From 5f3601e505460d98fd43a5ac05af644c5b7d7fc3 Mon Sep 17 00:00:00 2001 From: Sriharsha Chintalapani Date: Tue, 19 Nov 2024 16:44:47 -0800 Subject: [PATCH 01/38] Search Settings with Ranking config --- .../exception/SystemSettingsException.java | 12 + .../resources/system/SystemResource.java | 134 +++++ .../search/indexes/APIEndpointIndex.java | 3 - .../data/searchSettings/searchSettings.json | 503 ++++++++++++++++++ .../resources/system/SystemResourceTest.java | 222 +++++++- .../openmetadata/service/util/TestUtils.java | 8 + .../schema/configuration/searchSettings.json | 199 ++++++- 7 files changed, 1068 insertions(+), 13 deletions(-) create mode 100644 openmetadata-service/src/main/java/org/openmetadata/service/exception/SystemSettingsException.java create mode 100644 openmetadata-service/src/main/resources/json/data/searchSettings/searchSettings.json diff --git a/openmetadata-service/src/main/java/org/openmetadata/service/exception/SystemSettingsException.java b/openmetadata-service/src/main/java/org/openmetadata/service/exception/SystemSettingsException.java new file mode 100644 index 000000000000..886009acfd44 --- /dev/null +++ b/openmetadata-service/src/main/java/org/openmetadata/service/exception/SystemSettingsException.java @@ -0,0 +1,12 @@ +package org.openmetadata.service.exception; + +import javax.ws.rs.core.Response; +import org.openmetadata.sdk.exception.WebServiceException; + +public class SystemSettingsException extends WebServiceException { + private static final String ERROR_TYPE = "SYSTEM_SETTINGS_EXCEPTION"; + + public SystemSettingsException(String message) { + super(Response.Status.BAD_REQUEST, ERROR_TYPE, message); + } +} diff --git a/openmetadata-service/src/main/java/org/openmetadata/service/resources/system/SystemResource.java b/openmetadata-service/src/main/java/org/openmetadata/service/resources/system/SystemResource.java index 9bc3e78e303d..9ecb68997bf1 100644 --- a/openmetadata-service/src/main/java/org/openmetadata/service/resources/system/SystemResource.java +++ b/openmetadata-service/src/main/java/org/openmetadata/service/resources/system/SystemResource.java @@ -2,6 +2,7 @@ import static org.openmetadata.common.utils.CommonUtil.nullOrEmpty; import static org.openmetadata.schema.settings.SettingsType.LINEAGE_SETTINGS; +import static org.openmetadata.schema.settings.SettingsType.SEARCH_SETTINGS; import io.swagger.v3.oas.annotations.ExternalDocumentation; import io.swagger.v3.oas.annotations.Hidden; @@ -13,6 +14,8 @@ import io.swagger.v3.oas.annotations.parameters.RequestBody; import io.swagger.v3.oas.annotations.responses.ApiResponse; import io.swagger.v3.oas.annotations.tags.Tag; +import java.io.IOException; +import java.util.List; import javax.json.JsonPatch; import javax.validation.Valid; import javax.ws.rs.Consumes; @@ -30,6 +33,10 @@ import javax.ws.rs.core.SecurityContext; import javax.ws.rs.core.UriInfo; import lombok.extern.slf4j.Slf4j; +import org.openmetadata.common.utils.CommonUtil; +import org.openmetadata.schema.api.search.AssetTypeConfiguration; +import org.openmetadata.schema.api.search.GlobalSettings; +import org.openmetadata.schema.api.search.SearchSettings; import org.openmetadata.schema.auth.EmailRequest; import org.openmetadata.schema.settings.Settings; import org.openmetadata.schema.settings.SettingsType; @@ -42,7 +49,9 @@ import org.openmetadata.service.Entity; import org.openmetadata.service.OpenMetadataApplicationConfig; import org.openmetadata.service.clients.pipeline.PipelineServiceClientFactory; +import org.openmetadata.service.exception.SystemSettingsException; import org.openmetadata.service.exception.UnhandledServerException; +import org.openmetadata.service.jdbi3.EntityRepository; import org.openmetadata.service.jdbi3.ListFilter; import org.openmetadata.service.jdbi3.SystemRepository; import org.openmetadata.service.resources.Collection; @@ -50,6 +59,8 @@ import org.openmetadata.service.security.JwtFilter; import org.openmetadata.service.security.policyevaluator.OperationContext; import org.openmetadata.service.security.policyevaluator.ResourceContext; +import org.openmetadata.service.util.EntityUtil; +import org.openmetadata.service.util.JsonUtils; import org.openmetadata.service.util.ResultList; import org.openmetadata.service.util.email.EmailUtil; @@ -67,6 +78,7 @@ public class SystemResource { private OpenMetadataApplicationConfig applicationConfig; private PipelineServiceClientInterface pipelineServiceClient; private JwtFilter jwtFilter; + private SearchSettings defaultSearchSettingsCache = new SearchSettings(); public SystemResource(Authorizer authorizer) { this.systemRepository = Entity.getSystemRepository(); @@ -81,12 +93,50 @@ public void initialize(OpenMetadataApplicationConfig config) { this.jwtFilter = new JwtFilter(config.getAuthenticationConfiguration(), config.getAuthorizerConfiguration()); + loadDefaultSearchSettings(false); } public static class SettingsList extends ResultList { /* Required for serde */ } + public SearchSettings readDefaultSearchSettings() { + if (defaultSearchSettingsCache != null) { + try { + List jsonDataFiles = + EntityUtil.getJsonDataResources(".*/json/data/searchSettings/searchSettings.json"); + if (!jsonDataFiles.isEmpty()) { + String json = + CommonUtil.getResourceAsStream( + EntityRepository.class.getClassLoader(), jsonDataFiles.get(0)); + defaultSearchSettingsCache = JsonUtils.readValue(json, SearchSettings.class); + } + } catch (IOException e) { + LOG.error("Failed to read default search settings. Message: {}", e.getMessage(), e); + } + } + return defaultSearchSettingsCache; + } + + public SearchSettings loadDefaultSearchSettings(boolean force) { + SearchSettings searchSettings = readDefaultSearchSettings(); + if (!force) { + Settings existingSettings = + systemRepository.getConfigWithKey(String.valueOf(SEARCH_SETTINGS)); + if (existingSettings != null && existingSettings.getConfigValue() != null) { + SearchSettings existingSearchSettings = (SearchSettings) existingSettings.getConfigValue(); + if (existingSearchSettings.getGlobalSettings() != null) { + return searchSettings; + } + } + } + Settings settings = + new Settings().withConfigType(SettingsType.SEARCH_SETTINGS).withConfigValue(searchSettings); + systemRepository.createOrUpdate(settings); + LOG.info("Default searchSettings loaded successfully."); + return searchSettings; + } + @GET @Path("/settings") @Operation( @@ -186,9 +236,93 @@ public Response createOrUpdateSetting( @Context SecurityContext securityContext, @Valid Settings settingName) { authorizer.authorizeAdmin(securityContext); + if (SettingsType.SEARCH_SETTINGS + .value() + .equalsIgnoreCase(settingName.getConfigType().toString())) { + SearchSettings defaultSearchSettings = loadDefaultSearchSettings(false); + SearchSettings incomingSearchSettings = + JsonUtils.convertValue(settingName.getConfigValue(), SearchSettings.class); + + GlobalSettings defaultGlobalSettings = defaultSearchSettings.getGlobalSettings(); + GlobalSettings incomingGlobalSettings = incomingSearchSettings.getGlobalSettings(); + + GlobalSettings mergedGlobalSettings = new GlobalSettings(); + + mergedGlobalSettings.setMaxAggregateSize( + incomingGlobalSettings != null && incomingGlobalSettings.getMaxAggregateSize() != null + ? incomingGlobalSettings.getMaxAggregateSize() + : defaultGlobalSettings.getMaxAggregateSize()); + + mergedGlobalSettings.setMaxResultHits( + incomingGlobalSettings != null && incomingGlobalSettings.getMaxResultHits() != null + ? incomingGlobalSettings.getMaxResultHits() + : defaultGlobalSettings.getMaxResultHits()); + + mergedGlobalSettings.setMaxAnalyzedOffset( + incomingGlobalSettings != null && incomingGlobalSettings.getMaxAnalyzedOffset() != null + ? incomingGlobalSettings.getMaxAnalyzedOffset() + : defaultGlobalSettings.getMaxAnalyzedOffset()); + + mergedGlobalSettings.setAggregations(defaultGlobalSettings.getAggregations()); + mergedGlobalSettings.setHighlightFields(defaultGlobalSettings.getHighlightFields()); + + incomingSearchSettings.setGlobalSettings(mergedGlobalSettings); + + if (incomingSearchSettings.getDefaultConfiguration() == null) { + incomingSearchSettings.setDefaultConfiguration( + defaultSearchSettings.getDefaultConfiguration()); + } + + List defaultAssetTypes = + defaultSearchSettings.getAssetTypeConfigurations(); + List incomingAssetTypes = + incomingSearchSettings.getAssetTypeConfigurations(); + + for (AssetTypeConfiguration defaultConfig : defaultAssetTypes) { + String assetType = defaultConfig.getAssetType().toLowerCase(); + boolean exists = + incomingAssetTypes.stream() + .anyMatch(config -> config.getAssetType().equalsIgnoreCase(assetType)); + if (!exists) { + incomingAssetTypes.add(defaultConfig); + } + } + + settingName.setConfigValue(incomingSearchSettings); + } return systemRepository.createOrUpdate(settingName); } + @PUT + @Path("/settings/reset/{name}") + @Operation( + operationId = "resetSettingToDefault", + summary = "Reset a setting to default", + description = "Reset the specified setting to its default value.", + responses = { + @ApiResponse( + responseCode = "200", + description = "Settings reset to default", + content = + @Content( + mediaType = "application/json", + schema = @Schema(implementation = Settings.class))) + }) + public Response resetSettingToDefault( + @Context UriInfo uriInfo, + @Context SecurityContext securityContext, + @Parameter(description = "Name of the setting", schema = @Schema(type = "string")) + @PathParam("name") + String name) { + authorizer.authorizeAdmin(securityContext); + + if (!SettingsType.SEARCH_SETTINGS.value().equalsIgnoreCase(name)) { + throw new SystemSettingsException("Resetting of setting '" + name + "' is not supported."); + } + SearchSettings settings = loadDefaultSearchSettings(true); + return Response.ok(settings).build(); + } + @PUT @Path("/email/test") @Operation( diff --git a/openmetadata-service/src/main/java/org/openmetadata/service/search/indexes/APIEndpointIndex.java b/openmetadata-service/src/main/java/org/openmetadata/service/search/indexes/APIEndpointIndex.java index 9fbfa1555395..208c9a012a03 100644 --- a/openmetadata-service/src/main/java/org/openmetadata/service/search/indexes/APIEndpointIndex.java +++ b/openmetadata-service/src/main/java/org/openmetadata/service/search/indexes/APIEndpointIndex.java @@ -1,7 +1,5 @@ package org.openmetadata.service.search.indexes; -import static org.openmetadata.service.search.EntityBuilderConstant.ES_MESSAGE_SCHEMA_FIELD; - import java.util.ArrayList; import java.util.HashSet; import java.util.List; @@ -135,7 +133,6 @@ private void parseSchemaFields( public static Map getFields() { Map fields = SearchIndex.getDefaultFields(); - fields.put(ES_MESSAGE_SCHEMA_FIELD, 7.0f); fields.put("responseSchema.schemaFields.name.keyword", 5.0f); fields.put("responseSchema.schemaFields.description", 1.0f); fields.put("responseSchema.schemaFields.children.name", 7.0f); diff --git a/openmetadata-service/src/main/resources/json/data/searchSettings/searchSettings.json b/openmetadata-service/src/main/resources/json/data/searchSettings/searchSettings.json new file mode 100644 index 000000000000..b468cc4a6993 --- /dev/null +++ b/openmetadata-service/src/main/resources/json/data/searchSettings/searchSettings.json @@ -0,0 +1,503 @@ +{ + "enableAccessControl": false, + "globalSettings": { + "maxAggregateSize": 10000, + "maxResultHits": 10000, + "maxAnalyzedOffset": 1000, + "aggregations": [ + { + "name": "serviceType", + "type": "terms", + "field": "serviceType" + }, + { + "name": "service.displayName.keyword", + "type": "terms", + "field": "service.displayName.keyword" + }, + { + "name": "entityType", + "type": "terms", + "field": "entityType" + }, + { + "name": "tier.tagFQN", + "type": "terms", + "field": "tier.tagFQN" + }, + { + "name": "certification.tagLabel.tagFQN", + "type": "terms", + "field": "certification.tagLabel.tagFQN" + }, + { + "name": "owners.displayName.keyword", + "type": "terms", + "field": "owners.displayName.keyword" + }, + { + "name": "domain.displayName.keyword", + "type": "terms", + "field": "domain.displayName.keyword" + }, + { + "name": "tags.tagFQN", + "type": "terms", + "field": "tags.tagFQN" + } + ], + "highlightFields": ["name", "displayName", "description", "displayName.ngram", "name.ngram"] + }, + "assetTypeConfigurations": [ + { + "assetType": "table", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0, + "columns.name": 5.0, + "columns.displayName": 5.0, + "columns.description": 2.0, + "columns.children.name": 3.0 + }, + "mustMatch": ["name", "fullyQualifiedName"], + "shouldMatch": ["displayName", "description", "columns.name", "columns.description", "columns.children.name"], + "highlightFields": ["name","description","displayName","columns.name","columns.description","columns.children.name"], + "aggregations": [ + { + "name": "database", + "type": "terms", + "field": "database.displayName.keyword" + }, + { + "name": "databaseSchema", + "type": "terms", + "field": "databaseSchema.displayName.keyword" + }, + { + "name": "columns", + "type": "terms", + "field": "columns.name.keyword" + }, + { + "name": "columnNames", + "type": "terms", + "field": "columnNames" + }, + { + "name": "tableType", + "type": "terms", + "field": "tableType" + } + ], + "boosts": [ + { "field": "columns.name", "boost": 5.0 }, + { "field": "columns.displayName", "boost": 5.0 }, + { "field": "columns.description", "boost": 2.0 }, + { "field": "columns.children.name", "boost": 3.0 } + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "pipeline", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0, + "tasks.name": 