7)); - return new Stats(1, bytes); - } else { - return new Stats(1, 0); - } - } - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaLogQuery") - .getOrCreate(); - - JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext()); - - JavaRDD dataSet = (args.length == 1) ? jsc.textFile(args[0]) : jsc.parallelize(exampleApacheLogs); - - JavaPairRDD, Stats> extracted = - dataSet.mapToPair(s -> new Tuple2<>(extractKey(s), extractStats(s))); - - JavaPairRDD, Stats> counts = extracted.reduceByKey(Stats::merge); - - List, Stats>> output = counts.collect(); - for (Tuple2 t : output) { - System.out.println(t._1() + "\t" + t._2()); - } - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java deleted file mode 100644 index b5b4703..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java +++ /dev/null @@ -1,122 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples; - -import java.util.ArrayList; -import java.util.List; -import java.util.regex.Pattern; - -import scala.Tuple2; - -import com.google.common.collect.Iterables; - -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.function.Function2; -import org.apache.spark.sql.SparkSession; - -/** - * Computes the PageRank of URLs from an input file. Input file should - * be in format of: - * URL neighbor URL - * URL neighbor URL - * URL neighbor URL - * ... - * where URL and their neighbors are separated by space(s). - * - * This is an example implementation for learning how to use Spark. For more conventional use, - * please refer to org.apache.spark.graphx.lib.PageRank - * - * Example Usage: - * 
- * bin/run-example JavaPageRank data/mllib/pagerank_data.txt 10
- *
- */ -public final class JavaPageRank { - private static final Pattern SPACES = Pattern.compile("\\s+"); - - static void showWarning() { - String warning = "WARN: This is a naive implementation of PageRank " + - "and is given as an example! \n" + - "Please use the PageRank implementation found in " + - "org.apache.spark.graphx.lib.PageRank for more conventional use."; - System.err.println(warning); - } - - private static class Sum implements Function2 { - @Override - public Double call(Double a, Double b) { - return a + b; - } - } - - public static void main(String[] args) throws Exception { - if (args.length < 2) { - System.err.println("Usage: JavaPageRank "); - System.exit(1); - } - - showWarning(); - - SparkSession spark = SparkSession - .builder() - .appName("JavaPageRank") - .getOrCreate(); - - // Loads in input file. It should be in format of: - // URL neighbor URL - // URL neighbor URL - // URL neighbor URL - // ... - JavaRDD lines = spark.read().textFile(args[0]).javaRDD(); - - // Loads all URLs from input file and initialize their neighbors. - JavaPairRDD> links = lines.mapToPair(s -> { - String[] parts = SPACES.split(s); - return new Tuple2<>(parts[0], parts[1]); - }).distinct().groupByKey().cache(); - - // Loads all URLs with other URL(s) link to from input file and initialize ranks of them to one. - JavaPairRDD ranks = links.mapValues(rs -> 1.0); - - // Calculates and updates URL ranks continuously using PageRank algorithm. - for (int current = 0; current < Integer.parseInt(args[1]); current++) { - // Calculates URL contributions to the rank of other URLs. - JavaPairRDD contribs = links.join(ranks).values() - .flatMapToPair(s -> { - int urlCount = Iterables.size(s._1()); - List> results = new ArrayList<>(); - for (String n : s._1) { - results.add(new Tuple2<>(n, s._2() / urlCount)); - } - return results.iterator(); - }); - - // Re-calculates URL ranks based on neighbor contributions. - ranks = contribs.reduceByKey(new Sum()).mapValues(sum -> 0.15 + sum * 0.85); - } - - // Collects all URL ranks and dump them to console. - List> output = ranks.collect(); - for (Tuple2 tuple : output) { - System.out.println(tuple._1() + " has rank: " + tuple._2() + "."); - } - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java deleted file mode 100644 index 37bd8ff..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples; - -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.sql.SparkSession; - -import java.util.ArrayList; -import java.util.List; - -/** - * Computes an approximation to pi - * Usage: JavaSparkPi [partitions] - */ -public final class JavaSparkPi { - - public static void main(String[] args) throws Exception { - SparkSession spark = SparkSession - .builder() - .appName("JavaSparkPi") - .getOrCreate(); - - JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext()); - - int slices = (args.length == 1) ? Integer.parseInt(args[0]) : 2; - int n = 100000 * slices; - List l = new ArrayList<>(n); - for (int i = 0; i < n; i++) { - l.add(i); - } - - JavaRDD dataSet = jsc.parallelize(l, slices); - - int count = dataSet.map(integer -> { - double x = Math.random() * 2 - 1; - double y = Math.random() * 2 - 1; - return (x * x + y * y <= 1) ? 1 : 0; - }).reduce((integer, integer2) -> integer + integer2); - - System.out.println("Pi is roughly " + 4.0 * count / n); - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaStatusTrackerDemo.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaStatusTrackerDemo.java deleted file mode 100644 index b0ebedf..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaStatusTrackerDemo.java +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples; - -import org.apache.spark.SparkJobInfo; -import org.apache.spark.SparkStageInfo; -import org.apache.spark.api.java.JavaFutureAction; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.api.java.function.Function; -import org.apache.spark.sql.SparkSession; - -import java.util.Arrays; -import java.util.List; - -/** - * Example of using Spark's status APIs from Java. - */ -public final class JavaStatusTrackerDemo { - - public static final String APP_NAME = "JavaStatusAPIDemo"; - - public static final class IdentityWithDelay implements Function { - @Override - public T call(T x) throws Exception { - Thread.sleep(2 * 1000); // 2 seconds - return x; - } - } - - public static void main(String[] args) throws Exception { - SparkSession spark = SparkSession - .builder() - .appName(APP_NAME) - .getOrCreate(); - - JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext()); - - // Example of implementing a progress reporter for a simple job. - JavaRDD rdd = jsc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 5).map( - new IdentityWithDelay<>()); - JavaFutureAction> jobFuture = rdd.collectAsync(); - while (!jobFuture.isDone()) { - Thread.sleep(1000); // 1 second - List jobIds = jobFuture.jobIds(); - if (jobIds.isEmpty()) { - continue; - } - int currentJobId = jobIds.get(jobIds.size() - 1); - SparkJobInfo jobInfo = jsc.statusTracker().getJobInfo(currentJobId); - SparkStageInfo stageInfo = jsc.statusTracker().getStageInfo(jobInfo.stageIds()[0]); - System.out.println(stageInfo.numTasks() + " tasks total: " + stageInfo.numActiveTasks() + - " active, " + stageInfo.numCompletedTasks() + " complete"); - } - - System.out.println("Job results are: " + jobFuture.get()); - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaTC.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaTC.java deleted file mode 100644 index c9ca9c9..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaTC.java +++ /dev/null @@ -1,98 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples; - -import java.util.ArrayList; -import java.util.HashSet; -import java.util.List; -import java.util.Random; -import java.util.Set; - -import scala.Tuple2; - -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.api.java.function.PairFunction; -import org.apache.spark.sql.SparkSession; - -/** - * Transitive closure on a graph, implemented in Java. - * Usage: JavaTC [partitions] - */ -public final class JavaTC { - - private static final int numEdges = 200; - private static final int numVertices = 100; - private static final Random rand = new Random(42); - - static List> generateGraph() { - Set> edges = new HashSet<>(numEdges); - while (edges.size() < numEdges) { - int from = rand.nextInt(numVertices); - int to = rand.nextInt(numVertices); - Tuple2 e = new Tuple2<>(from, to); - if (from != to) { - edges.add(e); - } - } - return new ArrayList<>(edges); - } - - static class ProjectFn implements PairFunction>, - Integer, Integer> { - static final ProjectFn INSTANCE = new ProjectFn(); - - @Override - public Tuple2 call(Tuple2> triple) { - return new Tuple2<>(triple._2()._2(), triple._2()._1()); - } - } - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaTC") - .getOrCreate(); - - JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext()); - - Integer slices = (args.length > 0) ? Integer.parseInt(args[0]): 2; - JavaPairRDD tc = jsc.parallelizePairs(generateGraph(), slices).cache(); - - // Linear transitive closure: each round grows paths by one edge, - // by joining the graph's edges with the already-discovered paths. - // e.g. join the path (y, z) from the TC with the edge (x, y) from - // the graph to obtain the path (x, z). - - // Because join() joins on keys, the edges are stored in reversed order. - JavaPairRDD edges = tc.mapToPair(e -> new Tuple2<>(e._2(), e._1())); - - long oldCount; - long nextCount = tc.count(); - do { - oldCount = nextCount; - // Perform the join, obtaining an RDD of (y, (z, x)) pairs, - // then project the result to obtain the new (x, z) paths. - tc = tc.union(tc.join(edges).mapToPair(ProjectFn.INSTANCE)).distinct().cache(); - nextCount = tc.count(); - } while (nextCount != oldCount); - - System.out.println("TC has " + tc.count() + " edges."); - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java deleted file mode 100644 index f1ce1e9..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples; - -import scala.Tuple2; - -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.sql.SparkSession; - -import java.util.Arrays; -import java.util.List; -import java.util.regex.Pattern; - -public final class JavaWordCount { - private static final Pattern SPACE = Pattern.compile(" "); - - public static void main(String[] args) throws Exception { - - if (args.length < 1) { - System.err.println("Usage: JavaWordCount "); - System.exit(1); - } - - SparkSession spark = SparkSession - .builder() - .appName("JavaWordCount") - .getOrCreate(); - - JavaRDD lines = spark.read().textFile(args[0]).javaRDD(); - - JavaRDD words = lines.flatMap(s -> Arrays.asList(SPACE.split(s)).iterator()); - - JavaPairRDD ones = words.mapToPair(s -> new Tuple2<>(s, 1)); - - JavaPairRDD counts = ones.reduceByKey((i1, i2) -> i1 + i2); - - List> output = counts.collect(); - for (Tuple2 tuple : output) { - System.out.println(tuple._1() + ": " + tuple._2()); - } - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java deleted file mode 100644 index 7c741ff..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java +++ /dev/null @@ -1,82 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.regression.AFTSurvivalRegression; -import org.apache.spark.ml.regression.AFTSurvivalRegressionModel; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -/** - * An example demonstrating AFTSurvivalRegression. - * Run with - * 
- * bin/run-example ml.JavaAFTSurvivalRegressionExample
- *
- */ -public class JavaAFTSurvivalRegressionExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaAFTSurvivalRegressionExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(1.218, 1.0, Vectors.dense(1.560, -0.605)), - RowFactory.create(2.949, 0.0, Vectors.dense(0.346, 2.158)), - RowFactory.create(3.627, 0.0, Vectors.dense(1.380, 0.231)), - RowFactory.create(0.273, 1.0, Vectors.dense(0.520, 1.151)), - RowFactory.create(4.199, 0.0, Vectors.dense(0.795, -0.226)) - ); - StructType schema = new StructType(new StructField[]{ - new StructField("label", DataTypes.DoubleType, false, Metadata.empty()), - new StructField("censor", DataTypes.DoubleType, false, Metadata.empty()), - new StructField("features", new VectorUDT(), false, Metadata.empty()) - }); - Dataset training = spark.createDataFrame(data, schema); - double[] quantileProbabilities = new double[]{0.3, 0.6}; - AFTSurvivalRegression aft = new AFTSurvivalRegression() - .setQuantileProbabilities(quantileProbabilities) - .setQuantilesCol("quantiles"); - - AFTSurvivalRegressionModel model = aft.fit(training); - - // Print the coefficients, intercept and scale parameter for AFT survival regression - System.out.println("Coefficients: " + model.coefficients()); - System.out.println("Intercept: " + model.intercept()); - System.out.println("Scale: " + model.scale()); - model.transform(training).show(false); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java deleted file mode 100644 index fe4d6bc..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java +++ /dev/null @@ -1,127 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.io.Serializable; - -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.ml.evaluation.RegressionEvaluator; -import org.apache.spark.ml.recommendation.ALS; -import org.apache.spark.ml.recommendation.ALSModel; -// $example off$ - -public class JavaALSExample { - - // $example on$ - public static class Rating implements Serializable { - private int userId; - private int movieId; - private float rating; - private long timestamp; - - public Rating() {} - - public Rating(int userId, int movieId, float rating, long timestamp) { - this.userId = userId; - this.movieId = movieId; - this.rating = rating; - this.timestamp = timestamp; - } - - public int getUserId() { - return userId; - } - - public int getMovieId() { - return movieId; - } - - public float getRating() { - return rating; - } - - public long getTimestamp() { - return timestamp; - } - - public static Rating parseRating(String str) { - String[] fields = str.split("::"); - if (fields.length != 4) { - throw new IllegalArgumentException("Each line must contain 4 fields"); - } - int userId = Integer.parseInt(fields[0]); - int movieId = Integer.parseInt(fields[1]); - float rating = Float.parseFloat(fields[2]); - long timestamp = Long.parseLong(fields[3]); - return new Rating(userId, movieId, rating, timestamp); - } - } - // $example off$ - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaALSExample") - .getOrCreate(); - - // $example on$ - JavaRDD ratingsRDD = spark - .read().textFile("data/mllib/als/sample_movielens_ratings.txt").javaRDD() - .map(Rating::parseRating); - Dataset ratings = spark.createDataFrame(ratingsRDD, Rating.class); - Dataset[] splits = ratings.randomSplit(new double[]{0.8, 0.2}); - Dataset training = splits[0]; - Dataset test = splits[1]; - - // Build the recommendation model using ALS on the training data - ALS als = new ALS() - .setMaxIter(5) - .setRegParam(0.01) - .setUserCol("userId") - .setItemCol("movieId") - .setRatingCol("rating"); - ALSModel model = als.fit(training); - - // Evaluate the model by computing the RMSE on the test data - // Note we set cold start strategy to 'drop' to ensure we don't get NaN evaluation metrics - model.setColdStartStrategy("drop"); - Dataset predictions = model.transform(test); - - RegressionEvaluator evaluator = new RegressionEvaluator() - .setMetricName("rmse") - .setLabelCol("rating") - .setPredictionCol("prediction"); - Double rmse = evaluator.evaluate(predictions); - System.out.println("Root-mean-square error = " + rmse); - - // Generate top 10 movie recommendations for each user - Dataset userRecs = model.recommendForAllUsers(10); - // Generate top 10 user recommendations for each movie - Dataset movieRecs = model.recommendForAllItems(10); - // $example off$ - userRecs.show(); - movieRecs.show(); - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java deleted file mode 100644 index 3090d8f..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.Binarizer; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaBinarizerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaBinarizerExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0, 0.1), - RowFactory.create(1, 0.8), - RowFactory.create(2, 0.2) - ); - StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("feature", DataTypes.DoubleType, false, Metadata.empty()) - }); - Dataset continuousDataFrame = spark.createDataFrame(data, schema); - - Binarizer binarizer = new Binarizer() - .setInputCol("feature") - .setOutputCol("binarized_feature") - .setThreshold(0.5); - - Dataset binarizedDataFrame = binarizer.transform(continuousDataFrame); - - System.out.println("Binarizer output with Threshold = " + binarizer.getThreshold()); - binarizedDataFrame.show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaBisectingKMeansExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaBisectingKMeansExample.java deleted file mode 100644 index 8c82aaa..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaBisectingKMeansExample.java +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.clustering.BisectingKMeans; -import org.apache.spark.ml.clustering.BisectingKMeansModel; -import org.apache.spark.ml.linalg.Vector; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off$ -import org.apache.spark.sql.SparkSession; - - -/** - * An example demonstrating bisecting k-means clustering. - * Run with - * 
- * bin/run-example ml.JavaBisectingKMeansExample
- *
- */ -public class JavaBisectingKMeansExample { - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaBisectingKMeansExample") - .getOrCreate(); - - // $example on$ - // Loads data. - Dataset dataset = spark.read().format("libsvm").load("data/mllib/sample_kmeans_data.txt"); - - // Trains a bisecting k-means model. - BisectingKMeans bkm = new BisectingKMeans().setK(2).setSeed(1); - BisectingKMeansModel model = bkm.fit(dataset); - - // Evaluate clustering. - double cost = model.computeCost(dataset); - System.out.println("Within Set Sum of Squared Errors = " + cost); - - // Shows the result. - System.out.println("Cluster Centers: "); - Vector[] centers = model.clusterCenters(); - for (Vector center : centers) { - System.out.println(center); - } - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaBucketedRandomProjectionLSHExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaBucketedRandomProjectionLSHExample.java deleted file mode 100644 index ff917b7..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaBucketedRandomProjectionLSHExample.java +++ /dev/null @@ -1,110 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.BucketedRandomProjectionLSH; -import org.apache.spark.ml.feature.BucketedRandomProjectionLSHModel; -import org.apache.spark.ml.linalg.Vector; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; - -import static org.apache.spark.sql.functions.col; -// $example off$ - -/** - * An example demonstrating BucketedRandomProjectionLSH. - * Run with: - * bin/run-example ml.JavaBucketedRandomProjectionLSHExample - */ -public class JavaBucketedRandomProjectionLSHExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaBucketedRandomProjectionLSHExample") - .getOrCreate(); - - // $example on$ - List dataA = Arrays.asList( - RowFactory.create(0, Vectors.dense(1.0, 1.0)), - RowFactory.create(1, Vectors.dense(1.0, -1.0)), - RowFactory.create(2, Vectors.dense(-1.0, -1.0)), - RowFactory.create(3, Vectors.dense(-1.0, 1.0)) - ); - - List dataB = Arrays.asList( - RowFactory.create(4, Vectors.dense(1.0, 0.0)), - RowFactory.create(5, Vectors.dense(-1.0, 0.0)), - RowFactory.create(6, Vectors.dense(0.0, 1.0)), - RowFactory.create(7, Vectors.dense(0.0, -1.0)) - ); - - StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("features", new VectorUDT(), false, Metadata.empty()) - }); - Dataset dfA = spark.createDataFrame(dataA, schema); - Dataset dfB = spark.createDataFrame(dataB, schema); - - Vector key = Vectors.dense(1.0, 0.0); - - BucketedRandomProjectionLSH mh = new BucketedRandomProjectionLSH() - .setBucketLength(2.0) - .setNumHashTables(3) - .setInputCol("features") - .setOutputCol("hashes"); - - BucketedRandomProjectionLSHModel model = mh.fit(dfA); - - // Feature Transformation - System.out.println("The hashed dataset where hashed values are stored in the column 'hashes':"); - model.transform(dfA).show(); - - // Compute the locality sensitive hashes for the input rows, then perform approximate - // similarity join. - // We could avoid computing hashes by passing in the already-transformed dataset, e.g. - // `model.approxSimilarityJoin(transformedA, transformedB, 1.5)` - System.out.println("Approximately joining dfA and dfB on distance smaller than 1.5:"); - model.approxSimilarityJoin(dfA, dfB, 1.5, "EuclideanDistance") - .select(col("datasetA.id").alias("idA"), - col("datasetB.id").alias("idB"), - col("EuclideanDistance")).show(); - - // Compute the locality sensitive hashes for the input rows, then perform approximate nearest - // neighbor search. - // We could avoid computing hashes by passing in the already-transformed dataset, e.g. - // `model.approxNearestNeighbors(transformedA, key, 2)` - System.out.println("Approximately searching dfA for 2 nearest neighbors of the key:"); - model.approxNearestNeighbors(dfA, key, 2).show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaBucketizerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaBucketizerExample.java deleted file mode 100644 index f009938..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaBucketizerExample.java +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.Bucketizer; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaBucketizerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaBucketizerExample") - .getOrCreate(); - - // $example on$ - double[] splits = {Double.NEGATIVE_INFINITY, -0.5, 0.0, 0.5, Double.POSITIVE_INFINITY}; - - List data = Arrays.asList( - RowFactory.create(-999.9), - RowFactory.create(-0.5), - RowFactory.create(-0.3), - RowFactory.create(0.0), - RowFactory.create(0.2), - RowFactory.create(999.9) - ); - StructType schema = new StructType(new StructField[]{ - new StructField("features", DataTypes.DoubleType, false, Metadata.empty()) - }); - Dataset dataFrame = spark.createDataFrame(data, schema); - - Bucketizer bucketizer = new Bucketizer() - .setInputCol("features") - .setOutputCol("bucketedFeatures") - .setSplits(splits); - - // Transform original data into its bucket index. - Dataset bucketedData = bucketizer.transform(dataFrame); - - System.out.println("Bucketizer output with " + (bucketizer.getSplits().length-1) + " buckets"); - bucketedData.show(); - // $example off$ - - spark.stop(); - } -} - - diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java deleted file mode 100644 index 7373896..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.ChiSqSelector; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaChiSqSelectorExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaChiSqSelectorExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(7, Vectors.dense(0.0, 0.0, 18.0, 1.0), 1.0), - RowFactory.create(8, Vectors.dense(0.0, 1.0, 12.0, 0.0), 0.0), - RowFactory.create(9, Vectors.dense(1.0, 0.0, 15.0, 0.1), 0.0) - ); - StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("features", new VectorUDT(), false, Metadata.empty()), - new StructField("clicked", DataTypes.DoubleType, false, Metadata.empty()) - }); - - Dataset df = spark.createDataFrame(data, schema); - - ChiSqSelector selector = new ChiSqSelector() - .setNumTopFeatures(1) - .setFeaturesCol("features") - .setLabelCol("clicked") - .setOutputCol("selectedFeatures"); - - Dataset result = selector.fit(df).transform(df); - - System.out.println("ChiSqSelector output with top " + selector.getNumTopFeatures() - + " features selected"); - result.show(); - - // $example off$ - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSquareTestExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSquareTestExample.java deleted file mode 100644 index 4b39350..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSquareTestExample.java +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.ml.stat.ChiSquareTest; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.*; -// $example off$ - -/** - * An example for Chi-square hypothesis testing. - * Run with - * 
- * bin/run-example ml.JavaChiSquareTestExample
- *
- */ -public class JavaChiSquareTestExample { - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaChiSquareTestExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0.0, Vectors.dense(0.5, 10.0)), - RowFactory.create(0.0, Vectors.dense(1.5, 20.0)), - RowFactory.create(1.0, Vectors.dense(1.5, 30.0)), - RowFactory.create(0.0, Vectors.dense(3.5, 30.0)), - RowFactory.create(0.0, Vectors.dense(3.5, 40.0)), - RowFactory.create(1.0, Vectors.dense(3.5, 40.0)) - ); - - StructType schema = new StructType(new StructField[]{ - new StructField("label", DataTypes.DoubleType, false, Metadata.empty()), - new StructField("features", new VectorUDT(), false, Metadata.empty()), - }); - - Dataset df = spark.createDataFrame(data, schema); - Row r = ChiSquareTest.test(df, "features", "label").head(); - System.out.println("pValues: " + r.get(0).toString()); - System.out.println("degreesOfFreedom: " + r.getList(1).toString()); - System.out.println("statistics: " + r.get(2).toString()); - - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaCorrelationExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaCorrelationExample.java deleted file mode 100644 index 2a6d62a..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaCorrelationExample.java +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.ml.stat.Correlation; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.*; -// $example off$ - -/** - * An example for computing correlation matrix. - * Run with - * 
- * bin/run-example ml.JavaCorrelationExample
- *
- */ -public class JavaCorrelationExample { - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaCorrelationExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(Vectors.sparse(4, new int[]{0, 3}, new double[]{1.0, -2.0})), - RowFactory.create(Vectors.dense(4.0, 5.0, 0.0, 3.0)), - RowFactory.create(Vectors.dense(6.0, 7.0, 0.0, 8.0)), - RowFactory.create(Vectors.sparse(4, new int[]{0, 3}, new double[]{9.0, 1.0})) - ); - - StructType schema = new StructType(new StructField[]{ - new StructField("features", new VectorUDT(), false, Metadata.empty()), - }); - - Dataset df = spark.createDataFrame(data, schema); - Row r1 = Correlation.corr(df, "features").head(); - System.out.println("Pearson correlation matrix:\n" + r1.get(0).toString()); - - Row r2 = Correlation.corr(df, "features", "spearman").head(); - System.out.println("Spearman correlation matrix:\n" + r2.get(0).toString()); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaCountVectorizerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaCountVectorizerExample.java deleted file mode 100644 index ac2a86c..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaCountVectorizerExample.java +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.CountVectorizer; -import org.apache.spark.ml.feature.CountVectorizerModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.types.*; -// $example off$ - -public class JavaCountVectorizerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaCountVectorizerExample") - .getOrCreate(); - - // $example on$ - // Input data: Each row is a bag of words from a sentence or document. - List data = Arrays.asList( - RowFactory.create(Arrays.asList("a", "b", "c")), - RowFactory.create(Arrays.asList("a", "b", "b", "c", "a")) - ); - StructType schema = new StructType(new StructField [] { - new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty()) - }); - Dataset df = spark.createDataFrame(data, schema); - - // fit a CountVectorizerModel from the corpus - CountVectorizerModel cvModel = new CountVectorizer() - .setInputCol("text") - .setOutputCol("feature") - .setVocabSize(3) - .setMinDF(2) - .fit(df); - - // alternatively, define CountVectorizerModel with a-priori vocabulary - CountVectorizerModel cvm = new CountVectorizerModel(new String[]{"a", "b", "c"}) - .setInputCol("text") - .setOutputCol("feature"); - - cvModel.transform(df).show(false); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaDCTExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaDCTExample.java deleted file mode 100644 index 04546d2..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaDCTExample.java +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.DCT; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaDCTExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaDCTExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(Vectors.dense(0.0, 1.0, -2.0, 3.0)), - RowFactory.create(Vectors.dense(-1.0, 2.0, 4.0, -7.0)), - RowFactory.create(Vectors.dense(14.0, -2.0, -5.0, 1.0)) - ); - StructType schema = new StructType(new StructField[]{ - new StructField("features", new VectorUDT(), false, Metadata.empty()), - }); - Dataset df = spark.createDataFrame(data, schema); - - DCT dct = new DCT() - .setInputCol("features") - .setOutputCol("featuresDCT") - .setInverse(false); - - Dataset dctDf = dct.transform(df); - - dctDf.select("featuresDCT").show(false); - // $example off$ - - spark.stop(); - } -} - diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java deleted file mode 100644 index a9c6e7f..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java +++ /dev/null @@ -1,104 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -// scalastyle:off println -package org.apache.spark.examples.ml; -// $example on$ -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.classification.DecisionTreeClassifier; -import org.apache.spark.ml.classification.DecisionTreeClassificationModel; -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; -import org.apache.spark.ml.feature.*; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off$ - -public class JavaDecisionTreeClassificationExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaDecisionTreeClassificationExample") - .getOrCreate(); - - // $example on$ - // Load the data stored in LIBSVM format as a DataFrame. - Dataset data = spark - .read() - .format("libsvm") - .load("data/mllib/sample_libsvm_data.txt"); - - // Index labels, adding metadata to the label column. - // Fit on whole dataset to include all labels in index. - StringIndexerModel labelIndexer = new StringIndexer() - .setInputCol("label") - .setOutputCol("indexedLabel") - .fit(data); - - // Automatically identify categorical features, and index them. - VectorIndexerModel featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) // features with > 4 distinct values are treated as continuous. - .fit(data); - - // Split the data into training and test sets (30% held out for testing). - Dataset[] splits = data.randomSplit(new double[]{0.7, 0.3}); - Dataset trainingData = splits[0]; - Dataset testData = splits[1]; - - // Train a DecisionTree model. - DecisionTreeClassifier dt = new DecisionTreeClassifier() - .setLabelCol("indexedLabel") - .setFeaturesCol("indexedFeatures"); - - // Convert indexed labels back to original labels. - IndexToString labelConverter = new IndexToString() - .setInputCol("prediction") - .setOutputCol("predictedLabel") - .setLabels(labelIndexer.labels()); - - // Chain indexers and tree in a Pipeline. - Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter}); - - // Train model. This also runs the indexers. - PipelineModel model = pipeline.fit(trainingData); - - // Make predictions. - Dataset predictions = model.transform(testData); - - // Select example rows to display. - predictions.select("predictedLabel", "label", "features").show(5); - - // Select (prediction, true label) and compute test error. - MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() - .setLabelCol("indexedLabel") - .setPredictionCol("prediction") - .setMetricName("accuracy"); - double accuracy = evaluator.evaluate(predictions); - System.out.println("Test Error = " + (1.0 - accuracy)); - - DecisionTreeClassificationModel treeModel = - (DecisionTreeClassificationModel) (model.stages()[2]); - System.out.println("Learned classification tree model:\n" + treeModel.toDebugString()); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java deleted file mode 100644 index cffb713..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java +++ /dev/null @@ -1,89 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -// scalastyle:off println -package org.apache.spark.examples.ml; -// $example on$ -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.evaluation.RegressionEvaluator; -import org.apache.spark.ml.feature.VectorIndexer; -import org.apache.spark.ml.feature.VectorIndexerModel; -import org.apache.spark.ml.regression.DecisionTreeRegressionModel; -import org.apache.spark.ml.regression.DecisionTreeRegressor; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off$ - -public class JavaDecisionTreeRegressionExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaDecisionTreeRegressionExample") - .getOrCreate(); - // $example on$ - // Load the data stored in LIBSVM format as a DataFrame. - Dataset data = spark.read().format("libsvm") - .load("data/mllib/sample_libsvm_data.txt"); - - // Automatically identify categorical features, and index them. - // Set maxCategories so features with > 4 distinct values are treated as continuous. - VectorIndexerModel featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data); - - // Split the data into training and test sets (30% held out for testing). - Dataset[] splits = data.randomSplit(new double[]{0.7, 0.3}); - Dataset trainingData = splits[0]; - Dataset testData = splits[1]; - - // Train a DecisionTree model. - DecisionTreeRegressor dt = new DecisionTreeRegressor() - .setFeaturesCol("indexedFeatures"); - - // Chain indexer and tree in a Pipeline. - Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[]{featureIndexer, dt}); - - // Train model. This also runs the indexer. - PipelineModel model = pipeline.fit(trainingData); - - // Make predictions. - Dataset predictions = model.transform(testData); - - // Select example rows to display. - predictions.select("label", "features").show(5); - - // Select (prediction, true label) and compute test error. - RegressionEvaluator evaluator = new RegressionEvaluator() - .setLabelCol("label") - .setPredictionCol("prediction") - .setMetricName("rmse"); - double rmse = evaluator.evaluate(predictions); - System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse); - - DecisionTreeRegressionModel treeModel = - (DecisionTreeRegressionModel) (model.stages()[1]); - System.out.println("Learned regression tree model:\n" + treeModel.toDebugString()); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaDocument.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaDocument.java deleted file mode 100644 index 6459dab..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaDocument.java +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import java.io.Serializable; - -/** - * Unlabeled instance type, Spark SQL can infer schema from Java Beans. - */ -@SuppressWarnings("serial") -public class JavaDocument implements Serializable { - - private long id; - private String text; - - public JavaDocument(long id, String text) { - this.id = id; - this.text = text; - } - - public long getId() { - return this.id; - } - - public String getText() { - return this.text; - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaElementwiseProductExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaElementwiseProductExample.java deleted file mode 100644 index d2e70c2..