8.0, + "tasks.description": 1.0 + }, + "mustMatch": ["name", "fullyQualifiedName"], + "shouldMatch": ["displayName", "description", "tasks.name", "tasks.description"], + "highlightFields": ["tasks.name", "tasks.description"], + "aggregations": [ + { + "name": "tasks", + "type": "terms", + "field": "tasks.displayName.keyword" + } + ], + "boosts": [ + { "field": "tasks.name", "boost": 8.0 }, + { "field": "tasks.description", "boost": 1.0 } + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "mlmodel", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0, + "mlFeatures.name": 8.0, + "mlFeatures.description": 1.0 + }, + "mustMatch": ["name", "fullyQualifiedName"], + "shouldMatch": ["displayName", "description", "mlFeatures.name", "mlFeatures.description"], + "highlightFields": ["mlFeatures.name", "mlFeatures.description"], + "boosts": [ + { "field": "mlFeatures.name", "boost": 8.0 }, + { "field": "mlFeatures.description", "boost": 1.0 } + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "topic", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0, + "messageSchema.schemaText": 7.0, + "messageSchema.schemaFields.name.keyword": 5.0, + "messageSchema.schemaFields.description": 1.0, + "messageSchema.schemaFields.children.name": 7.0, + "messageSchema.schemaFields.children.keyword": 5.0 + }, + "mustMatch": ["name", "fullyQualifiedName"], + "shouldMatch": [ + "displayName", + "description", + "messageSchema.schemaText", + "messageSchema.schemaFields.name.keyword", + "messageSchema.schemaFields.description", + "messageSchema.schemaFields.children.name" + ], + "highlightFields": [], + "aggregations": [ + { + "name": "messageSchema", + "type": "terms", + "field": "messageSchema.schemaText.keyword" + }, + { + "name": "schemaFieldNames", + "type": "terms", + "field": "schemaFieldNames" + } + ], + "boosts": [ + { "field": "messageSchema.schemaText", "boost": 7.0 }, + { "field": "messageSchema.schemaFields.name.keyword", "boost": 5.0 }, + { "field": "messageSchema.schemaFields.description", "boost": 1.0 }, + { "field": "messageSchema.schemaFields.children.name", "boost": 7.0 }, + { "field": "messageSchema.schemaFields.children.keyword", "boost": 5.0 } + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "dashboard", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0, + "charts.name": 7.0, + "charts.displayName": 5.0 + }, + "mustMatch": ["name", "fullyQualifiedName"], + "shouldMatch": [ + "displayName", + "description", + "charts.name", + "charts.displayName" + ], + "highlightFields": [], + "aggregations": [ + { + "name": "dataModels.displayName.keyword", + "type": "terms", + "field": "dataModels.displayName.keyword" + }, + { + "name": "project.keyword", + "type": "terms", + "field": "project.keyword" + }, + { + "name": "charts.displayName.keyword", + "type": "terms", + "field": "charts.displayName.keyword" + } + ], + "boosts": [ + { "field": "charts.name", "boost": 2.0 }, + { "field": "charts.description", "boost": 5.0 } + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "dashboardDataModel", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0 + }, + "mustMatch": ["name", "fullyQualifiedName", "query", "query.ngram"], + "shouldMatch": ["columns.name", "columns.description", "columns.children.name", "columns.displayName"], + "highlightFields": [], + "aggregations": [ + { + "name": "dataModelType", + "type": "terms", + "field": "dataModelType" + }, + { + "name": "project.keyword", + "type": "terms", + "field": "project.keyword" + }, + { + "name": "columnNames", + "type": "terms", + "field": "columnNames" + }, + { + "name": "columns.name.keyword", + "type": "terms", + "field": "columns.name.keyword" + } + ], + "boosts": [ + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "container", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0 + }, + "mustMatch": ["name", "fullyQualifiedName"], + "shouldMatch": [ + "classfiication.name", + "description" + ], + "highlightFields": ["dataModel.columns.name", "dataModel.columns.description", "dataModel.columns.children.name"], + "aggregations": [ + { + "name": "dataModel.columns.name.keyword", + "type": "terms", + "field": "dataModel.columns.name.keyword" + }, + { + "name": "columnNames", + "type": "terms", + "field": "columnNames" + } + ], + "boosts": [ + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "storedProcedure", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0 + }, + "mustMatch": ["name", "fullyQualifiedName", "query", "query.ngram"], + "shouldMatch": [], + "highlightFields": [], + "aggregations": [ + ], + "boosts": [ + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "apiEndpoint", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0 + }, + "mustMatch": ["name", "fullyQualifiedName", "query", "query.ngram"], + "shouldMatch": ["responseSchema.schemaFields.name.keyword", "responseSchema.schemaFields.description", + "responseSchema.schemaFields.children.name", "responseSchema.schemaFields.children.keyword"], + "highlightFields": [], + "aggregations": [ + { + "name": "responseSchema.schemaFields.name", + "type": "terms", + "field": "responseSchema.schemaFields.name.keyword" + }, + { + "name": "fieldNames", + "type": "terms", + "field": "fieldNames" + } + ], + "boosts": [ + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "query", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0 + }, + "mustMatch": ["name", "fullyQualifiedName", "query", "query.ngram"], + "highlightFields": [], + "aggregations": [ + ], + "boosts": [ + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "dataAsset", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0 + }, + "mustMatch": ["name", "fullyQualifiedName"], + "shouldMatch": [ + "displayName", + "description", + "charts.name", + "charts.displayName" + ], + "highlightFields": [], + "aggregations": [ + ], + "boosts": [ + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "glossaryTerm", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0 + }, + "mustMatch": ["name", "fullyQualifiedName"], + "shouldMatch": [ + "displayName", + "description", + "synonyms", + "synonyms.ngram", + "glossary.name", + "glossary.displayName" + ], + "highlightFields": ["synonyms"], + "aggregations": [ + { + "name": "glossary.name.keyword", + "type": "terms", + "field": "glossary.name.keyword" + }, + { + "name": "fqnParts_agg", + "type": "terms", + "field": "fqnParts" + } + ], + "boosts": [ + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "tag", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0 + }, + "mustMatch": ["name", "fullyQualifiedName"], + "shouldMatch": [ + "classfiication.name", + "description" + ], + "highlightFields": [], + "aggregations": [ + { + "name": "classification.name.keyword", + "type": "terms", + "field": "classification.name.keyword" + } + ], + "boosts": [ + ], + "scoreMode": "sum", + "boostMode": "multiply" + }, + { + "assetType": "testCase", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0 + }, + "mustMatch": ["name", "fullyQualifiedName", "query", "query.ngram"], + "shouldMatch": ["testSuite.fullyQualifiedName", "testSuite.name", "testSuite.description", "entityLink", "entityFQN"], + "highlightFields": ["testSuite.name", "testSuite.description"], + "aggregations": [ + ], + "boosts": [ + ], + "scoreMode": "sum", + "boostMode": "multiply" + } + ], + "defaultConfiguration": { + "assetType": "default", + "fields": { + "name": 10.0, + "displayName": 10.0, + "description": 2.0, + "fullyQualifiedName": 5.0, + "fullyQualifiedNameParts": 5.0 + }, + "mustMatch": ["name", "fullyQualifiedName"], + "shouldMatch": ["displayName", "description"], + "highlightFields": [], + "aggregations": [], + "scoreMode": "sum", + "boostMode": "multiply" + } +} \ No newline at end of file diff --git a/openmetadata-service/src/test/java/org/openmetadata/service/resources/system/SystemResourceTest.java b/openmetadata-service/src/test/java/org/openmetadata/service/resources/system/SystemResourceTest.java index c0bd093aa5cc..c71251916a90 100644 --- a/openmetadata-service/src/test/java/org/openmetadata/service/resources/system/SystemResourceTest.java +++ b/openmetadata-service/src/test/java/org/openmetadata/service/resources/system/SystemResourceTest.java @@ -1,6 +1,8 @@ package org.openmetadata.service.resources.system; import static org.junit.jupiter.api.Assertions.assertEquals; +import static org.junit.jupiter.api.Assertions.assertFalse; +import static org.junit.jupiter.api.Assertions.assertNotNull; import static org.junit.jupiter.api.Assertions.assertTrue; import static org.openmetadata.service.util.TestUtils.ADMIN_AUTH_HEADERS; import static org.openmetadata.service.util.TestUtils.TEST_AUTH_HEADERS; @@ -14,6 +16,7 @@ import java.io.IOException; import java.net.URISyntaxException; import java.util.ArrayList; +import java.util.Arrays; import java.util.List; import javax.validation.Validator; import javax.ws.rs.client.WebTarget; @@ -25,7 +28,10 @@ import org.junit.jupiter.api.Order; import org.junit.jupiter.api.Test; import org.junit.jupiter.api.TestInfo; +import org.junit.jupiter.api.TestInstance; import org.junit.jupiter.api.TestMethodOrder; +import org.junit.jupiter.api.parallel.Execution; +import org.junit.jupiter.api.parallel.ExecutionMode; import org.openmetadata.api.configuration.LogoConfiguration; import org.openmetadata.api.configuration.ThemeConfiguration; import org.openmetadata.api.configuration.UiThemePreference; @@ -34,6 +40,9 @@ import org.openmetadata.schema.api.configuration.profiler.ProfilerConfiguration; import org.openmetadata.schema.api.data.*; import org.openmetadata.schema.api.lineage.LineageSettings; +import org.openmetadata.schema.api.search.Aggregation; +import org.openmetadata.schema.api.search.AssetTypeConfiguration; +import org.openmetadata.schema.api.search.Fields; import org.openmetadata.schema.api.search.SearchSettings; import org.openmetadata.schema.api.services.CreateDashboardService; import org.openmetadata.schema.api.services.CreateDatabaseService; @@ -85,6 +94,8 @@ @Slf4j @TestMethodOrder(MethodOrderer.OrderAnnotation.class) +@TestInstance(TestInstance.Lifecycle.PER_CLASS) +@Execution(ExecutionMode.CONCURRENT) public class SystemResourceTest extends OpenMetadataApplicationTest { static OpenMetadataApplicationConfig config; @@ -461,8 +472,9 @@ void testLoginConfigurationSettings() throws HttpResponseException { assertEquals(7200, updatedLoginConfig.getJwtTokenExpiryTime()); } + @Order(1) @Test - void testSearchSettings() throws HttpResponseException { + void testGetDefaultSearchSettings() throws HttpResponseException { // Retrieve the default search settings Settings searchSettings = getSystemConfig(SettingsType.SEARCH_SETTINGS); SearchSettings searchConfig = @@ -471,21 +483,208 @@ void testSearchSettings() throws HttpResponseException { // Assert default values assertEquals(false, searchConfig.getEnableAccessControl()); - // Update search settings + // Verify that global settings are loaded + assertNotNull(searchConfig.getGlobalSettings()); + assertEquals(10000, searchConfig.getGlobalSettings().getMaxAggregateSize()); + assertEquals(10000, searchConfig.getGlobalSettings().getMaxResultHits()); + assertEquals(1000, searchConfig.getGlobalSettings().getMaxAnalyzedOffset()); + + // Verify that aggregations are loaded + assertNotNull(searchConfig.getGlobalSettings().getAggregations()); + assertFalse(searchConfig.getGlobalSettings().getAggregations().isEmpty()); + + // Verify that highlight fields are loaded + assertNotNull(searchConfig.getGlobalSettings().getHighlightFields()); + assertFalse(searchConfig.getGlobalSettings().getHighlightFields().isEmpty()); + + // Verify that asset type configurations are loaded + assertNotNull(searchConfig.getAssetTypeConfigurations()); + assertFalse(searchConfig.getAssetTypeConfigurations().isEmpty()); + + // Check if 'table' asset type configuration exists + boolean tableConfigExists = + searchConfig.getAssetTypeConfigurations().stream() + .anyMatch(config -> "table".equalsIgnoreCase(config.getAssetType())); + assertTrue(tableConfigExists); + + // Verify default configuration is loaded + assertNotNull(searchConfig.getDefaultConfiguration()); + } + + @Test + void testResetSearchSettingsToDefault() throws HttpResponseException { + Settings searchSettings = getSystemConfig(SettingsType.SEARCH_SETTINGS); + SearchSettings searchConfig = + JsonUtils.convertValue(searchSettings.getConfigValue(), SearchSettings.class); + searchConfig.setEnableAccessControl(true); + searchConfig.getGlobalSettings().setMaxAggregateSize(5000); + + AssetTypeConfiguration tableConfig = + searchConfig.getAssetTypeConfigurations().stream() + .filter(config -> "table".equalsIgnoreCase(config.getAssetType())) + .findFirst() + .orElseThrow(() -> new AssertionError("Table configuration not found")); - Settings updatedSearchSettings = + tableConfig.getFields().getAdditionalProperties().put("name", 20.0); + + Settings updatedSettings = new Settings().withConfigType(SettingsType.SEARCH_SETTINGS).withConfigValue(searchConfig); - updateSystemConfig(updatedSearchSettings); + updateSystemConfig(updatedSettings); - // Retrieve the updated settings - Settings updatedSettings = getSystemConfig(SettingsType.SEARCH_SETTINGS); + Settings modifiedSettings = getSystemConfig(SettingsType.SEARCH_SETTINGS); + SearchSettings modifiedSearchConfig = + JsonUtils.convertValue(modifiedSettings.getConfigValue(), SearchSettings.class); + + assertEquals(true, modifiedSearchConfig.getEnableAccessControl()); + assertEquals(5000, modifiedSearchConfig.getGlobalSettings().getMaxAggregateSize()); + assertEquals( + 