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaElementwiseProductExample.java +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.ArrayList; -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.ElementwiseProduct; -import org.apache.spark.ml.linalg.Vector; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaElementwiseProductExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaElementwiseProductExample") - .getOrCreate(); - - // $example on$ - // Create some vector data; also works for sparse vectors - List data = Arrays.asList( - RowFactory.create("a", Vectors.dense(1.0, 2.0, 3.0)), - RowFactory.create("b", Vectors.dense(4.0, 5.0, 6.0)) - ); - - List fields = new ArrayList<>(2); - fields.add(DataTypes.createStructField("id", DataTypes.StringType, false)); - fields.add(DataTypes.createStructField("vector", new VectorUDT(), false)); - - StructType schema = DataTypes.createStructType(fields); - - Dataset dataFrame = spark.createDataFrame(data, schema); - - Vector transformingVector = Vectors.dense(0.0, 1.0, 2.0); - - ElementwiseProduct transformer = new ElementwiseProduct() - .setScalingVec(transformingVector) - .setInputCol("vector") - .setOutputCol("transformedVector"); - - // Batch transform the vectors to create new column: - transformer.transform(dataFrame).show(); - // $example off$ - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java deleted file mode 100644 index 9e07a0c..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java +++ /dev/null @@ -1,117 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.classification.LogisticRegression; -import org.apache.spark.ml.classification.LogisticRegressionModel; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.ml.param.ParamMap; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ -import org.apache.spark.sql.SparkSession; - -/** - * Java example for Estimator, Transformer, and Param. - */ -public class JavaEstimatorTransformerParamExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaEstimatorTransformerParamExample") - .getOrCreate(); - - // $example on$ - // Prepare training data. - List dataTraining = Arrays.asList( - RowFactory.create(1.0, Vectors.dense(0.0, 1.1, 0.1)), - RowFactory.create(0.0, Vectors.dense(2.0, 1.0, -1.0)), - RowFactory.create(0.0, Vectors.dense(2.0, 1.3, 1.0)), - RowFactory.create(1.0, Vectors.dense(0.0, 1.2, -0.5)) - ); - StructType schema = new StructType(new StructField[]{ - new StructField("label", DataTypes.DoubleType, false, Metadata.empty()), - new StructField("features", new VectorUDT(), false, Metadata.empty()) - }); - Dataset training = spark.createDataFrame(dataTraining, schema); - - // Create a LogisticRegression instance. This instance is an Estimator. - LogisticRegression lr = new LogisticRegression(); - // Print out the parameters, documentation, and any default values. - System.out.println("LogisticRegression parameters:\n" + lr.explainParams() + "\n"); - - // We may set parameters using setter methods. - lr.setMaxIter(10).setRegParam(0.01); - - // Learn a LogisticRegression model. This uses the parameters stored in lr. - LogisticRegressionModel model1 = lr.fit(training); - // Since model1 is a Model (i.e., a Transformer produced by an Estimator), - // we can view the parameters it used during fit(). - // This prints the parameter (name: value) pairs, where names are unique IDs for this - // LogisticRegression instance. - System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap()); - - // We may alternatively specify parameters using a ParamMap. - ParamMap paramMap = new ParamMap() - .put(lr.maxIter().w(20)) // Specify 1 Param. - .put(lr.maxIter(), 30) // This overwrites the original maxIter. - .put(lr.regParam().w(0.1), lr.threshold().w(0.55)); // Specify multiple Params. - - // One can also combine ParamMaps. - ParamMap paramMap2 = new ParamMap() - .put(lr.probabilityCol().w("myProbability")); // Change output column name - ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2); - - // Now learn a new model using the paramMapCombined parameters. - // paramMapCombined overrides all parameters set earlier via lr.set* methods. - LogisticRegressionModel model2 = lr.fit(training, paramMapCombined); - System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap()); - - // Prepare test documents. - List dataTest = Arrays.asList( - RowFactory.create(1.0, Vectors.dense(-1.0, 1.5, 1.3)), - RowFactory.create(0.0, Vectors.dense(3.0, 2.0, -0.1)), - RowFactory.create(1.0, Vectors.dense(0.0, 2.2, -1.5)) - ); - Dataset test = spark.createDataFrame(dataTest, schema); - - // Make predictions on test documents using the Transformer.transform() method. - // LogisticRegression.transform will only use the 'features' column. - // Note that model2.transform() outputs a 'myProbability' column instead of the usual - // 'probability' column since we renamed the lr.probabilityCol parameter previously. - Dataset results = model2.transform(test); - Dataset rows = results.select("features", "label", "myProbability", "prediction"); - for (Row r: rows.collectAsList()) { - System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2) - + ", prediction=" + r.get(3)); - } - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaFPGrowthExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaFPGrowthExample.java deleted file mode 100644 index 717ec21..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaFPGrowthExample.java +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.fpm.FPGrowth; -import org.apache.spark.ml.fpm.FPGrowthModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.types.*; -// $example off$ - -/** - * An example demonstrating FPGrowth. - * Run with - * 
- * bin/run-example ml.JavaFPGrowthExample
- *
- */ -public class JavaFPGrowthExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaFPGrowthExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(Arrays.asList("1 2 5".split(" "))), - RowFactory.create(Arrays.asList("1 2 3 5".split(" "))), - RowFactory.create(Arrays.asList("1 2".split(" "))) - ); - StructType schema = new StructType(new StructField[]{ new StructField( - "items", new ArrayType(DataTypes.StringType, true), false, Metadata.empty()) - }); - Dataset itemsDF = spark.createDataFrame(data, schema); - - FPGrowthModel model = new FPGrowth() - .setItemsCol("items") - .setMinSupport(0.5) - .setMinConfidence(0.6) - .fit(itemsDF); - - // Display frequent itemsets. - model.freqItemsets().show(); - - // Display generated association rules. - model.associationRules().show(); - - // transform examines the input items against all the association rules and summarize the - // consequents as prediction - model.transform(itemsDF).show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaGaussianMixtureExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaGaussianMixtureExample.java deleted file mode 100644 index 72bd5d0..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaGaussianMixtureExample.java +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.clustering.GaussianMixture; -import org.apache.spark.ml.clustering.GaussianMixtureModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off$ -import org.apache.spark.sql.SparkSession; - - -/** - * An example demonstrating Gaussian Mixture Model. - * Run with - * 
- * bin/run-example ml.JavaGaussianMixtureExample
- *
- */ -public class JavaGaussianMixtureExample { - - public static void main(String[] args) { - - // Creates a SparkSession - SparkSession spark = SparkSession - .builder() - .appName("JavaGaussianMixtureExample") - .getOrCreate(); - - // $example on$ - // Loads data - Dataset dataset = spark.read().format("libsvm").load("data/mllib/sample_kmeans_data.txt"); - - // Trains a GaussianMixture model - GaussianMixture gmm = new GaussianMixture() - .setK(2); - GaussianMixtureModel model = gmm.fit(dataset); - - // Output the parameters of the mixture model - for (int i = 0; i < model.getK(); i++) { - System.out.printf("Gaussian %d:\nweight=%f\nmu=%s\nsigma=\n%s\n\n", - i, model.weights()[i], model.gaussians()[i].mean(), model.gaussians()[i].cov()); - } - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaGeneralizedLinearRegressionExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaGeneralizedLinearRegressionExample.java deleted file mode 100644 index 3f072d1..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaGeneralizedLinearRegressionExample.java +++ /dev/null @@ -1,83 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import java.util.Arrays; - -import org.apache.spark.ml.regression.GeneralizedLinearRegression; -import org.apache.spark.ml.regression.GeneralizedLinearRegressionModel; -import org.apache.spark.ml.regression.GeneralizedLinearRegressionTrainingSummary; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off$ -import org.apache.spark.sql.SparkSession; - -/** - * An example demonstrating generalized linear regression. - * Run with - * 
- * bin/run-example ml.JavaGeneralizedLinearRegressionExample
- *
- */ - -public class JavaGeneralizedLinearRegressionExample { - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaGeneralizedLinearRegressionExample") - .getOrCreate(); - - // $example on$ - // Load training data - Dataset dataset = spark.read().format("libsvm") - .load("data/mllib/sample_linear_regression_data.txt"); - - GeneralizedLinearRegression glr = new GeneralizedLinearRegression() - .setFamily("gaussian") - .setLink("identity") - .setMaxIter(10) - .setRegParam(0.3); - - // Fit the model - GeneralizedLinearRegressionModel model = glr.fit(dataset); - - // Print the coefficients and intercept for generalized linear regression model - System.out.println("Coefficients: " + model.coefficients()); - System.out.println("Intercept: " + model.intercept()); - - // Summarize the model over the training set and print out some metrics - GeneralizedLinearRegressionTrainingSummary summary = model.summary(); - System.out.println("Coefficient Standard Errors: " - + Arrays.toString(summary.coefficientStandardErrors())); - System.out.println("T Values: " + Arrays.toString(summary.tValues())); - System.out.println("P Values: " + Arrays.toString(summary.pValues())); - System.out.println("Dispersion: " + summary.dispersion()); - System.out.println("Null Deviance: " + summary.nullDeviance()); - System.out.println("Residual Degree Of Freedom Null: " + summary.residualDegreeOfFreedomNull()); - System.out.println("Deviance: " + summary.deviance()); - System.out.println("Residual Degree Of Freedom: " + summary.residualDegreeOfFreedom()); - System.out.println("AIC: " + summary.aic()); - System.out.println("Deviance Residuals: "); - summary.residuals().show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java deleted file mode 100644 index 3e9eb99..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java +++ /dev/null @@ -1,105 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.classification.GBTClassificationModel; -import org.apache.spark.ml.classification.GBTClassifier; -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; -import org.apache.spark.ml.feature.*; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off$ - -public class JavaGradientBoostedTreeClassifierExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaGradientBoostedTreeClassifierExample") - .getOrCreate(); - - // $example on$ - // Load and parse the data file, converting it to a DataFrame. - Dataset data = spark - .read() - .format("libsvm") - .load("data/mllib/sample_libsvm_data.txt"); - - // Index labels, adding metadata to the label column. - // Fit on whole dataset to include all labels in index. - StringIndexerModel labelIndexer = new StringIndexer() - .setInputCol("label") - .setOutputCol("indexedLabel") - .fit(data); - // Automatically identify categorical features, and index them. - // Set maxCategories so features with > 4 distinct values are treated as continuous. - VectorIndexerModel featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data); - - // Split the data into training and test sets (30% held out for testing) - Dataset[] splits = data.randomSplit(new double[] {0.7, 0.3}); - Dataset trainingData = splits[0]; - Dataset testData = splits[1]; - - // Train a GBT model. - GBTClassifier gbt = new GBTClassifier() - .setLabelCol("indexedLabel") - .setFeaturesCol("indexedFeatures") - .setMaxIter(10); - - // Convert indexed labels back to original labels. - IndexToString labelConverter = new IndexToString() - .setInputCol("prediction") - .setOutputCol("predictedLabel") - .setLabels(labelIndexer.labels()); - - // Chain indexers and GBT in a Pipeline. - Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {labelIndexer, featureIndexer, gbt, labelConverter}); - - // Train model. This also runs the indexers. - PipelineModel model = pipeline.fit(trainingData); - - // Make predictions. - Dataset predictions = model.transform(testData); - - // Select example rows to display. - predictions.select("predictedLabel", "label", "features").show(5); - - // Select (prediction, true label) and compute test error. - MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() - .setLabelCol("indexedLabel") - .setPredictionCol("prediction") - .setMetricName("accuracy"); - double accuracy = evaluator.evaluate(predictions); - System.out.println("Test Error = " + (1.0 - accuracy)); - - GBTClassificationModel gbtModel = (GBTClassificationModel)(model.stages()[2]); - System.out.println("Learned classification GBT model:\n" + gbtModel.toDebugString()); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java deleted file mode 100644 index 769b5c3..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.evaluation.RegressionEvaluator; -import org.apache.spark.ml.feature.VectorIndexer; -import org.apache.spark.ml.feature.VectorIndexerModel; -import org.apache.spark.ml.regression.GBTRegressionModel; -import org.apache.spark.ml.regression.GBTRegressor; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off$ - -public class JavaGradientBoostedTreeRegressorExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaGradientBoostedTreeRegressorExample") - .getOrCreate(); - - // $example on$ - // Load and parse the data file, converting it to a DataFrame. - Dataset data = spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); - - // Automatically identify categorical features, and index them. - // Set maxCategories so features with > 4 distinct values are treated as continuous. - VectorIndexerModel featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data); - - // Split the data into training and test sets (30% held out for testing). - Dataset[] splits = data.randomSplit(new double[] {0.7, 0.3}); - Dataset trainingData = splits[0]; - Dataset testData = splits[1]; - - // Train a GBT model. - GBTRegressor gbt = new GBTRegressor() - .setLabelCol("label") - .setFeaturesCol("indexedFeatures") - .setMaxIter(10); - - // Chain indexer and GBT in a Pipeline. - Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] {featureIndexer, gbt}); - - // Train model. This also runs the indexer. - PipelineModel model = pipeline.fit(trainingData); - - // Make predictions. - Dataset predictions = model.transform(testData); - - // Select example rows to display. - predictions.select("prediction", "label", "features").show(5); - - // Select (prediction, true label) and compute test error. - RegressionEvaluator evaluator = new RegressionEvaluator() - .setLabelCol("label") - .setPredictionCol("prediction") - .setMetricName("rmse"); - double rmse = evaluator.evaluate(predictions); - System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse); - - GBTRegressionModel gbtModel = (GBTRegressionModel)(model.stages()[1]); - System.out.println("Learned regression GBT model:\n" + gbtModel.toDebugString()); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaImputerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaImputerExample.java deleted file mode 100644 index ac40ccd..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaImputerExample.java +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.Imputer; -import org.apache.spark.ml.feature.ImputerModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.types.*; -// $example off$ - -import static org.apache.spark.sql.types.DataTypes.*; - -/** - * An example demonstrating Imputer. - * Run with: - * bin/run-example ml.JavaImputerExample - */ -public class JavaImputerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaImputerExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(1.0, Double.NaN), - RowFactory.create(2.0, Double.NaN), - RowFactory.create(Double.NaN, 3.0), - RowFactory.create(4.0, 4.0), - RowFactory.create(5.0, 5.0) - ); - StructType schema = new StructType(new StructField[]{ - createStructField("a", DoubleType, false), - createStructField("b", DoubleType, false) - }); - Dataset df = spark.createDataFrame(data, schema); - - Imputer imputer = new Imputer() - .setInputCols(new String[]{"a", "b"}) - .setOutputCols(new String[]{"out_a", "out_b"}); - - ImputerModel model = imputer.fit(df); - model.transform(df).show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaIndexToStringExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaIndexToStringExample.java deleted file mode 100644 index 6965512..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaIndexToStringExample.java +++ /dev/null @@ -1,87 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.attribute.Attribute; -import org.apache.spark.ml.feature.IndexToString; -import org.apache.spark.ml.feature.StringIndexer; -import org.apache.spark.ml.feature.StringIndexerModel; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaIndexToStringExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaIndexToStringExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0, "a"), - RowFactory.create(1, "b"), - RowFactory.create(2, "c"), - RowFactory.create(3, "a"), - RowFactory.create(4, "a"), - RowFactory.create(5, "c") - ); - StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("category", DataTypes.StringType, false, Metadata.empty()) - }); - Dataset df = spark.createDataFrame(data, schema); - - StringIndexerModel indexer = new StringIndexer() - .setInputCol("category") - .setOutputCol("categoryIndex") - .fit(df); - Dataset indexed = indexer.transform(df); - - System.out.println("Transformed string column '" + indexer.getInputCol() + "' " + - "to indexed column '" + indexer.getOutputCol() + "'"); - indexed.show(); - - StructField inputColSchema = indexed.schema().apply(indexer.getOutputCol()); - System.out.println("StringIndexer will store labels in output column metadata: " + - Attribute.fromStructField(inputColSchema).toString() + "\n"); - - IndexToString converter = new IndexToString() - .setInputCol("categoryIndex") - .setOutputCol("originalCategory"); - Dataset converted = converter.transform(indexed); - - System.out.println("Transformed indexed column '" + converter.getInputCol() + "' back to " + - "original string column '" + converter.getOutputCol() + "' using labels in metadata"); - converted.select("id", "categoryIndex", "originalCategory").show(); - - // $example off$ - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaInteractionExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaInteractionExample.java deleted file mode 100644 index 3684a87..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaInteractionExample.java +++ /dev/null @@ -1,87 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.ml.feature.Interaction; -import org.apache.spark.ml.feature.VectorAssembler; -import org.apache.spark.sql.*; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; - -import java.util.Arrays; -import java.util.List; - -// $example on$ -// $example off$ - -public class JavaInteractionExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaInteractionExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(1, 1, 2, 3, 8, 4, 5), - RowFactory.create(2, 4, 3, 8, 7, 9, 8), - RowFactory.create(3, 6, 1, 9, 2, 3, 6), - RowFactory.create(4, 10, 8, 6, 9, 4, 5), - RowFactory.create(5, 9, 2, 7, 10, 7, 3), - RowFactory.create(6, 1, 1, 4, 2, 8, 4) - ); - - StructType schema = new StructType(new StructField[]{ - new StructField("id1", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("id2", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("id3", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("id4", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("id5", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("id6", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("id7", DataTypes.IntegerType, false, Metadata.empty()) - }); - - Dataset df = spark.createDataFrame(data, schema); - - VectorAssembler assembler1 = new VectorAssembler() - .setInputCols(new String[]{"id2", "id3", "id4"}) - .setOutputCol("vec1"); - - Dataset assembled1 = assembler1.transform(df); - - VectorAssembler assembler2 = new VectorAssembler() - .setInputCols(new String[]{"id5", "id6", "id7"}) - .setOutputCol("vec2"); - - Dataset assembled2 = assembler2.transform(assembled1).select("id1", "vec1", "vec2"); - - Interaction interaction = new Interaction() - .setInputCols(new String[]{"id1","vec1","vec2"}) - .setOutputCol("interactedCol"); - - Dataset interacted = interaction.transform(assembled2); - - interacted.show(false); - // $example off$ - - spark.stop(); - } -} - diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaIsotonicRegressionExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaIsotonicRegressionExample.java deleted file mode 100644 index a7de8e6..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaIsotonicRegressionExample.java +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.examples.ml; - -// $example on$ - -import org.apache.spark.ml.regression.IsotonicRegression; -import org.apache.spark.ml.regression.IsotonicRegressionModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off$ -import org.apache.spark.sql.SparkSession; - -/** - * An example demonstrating IsotonicRegression. - * Run with - * 
- * bin/run-example ml.JavaIsotonicRegressionExample
- *
- */ -public class JavaIsotonicRegressionExample { - - public static void main(String[] args) { - // Create a SparkSession. - SparkSession spark = SparkSession - .builder() - .appName("JavaIsotonicRegressionExample") - .getOrCreate(); - - // $example on$ - // Loads data. - Dataset dataset = spark.read().format("libsvm") - .load("data/mllib/sample_isotonic_regression_libsvm_data.txt"); - - // Trains an isotonic regression model. - IsotonicRegression ir = new IsotonicRegression(); - IsotonicRegressionModel model = ir.fit(dataset); - - System.out.println("Boundaries in increasing order: " + model.boundaries() + "\n"); - System.out.println("Predictions associated with the boundaries: " + model.predictions() + "\n"); - - // Makes predictions. - model.transform(dataset).show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java deleted file mode 100644 index d8f948a..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.clustering.KMeansModel; -import org.apache.spark.ml.clustering.KMeans; -import org.apache.spark.ml.linalg.Vector; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off$ -import org.apache.spark.sql.SparkSession; - - -/** - * An example demonstrating k-means clustering. - * Run with - * 
- * bin/run-example ml.JavaKMeansExample
- *
- */ -public class JavaKMeansExample { - - public static void main(String[] args) { - // Create a SparkSession. - SparkSession spark = SparkSession - .builder() - .appName("JavaKMeansExample") - .getOrCreate(); - - // $example on$ - // Loads data. - Dataset dataset = spark.read().format("libsvm").load("data/mllib/sample_kmeans_data.txt"); - - // Trains a k-means model. - KMeans kmeans = new KMeans().setK(2).setSeed(1L); - KMeansModel model = kmeans.fit(dataset); - - // Evaluate clustering by computing Within Set Sum of Squared Errors. - double WSSSE = model.computeCost(dataset); - System.out.println("Within Set Sum of Squared Errors = " + WSSSE); - - // Shows the result. - Vector[] centers = model.clusterCenters(); - System.out.println("Cluster Centers: "); - for (Vector center: centers) { - System.out.println(center); - } - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java deleted file mode 100644 index 0e5d005..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; -// $example on$ -import org.apache.spark.ml.clustering.LDA; -import org.apache.spark.ml.clustering.LDAModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off$ - -/** - * An example demonstrating LDA. - * Run with - * 
- * bin/run-example ml.JavaLDAExample
- *
- */ -public class JavaLDAExample { - - public static void main(String[] args) { - // Creates a SparkSession - SparkSession spark = SparkSession - .builder() - .appName("JavaLDAExample") - .getOrCreate(); - - // $example on$ - // Loads data. - Dataset dataset = spark.read().format("libsvm") - .load("data/mllib/sample_lda_libsvm_data.txt"); - - // Trains a LDA model. - LDA lda = new LDA().setK(10).setMaxIter(10); - LDAModel model = lda.fit(dataset); - - double ll = model.logLikelihood(dataset); - double lp = model.logPerplexity(dataset); - System.out.println("The lower bound on the log likelihood of the entire corpus: " + ll); - System.out.println("The upper bound on perplexity: " + lp); - - // Describe topics. - Dataset topics = model.describeTopics(3); - System.out.println("The topics described by their top-weighted terms:"); - topics.show(false); - - // Shows the result. - Dataset transformed = model.transform(dataset); - transformed.show(false); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLabeledDocument.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLabeledDocument.java deleted file mode 100644 index 68d1caf..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLabeledDocument.java +++ /dev/null @@ -1,38 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import java.io.Serializable; - -/** - * Labeled instance type, Spark SQL can infer schema from Java Beans. - */ -@SuppressWarnings("serial") -public class JavaLabeledDocument extends JavaDocument implements Serializable { - - private double label; - - public JavaLabeledDocument(long id, String text, double label) { - super(id, text); - this.label = label; - } - - public double getLabel() { - return this.label; - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java deleted file mode 100644 index a561b6d..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.regression.LinearRegression; -import org.apache.spark.ml.regression.LinearRegressionModel; -import org.apache.spark.ml.regression.LinearRegressionTrainingSummary; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off$ - -public class JavaLinearRegressionWithElasticNetExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaLinearRegressionWithElasticNetExample") - .getOrCreate(); - - // $example on$ - // Load training data. - Dataset training = spark.read().format("libsvm") - .load("data/mllib/sample_linear_regression_data.txt"); - - LinearRegression lr = new LinearRegression() - .setMaxIter(10) - .setRegParam(0.3) - .setElasticNetParam(0.8); - - // Fit the model. - LinearRegressionModel lrModel = lr.fit(training); - - // Print the coefficients and intercept for linear regression. - System.out.println("Coefficients: " - + lrModel.coefficients() + " Intercept: " + lrModel.intercept()); - - // Summarize the model over the training set and print out some metrics. - LinearRegressionTrainingSummary trainingSummary = lrModel.summary(); - System.out.println("numIterations: " + trainingSummary.totalIterations()); - System.out.println("objectiveHistory: " + Vectors.dense(trainingSummary.objectiveHistory())); - trainingSummary.residuals().show(); - System.out.println("RMSE: " + trainingSummary.rootMeanSquaredError()); - System.out.println("r2: " + trainingSummary.r2()); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearSVCExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearSVCExample.java deleted file mode 100644 index a18ed1d..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearSVCExample.java +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.classification.LinearSVC; -import org.apache.spark.ml.classification.LinearSVCModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off$ - -public class JavaLinearSVCExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaLinearSVCExample") - .getOrCreate(); - - // $example on$ - // Load training data - Dataset training = spark.read().format("libsvm") - .load("data/mllib/sample_libsvm_data.txt"); - - LinearSVC lsvc = new LinearSVC() - .setMaxIter(10) - .setRegParam(0.1); - - // Fit the model - LinearSVCModel lsvcModel = lsvc.fit(training); - - // Print the coefficients and intercept for LinearSVC - System.out.println("Coefficients: " - + lsvcModel.coefficients() + " Intercept: " + lsvcModel.intercept()); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java deleted file mode 100644 index dee5679..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java +++ /dev/null @@ -1,84 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.classification.BinaryLogisticRegressionSummary; -import org.apache.spark.ml.classification.LogisticRegression; -import org.apache.spark.ml.classification.LogisticRegressionModel; -import org.apache.spark.ml.classification.LogisticRegressionTrainingSummary; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.functions; -// $example off$ - -public class JavaLogisticRegressionSummaryExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaLogisticRegressionSummaryExample") - .getOrCreate(); - - // Load training data - Dataset training = spark.read().format("libsvm") - .load("data/mllib/sample_libsvm_data.txt"); - - LogisticRegression lr = new LogisticRegression() - .setMaxIter(10) - .setRegParam(0.3) - .setElasticNetParam(0.8); - - // Fit the model - LogisticRegressionModel lrModel = lr.fit(training); - - // $example on$ - // Extract the summary from the returned LogisticRegressionModel instance trained in the earlier - // example - LogisticRegressionTrainingSummary trainingSummary = lrModel.summary(); - - // Obtain the loss per iteration. - double[] objectiveHistory = trainingSummary.objectiveHistory(); - for (double lossPerIteration : objectiveHistory) { - System.out.println(lossPerIteration); - } - - // Obtain the metrics useful to judge performance on test data. - // We cast the summary to a BinaryLogisticRegressionSummary since the problem is a binary - // classification problem. - BinaryLogisticRegressionSummary binarySummary = - (BinaryLogisticRegressionSummary) trainingSummary; - - // Obtain the receiver-operating characteristic as a dataframe and areaUnderROC. - Dataset roc = binarySummary.roc(); - roc.show(); - roc.select("FPR").show(); - System.out.println(binarySummary.areaUnderROC()); - - // Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with - // this selected threshold. - Dataset fMeasure = binarySummary.fMeasureByThreshold(); - double maxFMeasure = fMeasure.select(functions.max("F-Measure")).head().getDouble(0); - double bestThreshold = fMeasure.where(fMeasure.col("F-Measure").equalTo(maxFMeasure)) - .select("threshold").head().getDouble(0); - lrModel.setThreshold(bestThreshold); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java deleted file mode 100644 index 4cdec21..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.classification.LogisticRegression; -import org.apache.spark.ml.classification.LogisticRegressionModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off$ - -public class JavaLogisticRegressionWithElasticNetExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaLogisticRegressionWithElasticNetExample") - .getOrCreate(); - - // $example on$ - // Load training data - Dataset training = spark.read().format("libsvm") - .load("data/mllib/sample_libsvm_data.txt"); - - LogisticRegression lr = new LogisticRegression() - .setMaxIter(10) - .setRegParam(0.3) - .setElasticNetParam(0.8); - - // Fit the model - LogisticRegressionModel lrModel = lr.fit(training); - - // Print the coefficients and intercept for logistic regression - System.out.println("Coefficients: " - + lrModel.coefficients() + " Intercept: " + lrModel.intercept()); - - // We can also use the multinomial family for binary classification - LogisticRegression mlr = new LogisticRegression() - .setMaxIter(10) - .setRegParam(0.3) - .setElasticNetParam(0.8) - .setFamily("multinomial"); - - // Fit the model - LogisticRegressionModel mlrModel = mlr.fit(training); - - // Print the coefficients and intercepts for logistic regression with multinomial family - System.out.println("Multinomial coefficients: " + lrModel.coefficientMatrix() - + "\nMultinomial intercepts: " + mlrModel.interceptVector()); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMaxAbsScalerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMaxAbsScalerExample.java deleted file mode 100644 index 9f1ce46..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMaxAbsScalerExample.java +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.MaxAbsScaler; -import org.apache.spark.ml.feature.MaxAbsScalerModel; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ -import org.apache.spark.sql.SparkSession; - -public class JavaMaxAbsScalerExample { - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaMaxAbsScalerExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0, Vectors.dense(1.0, 0.1, -8.0)), - RowFactory.create(1, Vectors.dense(2.0, 1.0, -4.0)), - RowFactory.create(2, Vectors.dense(4.0, 10.0, 8.0)) - ); - StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("features", new VectorUDT(), false, Metadata.empty()) - }); - Dataset dataFrame = spark.createDataFrame(data, schema); - - MaxAbsScaler scaler = new MaxAbsScaler() - .setInputCol("features") - .setOutputCol("scaledFeatures"); - - // Compute summary statistics and generate MaxAbsScalerModel - MaxAbsScalerModel scalerModel = scaler.fit(dataFrame); - - // rescale each feature to range [-1, 1]. - Dataset scaledData = scalerModel.transform(dataFrame); - scaledData.select("features", "scaledFeatures").show(); - // $example off$ - - spark.stop(); - } - -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMinHashLSHExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMinHashLSHExample.java deleted file mode 100644 index e164598..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMinHashLSHExample.java +++ /dev/null @@ -1,111 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.MinHashLSH; -import org.apache.spark.ml.feature.MinHashLSHModel; -import org.apache.spark.ml.linalg.Vector; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; - -import static org.apache.spark.sql.functions.col; -// $example off$ - -/** - * An example demonstrating MinHashLSH. - * Run with: - * bin/run-example ml.JavaMinHashLSHExample - */ -public class JavaMinHashLSHExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaMinHashLSHExample") - .getOrCreate(); - - // $example on$ - List dataA = Arrays.asList( - RowFactory.create(0, Vectors.sparse(6, new int[]{0, 1, 2}, new double[]{1.0, 1.0, 1.0})), - RowFactory.create(1, Vectors.sparse(6, new int[]{2, 3, 4}, new double[]{1.0, 1.0, 1.0})), - RowFactory.create(2, Vectors.sparse(6, new int[]{0, 2, 4}, new double[]{1.0, 1.0, 1.0})) - ); - - List dataB = Arrays.asList( - RowFactory.create(0, Vectors.sparse(6, new int[]{1, 3, 5}, new double[]{1.0, 1.0, 1.0})), - RowFactory.create(1, Vectors.sparse(6, new int[]{2, 3, 5}, new double[]{1.0, 1.0, 1.0})), - RowFactory.create(2, Vectors.sparse(6, new int[]{1, 2, 4}, new double[]{1.0, 1.0, 1.0})) - ); - - StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("features", new VectorUDT(), false, Metadata.empty()) - }); - Dataset dfA = spark.createDataFrame(dataA, schema); - Dataset dfB = spark.createDataFrame(dataB, schema); - - int[] indices = {1, 3}; - double[] values = {1.0, 1.0}; - Vector key = Vectors.sparse(6, indices, values); - - MinHashLSH mh = new MinHashLSH() - .setNumHashTables(5) - .setInputCol("features") - .setOutputCol("hashes"); - - MinHashLSHModel model = mh.fit(dfA); - - // Feature Transformation - System.out.println("The hashed dataset where hashed values are stored in the column 'hashes':"); - model.transform(dfA).show(); - - // Compute the locality sensitive hashes for the input rows, then perform approximate - // similarity join. - // We could avoid computing hashes by passing in the already-transformed dataset, e.g. - // `model.approxSimilarityJoin(transformedA, transformedB, 0.6)` - System.out.println("Approximately joining dfA and dfB on Jaccard distance smaller than 0.6:"); - model.approxSimilarityJoin(dfA, dfB, 0.6, "JaccardDistance") - .select(col("datasetA.id").alias("idA"), - col("datasetB.id").alias("idB"), - col("JaccardDistance")).show(); - - // Compute the locality sensitive hashes for the input rows, then perform approximate nearest - // neighbor search. - // We could avoid computing hashes by passing in the already-transformed dataset, e.g. - // `model.approxNearestNeighbors(transformedA, key, 2)` - // It may return less than 2 rows when not enough approximate near-neighbor candidates are - // found. - System.out.println("Approximately searching dfA for 2 nearest neighbors of the key:"); - model.approxNearestNeighbors(dfA, key, 2).show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMinMaxScalerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMinMaxScalerExample.java deleted file mode 100644 index 2757af8..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMinMaxScalerExample.java +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.MinMaxScaler; -import org.apache.spark.ml.feature.MinMaxScalerModel; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaMinMaxScalerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaMinMaxScalerExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0, Vectors.dense(1.0, 0.1, -1.0)), - RowFactory.create(1, Vectors.dense(2.0, 1.1, 1.0)), - RowFactory.create(2, Vectors.dense(3.0, 10.1, 3.0)) - ); - StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("features", new VectorUDT(), false, Metadata.empty()) - }); - Dataset dataFrame = spark.createDataFrame(data, schema); - - MinMaxScaler scaler = new MinMaxScaler() - .setInputCol("features") - .setOutputCol("scaledFeatures"); - - // Compute summary statistics and generate MinMaxScalerModel - MinMaxScalerModel scalerModel = scaler.fit(dataFrame); - - // rescale each feature to range [min, max]. - Dataset scaledData = scalerModel.transform(dataFrame); - System.out.println("Features scaled to range: [" + scaler.getMin() + ", " - + scaler.getMax() + "]"); - scaledData.select("features", "scaledFeatures").show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java deleted file mode 100644 index 975c65e..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java +++ /dev/null @@ -1,120 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import java.util.Arrays; -// $example off$ - -// $example on$ -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.classification.LogisticRegression; -import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator; -import org.apache.spark.ml.feature.HashingTF; -import org.apache.spark.ml.feature.Tokenizer; -import org.apache.spark.ml.param.ParamMap; -import org.apache.spark.ml.tuning.CrossValidator; -import org.apache.spark.ml.tuning.CrossValidatorModel; -import org.apache.spark.ml.tuning.ParamGridBuilder; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off$ -import org.apache.spark.sql.SparkSession; - -/** - * Java example for Model Selection via Cross Validation. - */ -public class JavaModelSelectionViaCrossValidationExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaModelSelectionViaCrossValidationExample") - .getOrCreate(); - - // $example on$ - // Prepare training documents, which are labeled. - Dataset training = spark.createDataFrame(Arrays.asList( - new JavaLabeledDocument(0L, "a b c d e spark", 1.0), - new JavaLabeledDocument(1L, "b d", 0.0), - new JavaLabeledDocument(2L,"spark f g h", 1.0), - new JavaLabeledDocument(3L, "hadoop mapreduce", 0.0), - new JavaLabeledDocument(4L, "b spark who", 1.0), - new JavaLabeledDocument(5L, "g d a y", 0.0), - new JavaLabeledDocument(6L, "spark fly", 1.0), - new JavaLabeledDocument(7L, "was mapreduce", 0.0), - new JavaLabeledDocument(8L, "e spark program", 1.0), - new JavaLabeledDocument(9L, "a e c l", 0.0), - new JavaLabeledDocument(10L, "spark compile", 1.0), - new JavaLabeledDocument(11L, "hadoop software", 0.0) - ), JavaLabeledDocument.class); - - // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr. - Tokenizer tokenizer = new Tokenizer() - .setInputCol("text") - .setOutputCol("words"); - HashingTF hashingTF = new HashingTF() - .setNumFeatures(1000) - .setInputCol(tokenizer.getOutputCol()) - .setOutputCol("features"); - LogisticRegression lr = new LogisticRegression() - .setMaxIter(10) - .setRegParam(0.01); - Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {tokenizer, hashingTF, lr}); - - // We use a ParamGridBuilder to construct a grid of parameters to search over. - // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam, - // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from. - ParamMap[] paramGrid = new ParamGridBuilder() - .addGrid(hashingTF.numFeatures(), new int[] {10, 100, 1000}) - .addGrid(lr.regParam(), new double[] {0.1, 0.01}) - .build(); - - // We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance. - // This will allow us to jointly choose parameters for all Pipeline stages. - // A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. - // Note that the evaluator here is a BinaryClassificationEvaluator and its default metric - // is areaUnderROC. - CrossValidator cv = new CrossValidator() - .setEstimator(pipeline) - .setEvaluator(new BinaryClassificationEvaluator()) - .setEstimatorParamMaps(paramGrid).setNumFolds(2); // Use 3+ in practice - - // Run cross-validation, and choose the best set of parameters. - CrossValidatorModel cvModel = cv.fit(training); - - // Prepare test documents, which are unlabeled. - Dataset test = spark.createDataFrame(Arrays.asList( - new JavaDocument(4L, "spark i j k"), - new JavaDocument(5L, "l m n"), - new JavaDocument(6L, "mapreduce spark"), - new JavaDocument(7L, "apache hadoop") - ), JavaDocument.class); - - // Make predictions on test documents. cvModel uses the best model found (lrModel). - Dataset predictions = cvModel.transform(test); - for (Row r : predictions.select("id", "text", "probability", "prediction").collectAsList()) { - System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2) - + ", prediction=" + r.get(3)); - } - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java deleted file mode 100644 index 9a4722b..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java +++ /dev/null @@ -1,87 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.evaluation.RegressionEvaluator; -import org.apache.spark.ml.param.ParamMap; -import org.apache.spark.ml.regression.LinearRegression; -import org.apache.spark.ml.tuning.ParamGridBuilder; -import org.apache.spark.ml.tuning.TrainValidationSplit; -import org.apache.spark.ml.tuning.TrainValidationSplitModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off$ -import org.apache.spark.sql.SparkSession; - -/** - * Java example demonstrating model selection using TrainValidationSplit. - * - * Run with - * {{{ - * bin/run-example ml.JavaModelSelectionViaTrainValidationSplitExample - * }}} - */ -public class JavaModelSelectionViaTrainValidationSplitExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaModelSelectionViaTrainValidationSplitExample") - .getOrCreate(); - - // $example on$ - Dataset data = spark.read().format("libsvm") - .load("data/mllib/sample_linear_regression_data.txt"); - - // Prepare training and test data. - Dataset[] splits = data.randomSplit(new double[] {0.9, 0.1}, 12345); - Dataset training = splits[0]; - Dataset test = splits[1]; - - LinearRegression lr = new LinearRegression(); - - // We use a ParamGridBuilder to construct a grid of parameters to search over. - // TrainValidationSplit will try all combinations of values and determine best model using - // the evaluator. - ParamMap[] paramGrid = new ParamGridBuilder() - .addGrid(lr.regParam(), new double[] {0.1, 0.01}) - .addGrid(lr.fitIntercept()) - .addGrid(lr.elasticNetParam(), new double[] {0.0, 0.5, 1.0}) - .build(); - - // In this case the estimator is simply the linear regression. - // A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. - TrainValidationSplit trainValidationSplit = new TrainValidationSplit() - .setEstimator(lr) - .setEvaluator(new RegressionEvaluator()) - .setEstimatorParamMaps(paramGrid) - .setTrainRatio(0.8); // 80% for training and the remaining 20% for validation - - // Run train validation split, and choose the best set of parameters. - TrainValidationSplitModel model = trainValidationSplit.fit(training); - - // Make predictions on test data. model is the model with combination of parameters - // that performed best. - model.transform(test) - .select("features", "label", "prediction") - .show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMulticlassLogisticRegressionWithElasticNetExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMulticlassLogisticRegressionWithElasticNetExample.java deleted file mode 100644 index da410cb..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMulticlassLogisticRegressionWithElasticNetExample.java +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.classification.LogisticRegression; -import org.apache.spark.ml.classification.LogisticRegressionModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off$ - -public class JavaMulticlassLogisticRegressionWithElasticNetExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaMulticlassLogisticRegressionWithElasticNetExample") - .getOrCreate(); - - // $example on$ - // Load training data - Dataset training = spark.read().format("libsvm") - .load("data/mllib/sample_multiclass_classification_data.txt"); - - LogisticRegression lr = new LogisticRegression() - .setMaxIter(10) - .setRegParam(0.3) - .setElasticNetParam(0.8); - - // Fit the model - LogisticRegressionModel lrModel = lr.fit(training); - - // Print the coefficients and intercept for multinomial logistic regression - System.out.println("Coefficients: \n" - + lrModel.coefficientMatrix() + " \nIntercept: " + lrModel.interceptVector()); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java deleted file mode 100644 index 43db41c..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java +++ /dev/null @@ -1,76 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel; -import org.apache.spark.ml.classification.MultilayerPerceptronClassifier; -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; -// $example off$ - -/** - * An example for Multilayer Perceptron Classification. - */ -public class JavaMultilayerPerceptronClassifierExample { - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaMultilayerPerceptronClassifierExample") - .getOrCreate(); - - // $example on$ - // Load training data - String path = "data/mllib/sample_multiclass_classification_data.txt"; - Dataset dataFrame = spark.read().format("libsvm").load(path); - - // Split the data into train and test - Dataset[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L); - Dataset train = splits[0]; - Dataset test = splits[1]; - - // specify layers for the neural network: - // input layer of size 4 (features), two intermediate of size 5 and 4 - // and output of size 3 (classes) - int[] layers = new int[] {4, 5, 4, 3}; - - // create the trainer and set its parameters - MultilayerPerceptronClassifier trainer = new MultilayerPerceptronClassifier() - .setLayers(layers) - .setBlockSize(128) - .setSeed(1234L) - .setMaxIter(100); - - // train the model - MultilayerPerceptronClassificationModel model = trainer.fit(train); - - // compute accuracy on the test set - Dataset result = model.transform(test); - Dataset predictionAndLabels = result.select("prediction", "label"); - MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() - .setMetricName("accuracy"); - - System.out.println("Test set accuracy = " + evaluator.evaluate(predictionAndLabels)); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaNGramExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaNGramExample.java deleted file mode 100644 index 5427e46..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaNGramExample.java +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.NGram; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaNGramExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaNGramExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0, Arrays.asList("Hi", "I", "heard", "about", "Spark")), - RowFactory.create(1, Arrays.asList("I", "wish", "Java", "could", "use", "case", "classes")), - RowFactory.create(2, Arrays.asList("Logistic", "regression", "models", "are", "neat")) - ); - - StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField( - "words", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty()) - }); - - Dataset wordDataFrame = spark.createDataFrame(data, schema); - - NGram ngramTransformer = new NGram().setN(2).setInputCol("words").setOutputCol("ngrams"); - - Dataset ngramDataFrame = ngramTransformer.transform(wordDataFrame); - ngramDataFrame.select("ngrams").show(false); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java deleted file mode 100644 index be578dc..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.classification.NaiveBayes; -import org.apache.spark.ml.classification.NaiveBayesModel; -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off$ - -/** - * An example for Naive Bayes Classification. - */ -public class JavaNaiveBayesExample { - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaNaiveBayesExample") - .getOrCreate(); - - // $example on$ - // Load training data - Dataset dataFrame = - spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); - // Split the data into train and test - Dataset[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L); - Dataset train = splits[0]; - Dataset test = splits[1]; - - // create the trainer and set its parameters - NaiveBayes nb = new NaiveBayes(); - - // train the model - NaiveBayesModel model = nb.fit(train); - - // Select example rows to display. - Dataset predictions = model.transform(test); - predictions.show(); - - // compute accuracy on the test set - MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() - .setLabelCol("label") - .setPredictionCol("prediction") - .setMetricName("accuracy"); - double accuracy = evaluator.evaluate(predictions); - System.out.println("Test set accuracy = " + accuracy); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaNormalizerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaNormalizerExample.java deleted file mode 100644 index f878c42..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaNormalizerExample.java +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.Normalizer; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaNormalizerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaNormalizerExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0, Vectors.dense(1.0, 0.1, -8.0)), - RowFactory.create(1, Vectors.dense(2.0, 1.0, -4.0)), - RowFactory.create(2, Vectors.dense(4.0, 10.0, 8.0)) - ); - StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("features", new VectorUDT(), false, Metadata.empty()) - }); - Dataset dataFrame = spark.createDataFrame(data, schema); - - // Normalize each Vector using $L^1$ norm. - Normalizer normalizer = new Normalizer() - .setInputCol("features") - .setOutputCol("normFeatures") - .setP(1.0); - - Dataset l1NormData = normalizer.transform(dataFrame); - l1NormData.show(); - - // Normalize each Vector using $L^\infty$ norm. - Dataset lInfNormData = - normalizer.transform(dataFrame, normalizer.p().w(Double.POSITIVE_INFINITY)); - lInfNormData.show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java deleted file mode 100644 index 99af376..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java +++ /dev/null @@ -1,79 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.OneHotEncoder; -import org.apache.spark.ml.feature.StringIndexer; -import org.apache.spark.ml.feature.StringIndexerModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaOneHotEncoderExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaOneHotEncoderExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0, "a"), - RowFactory.create(1, "b"), - RowFactory.create(2, "c"), - RowFactory.create(3, "a"), - RowFactory.create(4, "a"), - RowFactory.create(5, "c") - ); - - StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("category", DataTypes.StringType, false, Metadata.empty()) - }); - - Dataset df = spark.createDataFrame(data, schema); - - StringIndexerModel indexer = new StringIndexer() - .setInputCol("category") - .setOutputCol("categoryIndex") - .fit(df); - Dataset indexed = indexer.transform(df); - - OneHotEncoder encoder = new OneHotEncoder() - .setInputCol("categoryIndex") - .setOutputCol("categoryVec"); - - Dataset encoded = encoder.transform(indexed); - encoded.show(); - // $example off$ - - spark.stop(); - } -} - diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java deleted file mode 100644 index 82fb540..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java +++ /dev/null @@ -1,84 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.classification.LogisticRegression; -import org.apache.spark.ml.classification.OneVsRest; -import org.apache.spark.ml.classification.OneVsRestModel; -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off$ -import org.apache.spark.sql.SparkSession; - - -/** - * An example of Multiclass to Binary Reduction with One Vs Rest, - * using Logistic Regression as the base classifier. - * Run with - * 
- * bin/run-example ml.JavaOneVsRestExample
- *
- */ -public class JavaOneVsRestExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaOneVsRestExample") - .getOrCreate(); - - // $example on$ - // load data file. - Dataset inputData = spark.read().format("libsvm") - .load("data/mllib/sample_multiclass_classification_data.txt"); - - // generate the train/test split. - Dataset[] tmp = inputData.randomSplit(new double[]{0.8, 0.2}); - Dataset train = tmp[0]; - Dataset test = tmp[1]; - - // configure the base classifier. - LogisticRegression classifier = new LogisticRegression() - .setMaxIter(10) - .setTol(1E-6) - .setFitIntercept(true); - - // instantiate the One Vs Rest Classifier. - OneVsRest ovr = new OneVsRest().setClassifier(classifier); - - // train the multiclass model. - OneVsRestModel ovrModel = ovr.fit(train); - - // score the model on test data. - Dataset predictions = ovrModel.transform(test) - .select("prediction", "label"); - - // obtain evaluator. - MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() - .setMetricName("accuracy"); - - // compute the classification error on test data. - double accuracy = evaluator.evaluate(predictions); - System.out.println("Test Error = " + (1 - accuracy)); - // $example off$ - - spark.stop(); - } - -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaPCAExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaPCAExample.java deleted file mode 100644 index 6951a65..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaPCAExample.java +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.PCA; -import org.apache.spark.ml.feature.PCAModel; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaPCAExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaPCAExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(Vectors.sparse(5, new int[]{1, 3}, new double[]{1.0, 7.0})), - RowFactory.create(Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0)), - RowFactory.create(Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)) - ); - - StructType schema = new StructType(new StructField[]{ - new StructField("features", new VectorUDT(), false, Metadata.empty()), - }); - - Dataset df = spark.createDataFrame(data, schema); - - PCAModel pca = new PCA() - .setInputCol("features") - .setOutputCol("pcaFeatures") - .setK(3) - .fit(df); - - Dataset result = pca.transform(df).select("pcaFeatures"); - result.show(false); - // $example off$ - spark.stop(); - } -} - diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java deleted file mode 100644 index 4ccd8f6..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaPipelineExample.java +++ /dev/null @@ -1,88 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import java.util.Arrays; - -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.classification.LogisticRegression; -import org.apache.spark.ml.feature.HashingTF; -import org.apache.spark.ml.feature.Tokenizer; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off$ -import org.apache.spark.sql.SparkSession; - -/** - * Java example for simple text document 'Pipeline'. - */ -public class JavaPipelineExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaPipelineExample") - .getOrCreate(); - - // $example on$ - // Prepare training documents, which are labeled. - Dataset training = spark.createDataFrame(Arrays.asList( - new JavaLabeledDocument(0L, "a b c d e spark", 1.0), - new JavaLabeledDocument(1L, "b d", 0.0), - new JavaLabeledDocument(2L, "spark f g h", 1.0), - new JavaLabeledDocument(3L, "hadoop mapreduce", 0.0) - ), JavaLabeledDocument.class); - - // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr. - Tokenizer tokenizer = new Tokenizer() - .setInputCol("text") - .setOutputCol("words"); - HashingTF hashingTF = new HashingTF() - .setNumFeatures(1000) - .setInputCol(tokenizer.getOutputCol()) - .setOutputCol("features"); - LogisticRegression lr = new LogisticRegression() - .setMaxIter(10) - .setRegParam(0.001); - Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {tokenizer, hashingTF, lr}); - - // Fit the pipeline to training documents. - PipelineModel model = pipeline.fit(training); - - // Prepare test documents, which are unlabeled. - Dataset test = spark.createDataFrame(Arrays.asList( - new JavaDocument(4L, "spark i j k"), - new JavaDocument(5L, "l m n"), - new JavaDocument(6L, "spark hadoop spark"), - new JavaDocument(7L, "apache hadoop") - ), JavaDocument.class); - - // Make predictions on test documents. - Dataset predictions = model.transform(test); - for (Row r : predictions.select("id", "text", "probability", "prediction").collectAsList()) { - System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2) - + ", prediction=" + r.get(3)); - } - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java deleted file mode 100644 index 43c636c..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.PolynomialExpansion; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaPolynomialExpansionExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaPolynomialExpansionExample") - .getOrCreate(); - - // $example on$ - PolynomialExpansion polyExpansion = new PolynomialExpansion() - .setInputCol("features") - .setOutputCol("polyFeatures") - .setDegree(3); - - List data = Arrays.asList( - RowFactory.create(Vectors.dense(2.0, 1.0)), - RowFactory.create(Vectors.dense(0.0, 0.0)), - RowFactory.create(Vectors.dense(3.0, -1.0)) - ); - StructType schema = new StructType(new StructField[]{ - new StructField("features", new VectorUDT(), false, Metadata.empty()), - }); - Dataset df = spark.createDataFrame(data, schema); - - Dataset polyDF = polyExpansion.transform(df); - polyDF.show(false); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java deleted file mode 100644 index dd20cac..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.QuantileDiscretizer; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaQuantileDiscretizerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaQuantileDiscretizerExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0, 18.0), - RowFactory.create(1, 19.0), - RowFactory.create(2, 8.0), - RowFactory.create(3, 5.0), - RowFactory.create(4, 2.2) - ); - - StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("hour", DataTypes.DoubleType, false, Metadata.empty()) - }); - - Dataset df = spark.createDataFrame(data, schema); - // $example off$ - // Output of QuantileDiscretizer for such small datasets can depend on the number of - // partitions. Here we force a single partition to ensure consistent results. - // Note this is not necessary for normal use cases - df = df.repartition(1); - // $example on$ - QuantileDiscretizer discretizer = new QuantileDiscretizer() - .setInputCol("hour") - .setOutputCol("result") - .setNumBuckets(3); - - Dataset result = discretizer.fit(df).transform(df); - result.show(); - // $example off$ - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaRFormulaExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaRFormulaExample.java deleted file mode 100644 index 428067e..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaRFormulaExample.java +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.RFormula; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; - -import static org.apache.spark.sql.types.DataTypes.*; -// $example off$ - -public class JavaRFormulaExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaRFormulaExample") - .getOrCreate(); - - // $example on$ - StructType schema = createStructType(new StructField[]{ - createStructField("id", IntegerType, false), - createStructField("country", StringType, false), - createStructField("hour", IntegerType, false), - createStructField("clicked", DoubleType, false) - }); - - List data = Arrays.asList( - RowFactory.create(7, "US", 18, 1.0), - RowFactory.create(8, "CA", 12, 0.0), - RowFactory.create(9, "NZ", 15, 0.0) - ); - - Dataset dataset = spark.createDataFrame(data, schema); - RFormula formula = new RFormula() - .setFormula("clicked ~ country + hour") - .setFeaturesCol("features") - .setLabelCol("label"); - Dataset output = formula.fit(dataset).transform(dataset); - output.select("features", "label").show(); - // $example off$ - spark.stop(); - } -} - diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java deleted file mode 100644 index da2633e..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java +++ /dev/null @@ -1,101 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.classification.RandomForestClassificationModel; -import org.apache.spark.ml.classification.RandomForestClassifier; -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; -import org.apache.spark.ml.feature.*; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off$ - -public class JavaRandomForestClassifierExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaRandomForestClassifierExample") - .getOrCreate(); - - // $example on$ - // Load and parse the data file, converting it to a DataFrame. - Dataset data = spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); - - // Index labels, adding metadata to the label column. - // Fit on whole dataset to include all labels in index. - StringIndexerModel labelIndexer = new StringIndexer() - .setInputCol("label") - .setOutputCol("indexedLabel") - .fit(data); - // Automatically identify categorical features, and index them. - // Set maxCategories so features with > 4 distinct values are treated as continuous. - VectorIndexerModel featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data); - - // Split the data into training and test sets (30% held out for testing) - Dataset[] splits = data.randomSplit(new double[] {0.7, 0.3}); - Dataset trainingData = splits[0]; - Dataset testData = splits[1]; - - // Train a RandomForest model. - RandomForestClassifier rf = new RandomForestClassifier() - .setLabelCol("indexedLabel") - .setFeaturesCol("indexedFeatures"); - - // Convert indexed labels back to original labels. - IndexToString labelConverter = new IndexToString() - .setInputCol("prediction") - .setOutputCol("predictedLabel") - .setLabels(labelIndexer.labels()); - - // Chain indexers and forest in a Pipeline - Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {labelIndexer, featureIndexer, rf, labelConverter}); - - // Train model. This also runs the indexers. - PipelineModel model = pipeline.fit(trainingData); - - // Make predictions. - Dataset predictions = model.transform(testData); - - // Select example rows to display. - predictions.select("predictedLabel", "label", "features").show(5); - - // Select (prediction, true label) and compute test error - MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() - .setLabelCol("indexedLabel") - .setPredictionCol("prediction") - .setMetricName("accuracy"); - double accuracy = evaluator.evaluate(predictions); - System.out.println("Test Error = " + (1.0 - accuracy)); - - RandomForestClassificationModel rfModel = (RandomForestClassificationModel)(model.stages()[2]); - System.out.println("Learned classification forest model:\n" + rfModel.toDebugString()); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java deleted file mode 100644 index a707845..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.evaluation.RegressionEvaluator; -import org.apache.spark.ml.feature.VectorIndexer; -import org.apache.spark.ml.feature.VectorIndexerModel; -import org.apache.spark.ml.regression.RandomForestRegressionModel; -import org.apache.spark.ml.regression.RandomForestRegressor; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off$ - -public class JavaRandomForestRegressorExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaRandomForestRegressorExample") - .getOrCreate(); - - // $example on$ - // Load and parse the data file, converting it to a DataFrame. - Dataset data = spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); - - // Automatically identify categorical features, and index them. - // Set maxCategories so features with > 4 distinct values are treated as continuous. - VectorIndexerModel featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data); - - // Split the data into training and test sets (30% held out for testing) - Dataset[] splits = data.randomSplit(new double[] {0.7, 0.3}); - Dataset trainingData = splits[0]; - Dataset testData = splits[1]; - - // Train a RandomForest model. - RandomForestRegressor rf = new RandomForestRegressor() - .setLabelCol("label") - .setFeaturesCol("indexedFeatures"); - - // Chain indexer and forest in a Pipeline - Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {featureIndexer, rf}); - - // Train model. This also runs the indexer. - PipelineModel model = pipeline.fit(trainingData); - - // Make predictions. - Dataset predictions = model.transform(testData); - - // Select example rows to display. - predictions.select("prediction", "label", "features").show(5); - - // Select (prediction, true label) and compute test error - RegressionEvaluator evaluator = new RegressionEvaluator() - .setLabelCol("label") - .setPredictionCol("prediction") - .setMetricName("rmse"); - double rmse = evaluator.evaluate(predictions); - System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse); - - RandomForestRegressionModel rfModel = (RandomForestRegressionModel)(model.stages()[1]); - System.out.println("Learned regression forest model:\n" + rfModel.toDebugString()); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaSQLTransformerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaSQLTransformerExample.java deleted file mode 100644 index 2a3d62d..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaSQLTransformerExample.java +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.SQLTransformer; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.types.*; -// $example off$ - -public class JavaSQLTransformerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaSQLTransformerExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0, 1.0, 3.0), - RowFactory.create(2, 2.0, 5.0) - ); - StructType schema = new StructType(new StructField [] { - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("v1", DataTypes.DoubleType, false, Metadata.empty()), - new StructField("v2", DataTypes.DoubleType, false, Metadata.empty()) - }); - Dataset df = spark.createDataFrame(data, schema); - - SQLTransformer sqlTrans = new SQLTransformer().setStatement( - "SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__"); - - sqlTrans.transform(df).show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaStandardScalerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaStandardScalerExample.java deleted file mode 100644 index 08ea285..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaStandardScalerExample.java +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import org.apache.spark.ml.feature.StandardScaler; -import org.apache.spark.ml.feature.StandardScalerModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off$ - -public class JavaStandardScalerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaStandardScalerExample") - .getOrCreate(); - - // $example on$ - Dataset dataFrame = - spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); - - StandardScaler scaler = new StandardScaler() - .setInputCol("features") - .setOutputCol("scaledFeatures") - .setWithStd(true) - .setWithMean(false); - - // Compute summary statistics by fitting the StandardScaler - StandardScalerModel scalerModel = scaler.fit(dataFrame); - - // Normalize each feature to have unit standard deviation. - Dataset scaledData = scalerModel.transform(dataFrame); - scaledData.show(); - // $example off$ - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaStopWordsRemoverExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaStopWordsRemoverExample.java deleted file mode 100644 index 94ead62..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaStopWordsRemoverExample.java +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.StopWordsRemover; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaStopWordsRemoverExample { - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaStopWordsRemoverExample") - .getOrCreate(); - - // $example on$ - StopWordsRemover remover = new StopWordsRemover() - .setInputCol("raw") - .setOutputCol("filtered"); - - List data = Arrays.asList( - RowFactory.create(Arrays.asList("I", "saw", "the", "red", "balloon")), - RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb")) - ); - - StructType schema = new StructType(new StructField[]{ - new StructField( - "raw", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty()) - }); - - Dataset dataset = spark.createDataFrame(data, schema); - remover.transform(dataset).show(false); - // $example off$ - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaStringIndexerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaStringIndexerExample.java deleted file mode 100644 index cf9747a..