20.0, + modifiedSearchConfig.getAssetTypeConfigurations().stream() + .filter(config -> "table".equalsIgnoreCase(config.getAssetType())) + .findFirst() + .get() + .getFields() + .getAdditionalProperties() + .get("name")); + + // Step 2: Reset the settings to default + resetSystemConfig(SettingsType.SEARCH_SETTINGS); + + // Retrieve the settings after reset + Settings resetSettings = getSystemConfig(SettingsType.SEARCH_SETTINGS); + SearchSettings resetSearchConfig = + JsonUtils.convertValue(resetSettings.getConfigValue(), SearchSettings.class); + + // Verify that the settings are reset to default values + assertEquals(false, resetSearchConfig.getEnableAccessControl()); + assertEquals(10000, resetSearchConfig.getGlobalSettings().getMaxAggregateSize()); + assertEquals( + 10.0, + resetSearchConfig.getAssetTypeConfigurations().stream() + .filter(config -> "table".equalsIgnoreCase(config.getAssetType())) + .findFirst() + .get() + .getFields() + .getAdditionalProperties() + .get("name")); + } + + @Test + void testGlobalSettingsModification() throws HttpResponseException { + // Step 1: Retrieve current settings + Settings searchSettings = getSystemConfig(SettingsType.SEARCH_SETTINGS); + SearchSettings searchConfig = + JsonUtils.convertValue(searchSettings.getConfigValue(), SearchSettings.class); + SystemResource systemResource = new SystemResource(null); + SearchSettings defaultSearchSettings = systemResource.readDefaultSearchSettings(); + + // Step 2: Modify allowed fields in globalSettings + searchConfig.getGlobalSettings().setMaxAggregateSize(5000); + searchConfig.getGlobalSettings().setMaxResultHits(8000); + searchConfig.getGlobalSettings().setMaxAnalyzedOffset(2000); + + // Step 3: Attempt to modify restricted fields + List originalAggregations = searchConfig.getGlobalSettings().getAggregations(); + List originalHighlightFields = searchConfig.getGlobalSettings().getHighlightFields(); + + // Modify restricted fields + searchConfig + .getGlobalSettings() + .setAggregations(new ArrayList<>()); // Attempt to clear aggregations + searchConfig.getGlobalSettings().setHighlightFields(Arrays.asList("modifiedField")); + + // Step 4: Save the updated settings + Settings updatedSettings = + new Settings().withConfigType(SettingsType.SEARCH_SETTINGS).withConfigValue(searchConfig); + updateSystemConfig(updatedSettings); + + // Step 5: Retrieve the settings after update + Settings retrievedSettings = getSystemConfig(SettingsType.SEARCH_SETTINGS); SearchSettings updatedSearchConfig = - JsonUtils.convertValue(updatedSettings.getConfigValue(), SearchSettings.class); + JsonUtils.convertValue(retrievedSettings.getConfigValue(), SearchSettings.class); - // Assert updated values - assertEquals(true, updatedSearchConfig.getEnableAccessControl()); + // Step 6: Verify that allowed fields have been updated + assertEquals(5000, updatedSearchConfig.getGlobalSettings().getMaxAggregateSize()); + assertEquals(8000, updatedSearchConfig.getGlobalSettings().getMaxResultHits()); + assertEquals(2000, updatedSearchConfig.getGlobalSettings().getMaxAnalyzedOffset()); + + // Step 7: Verify that restricted fields have not changed + assertEquals( + defaultSearchSettings.getGlobalSettings().getAggregations(), + updatedSearchConfig.getGlobalSettings().getAggregations()); + assertEquals( + defaultSearchSettings.getGlobalSettings().getHighlightFields(), + updatedSearchConfig.getGlobalSettings().getHighlightFields()); + } + + @Test + void testCannotDeleteAssetType() throws HttpResponseException { + // Retrieve current settings + Settings searchSettings = getSystemConfig(SettingsType.SEARCH_SETTINGS); + SearchSettings searchConfig = + JsonUtils.convertValue(searchSettings.getConfigValue(), SearchSettings.class); + + // Remove an asset type, e.g., 'table' + searchConfig + .getAssetTypeConfigurations() + .removeIf(config -> "table".equalsIgnoreCase(config.getAssetType())); + + // Save the updated settings + Settings updatedSettings = + new Settings().withConfigType(SettingsType.SEARCH_SETTINGS).withConfigValue(searchConfig); + updateSystemConfig(updatedSettings); + + // Retrieve the settings after update + Settings retrievedSettings = getSystemConfig(SettingsType.SEARCH_SETTINGS); + SearchSettings updatedSearchConfig = + JsonUtils.convertValue(retrievedSettings.getConfigValue(), SearchSettings.class); + + // Verify that 'table' asset type still exists + boolean tableConfigExists = + updatedSearchConfig.getAssetTypeConfigurations().stream() + .anyMatch(config -> "table".equalsIgnoreCase(config.getAssetType())); + assertTrue(tableConfigExists); + } + + @Test + void testCanAddNewAssetType() throws HttpResponseException { + // Retrieve current settings + Settings searchSettings = getSystemConfig(SettingsType.SEARCH_SETTINGS); + SearchSettings searchConfig = + JsonUtils.convertValue(searchSettings.getConfigValue(), SearchSettings.class); + + // Create a new asset type configuration + AssetTypeConfiguration newAssetType = + new AssetTypeConfiguration() + .withAssetType("newAsset") + .withFields(new Fields()) + .withShouldMatch(Arrays.asList("description")) + .withHighlightFields(Arrays.asList("name", "description")) + .withAggregations(new ArrayList<>()) + .withBoosts(new ArrayList<>()) + .withScoreMode(AssetTypeConfiguration.ScoreMode.MULTIPLY) + .withBoostMode(AssetTypeConfiguration.BoostMode.MULTIPLY); + + // Add the new asset type + searchConfig.getAssetTypeConfigurations().add(newAssetType); + + // Save the updated settings + Settings updatedSettings = + new Settings().withConfigType(SettingsType.SEARCH_SETTINGS).withConfigValue(searchConfig); + updateSystemConfig(updatedSettings); + + // Retrieve the settings after update + Settings retrievedSettings = getSystemConfig(SettingsType.SEARCH_SETTINGS); + SearchSettings updatedSearchConfig = + JsonUtils.convertValue(retrievedSettings.getConfigValue(), SearchSettings.class); + + // Verify that the new asset type is added + boolean newAssetTypeExists = + updatedSearchConfig.getAssetTypeConfigurations().stream() + .anyMatch(config -> "newAsset".equalsIgnoreCase(config.getAssetType())); + assertTrue(newAssetTypeExists); } @Test @@ -637,4 +836,9 @@ private static void createSystemConfig(Settings updatedSetting) throws HttpRespo WebTarget target = getResource("system/settings"); TestUtils.put(target, updatedSetting, Response.Status.CREATED, ADMIN_AUTH_HEADERS); } + + private void resetSystemConfig(SettingsType settingsType) throws HttpResponseException { + WebTarget target = getResource("system/settings/reset/" + settingsType.value()); + TestUtils.put(target, Response.Status.OK, ADMIN_AUTH_HEADERS); + } } diff --git a/openmetadata-service/src/test/java/org/openmetadata/service/util/TestUtils.java b/openmetadata-service/src/test/java/org/openmetadata/service/util/TestUtils.java index e39f2d05a6e5..b16714d646d2 