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaStringIndexerExample.java +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.StringIndexer; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; - -import static org.apache.spark.sql.types.DataTypes.*; -// $example off$ - -public class JavaStringIndexerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaStringIndexerExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0, "a"), - RowFactory.create(1, "b"), - RowFactory.create(2, "c"), - RowFactory.create(3, "a"), - RowFactory.create(4, "a"), - RowFactory.create(5, "c") - ); - StructType schema = new StructType(new StructField[]{ - createStructField("id", IntegerType, false), - createStructField("category", StringType, false) - }); - Dataset df = spark.createDataFrame(data, schema); - - StringIndexer indexer = new StringIndexer() - .setInputCol("category") - .setOutputCol("categoryIndex"); - - Dataset indexed = indexer.fit(df).transform(df); - indexed.show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java deleted file mode 100644 index b740cd0..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java +++ /dev/null @@ -1,78 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.HashingTF; -import org.apache.spark.ml.feature.IDF; -import org.apache.spark.ml.feature.IDFModel; -import org.apache.spark.ml.feature.Tokenizer; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off$ - -public class JavaTfIdfExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaTfIdfExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0.0, "Hi I heard about Spark"), - RowFactory.create(0.0, "I wish Java could use case classes"), - RowFactory.create(1.0, "Logistic regression models are neat") - ); - StructType schema = new StructType(new StructField[]{ - new StructField("label", DataTypes.DoubleType, false, Metadata.empty()), - new StructField("sentence", DataTypes.StringType, false, Metadata.empty()) - }); - Dataset sentenceData = spark.createDataFrame(data, schema); - - Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words"); - Dataset wordsData = tokenizer.transform(sentenceData); - - int numFeatures = 20; - HashingTF hashingTF = new HashingTF() - .setInputCol("words") - .setOutputCol("rawFeatures") - .setNumFeatures(numFeatures); - - Dataset featurizedData = hashingTF.transform(wordsData); - // alternatively, CountVectorizer can also be used to get term frequency vectors - - IDF idf = new IDF().setInputCol("rawFeatures").setOutputCol("features"); - IDFModel idfModel = idf.fit(featurizedData); - - Dataset rescaledData = idfModel.transform(featurizedData); - rescaledData.select("label", "features").show(); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java deleted file mode 100644 index a0979aa..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java +++ /dev/null @@ -1,87 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import scala.collection.mutable.WrappedArray; - -import org.apache.spark.ml.feature.RegexTokenizer; -import org.apache.spark.ml.feature.Tokenizer; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.Metadata; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; - -// col("...") is preferable to df.col("...") -import static org.apache.spark.sql.functions.callUDF; -import static org.apache.spark.sql.functions.col; -// $example off$ - -public class JavaTokenizerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaTokenizerExample") - .getOrCreate(); - - // $example on$ - List data = Arrays.asList( - RowFactory.create(0, "Hi I heard about Spark"), - RowFactory.create(1, "I wish Java could use case classes"), - RowFactory.create(2, "Logistic,regression,models,are,neat") - ); - - StructType schema = new StructType(new StructField[]{ - new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), - new StructField("sentence", DataTypes.StringType, false, Metadata.empty()) - }); - - Dataset sentenceDataFrame = spark.createDataFrame(data, schema); - - Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words"); - - RegexTokenizer regexTokenizer = new RegexTokenizer() - .setInputCol("sentence") - .setOutputCol("words") - .setPattern("\\W"); // alternatively .setPattern("\\w+").setGaps(false); - - spark.udf().register( - "countTokens", (WrappedArray words) -> words.size(), DataTypes.IntegerType); - - Dataset tokenized = tokenizer.transform(sentenceDataFrame); - tokenized.select("sentence", "words") - .withColumn("tokens", callUDF("countTokens", col("words"))) - .show(false); - - Dataset regexTokenized = regexTokenizer.transform(sentenceDataFrame); - regexTokenized.select("sentence", "words") - .withColumn("tokens", callUDF("countTokens", col("words"))) - .show(false); - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorAssemblerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorAssemblerExample.java deleted file mode 100644 index 384e09c..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorAssemblerExample.java +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; - -import org.apache.spark.ml.feature.VectorAssembler; -import org.apache.spark.ml.linalg.VectorUDT; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.*; -import static org.apache.spark.sql.types.DataTypes.*; -// $example off$ - -public class JavaVectorAssemblerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaVectorAssemblerExample") - .getOrCreate(); - - // $example on$ - StructType schema = createStructType(new StructField[]{ - createStructField("id", IntegerType, false), - createStructField("hour", IntegerType, false), - createStructField("mobile", DoubleType, false), - createStructField("userFeatures", new VectorUDT(), false), - createStructField("clicked", DoubleType, false) - }); - Row row = RowFactory.create(0, 18, 1.0, Vectors.dense(0.0, 10.0, 0.5), 1.0); - Dataset dataset = spark.createDataFrame(Arrays.asList(row), schema); - - VectorAssembler assembler = new VectorAssembler() - .setInputCols(new String[]{"hour", "mobile", "userFeatures"}) - .setOutputCol("features"); - - Dataset output = assembler.transform(dataset); - System.out.println("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column " + - "'features'"); - output.select("features", "clicked").show(false); - // $example off$ - - spark.stop(); - } -} - diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorIndexerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorIndexerExample.java deleted file mode 100644 index dd9d757..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorIndexerExample.java +++ /dev/null @@ -1,61 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Map; - -import org.apache.spark.ml.feature.VectorIndexer; -import org.apache.spark.ml.feature.VectorIndexerModel; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off$ - -public class JavaVectorIndexerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaVectorIndexerExample") - .getOrCreate(); - - // $example on$ - Dataset data = spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); - - VectorIndexer indexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexed") - .setMaxCategories(10); - VectorIndexerModel indexerModel = indexer.fit(data); - - Map> categoryMaps = indexerModel.javaCategoryMaps(); - System.out.print("Chose " + categoryMaps.size() + " categorical features:"); - - for (Integer feature : categoryMaps.keySet()) { - System.out.print(" " + feature); - } - System.out.println(); - - // Create new column "indexed" with categorical values transformed to indices - Dataset indexedData = indexerModel.transform(data); - indexedData.show(); - // $example off$ - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorSlicerExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorSlicerExample.java deleted file mode 100644 index 1ae48be..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaVectorSlicerExample.java +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -import org.apache.spark.sql.SparkSession; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.attribute.Attribute; -import org.apache.spark.ml.attribute.AttributeGroup; -import org.apache.spark.ml.attribute.NumericAttribute; -import org.apache.spark.ml.feature.VectorSlicer; -import org.apache.spark.ml.linalg.Vectors; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.types.*; -// $example off$ - -public class JavaVectorSlicerExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaVectorSlicerExample") - .getOrCreate(); - - // $example on$ - Attribute[] attrs = { - NumericAttribute.defaultAttr().withName("f1"), - NumericAttribute.defaultAttr().withName("f2"), - NumericAttribute.defaultAttr().withName("f3") - }; - AttributeGroup group = new AttributeGroup("userFeatures", attrs); - - List data = Arrays.asList( - RowFactory.create(Vectors.sparse(3, new int[]{0, 1}, new double[]{-2.0, 2.3})), - RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0)) - ); - - Dataset dataset = - spark.createDataFrame(data, (new StructType()).add(group.toStructField())); - - VectorSlicer vectorSlicer = new VectorSlicer() - .setInputCol("userFeatures").setOutputCol("features"); - - vectorSlicer.setIndices(new int[]{1}).setNames(new String[]{"f3"}); - // or slicer.setIndices(new int[]{1, 2}), or slicer.setNames(new String[]{"f2", "f3"}) - - Dataset output = vectorSlicer.transform(dataset); - output.show(false); - // $example off$ - - spark.stop(); - } -} - diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaWord2VecExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaWord2VecExample.java deleted file mode 100644 index fc9b459..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/ml/JavaWord2VecExample.java +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.ml; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.ml.feature.Word2Vec; -import org.apache.spark.ml.feature.Word2VecModel; -import org.apache.spark.ml.linalg.Vector; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.RowFactory; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.types.*; -// $example off$ - -public class JavaWord2VecExample { - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("JavaWord2VecExample") - .getOrCreate(); - - // $example on$ - // Input data: Each row is a bag of words from a sentence or document. - List data = Arrays.asList( - RowFactory.create(Arrays.asList("Hi I heard about Spark".split(" "))), - RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))), - RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" "))) - ); - StructType schema = new StructType(new StructField[]{ - new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty()) - }); - Dataset documentDF = spark.createDataFrame(data, schema); - - // Learn a mapping from words to Vectors. - Word2Vec word2Vec = new Word2Vec() - .setInputCol("text") - .setOutputCol("result") - .setVectorSize(3) - .setMinCount(0); - - Word2VecModel model = word2Vec.fit(documentDF); - Dataset result = model.transform(documentDF); - - for (Row row : result.collectAsList()) { - List text = row.getList(0); - Vector vector = (Vector) row.get(1); - System.out.println("Text: " + text + " => \nVector: " + vector + "\n"); - } - // $example off$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java deleted file mode 100644 index 95a430f..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.api.java.function.Function; - -import org.apache.spark.mllib.recommendation.ALS; -import org.apache.spark.mllib.recommendation.MatrixFactorizationModel; -import org.apache.spark.mllib.recommendation.Rating; - -import java.util.Arrays; -import java.util.regex.Pattern; - -import scala.Tuple2; - -/** - * Example using MLlib ALS from Java. - */ -public final class JavaALS { - - static class ParseRating implements Function { - private static final Pattern COMMA = Pattern.compile(","); - - @Override - public Rating call(String line) { - String[] tok = COMMA.split(line); - int x = Integer.parseInt(tok[0]); - int y = Integer.parseInt(tok[1]); - double rating = Double.parseDouble(tok[2]); - return new Rating(x, y, rating); - } - } - - static class FeaturesToString implements Function, String> { - @Override - public String call(Tuple2 element) { - return element._1() + "," + Arrays.toString(element._2()); - } - } - - public static void main(String[] args) { - - if (args.length < 4) { - System.err.println( - "Usage: JavaALS []"); - System.exit(1); - } - SparkConf sparkConf = new SparkConf().setAppName("JavaALS"); - int rank = Integer.parseInt(args[1]); - int iterations = Integer.parseInt(args[2]); - String outputDir = args[3]; - int blocks = -1; - if (args.length == 5) { - blocks = Integer.parseInt(args[4]); - } - - JavaSparkContext sc = new JavaSparkContext(sparkConf); - JavaRDD lines = sc.textFile(args[0]); - - JavaRDD ratings = lines.map(new ParseRating()); - - MatrixFactorizationModel model = ALS.train(ratings.rdd(), rank, iterations, 0.01, blocks); - - model.userFeatures().toJavaRDD().map(new FeaturesToString()).saveAsTextFile( - outputDir + "/userFeatures"); - model.productFeatures().toJavaRDD().map(new FeaturesToString()).saveAsTextFile( - outputDir + "/productFeatures"); - System.out.println("Final user/product features written to " + outputDir); - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaAssociationRulesExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaAssociationRulesExample.java deleted file mode 100644 index 5f43603..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaAssociationRulesExample.java +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.Arrays; - -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.mllib.fpm.AssociationRules; -import org.apache.spark.mllib.fpm.FPGrowth; -import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset; -// $example off$ - -import org.apache.spark.SparkConf; - -public class JavaAssociationRulesExample { - - public static void main(String[] args) { - - SparkConf sparkConf = new SparkConf().setAppName("JavaAssociationRulesExample"); - JavaSparkContext sc = new JavaSparkContext(sparkConf); - - // $example on$ - JavaRDD> freqItemsets = sc.parallelize(Arrays.asList( - new FreqItemset<>(new String[] {"a"}, 15L), - new FreqItemset<>(new String[] {"b"}, 35L), - new FreqItemset<>(new String[] {"a", "b"}, 12L) - )); - - AssociationRules arules = new AssociationRules() - .setMinConfidence(0.8); - JavaRDD> results = arules.run(freqItemsets); - - for (AssociationRules.Rule rule : results.collect()) { - System.out.println( - rule.javaAntecedent() + " => " + rule.javaConsequent() + ", " + rule.confidence()); - } - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaBinaryClassificationMetricsExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaBinaryClassificationMetricsExample.java deleted file mode 100644 index b9d0313..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaBinaryClassificationMetricsExample.java +++ /dev/null @@ -1,102 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import scala.Tuple2; - -import org.apache.spark.api.java.*; -import org.apache.spark.mllib.classification.LogisticRegressionModel; -import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS; -import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; -// $example off$ -import org.apache.spark.SparkConf; -import org.apache.spark.SparkContext; - -public class JavaBinaryClassificationMetricsExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("Java Binary Classification Metrics Example"); - SparkContext sc = new SparkContext(conf); - // $example on$ - String path = "data/mllib/sample_binary_classification_data.txt"; - JavaRDD data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD(); - - // Split initial RDD into two... [60% training data, 40% testing data]. - JavaRDD[] splits = - data.randomSplit(new double[]{0.6, 0.4}, 11L); - JavaRDD training = splits[0].cache(); - JavaRDD test = splits[1]; - - // Run training algorithm to build the model. - LogisticRegressionModel model = new LogisticRegressionWithLBFGS() - .setNumClasses(2) - .run(training.rdd()); - - // Clear the prediction threshold so the model will return probabilities - model.clearThreshold(); - - // Compute raw scores on the test set. - JavaPairRDD predictionAndLabels = test.mapToPair(p -> - new Tuple2<>(model.predict(p.features()), p.label())); - - // Get evaluation metrics. - BinaryClassificationMetrics metrics = - new BinaryClassificationMetrics(predictionAndLabels.rdd()); - - // Precision by threshold - JavaRDD> precision = metrics.precisionByThreshold().toJavaRDD(); - System.out.println("Precision by threshold: " + precision.collect()); - - // Recall by threshold - JavaRDD recall = metrics.recallByThreshold().toJavaRDD(); - System.out.println("Recall by threshold: " + recall.collect()); - - // F Score by threshold - JavaRDD f1Score = metrics.fMeasureByThreshold().toJavaRDD(); - System.out.println("F1 Score by threshold: " + f1Score.collect()); - - JavaRDD f2Score = metrics.fMeasureByThreshold(2.0).toJavaRDD(); - System.out.println("F2 Score by threshold: " + f2Score.collect()); - - // Precision-recall curve - JavaRDD prc = metrics.pr().toJavaRDD(); - System.out.println("Precision-recall curve: " + prc.collect()); - - // Thresholds - JavaRDD thresholds = precision.map(t -> Double.parseDouble(t._1().toString())); - - // ROC Curve - JavaRDD roc = metrics.roc().toJavaRDD(); - System.out.println("ROC curve: " + roc.collect()); - - // AUPRC - System.out.println("Area under precision-recall curve = " + metrics.areaUnderPR()); - - // AUROC - System.out.println("Area under ROC = " + metrics.areaUnderROC()); - - // Save and load model - model.save(sc, "target/tmp/LogisticRegressionModel"); - LogisticRegressionModel.load(sc, "target/tmp/LogisticRegressionModel"); - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaBisectingKMeansExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaBisectingKMeansExample.java deleted file mode 100644 index f878b55..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaBisectingKMeansExample.java +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.Arrays; -import java.util.List; -// $example off$ -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; -// $example on$ -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.clustering.BisectingKMeans; -import org.apache.spark.mllib.clustering.BisectingKMeansModel; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -// $example off$ - -/** - * Java example for bisecting k-means clustering. - */ -public class JavaBisectingKMeansExample { - public static void main(String[] args) { - SparkConf sparkConf = new SparkConf().setAppName("JavaBisectingKMeansExample"); - JavaSparkContext sc = new JavaSparkContext(sparkConf); - - // $example on$ - List localData = Arrays.asList( - Vectors.dense(0.1, 0.1), Vectors.dense(0.3, 0.3), - Vectors.dense(10.1, 10.1), Vectors.dense(10.3, 10.3), - Vectors.dense(20.1, 20.1), Vectors.dense(20.3, 20.3), - Vectors.dense(30.1, 30.1), Vectors.dense(30.3, 30.3) - ); - JavaRDD data = sc.parallelize(localData, 2); - - BisectingKMeans bkm = new BisectingKMeans() - .setK(4); - BisectingKMeansModel model = bkm.run(data); - - System.out.println("Compute Cost: " + model.computeCost(data)); - - Vector[] clusterCenters = model.clusterCenters(); - for (int i = 0; i < clusterCenters.length; i++) { - Vector clusterCenter = clusterCenters[i]; - System.out.println("Cluster Center " + i + ": " + clusterCenter); - } - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaChiSqSelectorExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaChiSqSelectorExample.java deleted file mode 100644 index ce354af..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaChiSqSelectorExample.java +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; -// $example on$ -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.feature.ChiSqSelector; -import org.apache.spark.mllib.feature.ChiSqSelectorModel; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; -// $example off$ - -public class JavaChiSqSelectorExample { - public static void main(String[] args) { - - SparkConf conf = new SparkConf().setAppName("JavaChiSqSelectorExample"); - JavaSparkContext jsc = new JavaSparkContext(conf); - - // $example on$ - JavaRDD points = MLUtils.loadLibSVMFile(jsc.sc(), - "data/mllib/sample_libsvm_data.txt").toJavaRDD().cache(); - - // Discretize data in 16 equal bins since ChiSqSelector requires categorical features - // Although features are doubles, the ChiSqSelector treats each unique value as a category - JavaRDD discretizedData = points.map(lp -> { - double[] discretizedFeatures = new double[lp.features().size()]; - for (int i = 0; i < lp.features().size(); ++i) { - discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16); - } - return new LabeledPoint(lp.label(), Vectors.dense(discretizedFeatures)); - }); - - // Create ChiSqSelector that will select top 50 of 692 features - ChiSqSelector selector = new ChiSqSelector(50); - // Create ChiSqSelector model (selecting features) - ChiSqSelectorModel transformer = selector.fit(discretizedData.rdd()); - // Filter the top 50 features from each feature vector - JavaRDD filteredData = discretizedData.map(lp -> - new LabeledPoint(lp.label(), transformer.transform(lp.features()))); - // $example off$ - - System.out.println("filtered data: "); - filteredData.foreach(System.out::println); - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaCorrelationsExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaCorrelationsExample.java deleted file mode 100644 index c0fa0b3..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaCorrelationsExample.java +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; -// $example on$ -import java.util.Arrays; - -import org.apache.spark.api.java.JavaDoubleRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.linalg.Matrix; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.mllib.stat.Statistics; -// $example off$ - -public class JavaCorrelationsExample { - public static void main(String[] args) { - - SparkConf conf = new SparkConf().setAppName("JavaCorrelationsExample"); - JavaSparkContext jsc = new JavaSparkContext(conf); - - // $example on$ - JavaDoubleRDD seriesX = jsc.parallelizeDoubles( - Arrays.asList(1.0, 2.0, 3.0, 3.0, 5.0)); // a series - - // must have the same number of partitions and cardinality as seriesX - JavaDoubleRDD seriesY = jsc.parallelizeDoubles( - Arrays.asList(11.0, 22.0, 33.0, 33.0, 555.0)); - - // compute the correlation using Pearson's method. Enter "spearman" for Spearman's method. - // If a method is not specified, Pearson's method will be used by default. - Double correlation = Statistics.corr(seriesX.srdd(), seriesY.srdd(), "pearson"); - System.out.println("Correlation is: " + correlation); - - // note that each Vector is a row and not a column - JavaRDD data = jsc.parallelize( - Arrays.asList( - Vectors.dense(1.0, 10.0, 100.0), - Vectors.dense(2.0, 20.0, 200.0), - Vectors.dense(5.0, 33.0, 366.0) - ) - ); - - // calculate the correlation matrix using Pearson's method. - // Use "spearman" for Spearman's method. - // If a method is not specified, Pearson's method will be used by default. - Matrix correlMatrix = Statistics.corr(data.rdd(), "pearson"); - System.out.println(correlMatrix.toString()); - // $example off$ - - jsc.stop(); - } -} - diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java deleted file mode 100644 index 032c168..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java +++ /dev/null @@ -1,79 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.HashMap; -import java.util.Map; - -import scala.Tuple2; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.tree.DecisionTree; -import org.apache.spark.mllib.tree.model.DecisionTreeModel; -import org.apache.spark.mllib.util.MLUtils; -// $example off$ - -class JavaDecisionTreeClassificationExample { - - public static void main(String[] args) { - - // $example on$ - SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTreeClassificationExample"); - JavaSparkContext jsc = new JavaSparkContext(sparkConf); - - // Load and parse the data file. - String datapath = "data/mllib/sample_libsvm_data.txt"; - JavaRDD data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD(); - // Split the data into training and test sets (30% held out for testing) - JavaRDD[] splits = data.randomSplit(new double[]{0.7, 0.3}); - JavaRDD trainingData = splits[0]; - JavaRDD testData = splits[1]; - - // Set parameters. - // Empty categoricalFeaturesInfo indicates all features are continuous. - int numClasses = 2; - Map categoricalFeaturesInfo = new HashMap<>(); - String impurity = "gini"; - int maxDepth = 5; - int maxBins = 32; - - // Train a DecisionTree model for classification. - DecisionTreeModel model = DecisionTree.trainClassifier(trainingData, numClasses, - categoricalFeaturesInfo, impurity, maxDepth, maxBins); - - // Evaluate model on test instances and compute test error - JavaPairRDD predictionAndLabel = - testData.mapToPair(p -> new Tuple2<>(model.predict(p.features()), p.label())); - double testErr = - predictionAndLabel.filter(pl -> !pl._1().equals(pl._2())).count() / (double) testData.count(); - - System.out.println("Test Error: " + testErr); - System.out.println("Learned classification tree model:\n" + model.toDebugString()); - - // Save and load model - model.save(jsc.sc(), "target/tmp/myDecisionTreeClassificationModel"); - DecisionTreeModel sameModel = DecisionTreeModel - .load(jsc.sc(), "target/tmp/myDecisionTreeClassificationModel"); - // $example off$ - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeRegressionExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeRegressionExample.java deleted file mode 100644 index f222c38..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeRegressionExample.java +++ /dev/null @@ -1,79 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.HashMap; -import java.util.Map; - -import scala.Tuple2; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.tree.DecisionTree; -import org.apache.spark.mllib.tree.model.DecisionTreeModel; -import org.apache.spark.mllib.util.MLUtils; -// $example off$ - -class JavaDecisionTreeRegressionExample { - - public static void main(String[] args) { - - // $example on$ - SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTreeRegressionExample"); - JavaSparkContext jsc = new JavaSparkContext(sparkConf); - - // Load and parse the data file. - String datapath = "data/mllib/sample_libsvm_data.txt"; - JavaRDD data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD(); - // Split the data into training and test sets (30% held out for testing) - JavaRDD[] splits = data.randomSplit(new double[]{0.7, 0.3}); - JavaRDD trainingData = splits[0]; - JavaRDD testData = splits[1]; - - // Set parameters. - // Empty categoricalFeaturesInfo indicates all features are continuous. - Map categoricalFeaturesInfo = new HashMap<>(); - String impurity = "variance"; - int maxDepth = 5; - int maxBins = 32; - - // Train a DecisionTree model. - DecisionTreeModel model = DecisionTree.trainRegressor(trainingData, - categoricalFeaturesInfo, impurity, maxDepth, maxBins); - - // Evaluate model on test instances and compute test error - JavaPairRDD predictionAndLabel = - testData.mapToPair(p -> new Tuple2<>(model.predict(p.features()), p.label())); - double testMSE = predictionAndLabel.mapToDouble(pl -> { - double diff = pl._1() - pl._2(); - return diff * diff; - }).mean(); - System.out.println("Test Mean Squared Error: " + testMSE); - System.out.println("Learned regression tree model:\n" + model.toDebugString()); - - // Save and load model - model.save(jsc.sc(), "target/tmp/myDecisionTreeRegressionModel"); - DecisionTreeModel sameModel = DecisionTreeModel - .load(jsc.sc(), "target/tmp/myDecisionTreeRegressionModel"); - // $example off$ - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaElementwiseProductExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaElementwiseProductExample.java deleted file mode 100644 index 2d45c61..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaElementwiseProductExample.java +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.Arrays; -// $example off$ - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; -// $example on$ -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.feature.ElementwiseProduct; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -// $example off$ - -public class JavaElementwiseProductExample { - public static void main(String[] args) { - - SparkConf conf = new SparkConf().setAppName("JavaElementwiseProductExample"); - JavaSparkContext jsc = new JavaSparkContext(conf); - - // $example on$ - // Create some vector data; also works for sparse vectors - JavaRDD data = jsc.parallelize(Arrays.asList( - Vectors.dense(1.0, 2.0, 3.0), Vectors.dense(4.0, 5.0, 6.0))); - Vector transformingVector = Vectors.dense(0.0, 1.0, 2.0); - ElementwiseProduct transformer = new ElementwiseProduct(transformingVector); - - // Batch transform and per-row transform give the same results: - JavaRDD transformedData = transformer.transform(data); - JavaRDD transformedData2 = data.map(transformer::transform); - // $example off$ - - System.out.println("transformedData: "); - transformedData.foreach(System.out::println); - - System.out.println("transformedData2: "); - transformedData2.foreach(System.out::println); - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaGaussianMixtureExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaGaussianMixtureExample.java deleted file mode 100644 index 5792e5a..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaGaussianMixtureExample.java +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; - -// $example on$ -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.clustering.GaussianMixture; -import org.apache.spark.mllib.clustering.GaussianMixtureModel; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -// $example off$ - -public class JavaGaussianMixtureExample { - public static void main(String[] args) { - - SparkConf conf = new SparkConf().setAppName("JavaGaussianMixtureExample"); - JavaSparkContext jsc = new JavaSparkContext(conf); - - // $example on$ - // Load and parse data - String path = "data/mllib/gmm_data.txt"; - JavaRDD data = jsc.textFile(path); - JavaRDD parsedData = data.map(s -> { - String[] sarray = s.trim().split(" "); - double[] values = new double[sarray.length]; - for (int i = 0; i < sarray.length; i++) { - values[i] = Double.parseDouble(sarray[i]); - } - return Vectors.dense(values); - }); - parsedData.cache(); - - // Cluster the data into two classes using GaussianMixture - GaussianMixtureModel gmm = new GaussianMixture().setK(2).run(parsedData.rdd()); - - // Save and load GaussianMixtureModel - gmm.save(jsc.sc(), "target/org/apache/spark/JavaGaussianMixtureExample/GaussianMixtureModel"); - GaussianMixtureModel sameModel = GaussianMixtureModel.load(jsc.sc(), - "target/org.apache.spark.JavaGaussianMixtureExample/GaussianMixtureModel"); - - // Output the parameters of the mixture model - for (int j = 0; j < gmm.k(); j++) { - System.out.printf("weight=%f\nmu=%s\nsigma=\n%s\n", - gmm.weights()[j], gmm.gaussians()[j].mu(), gmm.gaussians()[j].sigma()); - } - // $example off$ - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingClassificationExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingClassificationExample.java deleted file mode 100644 index 521ee96..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingClassificationExample.java +++ /dev/null @@ -1,81 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.HashMap; -import java.util.Map; - -import scala.Tuple2; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.tree.GradientBoostedTrees; -import org.apache.spark.mllib.tree.configuration.BoostingStrategy; -import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel; -import org.apache.spark.mllib.util.MLUtils; -// $example off$ - -public class JavaGradientBoostingClassificationExample { - public static void main(String[] args) { - // $example on$ - SparkConf sparkConf = new SparkConf() - .setAppName("JavaGradientBoostedTreesClassificationExample"); - JavaSparkContext jsc = new JavaSparkContext(sparkConf); - - // Load and parse the data file. - String datapath = "data/mllib/sample_libsvm_data.txt"; - JavaRDD data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD(); - // Split the data into training and test sets (30% held out for testing) - JavaRDD[] splits = data.randomSplit(new double[]{0.7, 0.3}); - JavaRDD trainingData = splits[0]; - JavaRDD testData = splits[1]; - - // Train a GradientBoostedTrees model. - // The defaultParams for Classification use LogLoss by default. - BoostingStrategy boostingStrategy = BoostingStrategy.defaultParams("Classification"); - boostingStrategy.setNumIterations(3); // Note: Use more iterations in practice. - boostingStrategy.getTreeStrategy().setNumClasses(2); - boostingStrategy.getTreeStrategy().setMaxDepth(5); - // Empty categoricalFeaturesInfo indicates all features are continuous. - Map categoricalFeaturesInfo = new HashMap<>(); - boostingStrategy.treeStrategy().setCategoricalFeaturesInfo(categoricalFeaturesInfo); - - GradientBoostedTreesModel model = GradientBoostedTrees.train(trainingData, boostingStrategy); - - // Evaluate model on test instances and compute test error - JavaPairRDD predictionAndLabel = - testData.mapToPair(p -> new Tuple2<>(model.predict(p.features()), p.label())); - double testErr = - predictionAndLabel.filter(pl -> !pl._1().equals(pl._2())).count() / (double) testData.count(); - System.out.println("Test Error: " + testErr); - System.out.println("Learned classification GBT model:\n" + model.toDebugString()); - - // Save and load model - model.save(jsc.sc(), "target/tmp/myGradientBoostingClassificationModel"); - GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(jsc.sc(), - "target/tmp/myGradientBoostingClassificationModel"); - // $example off$ - - jsc.stop(); - } - -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingRegressionExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingRegressionExample.java deleted file mode 100644 index b345d19..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingRegressionExample.java +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.HashMap; -import java.util.Map; - -import scala.Tuple2; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.tree.GradientBoostedTrees; -import org.apache.spark.mllib.tree.configuration.BoostingStrategy; -import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel; -import org.apache.spark.mllib.util.MLUtils; -// $example off$ - -public class JavaGradientBoostingRegressionExample { - public static void main(String[] args) { - // $example on$ - SparkConf sparkConf = new SparkConf() - .setAppName("JavaGradientBoostedTreesRegressionExample"); - JavaSparkContext jsc = new JavaSparkContext(sparkConf); - // Load and parse the data file. - String datapath = "data/mllib/sample_libsvm_data.txt"; - JavaRDD data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD(); - // Split the data into training and test sets (30% held out for testing) - JavaRDD[] splits = data.randomSplit(new double[]{0.7, 0.3}); - JavaRDD trainingData = splits[0]; - JavaRDD testData = splits[1]; - - // Train a GradientBoostedTrees model. - // The defaultParams for Regression use SquaredError by default. - BoostingStrategy boostingStrategy = BoostingStrategy.defaultParams("Regression"); - boostingStrategy.setNumIterations(3); // Note: Use more iterations in practice. - boostingStrategy.getTreeStrategy().setMaxDepth(5); - // Empty categoricalFeaturesInfo indicates all features are continuous. - Map categoricalFeaturesInfo = new HashMap<>(); - boostingStrategy.treeStrategy().setCategoricalFeaturesInfo(categoricalFeaturesInfo); - - GradientBoostedTreesModel model = GradientBoostedTrees.train(trainingData, boostingStrategy); - - // Evaluate model on test instances and compute test error - JavaPairRDD predictionAndLabel = - testData.mapToPair(p -> new Tuple2<>(model.predict(p.features()), p.label())); - double testMSE = predictionAndLabel.mapToDouble(pl -> { - double diff = pl._1() - pl._2(); - return diff * diff; - }).mean(); - System.out.println("Test Mean Squared Error: " + testMSE); - System.out.println("Learned regression GBT model:\n" + model.toDebugString()); - - // Save and load model - model.save(jsc.sc(), "target/tmp/myGradientBoostingRegressionModel"); - GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(jsc.sc(), - "target/tmp/myGradientBoostingRegressionModel"); - // $example off$ - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaHypothesisTestingExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaHypothesisTestingExample.java deleted file mode 100644 index b48b95f..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaHypothesisTestingExample.java +++ /dev/null @@ -1,84 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; - -// $example on$ -import java.util.Arrays; - -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.linalg.Matrices; -import org.apache.spark.mllib.linalg.Matrix; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.stat.Statistics; -import org.apache.spark.mllib.stat.test.ChiSqTestResult; -// $example off$ - -public class JavaHypothesisTestingExample { - public static void main(String[] args) { - - SparkConf conf = new SparkConf().setAppName("JavaHypothesisTestingExample"); - JavaSparkContext jsc = new JavaSparkContext(conf); - - // $example on$ - // a vector composed of the frequencies of events - Vector vec = Vectors.dense(0.1, 0.15, 0.2, 0.3, 0.25); - - // compute the goodness of fit. If a second vector to test against is not supplied - // as a parameter, the test runs against a uniform distribution. - ChiSqTestResult goodnessOfFitTestResult = Statistics.chiSqTest(vec); - // summary of the test including the p-value, degrees of freedom, test statistic, - // the method used, and the null hypothesis. - System.out.println(goodnessOfFitTestResult + "\n"); - - // Create a contingency matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0)) - Matrix mat = Matrices.dense(3, 2, new double[]{1.0, 3.0, 5.0, 2.0, 4.0, 6.0}); - - // conduct Pearson's independence test on the input contingency matrix - ChiSqTestResult independenceTestResult = Statistics.chiSqTest(mat); - // summary of the test including the p-value, degrees of freedom... - System.out.println(independenceTestResult + "\n"); - - // an RDD of labeled points - JavaRDD obs = jsc.parallelize( - Arrays.asList( - new LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0)), - new LabeledPoint(1.0, Vectors.dense(1.0, 2.0, 0.0)), - new LabeledPoint(-1.0, Vectors.dense(-1.0, 0.0, -0.5)) - ) - ); - - // The contingency table is constructed from the raw (feature, label) pairs and used to conduct - // the independence test. Returns an array containing the ChiSquaredTestResult for every feature - // against the label. - ChiSqTestResult[] featureTestResults = Statistics.chiSqTest(obs.rdd()); - int i = 1; - for (ChiSqTestResult result : featureTestResults) { - System.out.println("Column " + i + ":"); - System.out.println(result + "\n"); // summary of the test - i++; - } - // $example off$ - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaHypothesisTestingKolmogorovSmirnovTestExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaHypothesisTestingKolmogorovSmirnovTestExample.java deleted file mode 100644 index fe611c9..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaHypothesisTestingKolmogorovSmirnovTestExample.java +++ /dev/null @@ -1,49 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; -// $example on$ -import java.util.Arrays; - -import org.apache.spark.api.java.JavaDoubleRDD; -import org.apache.spark.mllib.stat.Statistics; -import org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult; -// $example off$ - -public class JavaHypothesisTestingKolmogorovSmirnovTestExample { - public static void main(String[] args) { - - SparkConf conf = - new SparkConf().setAppName("JavaHypothesisTestingKolmogorovSmirnovTestExample"); - JavaSparkContext jsc = new JavaSparkContext(conf); - - // $example on$ - JavaDoubleRDD data = jsc.parallelizeDoubles(Arrays.asList(0.1, 0.15, 0.2, 0.3, 0.25)); - KolmogorovSmirnovTestResult testResult = - Statistics.kolmogorovSmirnovTest(data, "norm", 0.0, 1.0); - // summary of the test including the p-value, test statistic, and null hypothesis - // if our p-value indicates significance, we can reject the null hypothesis - System.out.println(testResult); - // $example off$ - - jsc.stop(); - } -} - diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java deleted file mode 100644 index adebafe..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.examples.mllib; - -// $example on$ - -import scala.Tuple2; -import scala.Tuple3; -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.regression.IsotonicRegression; -import org.apache.spark.mllib.regression.IsotonicRegressionModel; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; -// $example off$ -import org.apache.spark.SparkConf; - -public class JavaIsotonicRegressionExample { - public static void main(String[] args) { - SparkConf sparkConf = new SparkConf().setAppName("JavaIsotonicRegressionExample"); - JavaSparkContext jsc = new JavaSparkContext(sparkConf); - // $example on$ - JavaRDD data = MLUtils.loadLibSVMFile( - jsc.sc(), "data/mllib/sample_isotonic_regression_libsvm_data.txt").toJavaRDD(); - - // Create label, feature, weight tuples from input data with weight set to default value 1.0. - JavaRDD> parsedData = data.map(point -> - new Tuple3<>(point.label(), point.features().apply(0), 1.0)); - - // Split data into training (60%) and test (40%) sets. - JavaRDD>[] splits = - parsedData.randomSplit(new double[]{0.6, 0.4}, 11L); - JavaRDD> training = splits[0]; - JavaRDD> test = splits[1]; - - // Create isotonic regression model from training data. - // Isotonic parameter defaults to true so it is only shown for demonstration - IsotonicRegressionModel model = new IsotonicRegression().setIsotonic(true).run(training); - - // Create tuples of predicted and real labels. - JavaPairRDD predictionAndLabel = test.mapToPair(point -> - new Tuple2<>(model.predict(point._2()), point._1())); - - // Calculate mean squared error between predicted and real labels. - double meanSquaredError = predictionAndLabel.mapToDouble(pl -> { - double diff = pl._1() - pl._2(); - return diff * diff; - }).mean(); - System.out.println("Mean Squared Error = " + meanSquaredError); - - // Save and load model - model.save(jsc.sc(), "target/tmp/myIsotonicRegressionModel"); - IsotonicRegressionModel sameModel = - IsotonicRegressionModel.load(jsc.sc(), "target/tmp/myIsotonicRegressionModel"); - // $example off$ - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeansExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeansExample.java deleted file mode 100644 index f172756..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeansExample.java +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; - -// $example on$ -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.clustering.KMeans; -import org.apache.spark.mllib.clustering.KMeansModel; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -// $example off$ - -public class JavaKMeansExample { - public static void main(String[] args) { - - SparkConf conf = new SparkConf().setAppName("JavaKMeansExample"); - JavaSparkContext jsc = new JavaSparkContext(conf); - - // $example on$ - // Load and parse data - String path = "data/mllib/kmeans_data.txt"; - JavaRDD data = jsc.textFile(path); - JavaRDD parsedData = data.map(s -> { - String[] sarray = s.split(" "); - double[] values = new double[sarray.length]; - for (int i = 0; i < sarray.length; i++) { - values[i] = Double.parseDouble(sarray[i]); - } - return Vectors.dense(values); - }); - parsedData.cache(); - - // Cluster the data into two classes using KMeans - int numClusters = 2; - int numIterations = 20; - KMeansModel clusters = KMeans.train(parsedData.rdd(), numClusters, numIterations); - - System.out.println("Cluster centers:"); - for (Vector center: clusters.clusterCenters()) { - System.out.println(" " + center); - } - double cost = clusters.computeCost(parsedData.rdd()); - System.out.println("Cost: " + cost); - - // Evaluate clustering by computing Within Set Sum of Squared Errors - double WSSSE = clusters.computeCost(parsedData.rdd()); - System.out.println("Within Set Sum of Squared Errors = " + WSSSE); - - // Save and load model - clusters.save(jsc.sc(), "target/org/apache/spark/JavaKMeansExample/KMeansModel"); - KMeansModel sameModel = KMeansModel.load(jsc.sc(), - "target/org/apache/spark/JavaKMeansExample/KMeansModel"); - // $example off$ - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaKernelDensityEstimationExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaKernelDensityEstimationExample.java deleted file mode 100644 index 41de0d9..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaKernelDensityEstimationExample.java +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; -// $example on$ -import java.util.Arrays; - -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.stat.KernelDensity; -// $example off$ - -public class JavaKernelDensityEstimationExample { - public static void main(String[] args) { - - SparkConf conf = new SparkConf().setAppName("JavaKernelDensityEstimationExample"); - JavaSparkContext jsc = new JavaSparkContext(conf); - - // $example on$ - // an RDD of sample data - JavaRDD data = jsc.parallelize( - Arrays.asList(1.0, 1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 6.0, 7.0, 8.0, 9.0, 9.0)); - - // Construct the density estimator with the sample data - // and a standard deviation for the Gaussian kernels - KernelDensity kd = new KernelDensity().setSample(data).setBandwidth(3.0); - - // Find density estimates for the given values - double[] densities = kd.estimate(new double[]{-1.0, 2.0, 5.0}); - - System.out.println(Arrays.toString(densities)); - // $example off$ - - jsc.stop(); - } -} - diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaLBFGSExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaLBFGSExample.java deleted file mode 100644 index 3fdc03a..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaLBFGSExample.java +++ /dev/null @@ -1,101 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.Arrays; - -import scala.Tuple2; - -import org.apache.spark.api.java.*; -import org.apache.spark.mllib.classification.LogisticRegressionModel; -import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.mllib.optimization.*; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; -import org.apache.spark.SparkConf; -import org.apache.spark.SparkContext; -// $example off$ - -public class JavaLBFGSExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("L-BFGS Example"); - SparkContext sc = new SparkContext(conf); - - // $example on$ - String path = "data/mllib/sample_libsvm_data.txt"; - JavaRDD data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD(); - int numFeatures = data.take(1).get(0).features().size(); - - // Split initial RDD into two... [60% training data, 40% testing data]. - JavaRDD trainingInit = data.sample(false, 0.6, 11L); - JavaRDD test = data.subtract(trainingInit); - - // Append 1 into the training data as intercept. - JavaPairRDD training = data.mapToPair(p -> - new Tuple2<>(p.label(), MLUtils.appendBias(p.features()))); - training.cache(); - - // Run training algorithm to build the model. - int numCorrections = 10; - double convergenceTol = 1e-4; - int maxNumIterations = 20; - double regParam = 0.1; - Vector initialWeightsWithIntercept = Vectors.dense(new double[numFeatures + 1]); - - Tuple2 result = LBFGS.runLBFGS( - training.rdd(), - new LogisticGradient(), - new SquaredL2Updater(), - numCorrections, - convergenceTol, - maxNumIterations, - regParam, - initialWeightsWithIntercept); - Vector weightsWithIntercept = result._1(); - double[] loss = result._2(); - - LogisticRegressionModel model = new LogisticRegressionModel( - Vectors.dense(Arrays.copyOf(weightsWithIntercept.toArray(), weightsWithIntercept.size() - 1)), - (weightsWithIntercept.toArray())[weightsWithIntercept.size() - 1]); - - // Clear the default threshold. - model.clearThreshold(); - - // Compute raw scores on the test set. - JavaPairRDD scoreAndLabels = test.mapToPair(p -> - new Tuple2<>(model.predict(p.features()), p.label())); - - // Get evaluation metrics. - BinaryClassificationMetrics metrics = - new BinaryClassificationMetrics(scoreAndLabels.rdd()); - double auROC = metrics.areaUnderROC(); - - System.out.println("Loss of each step in training process"); - for (double l : loss) { - System.out.println(l); - } - System.out.println("Area under ROC = " + auROC); - // $example off$ - - sc.stop(); - } -} - diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaLatentDirichletAllocationExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaLatentDirichletAllocationExample.java deleted file mode 100644 index 887edf8..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaLatentDirichletAllocationExample.java +++ /dev/null @@ -1,82 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; - -// $example on$ -import scala.Tuple2; - -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.clustering.DistributedLDAModel; -import org.apache.spark.mllib.clustering.LDA; -import org.apache.spark.mllib.clustering.LDAModel; -import org.apache.spark.mllib.linalg.Matrix; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -// $example off$ - -public class JavaLatentDirichletAllocationExample { - public static void main(String[] args) { - - SparkConf conf = new SparkConf().setAppName("JavaKLatentDirichletAllocationExample"); - JavaSparkContext jsc = new JavaSparkContext(conf); - - // $example on$ - // Load and parse the data - String path = "data/mllib/sample_lda_data.txt"; - JavaRDD data = jsc.textFile(path); - JavaRDD parsedData = data.map(s -> { - String[] sarray = s.trim().split(" "); - double[] values = new double[sarray.length]; - for (int i = 0; i < sarray.length; i++) { - values[i] = Double.parseDouble(sarray[i]); - } - return Vectors.dense(values); - }); - // Index documents with unique IDs - JavaPairRDD corpus = - JavaPairRDD.fromJavaRDD(parsedData.zipWithIndex().map(Tuple2::swap)); - corpus.cache(); - - // Cluster the documents into three topics using LDA - LDAModel ldaModel = new LDA().setK(3).run(corpus); - - // Output topics. Each is a distribution over words (matching word count vectors) - System.out.println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize() - + " words):"); - Matrix topics = ldaModel.topicsMatrix(); - for (int topic = 0; topic < 3; topic++) { - System.out.print("Topic " + topic + ":"); - for (int word = 0; word < ldaModel.vocabSize(); word++) { - System.out.print(" " + topics.apply(word, topic)); - } - System.out.println(); - } - - ldaModel.save(jsc.sc(), - "target/org/apache/spark/JavaLatentDirichletAllocationExample/LDAModel"); - DistributedLDAModel sameModel = DistributedLDAModel.load(jsc.sc(), - "target/org/apache/spark/JavaLatentDirichletAllocationExample/LDAModel"); - // $example off$ - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaLinearRegressionWithSGDExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaLinearRegressionWithSGDExample.java deleted file mode 100644 index 324a781..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaLinearRegressionWithSGDExample.java +++ /dev/null @@ -1,81 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; - -// $example on$ -import scala.Tuple2; - -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.regression.LinearRegressionModel; -import org.apache.spark.mllib.regression.LinearRegressionWithSGD; -// $example off$ - -/** - * Example for LinearRegressionWithSGD. - */ -public class JavaLinearRegressionWithSGDExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("JavaLinearRegressionWithSGDExample"); - JavaSparkContext sc = new JavaSparkContext(conf); - - // $example on$ - // Load and parse the data - String path = "data/mllib/ridge-data/lpsa.data"; - JavaRDD data = sc.textFile(path); - JavaRDD parsedData = data.map(line -> { - String[] parts = line.split(","); - String[] features = parts[1].split(" "); - double[] v = new double[features.length]; - for (int i = 0; i < features.length - 1; i++) { - v[i] = Double.parseDouble(features[i]); - } - return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(v)); - }); - parsedData.cache(); - - // Building the model - int numIterations = 100; - double stepSize = 0.00000001; - LinearRegressionModel model = - LinearRegressionWithSGD.train(JavaRDD.toRDD(parsedData), numIterations, stepSize); - - // Evaluate model on training examples and compute training error - JavaPairRDD valuesAndPreds = parsedData.mapToPair(point -> - new Tuple2<>(model.predict(point.features()), point.label())); - - double MSE = valuesAndPreds.mapToDouble(pair -> { - double diff = pair._1() - pair._2(); - return diff * diff; - }).mean(); - System.out.println("training Mean Squared Error = " + MSE); - - // Save and load model - model.save(sc.sc(), "target/tmp/javaLinearRegressionWithSGDModel"); - LinearRegressionModel sameModel = LinearRegressionModel.load(sc.sc(), - "target/tmp/javaLinearRegressionWithSGDModel"); - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java deleted file mode 100644 index 26b8a6e..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.SparkContext; - -// $example on$ -import scala.Tuple2; - -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.classification.LogisticRegressionModel; -import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS; -import org.apache.spark.mllib.evaluation.MulticlassMetrics; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; -// $example off$ - -/** - * Example for LogisticRegressionWithLBFGS. - */ -public class JavaLogisticRegressionWithLBFGSExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("JavaLogisticRegressionWithLBFGSExample"); - SparkContext sc = new SparkContext(conf); - // $example on$ - String path = "data/mllib/sample_libsvm_data.txt"; - JavaRDD data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD(); - - // Split initial RDD into two... [60% training data, 40% testing data]. - JavaRDD[] splits = data.randomSplit(new double[] {0.6, 0.4}, 11L); - JavaRDD training = splits[0].cache(); - JavaRDD test = splits[1]; - - // Run training algorithm to build the model. - LogisticRegressionModel model = new LogisticRegressionWithLBFGS() - .setNumClasses(10) - .run(training.rdd()); - - // Compute raw scores on the test set. - JavaPairRDD predictionAndLabels = test.mapToPair(p -> - new Tuple2<>(model.predict(p.features()), p.label())); - - // Get evaluation metrics. - MulticlassMetrics metrics = new MulticlassMetrics(predictionAndLabels.rdd()); - double accuracy = metrics.accuracy(); - System.out.println("Accuracy = " + accuracy); - - // Save and load model - model.save(sc, "target/tmp/javaLogisticRegressionWithLBFGSModel"); - LogisticRegressionModel sameModel = LogisticRegressionModel.load(sc, - "target/tmp/javaLogisticRegressionWithLBFGSModel"); - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaMultiLabelClassificationMetricsExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaMultiLabelClassificationMetricsExample.java deleted file mode 100644 index bc99dc0..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaMultiLabelClassificationMetricsExample.java +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import scala.Tuple2; - -import org.apache.spark.api.java.*; -import org.apache.spark.mllib.evaluation.MultilabelMetrics; -import org.apache.spark.SparkConf; -// $example off$ - -public class JavaMultiLabelClassificationMetricsExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("Multilabel Classification Metrics Example"); - JavaSparkContext sc = new JavaSparkContext(conf); - // $example on$ - List> data = Arrays.asList( - new Tuple2<>(new double[]{0.0, 1.0}, new double[]{0.0, 2.0}), - new Tuple2<>(new double[]{0.0, 2.0}, new double[]{0.0, 1.0}), - new Tuple2<>(new double[]{}, new double[]{0.0}), - new Tuple2<>(new double[]{2.0}, new double[]{2.0}), - new Tuple2<>(new double[]{2.0, 0.0}, new double[]{2.0, 0.0}), - new Tuple2<>(new double[]{0.0, 1.0, 2.0}, new double[]{0.0, 1.0}), - new Tuple2<>(new double[]{1.0}, new double[]{1.0, 2.0}) - ); - JavaRDD> scoreAndLabels = sc.parallelize(data); - - // Instantiate metrics object - MultilabelMetrics metrics = new MultilabelMetrics(scoreAndLabels.rdd()); - - // Summary stats - System.out.format("Recall = %f\n", metrics.recall()); - System.out.format("Precision = %f\n", metrics.precision()); - System.out.format("F1 measure = %f\n", metrics.f1Measure()); - System.out.format("Accuracy = %f\n", metrics.accuracy()); - - // Stats by labels - for (int i = 0; i < metrics.labels().length - 1; i++) { - System.out.format("Class %1.1f precision = %f\n", metrics.labels()[i], metrics.precision( - metrics.labels()[i])); - System.out.format("Class %1.1f recall = %f\n", metrics.labels()[i], metrics.recall( - metrics.labels()[i])); - System.out.format("Class %1.1f F1 score = %f\n", metrics.labels()[i], metrics.f1Measure( - metrics.labels()[i])); - } - - // Micro stats - System.out.format("Micro recall = %f\n", metrics.microRecall()); - System.out.format("Micro precision = %f\n", metrics.microPrecision()); - System.out.format("Micro F1 measure = %f\n", metrics.microF1Measure()); - - // Hamming loss - System.out.format("Hamming loss = %f\n", metrics.hammingLoss()); - - // Subset accuracy - System.out.format("Subset accuracy = %f\n", metrics.subsetAccuracy()); - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java deleted file mode 100644 index 0367038..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaMulticlassClassificationMetricsExample.java +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import scala.Tuple2; - -import org.apache.spark.api.java.*; -import org.apache.spark.mllib.classification.LogisticRegressionModel; -import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS; -import org.apache.spark.mllib.evaluation.MulticlassMetrics; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; -import org.apache.spark.mllib.linalg.Matrix; -// $example off$ -import org.apache.spark.SparkConf; -import org.apache.spark.SparkContext; - -public class JavaMulticlassClassificationMetricsExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("Multi class Classification Metrics Example"); - SparkContext sc = new SparkContext(conf); - // $example on$ - String path = "data/mllib/sample_multiclass_classification_data.txt"; - JavaRDD data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD(); - - // Split initial RDD into two... [60% training data, 40% testing data]. - JavaRDD[] splits = data.randomSplit(new double[]{0.6, 0.4}, 11L); - JavaRDD training = splits[0].cache(); - JavaRDD test = splits[1]; - - // Run training algorithm to build the model. - LogisticRegressionModel model = new LogisticRegressionWithLBFGS() - .setNumClasses(3) - .run(training.rdd()); - - // Compute raw scores on the test set. - JavaPairRDD predictionAndLabels = test.mapToPair(p -> - new Tuple2<>(model.predict(p.features()), p.label())); - - // Get evaluation metrics. - MulticlassMetrics metrics = new MulticlassMetrics(predictionAndLabels.rdd()); - - // Confusion matrix - Matrix confusion = metrics.confusionMatrix(); - System.out.println("Confusion matrix: \n" + confusion); - - // Overall statistics - System.out.println("Accuracy = " + metrics.accuracy()); - - // Stats by labels - for (int i = 0; i < metrics.labels().length; i++) { - System.out.format("Class %f precision = %f\n", metrics.labels()[i],metrics.precision( - metrics.labels()[i])); - System.out.format("Class %f recall = %f\n", metrics.labels()[i], metrics.recall( - metrics.labels()[i])); - System.out.format("Class %f F1 score = %f\n", metrics.labels()[i], metrics.fMeasure( - metrics.labels()[i])); - } - - //Weighted stats - System.out.format("Weighted precision = %f\n", metrics.weightedPrecision()); - System.out.format("Weighted recall = %f\n", metrics.weightedRecall()); - System.out.format("Weighted F1 score = %f\n", metrics.weightedFMeasure()); - System.out.format("Weighted false positive rate = %f\n", metrics.weightedFalsePositiveRate()); - - // Save and load model - model.save(sc, "target/tmp/LogisticRegressionModel"); - LogisticRegressionModel sameModel = LogisticRegressionModel.load(sc, - "target/tmp/LogisticRegressionModel"); - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java deleted file mode 100644 index d80dbe8..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import scala.Tuple2; -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.mllib.classification.NaiveBayes; -import org.apache.spark.mllib.classification.NaiveBayesModel; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; -// $example off$ -import org.apache.spark.SparkConf; - -public class JavaNaiveBayesExample { - public static void main(String[] args) { - SparkConf sparkConf = new SparkConf().setAppName("JavaNaiveBayesExample"); - JavaSparkContext jsc = new JavaSparkContext(sparkConf); - // $example on$ - String path = "data/mllib/sample_libsvm_data.txt"; - JavaRDD inputData = MLUtils.loadLibSVMFile(jsc.sc(), path).toJavaRDD(); - JavaRDD[] tmp = inputData.randomSplit(new double[]{0.6, 0.4}); - JavaRDD training = tmp[0]; // training set - JavaRDD test = tmp[1]; // test set - NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0); - JavaPairRDD predictionAndLabel = - test.mapToPair(p -> new Tuple2<>(model.predict(p.features()), p.label())); - double accuracy = - predictionAndLabel.filter(pl -> pl._1().equals(pl._2())).count() / (double) test.count(); - - // Save and load model - model.save(jsc.sc(), "target/tmp/myNaiveBayesModel"); - NaiveBayesModel sameModel = NaiveBayesModel.load(jsc.sc(), "target/tmp/myNaiveBayesModel"); - // $example off$ - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaPCAExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaPCAExample.java deleted file mode 100644 index 0a7dc62..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaPCAExample.java +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.Arrays; -import java.util.List; -// $example off$ - -import org.apache.spark.SparkConf; -import org.apache.spark.SparkContext; -// $example on$ -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.mllib.linalg.Matrix; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.mllib.linalg.distributed.RowMatrix; -// $example off$ - -/** - * Example for compute principal components on a 'RowMatrix'. - */ -public class JavaPCAExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("PCA Example"); - SparkContext sc = new SparkContext(conf); - JavaSparkContext jsc = JavaSparkContext.fromSparkContext(sc); - - // $example on$ - List data = Arrays.asList( - Vectors.sparse(5, new int[] {1, 3}, new double[] {1.0, 7.0}), - Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0), - Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0) - ); - - JavaRDD rows = jsc.parallelize(data); - - // Create a RowMatrix from JavaRDD. - RowMatrix mat = new RowMatrix(rows.rdd()); - - // Compute the top 4 principal components. - // Principal components are stored in a local dense matrix. - Matrix pc = mat.computePrincipalComponents(4); - - // Project the rows to the linear space spanned by the top 4 principal components. - RowMatrix projected = mat.multiply(pc); - // $example off$ - Vector[] collectPartitions = (Vector[])projected.rows().collect(); - System.out.println("Projected vector of principal component:"); - for (Vector vector : collectPartitions) { - System.out.println("\t" + vector); - } - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaPowerIterationClusteringExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaPowerIterationClusteringExample.java deleted file mode 100644 index 5155f18..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaPowerIterationClusteringExample.java +++ /dev/null @@ -1,61 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import java.util.Arrays; - -import scala.Tuple3; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -// $example on$ -import org.apache.spark.mllib.clustering.PowerIterationClustering; -import org.apache.spark.mllib.clustering.PowerIterationClusteringModel; -// $example off$ - -/** - * Java example for graph clustering using power iteration clustering (PIC). - */ -public class JavaPowerIterationClusteringExample { - public static void main(String[] args) { - SparkConf sparkConf = new SparkConf().setAppName("JavaPowerIterationClusteringExample"); - JavaSparkContext sc = new JavaSparkContext(sparkConf); - - @SuppressWarnings("unchecked") - // $example on$ - JavaRDD> similarities = sc.parallelize(Arrays.asList( - new Tuple3<>(0L, 1L, 0.9), - new Tuple3<>(1L, 2L, 0.9), - new Tuple3<>(2L, 3L, 0.9), - new Tuple3<>(3L, 4L, 0.1), - new Tuple3<>(4L, 5L, 0.9))); - - PowerIterationClustering pic = new PowerIterationClustering() - .setK(2) - .setMaxIterations(10); - PowerIterationClusteringModel model = pic.run(similarities); - - for (PowerIterationClustering.Assignment a: model.assignments().toJavaRDD().collect()) { - System.out.println(a.id() + " -> " + a.cluster()); - } - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaPrefixSpanExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaPrefixSpanExample.java deleted file mode 100644 index 1634075..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaPrefixSpanExample.java +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.Arrays; -import java.util.List; -// $example off$ -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -// $example on$ -import org.apache.spark.mllib.fpm.PrefixSpan; -import org.apache.spark.mllib.fpm.PrefixSpanModel; -// $example off$ -import org.apache.spark.SparkConf; - -public class JavaPrefixSpanExample { - - public static void main(String[] args) { - - SparkConf sparkConf = new SparkConf().setAppName("JavaPrefixSpanExample"); - JavaSparkContext sc = new JavaSparkContext(sparkConf); - - // $example on$ - JavaRDD>> sequences = sc.parallelize(Arrays.asList( - Arrays.asList(Arrays.asList(1, 2), Arrays.asList(3)), - Arrays.asList(Arrays.asList(1), Arrays.asList(3, 2), Arrays.asList(1, 2)), - Arrays.asList(Arrays.asList(1, 2), Arrays.asList(5)), - Arrays.asList(Arrays.asList(6)) - ), 2); - PrefixSpan prefixSpan = new PrefixSpan() - .setMinSupport(0.5) - .setMaxPatternLength(5); - PrefixSpanModel model = prefixSpan.run(sequences); - for (PrefixSpan.FreqSequence freqSeq: model.freqSequences().toJavaRDD().collect()) { - System.out.println(freqSeq.javaSequence() + ", " + freqSeq.freq()); - } - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestClassificationExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestClassificationExample.java deleted file mode 100644 index 6998ce2..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestClassificationExample.java +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.HashMap; -import java.util.Map; - -import scala.Tuple2; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.tree.RandomForest; -import org.apache.spark.mllib.tree.model.RandomForestModel; -import org.apache.spark.mllib.util.MLUtils; -// $example off$ - -public class JavaRandomForestClassificationExample { - public static void main(String[] args) { - // $example on$ - SparkConf sparkConf = new SparkConf().setAppName("JavaRandomForestClassificationExample"); - JavaSparkContext jsc = new JavaSparkContext(sparkConf); - // Load and parse the data file. - String datapath = "data/mllib/sample_libsvm_data.txt"; - JavaRDD data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD(); - // Split the data into training and test sets (30% held out for testing) - JavaRDD[] splits = data.randomSplit(new double[]{0.7, 0.3}); - JavaRDD trainingData = splits[0]; - JavaRDD testData = splits[1]; - - // Train a RandomForest model. - // Empty categoricalFeaturesInfo indicates all features are continuous. - Integer numClasses = 2; - Map categoricalFeaturesInfo = new HashMap<>(); - Integer numTrees = 3; // Use more in practice. - String featureSubsetStrategy = "auto"; // Let the algorithm choose. - String impurity = "gini"; - Integer maxDepth = 5; - Integer maxBins = 32; - Integer seed = 12345; - - RandomForestModel model = RandomForest.trainClassifier(trainingData, numClasses, - categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, - seed); - - // Evaluate model on test instances and compute test error - JavaPairRDD predictionAndLabel = - testData.mapToPair(p -> new Tuple2<>(model.predict(p.features()), p.label())); - double testErr = - predictionAndLabel.filter(pl -> !pl._1().equals(pl._2())).count() / (double) testData.count(); - System.out.println("Test Error: " + testErr); - System.out.println("Learned classification forest model:\n" + model.toDebugString()); - - // Save and load model - model.save(jsc.sc(), "target/tmp/myRandomForestClassificationModel"); - RandomForestModel sameModel = RandomForestModel.load(jsc.sc(), - "target/tmp/myRandomForestClassificationModel"); - // $example off$ - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestRegressionExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestRegressionExample.java deleted file mode 100644 index 4a0f55f..