100644 --- a/openmetadata-service/src/test/java/org/openmetadata/service/util/TestUtils.java +++ b/openmetadata-service/src/test/java/org/openmetadata/service/util/TestUtils.java @@ -50,6 +50,7 @@ import javax.json.JsonObject; import javax.validation.constraints.Size; import javax.ws.rs.client.Entity; +import javax.ws.rs.client.Invocation; import javax.ws.rs.client.WebTarget; import javax.ws.rs.core.MediaType; import javax.ws.rs.core.Response; @@ -355,6 +356,13 @@ public static void put( readResponse(response, expectedStatus.getStatusCode()); } + public static void put(WebTarget target, Status expectedStatus, Map headers) + throws HttpResponseException { + Invocation.Builder builder = SecurityUtil.addHeaders(target, headers); + Response response = builder.method("PUT"); + readResponse(response, expectedStatus.getStatusCode()); + } + public static T put( WebTarget target, K request, Class clz, Status expectedStatus, Map headers) throws HttpResponseException { diff --git a/openmetadata-spec/src/main/resources/json/schema/configuration/searchSettings.json b/openmetadata-spec/src/main/resources/json/schema/configuration/searchSettings.json index 1441f4f472b9..a0d8267d4f1b 100644 --- a/openmetadata-spec/src/main/resources/json/schema/configuration/searchSettings.json +++ b/openmetadata-spec/src/main/resources/json/schema/configuration/searchSettings.json @@ -2,7 +2,7 @@ "$id": "https://open-metadata.org/schema/entity/configuration/searchSettings.json", "$schema": "http://json-schema.org/draft-07/schema#", "title": "SearchSettings", - "description": "This schema defines the Rbac Search Configuration.", + "description": "This schema defines the Search Configuration, including ranking logic and other settings per asset type.", "type": "object", "javaType": "org.openmetadata.schema.api.search.SearchSettings", "properties": { @@ -10,6 +10,203 @@ "type": "boolean", "description": "Flag to enable or disable the RBAC Search Configuration.", "default": false + }, + "globalSettings": { + "type": "object", + "description": "Global settings for search.", + "properties": { + "maxAggregateSize": { + "type": "integer", + "default": 10000 + }, + "maxResultHits": { + "type": "integer", + "default": 10000 + }, + "maxAnalyzedOffset": { + "type": "integer", + "default": 1000 + }, + "aggregations": { + "type": "array", + "description": "List of aggregations to include in the search query.", + "items": { + "$ref": "#/definitions/aggregation" + } + }, + "highlightFields": { + "type": "array", + "items": { "type": "string" }, + "description": "Fields to include in the highlights." + } + }, + "additionalProperties": false + }, + "assetTypeConfigurations": { + "type": "array", + "description": "List of search configurations for each asset type.", + "items": { + "$ref": "#/definitions/assetTypeConfiguration" + } + }, + "defaultConfiguration": { + "$ref": "#/definitions/assetTypeConfiguration", + "description": "Default search configuration when an entity doesn't match." + } + }, + "definitions": { + "assetTypeConfiguration": { + "type": "object", + "description": "Defines the search configuration for a specific asset type.", + "properties": { + "assetType": { + "type": "string", + "description": "The type of data asset this configuration applies to." + }, + "fields": { + "type": "object", + "description": "Fields to search with their boosts.", + "additionalProperties": { + "type": "number" + } + }, + "mustMatch": { + "type": "array", + "items": { "type": "string" }, + "description": "Fields that must match in the search query." + }, + "shouldMatch": { + "type": "array", + "items": { "type": "string" }, + "description": "Fields that should match in the search query." + }, + "mustNotMatch": { + "type": "array", + "items": { "type": "string" }, + "description": "Fields that must not match." + }, + "boosts": { + "type": "array", + "items": { + "$ref": "#/definitions/fieldBoost" + }, + "description": "Boost factors for specific fields." + }, + "tagBoosts": { + "type": "array", + "items": { + "$ref": "#/definitions/tagBoost" + }, + "description": "Boost factors for specific tags." + }, + "fieldValueBoosts": { + "type": "array", + "items": { + "$ref": "#/definitions/fieldValueBoost" + }, + "description": "Boost factors based on field values with function modifiers." + }, + "highlightFields": { + "type": "array", + "items": { "type": "string" }, + "description": "Fields to include in the highlights." + }, + "aggregations": { + "type": "array", + "description": "List of aggregations to include in the search query.", + "items": { + "$ref": "#/definitions/aggregation" + } + }, + "scoreMode": { + "type": "string", + "enum": ["multiply", "sum", "avg", "first", "max", "min"], + "description": "Determines how the computed scores are combined." + }, + "boostMode": { + "type": "string", + "enum": ["multiply", "replace", "sum", "avg", "max", "min"], + "description": "Determines how the combined score and the query score are combined." + }, + "additionalSettings": { + "type": "object", + "description": "Additional settings specific to the asset type.", + "additionalProperties": true + } + }, + "required": ["assetType", "fields"], + "additionalProperties": false + }, + "fieldBoost": { + "type": "object", + "properties": { + "field": { "type": "string", "description": "Field name to boost." }, + "boost": { "type": "number", "description": "Boost factor for the field." } + }, + "required": ["field", "boost"], + "additionalProperties": false + }, + "tagBoost": { + "type": "object", + "properties": { + "tagFQN": { "type": "string", "description": "Fully Qualified Name of the tag." }, + "boost": { "type": "number", "description": "Boost factor for the tag." } + }, + "required": ["tagFQN", "boost"], + "additionalProperties": false + }, + "fieldValueBoost": { + "type": "object", + "properties": { + "field": { "type": "string", "description": "Field name whose value is used for boosting." }, + "factor": { "type": "number", "description": "Factor by which to multiply the field value." }, + "modifier": { + "type": "string", + "enum": ["none", "log", "log1p", "log2p", "ln", "ln1p", "ln2p", "square", "sqrt", "reciprocal"], + "description": "Modifier function to apply to the field value." + }, + "missing": { "type": "number", "description": "Value to use if the field is missing." }, + "condition": { + "type": "object", + "description": "Condition to apply the boost.", + "properties": { + "range": { + "type": "object", + "properties": { + "gt": { "type": "number", "description": "Greater than value." }, + "gte": { "type": "number", "description": "Greater than or equal to value." }, + "lt": { "type": "number", "description": "Less than value." }, + "lte": { "type": "number", "description": "Less than or equal to