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRandomForestRegressionExample.java +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.HashMap; -import java.util.Map; - -import scala.Tuple2; - -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.tree.RandomForest; -import org.apache.spark.mllib.tree.model.RandomForestModel; -import org.apache.spark.mllib.util.MLUtils; -import org.apache.spark.SparkConf; -// $example off$ - -public class JavaRandomForestRegressionExample { - public static void main(String[] args) { - // $example on$ - SparkConf sparkConf = new SparkConf().setAppName("JavaRandomForestRegressionExample"); - JavaSparkContext jsc = new JavaSparkContext(sparkConf); - // Load and parse the data file. - String datapath = "data/mllib/sample_libsvm_data.txt"; - JavaRDD data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD(); - // Split the data into training and test sets (30% held out for testing) - JavaRDD[] splits = data.randomSplit(new double[]{0.7, 0.3}); - JavaRDD trainingData = splits[0]; - JavaRDD testData = splits[1]; - - // Set parameters. - // Empty categoricalFeaturesInfo indicates all features are continuous. - Map categoricalFeaturesInfo = new HashMap<>(); - int numTrees = 3; // Use more in practice. - String featureSubsetStrategy = "auto"; // Let the algorithm choose. - String impurity = "variance"; - int maxDepth = 4; - int maxBins = 32; - int seed = 12345; - // Train a RandomForest model. - RandomForestModel model = RandomForest.trainRegressor(trainingData, - categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed); - - // Evaluate model on test instances and compute test error - JavaPairRDD predictionAndLabel = - testData.mapToPair(p -> new Tuple2<>(model.predict(p.features()), p.label())); - double testMSE = predictionAndLabel.mapToDouble(pl -> { - double diff = pl._1() - pl._2(); - return diff * diff; - }).mean(); - System.out.println("Test Mean Squared Error: " + testMSE); - System.out.println("Learned regression forest model:\n" + model.toDebugString()); - - // Save and load model - model.save(jsc.sc(), "target/tmp/myRandomForestRegressionModel"); - RandomForestModel sameModel = RandomForestModel.load(jsc.sc(), - "target/tmp/myRandomForestRegressionModel"); - // $example off$ - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRankingMetricsExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRankingMetricsExample.java deleted file mode 100644 index dc9970d..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRankingMetricsExample.java +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.*; - -import scala.Tuple2; - -import org.apache.spark.api.java.*; -import org.apache.spark.mllib.evaluation.RegressionMetrics; -import org.apache.spark.mllib.evaluation.RankingMetrics; -import org.apache.spark.mllib.recommendation.ALS; -import org.apache.spark.mllib.recommendation.MatrixFactorizationModel; -import org.apache.spark.mllib.recommendation.Rating; -// $example off$ -import org.apache.spark.SparkConf; - -public class JavaRankingMetricsExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("Java Ranking Metrics Example"); - JavaSparkContext sc = new JavaSparkContext(conf); - // $example on$ - String path = "data/mllib/sample_movielens_data.txt"; - JavaRDD data = sc.textFile(path); - JavaRDD ratings = data.map(line -> { - String[] parts = line.split("::"); - return new Rating(Integer.parseInt(parts[0]), Integer.parseInt(parts[1]), Double - .parseDouble(parts[2]) - 2.5); - }); - ratings.cache(); - - // Train an ALS model - MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), 10, 10, 0.01); - - // Get top 10 recommendations for every user and scale ratings from 0 to 1 - JavaRDD> userRecs = model.recommendProductsForUsers(10).toJavaRDD(); - JavaRDD> userRecsScaled = userRecs.map(t -> { - Rating[] scaledRatings = new Rating[t._2().length]; - for (int i = 0; i < scaledRatings.length; i++) { - double newRating = Math.max(Math.min(t._2()[i].rating(), 1.0), 0.0); - scaledRatings[i] = new Rating(t._2()[i].user(), t._2()[i].product(), newRating); - } - return new Tuple2<>(t._1(), scaledRatings); - }); - JavaPairRDD userRecommended = JavaPairRDD.fromJavaRDD(userRecsScaled); - - // Map ratings to 1 or 0, 1 indicating a movie that should be recommended - JavaRDD binarizedRatings = ratings.map(r -> { - double binaryRating; - if (r.rating() > 0.0) { - binaryRating = 1.0; - } else { - binaryRating = 0.0; - } - return new Rating(r.user(), r.product(), binaryRating); - }); - - // Group ratings by common user - JavaPairRDD> userMovies = binarizedRatings.groupBy(Rating::user); - - // Get true relevant documents from all user ratings - JavaPairRDD> userMoviesList = userMovies.mapValues(docs -> { - List products = new ArrayList<>(); - for (Rating r : docs) { - if (r.rating() > 0.0) { - products.add(r.product()); - } - } - return products; - }); - - // Extract the product id from each recommendation - JavaPairRDD> userRecommendedList = userRecommended.mapValues(docs -> { - List products = new ArrayList<>(); - for (Rating r : docs) { - products.add(r.product()); - } - return products; - }); - JavaRDD, List>> relevantDocs = userMoviesList.join( - userRecommendedList).values(); - - // Instantiate the metrics object - RankingMetrics metrics = RankingMetrics.of(relevantDocs); - - // Precision and NDCG at k - Integer[] kVector = {1, 3, 5}; - for (Integer k : kVector) { - System.out.format("Precision at %d = %f\n", k, metrics.precisionAt(k)); - System.out.format("NDCG at %d = %f\n", k, metrics.ndcgAt(k)); - } - - // Mean average precision - System.out.format("Mean average precision = %f\n", metrics.meanAveragePrecision()); - - // Evaluate the model using numerical ratings and regression metrics - JavaRDD> userProducts = - ratings.map(r -> new Tuple2<>(r.user(), r.product())); - - JavaPairRDD, Object> predictions = JavaPairRDD.fromJavaRDD( - model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(r -> - new Tuple2<>(new Tuple2<>(r.user(), r.product()), r.rating()))); - JavaRDD> ratesAndPreds = - JavaPairRDD.fromJavaRDD(ratings.map(r -> - new Tuple2, Object>( - new Tuple2<>(r.user(), r.product()), - r.rating()) - )).join(predictions).values(); - - // Create regression metrics object - RegressionMetrics regressionMetrics = new RegressionMetrics(ratesAndPreds.rdd()); - - // Root mean squared error - System.out.format("RMSE = %f\n", regressionMetrics.rootMeanSquaredError()); - - // R-squared - System.out.format("R-squared = %f\n", regressionMetrics.r2()); - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRecommendationExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRecommendationExample.java deleted file mode 100644 index 1ee68da..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRecommendationExample.java +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import scala.Tuple2; - -import org.apache.spark.api.java.*; -import org.apache.spark.mllib.recommendation.ALS; -import org.apache.spark.mllib.recommendation.MatrixFactorizationModel; -import org.apache.spark.mllib.recommendation.Rating; -import org.apache.spark.SparkConf; -// $example off$ - -public class JavaRecommendationExample { - public static void main(String[] args) { - // $example on$ - SparkConf conf = new SparkConf().setAppName("Java Collaborative Filtering Example"); - JavaSparkContext jsc = new JavaSparkContext(conf); - - // Load and parse the data - String path = "data/mllib/als/test.data"; - JavaRDD data = jsc.textFile(path); - JavaRDD ratings = data.map(s -> { - String[] sarray = s.split(","); - return new Rating(Integer.parseInt(sarray[0]), - Integer.parseInt(sarray[1]), - Double.parseDouble(sarray[2])); - }); - - // Build the recommendation model using ALS - int rank = 10; - int numIterations = 10; - MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01); - - // Evaluate the model on rating data - JavaRDD> userProducts = - ratings.map(r -> new Tuple2<>(r.user(), r.product())); - JavaPairRDD, Double> predictions = JavaPairRDD.fromJavaRDD( - model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD() - .map(r -> new Tuple2<>(new Tuple2<>(r.user(), r.product()), r.rating())) - ); - JavaRDD> ratesAndPreds = JavaPairRDD.fromJavaRDD( - ratings.map(r -> new Tuple2<>(new Tuple2<>(r.user(), r.product()), r.rating()))) - .join(predictions).values(); - double MSE = ratesAndPreds.mapToDouble(pair -> { - double err = pair._1() - pair._2(); - return err * err; - }).mean(); - System.out.println("Mean Squared Error = " + MSE); - - // Save and load model - model.save(jsc.sc(), "target/tmp/myCollaborativeFilter"); - MatrixFactorizationModel sameModel = MatrixFactorizationModel.load(jsc.sc(), - "target/tmp/myCollaborativeFilter"); - // $example off$ - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRegressionMetricsExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRegressionMetricsExample.java deleted file mode 100644 index 00033b5..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaRegressionMetricsExample.java +++ /dev/null @@ -1,83 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import scala.Tuple2; - -import org.apache.spark.api.java.*; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.regression.LinearRegressionModel; -import org.apache.spark.mllib.regression.LinearRegressionWithSGD; -import org.apache.spark.mllib.evaluation.RegressionMetrics; -import org.apache.spark.SparkConf; -// $example off$ - -public class JavaRegressionMetricsExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("Java Regression Metrics Example"); - JavaSparkContext sc = new JavaSparkContext(conf); - // $example on$ - // Load and parse the data - String path = "data/mllib/sample_linear_regression_data.txt"; - JavaRDD data = sc.textFile(path); - JavaRDD parsedData = data.map(line -> { - String[] parts = line.split(" "); - double[] v = new double[parts.length - 1]; - for (int i = 1; i < parts.length; i++) { - v[i - 1] = Double.parseDouble(parts[i].split(":")[1]); - } - return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(v)); - }); - parsedData.cache(); - - // Building the model - int numIterations = 100; - LinearRegressionModel model = LinearRegressionWithSGD.train(JavaRDD.toRDD(parsedData), - numIterations); - - // Evaluate model on training examples and compute training error - JavaPairRDD valuesAndPreds = parsedData.mapToPair(point -> - new Tuple2<>(model.predict(point.features()), point.label())); - - // Instantiate metrics object - RegressionMetrics metrics = new RegressionMetrics(valuesAndPreds.rdd()); - - // Squared error - System.out.format("MSE = %f\n", metrics.meanSquaredError()); - System.out.format("RMSE = %f\n", metrics.rootMeanSquaredError()); - - // R-squared - System.out.format("R Squared = %f\n", metrics.r2()); - - // Mean absolute error - System.out.format("MAE = %f\n", metrics.meanAbsoluteError()); - - // Explained variance - System.out.format("Explained Variance = %f\n", metrics.explainedVariance()); - - // Save and load model - model.save(sc.sc(), "target/tmp/LogisticRegressionModel"); - LinearRegressionModel sameModel = LinearRegressionModel.load(sc.sc(), - "target/tmp/LogisticRegressionModel"); - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaSVDExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaSVDExample.java deleted file mode 100644 index 802be39..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaSVDExample.java +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.Arrays; -import java.util.List; -// $example off$ - -import org.apache.spark.SparkConf; -import org.apache.spark.SparkContext; -// $example on$ -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.mllib.linalg.Matrix; -import org.apache.spark.mllib.linalg.SingularValueDecomposition; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.mllib.linalg.distributed.RowMatrix; -// $example off$ - -/** - * Example for SingularValueDecomposition. - */ -public class JavaSVDExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("SVD Example"); - SparkContext sc = new SparkContext(conf); - JavaSparkContext jsc = JavaSparkContext.fromSparkContext(sc); - - // $example on$ - List data = Arrays.asList( - Vectors.sparse(5, new int[] {1, 3}, new double[] {1.0, 7.0}), - Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0), - Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0) - ); - - JavaRDD rows = jsc.parallelize(data); - - // Create a RowMatrix from JavaRDD. - RowMatrix mat = new RowMatrix(rows.rdd()); - - // Compute the top 5 singular values and corresponding singular vectors. - SingularValueDecomposition svd = mat.computeSVD(5, true, 1.0E-9d); - RowMatrix U = svd.U(); // The U factor is a RowMatrix. - Vector s = svd.s(); // The singular values are stored in a local dense vector. - Matrix V = svd.V(); // The V factor is a local dense matrix. - // $example off$ - Vector[] collectPartitions = (Vector[]) U.rows().collect(); - System.out.println("U factor is:"); - for (Vector vector : collectPartitions) { - System.out.println("\t" + vector); - } - System.out.println("Singular values are: " + s); - System.out.println("V factor is:\n" + V); - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaSVMWithSGDExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaSVMWithSGDExample.java deleted file mode 100644 index 866a221..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaSVMWithSGDExample.java +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.SparkContext; - -// $example on$ -import scala.Tuple2; - -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.classification.SVMModel; -import org.apache.spark.mllib.classification.SVMWithSGD; -import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; -// $example off$ - -/** - * Example for SVMWithSGD. - */ -public class JavaSVMWithSGDExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("JavaSVMWithSGDExample"); - SparkContext sc = new SparkContext(conf); - // $example on$ - String path = "data/mllib/sample_libsvm_data.txt"; - JavaRDD data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD(); - - // Split initial RDD into two... [60% training data, 40% testing data]. - JavaRDD training = data.sample(false, 0.6, 11L); - training.cache(); - JavaRDD test = data.subtract(training); - - // Run training algorithm to build the model. - int numIterations = 100; - SVMModel model = SVMWithSGD.train(training.rdd(), numIterations); - - // Clear the default threshold. - model.clearThreshold(); - - // Compute raw scores on the test set. - JavaRDD> scoreAndLabels = test.map(p -> - new Tuple2<>(model.predict(p.features()), p.label())); - - // Get evaluation metrics. - BinaryClassificationMetrics metrics = - new BinaryClassificationMetrics(JavaRDD.toRDD(scoreAndLabels)); - double auROC = metrics.areaUnderROC(); - - System.out.println("Area under ROC = " + auROC); - - // Save and load model - model.save(sc, "target/tmp/javaSVMWithSGDModel"); - SVMModel sameModel = SVMModel.load(sc, "target/tmp/javaSVMWithSGDModel"); - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaSimpleFPGrowth.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaSimpleFPGrowth.java deleted file mode 100644 index f9198e7..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaSimpleFPGrowth.java +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import java.util.Arrays; -import java.util.List; - -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.mllib.fpm.AssociationRules; -import org.apache.spark.mllib.fpm.FPGrowth; -import org.apache.spark.mllib.fpm.FPGrowthModel; -// $example off$ - -import org.apache.spark.SparkConf; - -public class JavaSimpleFPGrowth { - - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("FP-growth Example"); - JavaSparkContext sc = new JavaSparkContext(conf); - - // $example on$ - JavaRDD data = sc.textFile("data/mllib/sample_fpgrowth.txt"); - - JavaRDD> transactions = data.map(line -> Arrays.asList(line.split(" "))); - - FPGrowth fpg = new FPGrowth() - .setMinSupport(0.2) - .setNumPartitions(10); - FPGrowthModel model = fpg.run(transactions); - - for (FPGrowth.FreqItemset itemset: model.freqItemsets().toJavaRDD().collect()) { - System.out.println("[" + itemset.javaItems() + "], " + itemset.freq()); - } - - double minConfidence = 0.8; - for (AssociationRules.Rule rule - : model.generateAssociationRules(minConfidence).toJavaRDD().collect()) { - System.out.println( - rule.javaAntecedent() + " => " + rule.javaConsequent() + ", " + rule.confidence()); - } - // $example off$ - - sc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaStratifiedSamplingExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaStratifiedSamplingExample.java deleted file mode 100644 index 286b95c..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaStratifiedSamplingExample.java +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import com.google.common.collect.ImmutableMap; -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; - -// $example on$ -import java.util.*; - -import scala.Tuple2; - -import org.apache.spark.api.java.JavaPairRDD; -// $example off$ - -public class JavaStratifiedSamplingExample { - public static void main(String[] args) { - - SparkConf conf = new SparkConf().setAppName("JavaStratifiedSamplingExample"); - JavaSparkContext jsc = new JavaSparkContext(conf); - - @SuppressWarnings("unchecked") - // $example on$ - List> list = Arrays.asList( - new Tuple2<>(1, 'a'), - new Tuple2<>(1, 'b'), - new Tuple2<>(2, 'c'), - new Tuple2<>(2, 'd'), - new Tuple2<>(2, 'e'), - new Tuple2<>(3, 'f') - ); - - JavaPairRDD data = jsc.parallelizePairs(list); - - // specify the exact fraction desired from each key Map - ImmutableMap fractions = ImmutableMap.of(1, 0.1, 2, 0.6, 3, 0.3); - - // Get an approximate sample from each stratum - JavaPairRDD approxSample = data.sampleByKey(false, fractions); - // Get an exact sample from each stratum - JavaPairRDD exactSample = data.sampleByKeyExact(false, fractions); - // $example off$ - - System.out.println("approxSample size is " + approxSample.collect().size()); - for (Tuple2 t : approxSample.collect()) { - System.out.println(t._1() + " " + t._2()); - } - - System.out.println("exactSample size is " + exactSample.collect().size()); - for (Tuple2 t : exactSample.collect()) { - System.out.println(t._1() + " " + t._2()); - } - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaStreamingTestExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaStreamingTestExample.java deleted file mode 100644 index 4be702c..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaStreamingTestExample.java +++ /dev/null @@ -1,104 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -// $example on$ -import org.apache.spark.mllib.stat.test.BinarySample; -import org.apache.spark.mllib.stat.test.StreamingTest; -import org.apache.spark.mllib.stat.test.StreamingTestResult; -// $example off$ -import org.apache.spark.SparkConf; -import org.apache.spark.streaming.Duration; -import org.apache.spark.streaming.Seconds; -import org.apache.spark.streaming.api.java.JavaDStream; -import org.apache.spark.streaming.api.java.JavaStreamingContext; -import org.apache.spark.util.Utils; - - -/** - * Perform streaming testing using Welch's 2-sample t-test on a stream of data, where the data - * stream arrives as text files in a directory. Stops when the two groups are statistically - * significant (p-value < 0.05) or after a user-specified timeout in number of batches is exceeded. - * - * The rows of the text files must be in the form `Boolean, Double`. For example: - * false, -3.92 - * true, 99.32 - * - * Usage: - * JavaStreamingTestExample - * - * To run on your local machine using the directory `dataDir` with 5 seconds between each batch and - * a timeout after 100 insignificant batches, call: - * $ bin/run-example mllib.JavaStreamingTestExample dataDir 5 100 - * - * As you add text files to `dataDir` the significance test wil continually update every - * `batchDuration` seconds until the test becomes significant (p-value < 0.05) or the number of - * batches processed exceeds `numBatchesTimeout`. - */ -public class JavaStreamingTestExample { - - private static int timeoutCounter = 0; - - public static void main(String[] args) throws Exception { - if (args.length != 3) { - System.err.println("Usage: JavaStreamingTestExample " + - " "); - System.exit(1); - } - - String dataDir = args[0]; - Duration batchDuration = Seconds.apply(Long.parseLong(args[1])); - int numBatchesTimeout = Integer.parseInt(args[2]); - - SparkConf conf = new SparkConf().setMaster("local").setAppName("StreamingTestExample"); - JavaStreamingContext ssc = new JavaStreamingContext(conf, batchDuration); - - ssc.checkpoint(Utils.createTempDir(System.getProperty("java.io.tmpdir"), "spark").toString()); - - // $example on$ - JavaDStream data = ssc.textFileStream(dataDir).map(line -> { - String[] ts = line.split(","); - boolean label = Boolean.parseBoolean(ts[0]); - double value = Double.parseDouble(ts[1]); - return new BinarySample(label, value); - }); - - StreamingTest streamingTest = new StreamingTest() - .setPeacePeriod(0) - .setWindowSize(0) - .setTestMethod("welch"); - - JavaDStream out = streamingTest.registerStream(data); - out.print(); - // $example off$ - - // Stop processing if test becomes significant or we time out - timeoutCounter = numBatchesTimeout; - - out.foreachRDD(rdd -> { - timeoutCounter -= 1; - boolean anySignificant = !rdd.filter(v -> v.pValue() < 0.05).isEmpty(); - if (timeoutCounter <= 0 || anySignificant) { - rdd.context().stop(); - } - }); - - ssc.start(); - ssc.awaitTermination(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaSummaryStatisticsExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaSummaryStatisticsExample.java deleted file mode 100644 index 278706b..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/mllib/JavaSummaryStatisticsExample.java +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.mllib; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; -// $example on$ -import java.util.Arrays; - -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.mllib.stat.MultivariateStatisticalSummary; -import org.apache.spark.mllib.stat.Statistics; -// $example off$ - -public class JavaSummaryStatisticsExample { - public static void main(String[] args) { - - SparkConf conf = new SparkConf().setAppName("JavaSummaryStatisticsExample"); - JavaSparkContext jsc = new JavaSparkContext(conf); - - // $example on$ - JavaRDD mat = jsc.parallelize( - Arrays.asList( - Vectors.dense(1.0, 10.0, 100.0), - Vectors.dense(2.0, 20.0, 200.0), - Vectors.dense(3.0, 30.0, 300.0) - ) - ); // an RDD of Vectors - - // Compute column summary statistics. - MultivariateStatisticalSummary summary = Statistics.colStats(mat.rdd()); - System.out.println(summary.mean()); // a dense vector containing the mean value for each column - System.out.println(summary.variance()); // column-wise variance - System.out.println(summary.numNonzeros()); // number of nonzeros in each column - // $example off$ - - jsc.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java deleted file mode 100644 index 95859c5..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java +++ /dev/null @@ -1,288 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.examples.sql; - -// $example on:schema_merging$ -import java.io.Serializable; -import java.util.ArrayList; -import java.util.Arrays; -import java.util.List; -// $example off:schema_merging$ -import java.util.Properties; - -// $example on:basic_parquet_example$ -import org.apache.spark.api.java.function.MapFunction; -import org.apache.spark.sql.Encoders; -// $example on:schema_merging$ -// $example on:json_dataset$ -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off:json_dataset$ -// $example off:schema_merging$ -// $example off:basic_parquet_example$ -import org.apache.spark.sql.SparkSession; - -public class JavaSQLDataSourceExample { - - // $example on:schema_merging$ - public static class Square implements Serializable { - private int value; - private int square; - - // Getters and setters... - // $example off:schema_merging$ - public int getValue() { - return value; - } - - public void setValue(int value) { - this.value = value; - } - - public int getSquare() { - return square; - } - - public void setSquare(int square) { - this.square = square; - } - // $example on:schema_merging$ - } - // $example off:schema_merging$ - - // $example on:schema_merging$ - public static class Cube implements Serializable { - private int value; - private int cube; - - // Getters and setters... - // $example off:schema_merging$ - public int getValue() { - return value; - } - - public void setValue(int value) { - this.value = value; - } - - public int getCube() { - return cube; - } - - public void setCube(int cube) { - this.cube = cube; - } - // $example on:schema_merging$ - } - // $example off:schema_merging$ - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("Java Spark SQL data sources example") - .config("spark.some.config.option", "some-value") - .getOrCreate(); - - runBasicDataSourceExample(spark); - runBasicParquetExample(spark); - runParquetSchemaMergingExample(spark); - runJsonDatasetExample(spark); - runJdbcDatasetExample(spark); - - spark.stop(); - } - - private static void runBasicDataSourceExample(SparkSession spark) { - // $example on:generic_load_save_functions$ - Dataset usersDF = spark.read().load("examples/src/main/resources/users.parquet"); - usersDF.select("name", "favorite_color").write().save("namesAndFavColors.parquet"); - // $example off:generic_load_save_functions$ - // $example on:manual_load_options$ - Dataset peopleDF = - spark.read().format("json").load("examples/src/main/resources/people.json"); - peopleDF.select("name", "age").write().format("parquet").save("namesAndAges.parquet"); - // $example off:manual_load_options$ - // $example on:direct_sql$ - Dataset sqlDF = - spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`"); - // $example off:direct_sql$ - // $example on:write_sorting_and_bucketing$ - peopleDF.write().bucketBy(42, "name").sortBy("age").saveAsTable("people_bucketed"); - // $example off:write_sorting_and_bucketing$ - // $example on:write_partitioning$ - usersDF - .write() - .partitionBy("favorite_color") - .format("parquet") - .save("namesPartByColor.parquet"); - // $example off:write_partitioning$ - // $example on:write_partition_and_bucket$ - peopleDF - .write() - .partitionBy("favorite_color") - .bucketBy(42, "name") - .saveAsTable("people_partitioned_bucketed"); - // $example off:write_partition_and_bucket$ - - spark.sql("DROP TABLE IF EXISTS people_bucketed"); - spark.sql("DROP TABLE IF EXISTS people_partitioned_bucketed"); - } - - private static void runBasicParquetExample(SparkSession spark) { - // $example on:basic_parquet_example$ - Dataset peopleDF = spark.read().json("examples/src/main/resources/people.json"); - - // DataFrames can be saved as Parquet files, maintaining the schema information - peopleDF.write().parquet("people.parquet"); - - // Read in the Parquet file created above. - // Parquet files are self-describing so the schema is preserved - // The result of loading a parquet file is also a DataFrame - Dataset parquetFileDF = spark.read().parquet("people.parquet"); - - // Parquet files can also be used to create a temporary view and then used in SQL statements - parquetFileDF.createOrReplaceTempView("parquetFile"); - Dataset namesDF = spark.sql("SELECT name FROM parquetFile WHERE age BETWEEN 13 AND 19"); - Dataset namesDS = namesDF.map( - (MapFunction) row -> "Name: " + row.getString(0), - Encoders.STRING()); - namesDS.show(); - // +------------+ - // | value| - // +------------+ - // |Name: Justin| - // +------------+ - // $example off:basic_parquet_example$ - } - - private static void runParquetSchemaMergingExample(SparkSession spark) { - // $example on:schema_merging$ - List squares = new ArrayList<>(); - for (int value = 1; value <= 5; value++) { - Square square = new Square(); - square.setValue(value); - square.setSquare(value * value); - squares.add(square); - } - - // Create a simple DataFrame, store into a partition directory - Dataset squaresDF = spark.createDataFrame(squares, Square.class); - squaresDF.write().parquet("data/test_table/key=1"); - - List cubes = new ArrayList<>(); - for (int value = 6; value <= 10; value++) { - Cube cube = new Cube(); - cube.setValue(value); - cube.setCube(value * value * value); - cubes.add(cube); - } - - // Create another DataFrame in a new partition directory, - // adding a new column and dropping an existing column - Dataset cubesDF = spark.createDataFrame(cubes, Cube.class); - cubesDF.write().parquet("data/test_table/key=2"); - - // Read the partitioned table - Dataset mergedDF = spark.read().option("mergeSchema", true).parquet("data/test_table"); - mergedDF.printSchema(); - - // The final schema consists of all 3 columns in the Parquet files together - // with the partitioning column appeared in the partition directory paths - // root - // |-- value: int (nullable = true) - // |-- square: int (nullable = true) - // |-- cube: int (nullable = true) - // |-- key: int (nullable = true) - // $example off:schema_merging$ - } - - private static void runJsonDatasetExample(SparkSession spark) { - // $example on:json_dataset$ - // A JSON dataset is pointed to by path. - // The path can be either a single text file or a directory storing text files - Dataset people = spark.read().json("examples/src/main/resources/people.json"); - - // The inferred schema can be visualized using the printSchema() method - people.printSchema(); - // root - // |-- age: long (nullable = true) - // |-- name: string (nullable = true) - - // Creates a temporary view using the DataFrame - people.createOrReplaceTempView("people"); - - // SQL statements can be run by using the sql methods provided by spark - Dataset namesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19"); - namesDF.show(); - // +------+ - // | name| - // +------+ - // |Justin| - // +------+ - - // Alternatively, a DataFrame can be created for a JSON dataset represented by - // a Dataset storing one JSON object per string. - List jsonData = Arrays.asList( - "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}"); - Dataset anotherPeopleDataset = spark.createDataset(jsonData, Encoders.STRING()); - Dataset anotherPeople = spark.read().json(anotherPeopleDataset); - anotherPeople.show(); - // +---------------+----+ - // | address|name| - // +---------------+----+ - // |[Columbus,Ohio]| Yin| - // +---------------+----+ - // $example off:json_dataset$ - } - - private static void runJdbcDatasetExample(SparkSession spark) { - // $example on:jdbc_dataset$ - // Note: JDBC loading and saving can be achieved via either the load/save or jdbc methods - // Loading data from a JDBC source - Dataset jdbcDF = spark.read() - .format("jdbc") - .option("url", "jdbc:postgresql:dbserver") - .option("dbtable", "schema.tablename") - .option("user", "username") - .option("password", "password") - .load(); - - Properties connectionProperties = new Properties(); - connectionProperties.put("user", "username"); - connectionProperties.put("password", "password"); - Dataset jdbcDF2 = spark.read() - .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties); - - // Saving data to a JDBC source - jdbcDF.write() - .format("jdbc") - .option("url", "jdbc:postgresql:dbserver") - .option("dbtable", "schema.tablename") - .option("user", "username") - .option("password", "password") - .save(); - - jdbcDF2.write() - .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties); - - // Specifying create table column data types on write - jdbcDF.write() - .option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)") - .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties); - // $example off:jdbc_dataset$ - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java deleted file mode 100644 index 8605852..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java +++ /dev/null @@ -1,344 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.examples.sql; - -// $example on:programmatic_schema$ -import java.util.ArrayList; -import java.util.List; -// $example off:programmatic_schema$ -// $example on:create_ds$ -import java.util.Arrays; -import java.util.Collections; -import java.io.Serializable; -// $example off:create_ds$ - -// $example on:schema_inferring$ -// $example on:programmatic_schema$ -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.function.Function; -// $example off:programmatic_schema$ -// $example on:create_ds$ -import org.apache.spark.api.java.function.MapFunction; -// $example on:create_df$ -// $example on:run_sql$ -// $example on:programmatic_schema$ -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -// $example off:programmatic_schema$ -// $example off:create_df$ -// $example off:run_sql$ -import org.apache.spark.sql.Encoder; -import org.apache.spark.sql.Encoders; -// $example off:create_ds$ -// $example off:schema_inferring$ -import org.apache.spark.sql.RowFactory; -// $example on:init_session$ -import org.apache.spark.sql.SparkSession; -// $example off:init_session$ -// $example on:programmatic_schema$ -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off:programmatic_schema$ -import org.apache.spark.sql.AnalysisException; - -// $example on:untyped_ops$ -// col("...") is preferable to df.col("...") -import static org.apache.spark.sql.functions.col; -// $example off:untyped_ops$ - -public class JavaSparkSQLExample { - // $example on:create_ds$ - public static class Person implements Serializable { - private String name; - private int age; - - public String getName() { - return name; - } - - public void setName(String name) { - this.name = name; - } - - public int getAge() { - return age; - } - - public void setAge(int age) { - this.age = age; - } - } - // $example off:create_ds$ - - public static void main(String[] args) throws AnalysisException { - // $example on:init_session$ - SparkSession spark = SparkSession - .builder() - .appName("Java Spark SQL basic example") - .config("spark.some.config.option", "some-value") - .getOrCreate(); - // $example off:init_session$ - - runBasicDataFrameExample(spark); - runDatasetCreationExample(spark); - runInferSchemaExample(spark); - runProgrammaticSchemaExample(spark); - - spark.stop(); - } - - private static void runBasicDataFrameExample(SparkSession spark) throws AnalysisException { - // $example on:create_df$ - Dataset df = spark.read().json("examples/src/main/resources/people.json"); - - // Displays the content of the DataFrame to stdout - df.show(); - // +----+-------+ - // | age| name| - // +----+-------+ - // |null|Michael| - // | 30| Andy| - // | 19| Justin| - // +----+-------+ - // $example off:create_df$ - - // $example on:untyped_ops$ - // Print the schema in a tree format - df.printSchema(); - // root - // |-- age: long (nullable = true) - // |-- name: string (nullable = true) - - // Select only the "name" column - df.select("name").show(); - // +-------+ - // | name| - // +-------+ - // |Michael| - // | Andy| - // | Justin| - // +-------+ - - // Select everybody, but increment the age by 1 - df.select(col("name"), col("age").plus(1)).show(); - // +-------+---------+ - // | name|(age + 1)| - // +-------+---------+ - // |Michael| null| - // | Andy| 31| - // | Justin| 20| - // +-------+---------+ - - // Select people older than 21 - df.filter(col("age").gt(21)).show(); - // +---+----+ - // |age|name| - // +---+----+ - // | 30|Andy| - // +---+----+ - - // Count people by age - df.groupBy("age").count().show(); - // +----+-----+ - // | age|count| - // +----+-----+ - // | 19| 1| - // |null| 1| - // | 30| 1| - // +----+-----+ - // $example off:untyped_ops$ - - // $example on:run_sql$ - // Register the DataFrame as a SQL temporary view - df.createOrReplaceTempView("people"); - - Dataset sqlDF = spark.sql("SELECT * FROM people"); - sqlDF.show(); - // +----+-------+ - // | age| name| - // +----+-------+ - // |null|Michael| - // | 30| Andy| - // | 19| Justin| - // +----+-------+ - // $example off:run_sql$ - - // $example on:global_temp_view$ - // Register the DataFrame as a global temporary view - df.createGlobalTempView("people"); - - // Global temporary view is tied to a system preserved database `global_temp` - spark.sql("SELECT * FROM global_temp.people").show(); - // +----+-------+ - // | age| name| - // +----+-------+ - // |null|Michael| - // | 30| Andy| - // | 19| Justin| - // +----+-------+ - - // Global temporary view is cross-session - spark.newSession().sql("SELECT * FROM global_temp.people").show(); - // +----+-------+ - // | age| name| - // +----+-------+ - // |null|Michael| - // | 30| Andy| - // | 19| Justin| - // +----+-------+ - // $example off:global_temp_view$ - } - - private static void runDatasetCreationExample(SparkSession spark) { - // $example on:create_ds$ - // Create an instance of a Bean class - Person person = new Person(); - person.setName("Andy"); - person.setAge(32); - - // Encoders are created for Java beans - Encoder personEncoder = Encoders.bean(Person.class); - Dataset javaBeanDS = spark.createDataset( - Collections.singletonList(person), - personEncoder - ); - javaBeanDS.show(); - // +---+----+ - // |age|name| - // +---+----+ - // | 32|Andy| - // +---+----+ - - // Encoders for most common types are provided in class Encoders - Encoder integerEncoder = Encoders.INT(); - Dataset primitiveDS = spark.createDataset(Arrays.asList(1, 2, 3), integerEncoder); - Dataset transformedDS = primitiveDS.map( - (MapFunction) value -> value + 1, - integerEncoder); - transformedDS.collect(); // Returns [2, 3, 4] - - // DataFrames can be converted to a Dataset by providing a class. Mapping based on name - String path = "examples/src/main/resources/people.json"; - Dataset peopleDS = spark.read().json(path).as(personEncoder); - peopleDS.show(); - // +----+-------+ - // | age| name| - // +----+-------+ - // |null|Michael| - // | 30| Andy| - // | 19| Justin| - // +----+-------+ - // $example off:create_ds$ - } - - private static void runInferSchemaExample(SparkSession spark) { - // $example on:schema_inferring$ - // Create an RDD of Person objects from a text file - JavaRDD peopleRDD = spark.read() - .textFile("examples/src/main/resources/people.txt") - .javaRDD() - .map(line -> { - String[] parts = line.split(","); - Person person = new Person(); - person.setName(parts[0]); - person.setAge(Integer.parseInt(parts[1].trim())); - return person; - }); - - // Apply a schema to an RDD of JavaBeans to get a DataFrame - Dataset peopleDF = spark.createDataFrame(peopleRDD, Person.class); - // Register the DataFrame as a temporary view - peopleDF.createOrReplaceTempView("people"); - - // SQL statements can be run by using the sql methods provided by spark - Dataset teenagersDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19"); - - // The columns of a row in the result can be accessed by field index - Encoder stringEncoder = Encoders.STRING(); - Dataset teenagerNamesByIndexDF = teenagersDF.map( - (MapFunction) row -> "Name: " + row.getString(0), - stringEncoder); - teenagerNamesByIndexDF.show(); - // +------------+ - // | value| - // +------------+ - // |Name: Justin| - // +------------+ - - // or by field name - Dataset teenagerNamesByFieldDF = teenagersDF.map( - (MapFunction) row -> "Name: " + row.getAs("name"), - stringEncoder); - teenagerNamesByFieldDF.show(); - // +------------+ - // | value| - // +------------+ - // |Name: Justin| - // +------------+ - // $example off:schema_inferring$ - } - - private static void runProgrammaticSchemaExample(SparkSession spark) { - // $example on:programmatic_schema$ - // Create an RDD - JavaRDD peopleRDD = spark.sparkContext() - .textFile("examples/src/main/resources/people.txt", 1) - .toJavaRDD(); - - // The schema is encoded in a string - String schemaString = "name age"; - - // Generate the schema based on the string of schema - List fields = new ArrayList<>(); - for (String fieldName : schemaString.split(" ")) { - StructField field = DataTypes.createStructField(fieldName, DataTypes.StringType, true); - fields.add(field); - } - StructType schema = DataTypes.createStructType(fields); - - // Convert records of the RDD (people) to Rows - JavaRDD rowRDD = peopleRDD.map((Function) record -> { - String[] attributes = record.split(","); - return RowFactory.create(attributes[0], attributes[1].trim()); - }); - - // Apply the schema to the RDD - Dataset peopleDataFrame = spark.createDataFrame(rowRDD, schema); - - // Creates a temporary view using the DataFrame - peopleDataFrame.createOrReplaceTempView("people"); - - // SQL can be run over a temporary view created using DataFrames - Dataset results = spark.sql("SELECT name FROM people"); - - // The results of SQL queries are DataFrames and support all the normal RDD operations - // The columns of a row in the result can be accessed by field index or by field name - Dataset namesDS = results.map( - (MapFunction) row -> "Name: " + row.getString(0), - Encoders.STRING()); - namesDS.show(); - // +-------------+ - // | value| - // +-------------+ - // |Name: Michael| - // | Name: Andy| - // | Name: Justin| - // +-------------+ - // $example off:programmatic_schema$ - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedTypedAggregation.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedTypedAggregation.java deleted file mode 100644 index 78e9011..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedTypedAggregation.java +++ /dev/null @@ -1,160 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.examples.sql; - -// $example on:typed_custom_aggregation$ -import java.io.Serializable; - -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Encoder; -import org.apache.spark.sql.Encoders; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.TypedColumn; -import org.apache.spark.sql.expressions.Aggregator; -// $example off:typed_custom_aggregation$ - -public class JavaUserDefinedTypedAggregation { - - // $example on:typed_custom_aggregation$ - public static class Employee implements Serializable { - private String name; - private long salary; - - // Constructors, getters, setters... - // $example off:typed_custom_aggregation$ - public String getName() { - return name; - } - - public void setName(String name) { - this.name = name; - } - - public long getSalary() { - return salary; - } - - public void setSalary(long salary) { - this.salary = salary; - } - // $example on:typed_custom_aggregation$ - } - - public static class Average implements Serializable { - private long sum; - private long count; - - // Constructors, getters, setters... - // $example off:typed_custom_aggregation$ - public Average() { - } - - public Average(long sum, long count) { - this.sum = sum; - this.count = count; - } - - public long getSum() { - return sum; - } - - public void setSum(long sum) { - this.sum = sum; - } - - public long getCount() { - return count; - } - - public void setCount(long count) { - this.count = count; - } - // $example on:typed_custom_aggregation$ - } - - public static class MyAverage extends Aggregator { - // A zero value for this aggregation. Should satisfy the property that any b + zero = b - public Average zero() { - return new Average(0L, 0L); - } - // Combine two values to produce a new value. For performance, the function may modify `buffer` - // and return it instead of constructing a new object - public Average reduce(Average buffer, Employee employee) { - long newSum = buffer.getSum() + employee.getSalary(); - long newCount = buffer.getCount() + 1; - buffer.setSum(newSum); - buffer.setCount(newCount); - return buffer; - } - // Merge two intermediate values - public Average merge(Average b1, Average b2) { - long mergedSum = b1.getSum() + b2.getSum(); - long mergedCount = b1.getCount() + b2.getCount(); - b1.setSum(mergedSum); - b1.setCount(mergedCount); - return b1; - } - // Transform the output of the reduction - public Double finish(Average reduction) { - return ((double) reduction.getSum()) / reduction.getCount(); - } - // Specifies the Encoder for the intermediate value type - public Encoder bufferEncoder() { - return Encoders.bean(Average.class); - } - // Specifies the Encoder for the final output value type - public Encoder outputEncoder() { - return Encoders.DOUBLE(); - } - } - // $example off:typed_custom_aggregation$ - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("Java Spark SQL user-defined Datasets aggregation example") - .getOrCreate(); - - // $example on:typed_custom_aggregation$ - Encoder employeeEncoder = Encoders.bean(Employee.class); - String path = "examples/src/main/resources/employees.json"; - Dataset ds = spark.read().json(path).as(employeeEncoder); - ds.show(); - // +-------+------+ - // | name|salary| - // +-------+------+ - // |Michael| 3000| - // | Andy| 4500| - // | Justin| 3500| - // | Berta| 4000| - // +-------+------+ - - MyAverage myAverage = new MyAverage(); - // Convert the function to a `TypedColumn` and give it a name - TypedColumn averageSalary = myAverage.toColumn().name("average_salary"); - Dataset result = ds.select(averageSalary); - result.show(); - // +--------------+ - // |average_salary| - // +--------------+ - // | 3750.0| - // +--------------+ - // $example off:typed_custom_aggregation$ - spark.stop(); - } - -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedUntypedAggregation.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedUntypedAggregation.java deleted file mode 100644 index 6da60a1..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/JavaUserDefinedUntypedAggregation.java +++ /dev/null @@ -1,132 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.examples.sql; - -// $example on:untyped_custom_aggregation$ -import java.util.ArrayList; -import java.util.List; - -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.expressions.MutableAggregationBuffer; -import org.apache.spark.sql.expressions.UserDefinedAggregateFunction; -import org.apache.spark.sql.types.DataType; -import org.apache.spark.sql.types.DataTypes; -import org.apache.spark.sql.types.StructField; -import org.apache.spark.sql.types.StructType; -// $example off:untyped_custom_aggregation$ - -public class JavaUserDefinedUntypedAggregation { - - // $example on:untyped_custom_aggregation$ - public static class MyAverage extends UserDefinedAggregateFunction { - - private StructType inputSchema; - private StructType bufferSchema; - - public MyAverage() { - List inputFields = new ArrayList<>(); - inputFields.add(DataTypes.createStructField("inputColumn", DataTypes.LongType, true)); - inputSchema = DataTypes.createStructType(inputFields); - - List bufferFields = new ArrayList<>(); - bufferFields.add(DataTypes.createStructField("sum", DataTypes.LongType, true)); - bufferFields.add(DataTypes.createStructField("count", DataTypes.LongType, true)); - bufferSchema = DataTypes.createStructType(bufferFields); - } - // Data types of input arguments of this aggregate function - public StructType inputSchema() { - return inputSchema; - } - // Data types of values in the aggregation buffer - public StructType bufferSchema() { - return bufferSchema; - } - // The data type of the returned value - public DataType dataType() { - return DataTypes.DoubleType; - } - // Whether this function always returns the same output on the identical input - public boolean deterministic() { - return true; - } - // Initializes the given aggregation buffer. The buffer itself is a `Row` that in addition to - // standard methods like retrieving a value at an index (e.g., get(), getBoolean()), provides - // the opportunity to update its values. Note that arrays and maps inside the buffer are still - // immutable. - public void initialize(MutableAggregationBuffer buffer) { - buffer.update(0, 0L); - buffer.update(1, 0L); - } - // Updates the given aggregation buffer `buffer` with new input data from `input` - public void update(MutableAggregationBuffer buffer, Row input) { - if (!input.isNullAt(0)) { - long updatedSum = buffer.getLong(0) + input.getLong(0); - long updatedCount = buffer.getLong(1) + 1; - buffer.update(0, updatedSum); - buffer.update(1, updatedCount); - } - } - // Merges two aggregation buffers and stores the updated buffer values back to `buffer1` - public void merge(MutableAggregationBuffer buffer1, Row buffer2) { - long mergedSum = buffer1.getLong(0) + buffer2.getLong(0); - long mergedCount = buffer1.getLong(1) + buffer2.getLong(1); - buffer1.update(0, mergedSum); - buffer1.update(1, mergedCount); - } - // Calculates the final result - public Double evaluate(Row buffer) { - return ((double) buffer.getLong(0)) / buffer.getLong(1); - } - } - // $example off:untyped_custom_aggregation$ - - public static void main(String[] args) { - SparkSession spark = SparkSession - .builder() - .appName("Java Spark SQL user-defined DataFrames aggregation example") - .getOrCreate(); - - // $example on:untyped_custom_aggregation$ - // Register the function to access it - spark.udf().register("myAverage", new MyAverage()); - - Dataset df = spark.read().json("examples/src/main/resources/employees.json"); - df.createOrReplaceTempView("employees"); - df.show(); - // +-------+------+ - // | name|salary| - // +-------+------+ - // |Michael| 3000| - // | Andy| 4500| - // | Justin| 3500| - // | Berta| 4000| - // +-------+------+ - - Dataset result = spark.sql("SELECT myAverage(salary) as average_salary FROM employees"); - result.show(); - // +--------------+ - // |average_salary| - // +--------------+ - // | 3750.0| - // +--------------+ - // $example off:untyped_custom_aggregation$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/hive/JavaSparkHiveExample.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/hive/JavaSparkHiveExample.java deleted file mode 100644 index 575a463..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/hive/JavaSparkHiveExample.java +++ /dev/null @@ -1,129 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.examples.sql.hive; - -// $example on:spark_hive$ -import java.io.File; -import java.io.Serializable; -import java.util.ArrayList; -import java.util.List; - -import org.apache.spark.api.java.function.MapFunction; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Encoders; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -// $example off:spark_hive$ - -public class JavaSparkHiveExample { - - // $example on:spark_hive$ - public static class Record implements Serializable { - private int key; - private String value; - - public int getKey() { - return key; - } - - public void setKey(int key) { - this.key = key; - } - - public String getValue() { - return value; - } - - public void setValue(String value) { - this.value = value; - } - } - // $example off:spark_hive$ - - public static void main(String[] args) { - // $example on:spark_hive$ - // warehouseLocation points to the default location for managed databases and tables - String warehouseLocation = new File("spark-warehouse").getAbsolutePath(); - SparkSession spark = SparkSession - .builder() - .appName("Java Spark Hive Example") - .config("spark.sql.warehouse.dir", warehouseLocation) - .enableHiveSupport() - .getOrCreate(); - - spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive"); - spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src"); - - // Queries are expressed in HiveQL - spark.sql("SELECT * FROM src").show(); - // +---+-------+ - // |key| value| - // +---+-------+ - // |238|val_238| - // | 86| val_86| - // |311|val_311| - // ... - - // Aggregation queries are also supported. - spark.sql("SELECT COUNT(*) FROM src").show(); - // +--------+ - // |count(1)| - // +--------+ - // | 500 | - // +--------+ - - // The results of SQL queries are themselves DataFrames and support all normal functions. - Dataset sqlDF = spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key"); - - // The items in DataFrames are of type Row, which lets you to access each column by ordinal. - Dataset stringsDS = sqlDF.map( - (MapFunction) row -> "Key: " + row.get(0) + ", Value: " + row.get(1), - Encoders.STRING()); - stringsDS.show(); - // +--------------------+ - // | value| - // +--------------------+ - // |Key: 0, Value: val_0| - // |Key: 0, Value: val_0| - // |Key: 0, Value: val_0| - // ... - - // You can also use DataFrames to create temporary views within a SparkSession. - List records = new ArrayList<>(); - for (int key = 1; key < 100; key++) { - Record record = new Record(); - record.setKey(key); - record.setValue("val_" + key); - records.add(record); - } - Dataset recordsDF = spark.createDataFrame(records, Record.class); - recordsDF.createOrReplaceTempView("records"); - - // Queries can then join DataFrames data with data stored in Hive. - spark.sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show(); - // +---+------+---+------+ - // |key| value|key| value| - // +---+------+---+------+ - // | 2| val_2| 2| val_2| - // | 2| val_2| 2| val_2| - // | 4| val_4| 4| val_4| - // ... - // $example off:spark_hive$ - - spark.stop(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredKafkaWordCount.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredKafkaWordCount.java deleted file mode 100644 index 4e02719..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredKafkaWordCount.java +++ /dev/null @@ -1,92 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.sql.streaming; - -import org.apache.spark.api.java.function.FlatMapFunction; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Encoders; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.streaming.StreamingQuery; - -import java.util.Arrays; - -/** - * Consumes messages from one or more topics in Kafka and does wordcount. - * Usage: JavaStructuredKafkaWordCount - * The Kafka "bootstrap.servers" configuration. A - * comma-separated list of host:port. - * There are three kinds of type, i.e. 'assign', 'subscribe', - * 'subscribePattern'. - * |- Specific TopicPartitions to consume. Json string - * | {"topicA":[0,1],"topicB":[2,4]}. - * |- The topic list to subscribe. A comma-separated list of - * | topics. - * |- The pattern used to subscribe to topic(s). - * | Java regex string. - * |- Only one of "assign, "subscribe" or "subscribePattern" options can be - * | specified for Kafka source. - * Different value format depends on the value of 'subscribe-type'. - * - * Example: - * `$ bin/run-example \ - * sql.streaming.JavaStructuredKafkaWordCount host1:port1,host2:port2 \ - * subscribe topic1,topic2` - */ -public final class JavaStructuredKafkaWordCount { - - public static void main(String[] args) throws Exception { - if (args.length < 3) { - System.err.println("Usage: JavaStructuredKafkaWordCount " + - " "); - System.exit(1); - } - - String bootstrapServers = args[0]; - String subscribeType = args[1]; - String topics = args[2]; - - SparkSession spark = SparkSession - .builder() - .appName("JavaStructuredKafkaWordCount") - .getOrCreate(); - - // Create DataSet representing the stream of input lines from kafka - Dataset lines = spark - .readStream() - .format("kafka") - .option("kafka.bootstrap.servers", bootstrapServers) - .option(subscribeType, topics) - .load() - .selectExpr("CAST(value AS STRING)") - .as(Encoders.STRING()); - - // Generate running word count - Dataset wordCounts = lines.flatMap( - (FlatMapFunction) x -> Arrays.asList(x.split(" ")).iterator(), - Encoders.STRING()).groupBy("value").count(); - - // Start running the query that prints the running counts to the console - StreamingQuery query = wordCounts.writeStream() - .outputMode("complete") - .format("console") - .start(); - - query.awaitTermination(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCount.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCount.java deleted file mode 100644 index 3af7869..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCount.java +++ /dev/null @@ -1,78 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.examples.sql.streaming; - -import org.apache.spark.api.java.function.FlatMapFunction; -import org.apache.spark.sql.*; -import org.apache.spark.sql.streaming.StreamingQuery; - -import java.util.Arrays; - -/** - * Counts words in UTF8 encoded, '\n' delimited text received from the network. - * - * Usage: JavaStructuredNetworkWordCount - * and describe the TCP server that Structured Streaming - * would connect to receive data. - * - * To run this on your local machine, you need to first run a Netcat server - * `$ nc -lk 9999` - * and then run the example - * `$ bin/run-example sql.streaming.JavaStructuredNetworkWordCount - * localhost 9999` - */ -public final class JavaStructuredNetworkWordCount { - - public static void main(String[] args) throws Exception { - if (args.length < 2) { - System.err.println("Usage: JavaStructuredNetworkWordCount "); - System.exit(1); - } - - String host = args[0]; - int port = Integer.parseInt(args[1]); - - SparkSession spark = SparkSession - .builder() - .appName("JavaStructuredNetworkWordCount") - .getOrCreate(); - - // Create DataFrame representing the stream of input lines from connection to host:port - Dataset lines = spark - .readStream() - .format("socket") - .option("host", host) - .option("port", port) - .load(); - - // Split the lines into words - Dataset words = lines.as(Encoders.STRING()).flatMap( - (FlatMapFunction) x -> Arrays.asList(x.split(" ")).iterator(), - Encoders.STRING()); - - // Generate running word count - Dataset wordCounts = words.groupBy("value").count(); - - // Start running the query that prints the running counts to the console - StreamingQuery query = wordCounts.writeStream() - .outputMode("complete") - .format("console") - .start(); - - query.awaitTermination(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCountWindowed.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCountWindowed.java deleted file mode 100644 index 93ec5e2..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCountWindowed.java +++ /dev/null @@ -1,112 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.examples.sql.streaming; - -import org.apache.spark.api.java.function.FlatMapFunction; -import org.apache.spark.sql.*; -import org.apache.spark.sql.streaming.StreamingQuery; -import scala.Tuple2; - -import java.sql.Timestamp; -import java.util.ArrayList; -import java.util.List; - -/** - * Counts words in UTF8 encoded, '\n' delimited text received from the network over a - * sliding window of configurable duration. Each line from the network is tagged - * with a timestamp that is used to determine the windows into which it falls. - * - * Usage: JavaStructuredNetworkWordCountWindowed - * [] - * and describe the TCP server that Structured Streaming - * would connect to receive data. - * gives the size of window, specified as integer number of seconds - * gives the amount of time successive windows are offset from one another, - * given in the same units as above. should be less than or equal to - * . If the two are equal, successive windows have no overlap. If - * is not provided, it defaults to . - * - * To run this on your local machine, you need to first run a Netcat server - * `$ nc -lk 9999` - * and then run the example - * `$ bin/run-example sql.streaming.JavaStructuredNetworkWordCountWindowed - * localhost 9999 []` - * - * One recommended , pair is 10, 5 - */ -public final class JavaStructuredNetworkWordCountWindowed { - - public static void main(String[] args) throws Exception { - if (args.length < 3) { - System.err.println("Usage: JavaStructuredNetworkWordCountWindowed " + - " []"); - System.exit(1); - } - - String host = args[0]; - int port = Integer.parseInt(args[1]); - int windowSize = Integer.parseInt(args[2]); - int slideSize = (args.length == 3) ? windowSize : Integer.parseInt(args[3]); - if (slideSize > windowSize) { - System.err.println(" must be less than or equal to "); - } - String windowDuration = windowSize + " seconds"; - String slideDuration = slideSize + " seconds"; - - SparkSession spark = SparkSession - .builder() - .appName("JavaStructuredNetworkWordCountWindowed") - .getOrCreate(); - - // Create DataFrame representing the stream of input lines from connection to host:port - Dataset lines = spark - .readStream() - .format("socket") - .option("host", host) - .option("port", port) - .option("includeTimestamp", true) - .load(); - - // Split the lines into words, retaining timestamps - Dataset words = lines - .as(Encoders.tuple(Encoders.STRING(), Encoders.TIMESTAMP())) - .flatMap((FlatMapFunction, Tuple2>) t -> { - List> result = new ArrayList<>(); - for (String word : t._1.split(" ")) { - result.add(new Tuple2<>(word, t._2)); - } - return result.iterator(); - }, - Encoders.tuple(Encoders.STRING(), Encoders.TIMESTAMP()) - ).toDF("word", "timestamp"); - - // Group the data by window and word and compute the count of each group - Dataset windowedCounts = words.groupBy( - functions.window(words.col("timestamp"), windowDuration, slideDuration), - words.col("word") - ).count().orderBy("window"); - - // Start running the query that prints the windowed word counts to the console - StreamingQuery query = windowedCounts.writeStream() - .outputMode("complete") - .format("console") - .option("truncate", "false") - .start(); - - query.awaitTermination(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredSessionization.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredSessionization.java deleted file mode 100644 index 6b8e655..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredSessionization.java +++ /dev/null @@ -1,252 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.examples.sql.streaming; - -import org.apache.spark.api.java.function.FlatMapFunction; -import org.apache.spark.api.java.function.MapFunction; -import org.apache.spark.api.java.function.MapGroupsWithStateFunction; -import org.apache.spark.sql.*; -import org.apache.spark.sql.streaming.GroupState; -import org.apache.spark.sql.streaming.GroupStateTimeout; -import org.apache.spark.sql.streaming.StreamingQuery; - -import java.io.Serializable; -import java.sql.Timestamp; -import java.util.*; - -/** - * Counts words in UTF8 encoded, '\n' delimited text received from the network. - *

- * Usage: JavaStructuredNetworkWordCount - * and describe the TCP server that Structured Streaming - * would connect to receive data. - *

- * To run this on your local machine, you need to first run a Netcat server - * `$ nc -lk 9999` - * and then run the example - * `$ bin/run-example sql.streaming.JavaStructuredSessionization - * localhost 9999` - */ -public final class JavaStructuredSessionization { - - public static void main(String[] args) throws Exception { - if (args.length < 2) { - System.err.println("Usage: JavaStructuredSessionization "); - System.exit(1); - } - - String host = args[0]; - int port = Integer.parseInt(args[1]); - - SparkSession spark = SparkSession - .builder() - .appName("JavaStructuredSessionization") - .getOrCreate(); - - // Create DataFrame representing the stream of input lines from connection to host:port - Dataset lines = spark - .readStream() - .format("socket") - .option("host", host) - .option("port", port) - .option("includeTimestamp", true) - .load(); - - FlatMapFunction linesToEvents = - new FlatMapFunction() { - @Override - public Iterator call(LineWithTimestamp lineWithTimestamp) throws Exception { - ArrayList eventList = new ArrayList(); - for (String word : lineWithTimestamp.getLine().split(" ")) { - eventList.add(new Event(word, lineWithTimestamp.getTimestamp())); - } - return eventList.iterator(); - } - }; - - // Split the lines into words, treat words as sessionId of events - Dataset events = lines - .withColumnRenamed("value", "line") - .as(Encoders.bean(LineWithTimestamp.class)) - .flatMap(linesToEvents, Encoders.bean(Event.class)); - - // Sessionize the events. Track number of events, start and end timestamps of session, and - // and report session updates. - // - // Step 1: Define the state update function - MapGroupsWithStateFunction stateUpdateFunc = - new MapGroupsWithStateFunction() { - @Override public SessionUpdate call( - String sessionId, Iterator events, GroupState state) - throws Exception { - // If timed out, then remove session and send final update - if (state.hasTimedOut()) { - SessionUpdate finalUpdate = new SessionUpdate( - sessionId, state.get().calculateDuration(), state.get().getNumEvents(), true); - state.remove(); - return finalUpdate; - - } else { - // Find max and min timestamps in events - long maxTimestampMs = Long.MIN_VALUE; - long minTimestampMs = Long.MAX_VALUE; - int numNewEvents = 0; - while (events.hasNext()) { - Event e = events.next(); - long timestampMs = e.getTimestamp().getTime(); - maxTimestampMs = Math.max(timestampMs, maxTimestampMs); - minTimestampMs = Math.min(timestampMs, minTimestampMs); - numNewEvents += 1; - } - SessionInfo updatedSession = new SessionInfo(); - - // Update start and end timestamps in session - if (state.exists()) { - SessionInfo oldSession = state.get(); - updatedSession.setNumEvents(oldSession.numEvents + numNewEvents); - updatedSession.setStartTimestampMs(oldSession.startTimestampMs); - updatedSession.setEndTimestampMs(Math.max(oldSession.endTimestampMs, maxTimestampMs)); - } else { - updatedSession.setNumEvents(numNewEvents); - updatedSession.setStartTimestampMs(minTimestampMs); - updatedSession.setEndTimestampMs(maxTimestampMs); - } - state.update(updatedSession); - // Set timeout such that the session will be expired if no data received for 10 seconds - state.setTimeoutDuration("10 seconds"); - return new SessionUpdate( - sessionId, state.get().calculateDuration(), state.get().getNumEvents(), false); - } - } - }; - - // Step 2: Apply the state update function to the events streaming Dataset grouped by sessionId - Dataset sessionUpdates = events - .groupByKey( - new MapFunction() { - @Override public String call(Event event) throws Exception { - return event.getSessionId(); - } - }, Encoders.STRING()) - .mapGroupsWithState( - stateUpdateFunc, - Encoders.bean(SessionInfo.class), - Encoders.bean(SessionUpdate.class), - GroupStateTimeout.ProcessingTimeTimeout()); - - // Start running the query that prints the session updates to the console - StreamingQuery query = sessionUpdates - .writeStream() - .outputMode("update") - .format("console") - .start(); - - query.awaitTermination(); - } - - /** - * User-defined data type representing the raw lines with timestamps. - */ - public static class LineWithTimestamp implements Serializable { - private String line; - private Timestamp timestamp; - - public Timestamp getTimestamp() { return timestamp; } - public void setTimestamp(Timestamp timestamp) { this.timestamp = timestamp; } - - public String getLine() { return line; } - public void setLine(String sessionId) { this.line = sessionId; } - } - - /** - * User-defined data type representing the input events - */ - public static class Event implements Serializable { - private String sessionId; - private Timestamp timestamp; - - public Event() { } - public Event(String sessionId, Timestamp timestamp) { - this.sessionId = sessionId; - this.timestamp = timestamp; - } - - public Timestamp getTimestamp() { return timestamp; } - public void setTimestamp(Timestamp timestamp) { this.timestamp = timestamp; } - - public String getSessionId() { return sessionId; } - public void setSessionId(String sessionId) { this.sessionId = sessionId; } - } - - /** - * User-defined data type for storing a session information as state in mapGroupsWithState. - */ - public static class SessionInfo implements Serializable { - private int numEvents = 0; - private long startTimestampMs = -1; - private long endTimestampMs = -1; - - public int getNumEvents() { return numEvents; } - public void setNumEvents(int numEvents) { this.numEvents = numEvents; } - - public long getStartTimestampMs() { return startTimestampMs; } - public void setStartTimestampMs(long startTimestampMs) { - this.startTimestampMs = startTimestampMs; - } - - public long getEndTimestampMs() { return endTimestampMs; } - public void setEndTimestampMs(long endTimestampMs) { this.endTimestampMs = endTimestampMs; } - - public long calculateDuration() { return endTimestampMs - startTimestampMs; } - - @Override public String toString() { - return "SessionInfo(numEvents = " + numEvents + - ", timestamps = " + startTimestampMs + " to " + endTimestampMs + ")"; - } - } - - /** - * User-defined data type representing the update information returned by mapGroupsWithState. - */ - public static class SessionUpdate implements Serializable { - private String id; - private long durationMs; - private int numEvents; - private boolean expired; - - public SessionUpdate() { } - - public SessionUpdate(String id, long durationMs, int numEvents, boolean expired) { - this.id = id; - this.durationMs = durationMs; - this.numEvents = numEvents; - this.expired = expired; - } - - public String getId() { return id; } - public void setId(String id) { this.id = id; } - - public long getDurationMs() { return durationMs; } - public void setDurationMs(long durationMs) { this.durationMs = durationMs; } - - public int getNumEvents() { return numEvents; } - public void setNumEvents(int numEvents) { this.numEvents = numEvents; } - - public boolean isExpired() { return expired; } - public void setExpired(boolean expired) { this.expired = expired; } - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java deleted file mode 100644 index 47692ec..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java +++ /dev/null @@ -1,134 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.streaming; - -import com.google.common.io.Closeables; - -import org.apache.spark.SparkConf; -import org.apache.spark.storage.StorageLevel; -import org.apache.spark.streaming.Duration; -import org.apache.spark.streaming.api.java.JavaDStream; -import org.apache.spark.streaming.api.java.JavaPairDStream; -import org.apache.spark.streaming.api.java.JavaReceiverInputDStream; -import org.apache.spark.streaming.api.java.JavaStreamingContext; -import org.apache.spark.streaming.receiver.Receiver; -import scala.Tuple2; - -import java.io.BufferedReader; -import java.io.InputStreamReader; -import java.net.ConnectException; -import java.net.Socket; -import java.nio.charset.StandardCharsets; -import java.util.Arrays; -import java.util.regex.Pattern; - -/** - * Custom Receiver that receives data over a socket. Received bytes is interpreted as - * text and \n delimited lines are considered as records. They are then counted and printed. - * - * Usage: JavaCustomReceiver - * is the Spark master URL. In local mode, should be 'local[n]' with n > 1. - * and of the TCP server that Spark Streaming would connect to receive data. - * - * To run this on your local machine, you need to first run a Netcat server - * `$ nc -lk 9999` - * and then run the example - * `$ bin/run-example org.apache.spark.examples.streaming.JavaCustomReceiver localhost 9999` - */ - -public class JavaCustomReceiver extends Receiver { - private static final Pattern SPACE = Pattern.compile(" "); - - public static void main(String[] args) throws Exception { - if (args.length < 2) { - System.err.println("Usage: JavaCustomReceiver "); - System.exit(1); - } - - StreamingExamples.setStreamingLogLevels(); - - // Create the context with a 1 second batch size - SparkConf sparkConf = new SparkConf().setAppName("JavaCustomReceiver"); - JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000)); - - // Create an input stream with the custom receiver on target ip:port and count the - // words in input stream of \n delimited text (eg. generated by 'nc') - JavaReceiverInputDStream lines = ssc.receiverStream( - new JavaCustomReceiver(args[0], Integer.parseInt(args[1]))); - JavaDStream words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator()); - JavaPairDStream wordCounts = words.mapToPair(s -> new Tuple2<>(s, 1)) - .reduceByKey((i1, i2) -> i1 + i2); - - wordCounts.print(); - ssc.start(); - ssc.awaitTermination(); - } - - // ============= Receiver code that receives data over a socket ============== - - String host = null; - int port = -1; - - public JavaCustomReceiver(String host_ , int port_) { - super(StorageLevel.MEMORY_AND_DISK_2()); - host = host_; - port = port_; - } - - @Override - public void onStart() { - // Start the thread that receives data over a connection - new Thread(this::receive).start(); - } - - @Override - public void onStop() { - // There is nothing much to do as the thread calling receive() - // is designed to stop by itself isStopped() returns false - } - - /** Create a socket connection and receive data until receiver is stopped */ - private void receive() { - try { - Socket socket = null; - BufferedReader reader = null; - try { - // connect to the server - socket = new Socket(host, port); - reader = new BufferedReader( - new InputStreamReader(socket.getInputStream(), StandardCharsets.UTF_8)); - // Until stopped or connection broken continue reading - String userInput; - while (!isStopped() && (userInput = reader.readLine()) != null) { - System.out.println("Received data '" + userInput + "'"); - store(userInput); - } - } finally { - Closeables.close(reader, /* swallowIOException = */ true); - Closeables.close(socket, /* swallowIOException = */ true); - } - // Restart in an attempt to connect again when server is active again - restart("Trying to connect again"); - } catch(ConnectException ce) { - // restart if could not connect to server - restart("Could not connect", ce); - } catch(Throwable t) { - restart("Error receiving data", t); - } - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java deleted file mode 100644 index 5e5ae62..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java +++ /dev/null @@ -1,93 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.streaming; - -import java.util.HashMap; -import java.util.HashSet; -import java.util.Arrays; -import java.util.Map; -import java.util.Set; -import java.util.regex.Pattern; - -import scala.Tuple2; - -import kafka.serializer.StringDecoder; - -import org.apache.spark.SparkConf; -import org.apache.spark.streaming.api.java.*; -import org.apache.spark.streaming.kafka.KafkaUtils; -import org.apache.spark.streaming.Durations; - -/** - * Consumes messages from one or more topics in Kafka and does wordcount. - * Usage: JavaDirectKafkaWordCount - * is a list of one or more Kafka brokers - * is a list of one or more kafka topics to consume from - * - * Example: - * $ bin/run-example streaming.JavaDirectKafkaWordCount broker1-host:port,broker2-host:port \ - * topic1,topic2 - */ - -public final class JavaDirectKafkaWordCount { - private static final Pattern SPACE = Pattern.compile(" "); - - public static void main(String[] args) throws Exception { - if (args.length < 2) { - System.err.println("Usage: JavaDirectKafkaWordCount \n" + - " is a list of one or more Kafka brokers\n" + - " is a list of one or more kafka topics to consume from\n\n"); - System.exit(1); - } - - StreamingExamples.setStreamingLogLevels(); - - String brokers = args[0]; - String topics = args[1]; - - // Create context with a 2 seconds batch interval - SparkConf sparkConf = new SparkConf().setAppName("JavaDirectKafkaWordCount"); - JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Durations.seconds(2)); - - Set topicsSet = new HashSet<>(Arrays.asList(topics.split(","))); - Map kafkaParams = new HashMap<>(); - kafkaParams.put("metadata.broker.list", brokers); - - // Create direct kafka stream with brokers and topics - JavaPairInputDStream messages = KafkaUtils.createDirectStream( - jssc, - String.class, - String.class, - StringDecoder.class, - StringDecoder.class, - kafkaParams, - topicsSet - ); - - // Get the lines, split them into words, count the words and print - JavaDStream lines = messages.map(Tuple2::_2); - JavaDStream words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator()); - JavaPairDStream wordCounts = words.mapToPair(s -> new Tuple2<>(s, 1)) - .reduceByKey((i1, i2) -> i1 + i2); - wordCounts.print(); - - // Start the computation - jssc.start(); - jssc.awaitTermination(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java deleted file mode 100644 index 0c65104..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.streaming; - -import org.apache.spark.SparkConf; -import org.apache.spark.streaming.*; -import org.apache.spark.streaming.api.java.*; -import org.apache.spark.streaming.flume.FlumeUtils; -import org.apache.spark.streaming.flume.SparkFlumeEvent; - -/** - * Produces a count of events received from Flume. - * - * This should be used in conjunction with an AvroSink in Flume. It will start - * an Avro server on at the request host:port address and listen for requests. - * Your Flume AvroSink should be pointed to this address. - * - * Usage: JavaFlumeEventCount - * is the host the Flume receiver will be started on - a receiver - * creates a server and listens for flume events. - * is the port the Flume receiver will listen on. - * - * To run this example: - * `$ bin/run-example org.apache.spark.examples.streaming.JavaFlumeEventCount ` - */ -public final class JavaFlumeEventCount { - private JavaFlumeEventCount() { - } - - public static void main(String[] args) throws Exception { - if (args.length != 2) { - System.err.println("Usage: JavaFlumeEventCount "); - System.exit(1); - } - - StreamingExamples.setStreamingLogLevels(); - - String host = args[0]; - int port = Integer.parseInt(args[1]); - - Duration batchInterval = new Duration(2000); - SparkConf sparkConf = new SparkConf().setAppName("JavaFlumeEventCount"); - JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, batchInterval); - JavaReceiverInputDStream flumeStream = - FlumeUtils.createStream(ssc, host, port); - - flumeStream.count(); - - flumeStream.count().map(in -> "Received " + in + " flume events.").print(); - - ssc.start(); - ssc.awaitTermination(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java deleted file mode 100644 index ce5acdc..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java +++ /dev/null @@ -1,87 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.streaming; - -import java.util.Arrays; -import java.util.Map; -import java.util.HashMap; -import java.util.regex.Pattern; - -import scala.Tuple2; - -import org.apache.spark.SparkConf; -import org.apache.spark.streaming.Duration; -import org.apache.spark.streaming.api.java.JavaDStream; -import org.apache.spark.streaming.api.java.JavaPairDStream; -import org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream; -import org.apache.spark.streaming.api.java.JavaStreamingContext; -import org.apache.spark.streaming.kafka.KafkaUtils; - -/** - * Consumes messages from one or more topics in Kafka and does wordcount. - * - * Usage: JavaKafkaWordCount - * is a list of one or more zookeeper servers that make quorum - * is the name of kafka consumer group - * is a list of one or more kafka topics to consume from - * is the number of threads the kafka consumer should use - * - * To run this example: - * `$ bin/run-example org.apache.spark.examples.streaming.JavaKafkaWordCount zoo01,zoo02, \ - * zoo03 my-consumer-group topic1,topic2 1` - */ - -public final class JavaKafkaWordCount { - private static final Pattern SPACE = Pattern.compile(" "); - - private JavaKafkaWordCount() { - } - - public static void main(String[] args) throws Exception { - if (args.length < 4) { - System.err.println("Usage: JavaKafkaWordCount "); - System.exit(1); - } - - StreamingExamples.setStreamingLogLevels(); - SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaWordCount"); - // Create the context with 2 seconds batch size - JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000)); - - int numThreads = Integer.parseInt(args[3]); - Map topicMap = new HashMap<>(); - String[] topics = args[2].split(","); - for (String topic: topics) { - topicMap.put(topic, numThreads); - } - - JavaPairReceiverInputDStream messages = - KafkaUtils.createStream(jssc, args[0], args[1], topicMap); - - JavaDStream lines = messages.map(Tuple2::_2); - - JavaDStream words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator()); - - JavaPairDStream wordCounts = words.mapToPair(s -> new Tuple2<>(s, 1)) - .reduceByKey((i1, i2) -> i1 + i2); - - wordCounts.print(); - jssc.start(); - jssc.awaitTermination(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java deleted file mode 100644 index b217672..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.streaming; - -import java.util.Arrays; -import java.util.regex.Pattern; - -import scala.Tuple2; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.StorageLevels; -import org.apache.spark.streaming.Durations; -import org.apache.spark.streaming.api.java.JavaDStream; -import org.apache.spark.streaming.api.java.JavaPairDStream; -import org.apache.spark.streaming.api.java.JavaReceiverInputDStream; -import org.apache.spark.streaming.api.java.JavaStreamingContext; - -/** - * Counts words in UTF8 encoded, '\n' delimited text received from the network every second. - * - * Usage: JavaNetworkWordCount - * and describe the TCP server that Spark Streaming would connect to receive data. - * - * To run this on your local machine, you need to first run a Netcat server - * `$ nc -lk 9999` - * and then run the example - * `$ bin/run-example org.apache.spark.examples.streaming.JavaNetworkWordCount localhost 9999` - */ -public final class JavaNetworkWordCount { - private static final Pattern SPACE = Pattern.compile(" "); - - public static void main(String[] args) throws Exception { - if (args.length < 2) { - System.err.println("Usage: JavaNetworkWordCount "); - System.exit(1); - } - - StreamingExamples.setStreamingLogLevels(); - - // Create the context with a 1 second batch size - SparkConf sparkConf = new SparkConf().setAppName("JavaNetworkWordCount"); - JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1)); - - // Create a JavaReceiverInputDStream on target ip:port and count the - // words in input stream of \n delimited text (eg. generated by 'nc') - // Note that no duplication in storage level only for running locally. - // Replication necessary in distributed scenario for fault tolerance. - JavaReceiverInputDStream lines = ssc.socketTextStream( - args[0], Integer.parseInt(args[1]), StorageLevels.MEMORY_AND_DISK_SER); - JavaDStream words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator()); - JavaPairDStream wordCounts = words.mapToPair(s -> new Tuple2<>(s, 1)) - .reduceByKey((i1, i2) -> i1 + i2); - - wordCounts.print(); - ssc.start(); - ssc.awaitTermination(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java deleted file mode 100644 index e86f8ab..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.streaming; - -import java.util.ArrayList; -import java.util.LinkedList; -import java.util.List; -import java.util.Queue; - -import scala.Tuple2; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.streaming.Duration; -import org.apache.spark.streaming.api.java.JavaDStream; -import org.apache.spark.streaming.api.java.JavaPairDStream; -import org.apache.spark.streaming.api.java.JavaStreamingContext; - -public final class JavaQueueStream { - private JavaQueueStream() { - } - - public static void main(String[] args) throws Exception { - - StreamingExamples.setStreamingLogLevels(); - SparkConf sparkConf = new SparkConf().setAppName("JavaQueueStream"); - - // Create the context - JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000)); - - // Create the queue through which RDDs can be pushed to - // a QueueInputDStream - - // Create and push some RDDs into the queue - List list = new ArrayList<>(); - for (int i = 0; i < 1000; i++) { - list.add(i); - } - - Queue> rddQueue = new LinkedList<>(); - for (int i = 0; i < 30; i++) { - rddQueue.add(ssc.sparkContext().parallelize(list)); - } - - // Create the QueueInputDStream and use it do some processing - JavaDStream inputStream = ssc.queueStream(rddQueue); - JavaPairDStream mappedStream = inputStream.mapToPair( - i -> new Tuple2<>(i % 10, 1)); - JavaPairDStream reducedStream = mappedStream.reduceByKey( - (i1, i2) -> i1 + i2); - - reducedStream.print(); - ssc.start(); - ssc.awaitTermination(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecord.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecord.java deleted file mode 100644 index e63697a..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecord.java +++ /dev/null @@ -1,31 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.streaming; - -/** Java Bean class to be used with the example JavaSqlNetworkWordCount. */ -public class JavaRecord implements java.io.Serializable { - private String word; - - public String getWord() { - return word; - } - - public void setWord(String word) { - this.word = word; - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java deleted file mode 100644 index 45a876d..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java +++ /dev/null @@ -1,191 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.streaming; - -import java.io.File; -import java.nio.charset.Charset; -import java.util.Arrays; -import java.util.List; -import java.util.regex.Pattern; - -import scala.Tuple2; - -import com.google.common.io.Files; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.api.java.function.*; -import org.apache.spark.broadcast.Broadcast; -import org.apache.spark.streaming.Durations; -import org.apache.spark.streaming.api.java.JavaDStream; -import org.apache.spark.streaming.api.java.JavaPairDStream; -import org.apache.spark.streaming.api.java.JavaReceiverInputDStream; -import org.apache.spark.streaming.api.java.JavaStreamingContext; -import org.apache.spark.util.LongAccumulator; - -/** - * Use this singleton to get or register a Broadcast variable. - */ -class JavaWordBlacklist { - - private static volatile Broadcast> instance = null; - - public static Broadcast> getInstance(JavaSparkContext jsc) { - if (instance == null) { - synchronized (JavaWordBlacklist.class) { - if (instance == null) { - List wordBlacklist = Arrays.asList("a", "b", "c"); - instance = jsc.broadcast(wordBlacklist); - } - } - } - return instance; - } -} - -/** - * Use this singleton to get or register an Accumulator. - */ -class JavaDroppedWordsCounter { - - private static volatile LongAccumulator instance = null; - - public static LongAccumulator getInstance(JavaSparkContext jsc) { - if (instance == null) { - synchronized (JavaDroppedWordsCounter.class) { - if (instance == null) { - instance = jsc.sc().longAccumulator("WordsInBlacklistCounter"); - } - } - } - return instance; - } -} - -/** - * Counts words in text encoded with UTF8 received from the network every second. This example also - * shows how to use lazily instantiated singleton instances for Accumulator and Broadcast so that - * they can be registered on driver failures. - * - * Usage: JavaRecoverableNetworkWordCount - * and describe the TCP server that Spark Streaming would connect to receive - * data. directory to HDFS-compatible file system which checkpoint data - * file to which the word counts will be appended - * - * and must be absolute paths - * - * To run this on your local machine, you need to first run a Netcat server - * - * `$ nc -lk 9999` - * - * and run the example as - * - * `$ ./bin/run-example org.apache.spark.examples.streaming.JavaRecoverableNetworkWordCount \ - * localhost 9999 ~/checkpoint/ ~/out` - * - * If the directory ~/checkpoint/ does not exist (e.g. running for the first time), it will create - * a new StreamingContext (will print "Creating new context" to the console). Otherwise, if - * checkpoint data exists in ~/checkpoint/, then it will create StreamingContext from - * the checkpoint data. - * - * Refer to the online documentation for more details. - */ -public final class JavaRecoverableNetworkWordCount { - private static final Pattern SPACE = Pattern.compile(" "); - - private static JavaStreamingContext createContext(String ip, - int port, - String checkpointDirectory, - String outputPath) { - - // If you do not see this printed, that means the StreamingContext has been loaded - // from the new checkpoint - System.out.println("Creating new context"); - File outputFile = new File(outputPath); - if (outputFile.exists()) { - outputFile.delete(); - } - SparkConf sparkConf = new SparkConf().setAppName("JavaRecoverableNetworkWordCount"); - // Create the context with a 1 second batch size - JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1)); - ssc.checkpoint(checkpointDirectory); - - // Create a socket stream on target ip:port and count the - // words in input stream of \n delimited text (eg. generated by 'nc') - JavaReceiverInputDStream lines = ssc.socketTextStream(ip, port); - JavaDStream words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator()); - JavaPairDStream wordCounts = words.mapToPair(s -> new Tuple2<>(s, 1)) - .reduceByKey((i1, i2) -> i1 + i2); - - wordCounts.foreachRDD((rdd, time) -> { - // Get or register the blacklist Broadcast - Broadcast> blacklist = - JavaWordBlacklist.getInstance(new JavaSparkContext(rdd.context())); - // Get or register the droppedWordsCounter Accumulator - LongAccumulator droppedWordsCounter = - JavaDroppedWordsCounter.getInstance(new JavaSparkContext(rdd.context())); - // Use blacklist to drop words and use droppedWordsCounter to count them - String counts = rdd.filter(wordCount -> { - if (blacklist.value().contains(wordCount._1())) { - droppedWordsCounter.add(wordCount._2()); - return false; - } else { - return true; - } - }).collect().toString(); - String output = "Counts at time " + time + " " + counts; - System.out.println(output); - System.out.println("Dropped " + droppedWordsCounter.value() + " word(s) totally"); - System.out.println("Appending to " + outputFile.getAbsolutePath()); - Files.append(output + "\n", outputFile, Charset.defaultCharset()); - }); - - return ssc; - } - - public static void main(String[] args) throws Exception { - if (args.length != 4) { - System.err.println("You arguments were " + Arrays.asList(args)); - System.err.println( - "Usage: JavaRecoverableNetworkWordCount \n" + - " . and describe the TCP server that Spark\n" + - " Streaming would connect to receive data. directory to\n" + - " HDFS-compatible file system which checkpoint data file to which\n" + - " the word counts will be appended\n" + - "\n" + - "In local mode, should be 'local[n]' with n > 1\n" + - "Both and must be absolute paths"); - System.exit(1); - } - - String ip = args[0]; - int port = Integer.parseInt(args[1]); - String checkpointDirectory = args[2]; - String outputPath = args[3]; - - // Function to create JavaStreamingContext without any output operations - // (used to detect the new context) - Function0 createContextFunc = - () -> createContext(ip, port, checkpointDirectory, outputPath); - - JavaStreamingContext ssc = - JavaStreamingContext.getOrCreate(checkpointDirectory, createContextFunc); - ssc.start(); - ssc.awaitTermination(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java deleted file mode 100644 index 948d1a2..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java +++ /dev/null @@ -1,108 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.streaming; - -import java.util.Arrays; -import java.util.regex.Pattern; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SparkSession; -import org.apache.spark.api.java.StorageLevels; -import org.apache.spark.streaming.Durations; -import org.apache.spark.streaming.api.java.JavaDStream; -import org.apache.spark.streaming.api.java.JavaReceiverInputDStream; -import org.apache.spark.streaming.api.java.JavaStreamingContext; - -/** - * Use DataFrames and SQL to count words in UTF8 encoded, '\n' delimited text received from the - * network every second. - * - * Usage: JavaSqlNetworkWordCount - * and describe the TCP server that Spark Streaming would connect to receive data. - * - * To run this on your local machine, you need to first run a Netcat server - * `$ nc -lk 9999` - * and then run the example - * `$ bin/run-example org.apache.spark.examples.streaming.JavaSqlNetworkWordCount localhost 9999` - */ -public final class JavaSqlNetworkWordCount { - private static final Pattern SPACE = Pattern.compile(" "); - - public static void main(String[] args) throws Exception { - if (args.length < 2) { - System.err.println("Usage: JavaNetworkWordCount "); - System.exit(1); - } - - StreamingExamples.setStreamingLogLevels(); - - // Create the context with a 1 second batch size - SparkConf sparkConf = new SparkConf().setAppName("JavaSqlNetworkWordCount"); - JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1)); - - // Create a JavaReceiverInputDStream on target ip:port and count the - // words in input stream of \n delimited text (eg. generated by 'nc') - // Note that no duplication in storage level only for running locally. - // Replication necessary in distributed scenario for fault tolerance. - JavaReceiverInputDStream lines = ssc.socketTextStream( - args[0], Integer.parseInt(args[1]), StorageLevels.MEMORY_AND_DISK_SER); - JavaDStream words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator()); - - // Convert RDDs of the words DStream to DataFrame and run SQL query - words.foreachRDD((rdd, time) -> { - SparkSession spark = JavaSparkSessionSingleton.getInstance(rdd.context().getConf()); - - // Convert JavaRDD[String] to JavaRDD[bean class] to DataFrame - JavaRDD rowRDD = rdd.map(word -> { - JavaRecord record = new JavaRecord(); - record.setWord(word); - return record; - }); - Dataset wordsDataFrame = spark.createDataFrame(rowRDD, JavaRecord.class); - - // Creates a temporary view using the DataFrame - wordsDataFrame.createOrReplaceTempView("words"); - - // Do word count on table using SQL and print it - Dataset wordCountsDataFrame = - spark.sql("select word, count(*) as total from words group by word"); - System.out.println("========= " + time + "========="); - wordCountsDataFrame.show(); - }); - - ssc.start(); - ssc.awaitTermination(); - } -} - -/** Lazily instantiated singleton instance of SparkSession */ -class JavaSparkSessionSingleton { - private static transient SparkSession instance = null; - public static SparkSession getInstance(SparkConf sparkConf) { - if (instance == null) { - instance = SparkSession - .builder() - .config(sparkConf) - .getOrCreate(); - } - return instance; - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java b/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java deleted file mode 100644 index 9d8bd7f..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java +++ /dev/null @@ -1,95 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.examples.streaming; - -import java.util.Arrays; -import java.util.List; -import java.util.regex.Pattern; - -import scala.Tuple2; - -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.function.*; -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.Optional; -import org.apache.spark.api.java.StorageLevels; -import org.apache.spark.streaming.Durations; -import org.apache.spark.streaming.State; -import org.apache.spark.streaming.StateSpec; -import org.apache.spark.streaming.api.java.*; - -/** - * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every - * second starting with initial value of word count. - * Usage: JavaStatefulNetworkWordCount - * and describe the TCP server that Spark Streaming would connect to receive - * data. - *

- * To run this on your local machine, you need to first run a Netcat server - * `$ nc -lk 9999` - * and then run the example - * `$ bin/run-example - * org.apache.spark.examples.streaming.JavaStatefulNetworkWordCount localhost 9999` - */ -public class JavaStatefulNetworkWordCount { - private static final Pattern SPACE = Pattern.compile(" "); - - public static void main(String[] args) throws Exception { - if (args.length < 2) { - System.err.println("Usage: JavaStatefulNetworkWordCount "); - System.exit(1); - } - - StreamingExamples.setStreamingLogLevels(); - - // Create the context with a 1 second batch size - SparkConf sparkConf = new SparkConf().setAppName("JavaStatefulNetworkWordCount"); - JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1)); - ssc.checkpoint("."); - - // Initial state RDD input to mapWithState - @SuppressWarnings("unchecked") - List> tuples = - Arrays.asList(new Tuple2<>("hello", 1), new Tuple2<>("world", 1)); - JavaPairRDD initialRDD = ssc.sparkContext().parallelizePairs(tuples); - - JavaReceiverInputDStream lines = ssc.socketTextStream( - args[0], Integer.parseInt(args[1]), StorageLevels.MEMORY_AND_DISK_SER_2); - - JavaDStream words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator()); - - JavaPairDStream wordsDstream = words.mapToPair(s -> new Tuple2<>(s, 1)); - - // Update the cumulative count function - Function3, State, Tuple2> mappingFunc = - (word, one, state) -> { - int sum = one.orElse(0) + (state.exists() ? state.get() : 0); - Tuple2 output = new Tuple2<>(word, sum); - state.update(sum); - return output; - }; - - // DStream made of get cumulative counts that get updated in every batch - JavaMapWithStateDStream> stateDstream = - wordsDstream.mapWithState(StateSpec.function(mappingFunc).initialState(initialRDD)); - - stateDstream.print(); - ssc.start(); - ssc.awaitTermination(); - } -} diff --git a/image_content/config/spark/opt/spark/examples/src/main/python/als.py b/image_content/config/spark/opt/spark/examples/src/main/python/als.py deleted file mode 100755 index 6d32418..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/python/als.py +++ /dev/null @@ -1,108 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This is an example implementation of ALS for learning how to use Spark. Please refer to -pyspark.ml.recommendation.ALS for more conventional use. - -This example requires numpy (http://www.numpy.org/) -""" -from __future__ import print_function - -import sys - -import numpy as np -from numpy.random import rand -from numpy import matrix -from pyspark.sql import SparkSession - -LAMBDA = 0.01 # regularization -np.random.seed(42) - - -def rmse(R, ms, us): - diff = R - ms * us.T - return np.sqrt(np.sum(np.power(diff, 2)) / (M * U)) - - -def update(i, mat, ratings): - uu = mat.shape[0] - ff = mat.shape[1] - - XtX = mat.T * mat - Xty = mat.T * ratings[i, :].T - - for j in range(ff): - XtX[j, j] += LAMBDA * uu - - return np.linalg.solve(XtX, Xty) - - -if __name__ == "__main__": - - """ - Usage: als [M] [U] [F] [iterations] [partitions]" - """ - - print("""WARN: This is a naive implementation of ALS and is given as an - example. Please use pyspark.ml.recommendation.ALS for more - conventional use.""", file=sys.stderr) - - spark = SparkSession\ - .builder\ - .appName("PythonALS")\ - .getOrCreate() - - sc = spark.sparkContext - - M = int(sys.argv[1]) if len(sys.argv) > 1 else 100 - U = int(sys.argv[2]) if len(sys.argv) > 2 else 500 - F = int(sys.argv[3]) if len(sys.argv) > 3 else 10 - ITERATIONS = int(sys.argv[4]) if len(sys.argv) > 4 else 5 - partitions = int(sys.argv[5]) if len(sys.argv) > 5 else 2 - - print("Running ALS with M=%d, U=%d, F=%d, iters=%d, partitions=%d\n" % - (M, U, F, ITERATIONS, partitions)) - - R = matrix(rand(M, F)) * matrix(rand(U, F).T) - ms = matrix(rand(M, F)) - us = matrix(rand(U, F)) - - Rb = sc.broadcast(R) - msb = sc.broadcast(ms) - usb = sc.broadcast(us) - - for i in range(ITERATIONS): - ms = sc.parallelize(range(M), partitions) \ - .map(lambda x: update(x, usb.value, Rb.value)) \ - .collect() - # collect() returns a list, so array ends up being - # a 3-d array, we take the first 2 dims for the matrix - ms = matrix(np.array(ms)[:, :, 0]) - msb = sc.broadcast(ms) - - us = sc.parallelize(range(U), partitions) \ - .map(lambda x: update(x, msb.value, Rb.value.T)) \ - .collect() - us = matrix(np.array(us)[:, :, 0]) - usb = sc.broadcast(us) - - error = rmse(R, ms, us) - print("Iteration %d:" % i) - print("\nRMSE: %5.4f\n" % error) - - spark.stop() diff --git a/image_content/config/spark/opt/spark/examples/src/main/python/avro_inputformat.py b/image_content/config/spark/opt/spark/examples/src/main/python/avro_inputformat.py deleted file mode 100644 index 4422f9e..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/python/avro_inputformat.py +++ /dev/null @@ -1,91 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -from __future__ import print_function - -import sys - -from functools import reduce -from pyspark.sql import SparkSession - -""" -Read data file users.avro in local Spark distro: - -$ cd $SPARK_HOME -$ ./bin/spark-submit --driver-class-path /path/to/example/jar \ -> ./examples/src/main/python/avro_inputformat.py \ -> examples/src/main/resources/users.avro -{u'favorite_color': None, u'name': u'Alyssa', u'favorite_numbers': [3, 9, 15, 20]} -{u'favorite_color': u'red', u'name': u'Ben', u'favorite_numbers': []} - -To read name and favorite_color fields only, specify the following reader schema: - -$ cat examples/src/main/resources/user.avsc -{"namespace": "example.avro", - "type": "record", - "name": "User", - "fields": [ - {"name": "name", "type": "string"}, - {"name": "favorite_color", "type": ["string", "null"]} - ] -} - -$ ./bin/spark-submit --driver-class-path /path/to/example/jar \ -> ./examples/src/main/python/avro_inputformat.py \ -> examples/src/main/resources/users.avro examples/src/main/resources/user.avsc -{u'favorite_color': None, u'name': u'Alyssa'} -{u'favorite_color': u'red', u'name': u'Ben'} -""" -if __name__ == "__main__": - if len(sys.argv) != 2 and len(sys.argv) != 3: - print(""" - Usage: avro_inputformat [reader_schema_file] - - Run with example jar: - ./bin/spark-submit --driver-class-path /path/to/example/jar \ - /path/to/examples/avro_inputformat.py [reader_schema_file] - Assumes you have Avro data stored in . Reader schema can be optionally specified - in [reader_schema_file]. - """, file=sys.stderr) - exit(-1) - - path = sys.argv[1] - - spark = SparkSession\ - .builder\ - .appName("AvroKeyInputFormat")\ - .getOrCreate() - - sc = spark.sparkContext - - conf = None - if len(sys.argv) == 3: - schema_rdd = sc.textFile(sys.argv[2], 1).collect() - conf = {"avro.schema.input.key": reduce(lambda x, y: x + y, schema_rdd)} - - avro_rdd = sc.newAPIHadoopFile( - path, - "org.apache.avro.mapreduce.AvroKeyInputFormat", - "org.apache.avro.mapred.AvroKey", - "org.apache.hadoop.io.NullWritable", - keyConverter="org.apache.spark.examples.pythonconverters.AvroWrapperToJavaConverter", - conf=conf) - output = avro_rdd.map(lambda x: x[0]).collect() - for k in output: - print(k) - - spark.stop() diff --git a/image_content/config/spark/opt/spark/examples/src/main/python/kmeans.py b/image_content/config/spark/opt/spark/examples/src/main/python/kmeans.py deleted file mode 100755 index 92e0a3a..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/python/kmeans.py +++ /dev/null @@ -1,86 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -The K-means algorithm written from scratch against PySpark. In practice, -one may prefer to use the KMeans algorithm in ML, as shown in -examples/src/main/python/ml/kmeans_example.py. - -This example requires NumPy (http://www.numpy.org/). -""" -from __future__ import print_function - -import sys - -import numpy as np -from pyspark.sql import SparkSession - - -def parseVector(line): - return np.array([float(x) for x in line.split(' ')]) - - -def closestPoint(p, centers): - bestIndex = 0 - closest = float("+inf") - for i in range(len(centers)): - tempDist = np.sum((p - centers[i]) ** 2) - if tempDist < closest: - closest = tempDist - bestIndex = i - return bestIndex - - -if __name__ == "__main__": - - if len(sys.argv) != 4: - print("Usage: kmeans ", file=sys.stderr) - exit(-1) - - print("""WARN: This is a naive implementation of KMeans Clustering and is given - as an example! Please refer to examples/src/main/python/ml/kmeans_example.py for an - example on how to use ML's KMeans implementation.""", file=sys.stderr) - - spark = SparkSession\ - .builder\ - .appName("PythonKMeans")\ - .getOrCreate() - - lines = spark.read.text(sys.argv[1]).rdd.map(lambda r: r[0]) - data = lines.map(parseVector).cache() - K = int(sys.argv[2]) - convergeDist = float(sys.argv[3]) - - kPoints = data.takeSample(False, K, 1) - tempDist = 1.0 - - while tempDist > convergeDist: - closest = data.map( - lambda p: (closestPoint(p, kPoints), (p, 1))) - pointStats = closest.reduceByKey( - lambda p1_c1, p2_c2: (p1_c1[0] + p2_c2[0], p1_c1[1] + p2_c2[1])) - newPoints = pointStats.map( - lambda st: (st[0], st[1][0] / st[1][1])).collect() - - tempDist = sum(np.sum((kPoints[iK] - p) ** 2) for (iK, p) in newPoints) - - for (iK, p) in newPoints: - kPoints[iK] = p - - print("Final centers: " + str(kPoints)) - - spark.stop() diff --git a/image_content/config/spark/opt/spark/examples/src/main/python/logistic_regression.py b/image_content/config/spark/opt/spark/examples/src/main/python/logistic_regression.py deleted file mode 100755 index 01c9384..0000000 --- a/image_content/config/spark/opt/spark/examples/src/main/python/logistic_regression.py +++ /dev/null @@ -1,88 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -A logistic regression implementation that uses NumPy (http://www.numpy.org) -to act on batches of input data using efficient matrix operations. - -In practice, one may prefer to use the LogisticRegression algorithm in -ML, as shown in examples/src/main/python/ml/logistic_regression_with_elastic_net.py. -""" -from __future__ import print_function - -import sys - -import numpy as np -from pyspark.sql import SparkSession - - -D = 10 # Number of dimensions - - -# Read a batch of points from the input file into a NumPy matrix object. We operate on batches to -# make further computations faster. -# The data file contains lines of the form

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b/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/legend.js deleted file mode 100644 index 10d2c53..0000000 --- a/image_content/config/grafana/var/lib/grafana/plugins/grafana-piechart-panel/src/legend.js +++ /dev/null @@ -1,180 +0,0 @@ -import angular from 'angular'; -//import _ from 'lodash'; -import kbn from 'app/core/utils/kbn'; -import $ from 'jquery'; -import 'jquery.flot'; -import 'jquery.flot.time'; - -angular.module('grafana.directives').directive('piechartLegend', function(popoverSrv, $timeout) { - return { - link: function(scope, elem) { - var $container = $('