value." } + }, + "additionalProperties": false + } + }, + "additionalProperties": false + } + }, + "required": ["field", "factor"], + "additionalProperties": false + }, + "aggregation": { + "type": "object", + "description": "Defines an aggregation for the search query.", + "properties": { + "name": { + "type": "string", + "description": "The name of the aggregation." + }, + "type": { + "type": "string", + "enum": ["terms", "range", "histogram", "date_histogram", "filters", "missing", "nested", "reverse_nested", "top_hits", "max", "min", "avg", "sum", "stats"], + "description": "The type of aggregation." + }, + "field": { + "type": "string", + "description": "The field to aggregate on." + } + }, + "required": ["name", "type", "field"], + "additionalProperties": false } }, "additionalProperties": false From 6bb05e153525aaa2f2d536ce0ca9ee602cf59623 Mon Sep 17 00:00:00 2001 From: Teddy Date: Wed, 20 Nov 2024 08:20:13 +0100 Subject: [PATCH 02/38] MINOR: Move from Date to LocalDate for thread safe operations (#18679) * fix: move from Date to LocalDate for thread safe operations * style: ran java linting * fix: invalid range in test case --- .../openmetadata/common/utils/CommonUtil.java | 48 +++++++++---------- .../service/jdbi3/TableRepository.java | 4 +- .../resources/usage/UsageResource.java | 6 +-- .../openmetadata/service/util/EntityUtil.java | 4 +- .../openmetadata/service/util/RestUtil.java | 17 ++++--- .../databases/TableResourceTest.java | 2 +- .../resources/usage/UsageResourceTest.java | 8 ++-- 7 files changed, 45 insertions(+), 44 deletions(-) diff --git a/common/src/main/java/org/openmetadata/common/utils/CommonUtil.java b/common/src/main/java/org/openmetadata/common/utils/CommonUtil.java index b6445acfc382..7e0da8b6ac45 100644 --- a/common/src/main/java/org/openmetadata/common/utils/CommonUtil.java +++ b/common/src/main/java/org/openmetadata/common/utils/CommonUtil.java @@ -24,22 +24,20 @@ import java.nio.file.Files; import java.nio.file.Path; import java.nio.file.Paths; -import java.text.DateFormat; -import java.text.ParseException; +import java.time.LocalDate; +import java.time.format.DateTimeFormatter; +import java.time.format.DateTimeParseException; import java.util.ArrayList; import java.util.Arrays; import java.util.Base64; -import java.util.Calendar; import java.util.Collection; import java.util.Collections; -import java.util.Date; import java.util.Enumeration; import java.util.List; import java.util.Map; import java.util.Objects; import java.util.Optional; import java.util.Set; -import java.util.TimeZone; import java.util.UUID; import java.util.regex.Pattern; import java.util.stream.Collectors; @@ -121,42 +119,42 @@ public static Collection getResourcesFromDirectory(File file, Pattern pa } /** Get date after {@code days} from the given date or before i{@code days} when it is negative */ - public static Date getDateByOffset(Date date, int days) { - Calendar calendar = Calendar.getInstance(TimeZone.getTimeZone("UTC")); - calendar.setTime(date); - calendar.add(Calendar.DATE, days); - return calendar.getTime(); + public static LocalDate getDateByOffset(LocalDate localDate, int days) { + return localDate.plusDays(days); } /** Get date after {@code days} from the given date or before i{@code days} when it is negative */ - public static Date getDateByOffset(DateFormat dateFormat, String strDate, int days) { - Date date; + public static LocalDate getDateByOffset(DateTimeFormatter dateFormat, String strDate, int days) { + LocalDate localDate; try { - date = dateFormat.parse(strDate); - } catch (ParseException e) { + localDate = LocalDate.parse(strDate, dateFormat); + } catch (DateTimeParseException e) { throw new IllegalArgumentException("Failed to parse date " + strDate, e); } - return getDateByOffset(date, days); + return getDateByOffset(localDate, days); } /** Get date after {@code days} from the given date or before i{@code days} when it is negative */ - public static String getDateStringByOffset(DateFormat dateFormat, String strDate, int days) { - return dateFormat.format(getDateByOffset(dateFormat, strDate, days)); + public static String getDateStringByOffset( + DateTimeFormatter dateFormat, String strDate, int days) { + LocalDate localDate = getDateByOffset(dateFormat, strDate, days); + return localDate.format(dateFormat); } /** Check if given date is with in today - pastDays and today + futureDays */ public static boolean dateInRange( - DateFormat dateFormat, String date, int futureDays, int pastDays) { - Date today = new Date(); - Date startDate = getDateByOffset(today, -pastDays); - Date endDate = getDateByOffset(today, futureDays); - Date givenDate; + DateTimeFormatter dateFormat, String date, int futureDays, int pastDays) { + LocalDate today = LocalDate.now(); + LocalDate startDate = getDateByOffset(today, -pastDays); + LocalDate endDate = getDateByOffset(today, futureDays); + LocalDate givenDate; try { - givenDate = dateFormat.parse(date); - } catch (ParseException e) { + givenDate = LocalDate.parse(date, dateFormat); + } catch (DateTimeParseException e) { throw new IllegalArgumentException("Failed to parse date " + date, e); } - return givenDate.after(startDate) && givenDate.before(endDate); + return (givenDate.isAfter(startDate) || givenDate.equals(startDate)) + && (givenDate.isBefore(endDate) || givenDate.equals(endDate)); } public static final String HMAC_SHA256_ALGORITHM = "HmacSHA256"; diff --git a/openmetadata-service/src/main/java/org/openmetadata/service/jdbi3/TableRepository.java b/openmetadata-service/src/main/java/org/openmetadata/service/jdbi3/TableRepository.java index 3c85de74f59a..bb428941ce55 100644 --- a/openmetadata-service/src/main/java/org/openmetadata/service/jdbi3/TableRepository.java +++ b/openmetadata-service/src/main/java/org/openmetadata/service/jdbi3/TableRepository.java @@ -36,10 +36,10 @@ import com.google.common.collect.Streams; import java.io.IOException; +import java.time.LocalDate; import java.util.ArrayList; import java.util.Arrays; import java.util.Collections; -import java.util.Date; import java.util.List; import java.util.Map; import java.util.Optional; @@ -1004,7 +1004,7 @@ private List aggregateAndFilterDailyCounts( } private TableJoins getJoins(Table table) { - String today = RestUtil.DATE_FORMAT.format(new Date()); + String today = RestUtil.DATE_FORMAT.format(LocalDate.now()); String todayMinus30Days = CommonUtil.getDateStringByOffset(RestUtil.DATE_FORMAT, today, -30); return new TableJoins() .withStartDate(todayMinus30Days) diff --git a/openmetadata-service/src/main/java/org/openmetadata/service/resources/usage/UsageResource.java b/openmetadata-service/src/main/java/org/openmetadata/service/resources/usage/UsageResource.java index 99231ea25613..942c8a8ca280 100644 --- a/openmetadata-service/src/main/java/org/openmetadata/service/resources/usage/UsageResource.java +++ b/openmetadata-service/src/main/java/org/openmetadata/service/resources/usage/UsageResource.java @@ -19,7 +19,7 @@ import io.swagger.v3.oas.annotations.media.Schema; import io.swagger.v3.oas.annotations.responses.ApiResponse; import io.swagger.v3.oas.annotations.tags.Tag; -import java.util.Date; +import java.time.LocalDate; import java.util.UUID; import javax.validation.Valid; import javax.ws.rs.Consumes; @@ -105,7 +105,7 @@ public EntityUsage get( ResourceContext resourceContext = new ResourceContext(entity); authorizer.authorize(securityContext, operationContext, resourceContext); int actualDays = Math.min(Math.max(days, 1), 30); - String actualDate = date == null ? RestUtil.DATE_FORMAT.format(new Date()) : date; + String actualDate = date == null ? RestUtil.DATE_FORMAT.format(LocalDate.now()) : date; return addHref(uriInfo, dao.get(entity, id, actualDate, actualDays)); } @@ -155,7 +155,7 @@ public EntityUsage getByName( ResourceContext resourceContext = new ResourceContext<>(entity, null, fqn); authorizer.authorize(securityContext, operationContext, resourceContext); int actualDays = Math.min(Math.max(days, 1), 30); - String actualDate = date == null ? RestUtil.DATE_FORMAT.format(new Date()) : date; + String actualDate = date == null ? RestUtil.DATE_FORMAT.format(LocalDate.now()) : date; return addHref(uriInfo, dao.getByName(entity, fqn, actualDate, actualDays)); } diff --git a/openmetadata-service/src/main/java/org/openmetadata/service/util/EntityUtil.java b/openmetadata-service/src/main/java/org/openmetadata/service/util/EntityUtil.java index f93c00a919aa..e08ff1e910fb 100644 --- a/openmetadata-service/src/main/java/org/openmetadata/service/util/EntityUtil.java +++ b/openmetadata-service/src/main/java/org/openmetadata/service/util/EntityUtil.java @@ -22,11 +22,11 @@ import java.io.IOException; import java.security.MessageDigest; +import java.time.LocalDate; import java.util.ArrayList; import java.util.Arrays; import java.util.Collections; import java.util.Comparator; -import java.util.Date; import java.util.HashSet; import java.util.Iterator; import java.util.List; @@ -219,7 +219,7 @@ public static UsageDetails getLatestUsage(UsageDAO usageDAO, UUID entityId) { .withDailyStats(stats) .withWeeklyStats(stats) .withMonthlyStats(stats) - .withDate(RestUtil.DATE_FORMAT.format(new Date())); + .withDate(RestUtil.DATE_FORMAT.format(LocalDate.now())); } return details; } diff --git a/openmetadata-service/src/main/java/org/openmetadata/service/util/RestUtil.java b/openmetadata-service/src/main/java/org/openmetadata/service/util/RestUtil.java index 2f5f8bbd92a1..76fe345bf704 100644 --- a/openmetadata-service/src/main/java/org/openmetadata/service/util/RestUtil.java +++ b/openmetadata-service/src/main/java/org/openmetadata/service/util/RestUtil.java @@ -24,8 +24,11 @@ import java.text.DateFormat; import java.text.ParseException; import java.text.SimpleDateFormat; +import java.time.LocalDate; +import java.time.LocalDateTime; +import java.time.ZoneId; +import java.time.format.DateTimeFormatter; import java.util.Base64; -import java.util.Date; import java.util.TimeZone; import java.util.UUID; import javax.ws.rs.BadRequestException; @@ -42,15 +45,14 @@ public final class RestUtil { public static final String CHANGE_CUSTOM_HEADER = "X-OpenMetadata-Change"; public static final String SIGNATURE_HEADER = "X-OM-Signature"; public static final DateFormat DATE_TIME_FORMAT; - public static final DateFormat DATE_FORMAT; + public static final DateTimeFormatter DATE_FORMAT; static { // Quoted "Z" to indicate UTC, no timezone offset DATE_TIME_FORMAT = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSSSS'Z'"); DATE_TIME_FORMAT.setTimeZone(TimeZone.getTimeZone("UTC")); - DATE_FORMAT = new SimpleDateFormat("yyyy-MM-dd"); - DATE_FORMAT.setTimeZone(TimeZone.getTimeZone("UTC")); + DATE_FORMAT = DateTimeFormatter.ofPattern("yyyy-MM-dd").withZone(ZoneId.of("UTC")); } private RestUtil() {} @@ -91,12 +93,13 @@ public static URI getHref(UriInfo uriInfo, String collectionPath, UUID id) { } public static int compareDates(String date1, String date2) throws ParseException { - return DATE_FORMAT.parse(date1).compareTo(DATE_FORMAT.parse(date2)); + return LocalDateTime.parse(date1, DATE_FORMAT) + .compareTo(LocalDateTime.parse(date2, DATE_FORMAT)); } public static String today(int offsetDays) { - Date date = CommonUtil.getDateByOffset(new Date(), offsetDays); - return DATE_FORMAT.format(date); + LocalDate localDate = CommonUtil.getDateByOffset(LocalDate.now(), offsetDays); + return localDate.format(DATE_FORMAT); } public static void validateCursors(String before, String after) { diff --git a/openmetadata-service/src/test/java/org/openmetadata/service/resources/databases/TableResourceTest.java b/openmetadata-service/src/test/java/org/openmetadata/service/resources/databases/TableResourceTest.java index 1f099ed5449d..629fba50de37 100644 --- a/openmetadata-service/src/test/java/org/openmetadata/service/resources/databases/TableResourceTest.java +++ b/openmetadata-service/src/test/java/org/openmetadata/service/resources/databases/TableResourceTest.java @@ -1001,7 +1001,7 @@ void put_tableJoinsInvalidColumnName_4xx(TestInfo test) throws IOException { // Invalid date older than 30 days String invalidColumnFQN4 = table2.getFullyQualifiedName() + ".c1"; TableJoins tableJoins4 = - getTableJoins(getColumnJoin(C1, invalidColumnFQN4)).withStartDate(RestUtil.today(-30)); + getTableJoins(getColumnJoin(C1, invalidColumnFQN4)).withStartDate(RestUtil.today(-31)); assertResponse( () -> putJoins(table1.getId(), tableJoins4, ADMIN_AUTH_HEADERS), BAD_REQUEST, diff --git a/openmetadata-service/src/test/java/org/openmetadata/service/resources/usage/UsageResourceTest.java b/openmetadata-service/src/test/java/org/openmetadata/service/resources/usage/UsageResourceTest.java index 05c27f1b0c20..e99190f8d50a 100644 --- a/openmetadata-service/src/test/java/org/openmetadata/service/resources/usage/UsageResourceTest.java +++ b/openmetadata-service/src/test/java/org/openmetadata/service/resources/usage/UsageResourceTest.java @@ -31,8 +31,8 @@ import java.io.IOException; import java.net.URISyntaxException; +import java.time.LocalDate; import java.util.ArrayList; -import java.util.Date; import java.util.List; import java.util.Map; import java.util.Random; @@ -200,7 +200,7 @@ void testValidUsageByName(TestInfo test, String methodType, Map Table table = tableResourceTest.createEntity(tableResourceTest.createRequest(test), ADMIN_AUTH_HEADERS); DailyCount usageReport = - usageReport().withCount(100).withDate(RestUtil.DATE_FORMAT.format(new Date())); + usageReport().withCount(100).withDate(RestUtil.DATE_FORMAT.format(LocalDate.now())); reportUsageByNameAndCheckPut( TABLE, table.getFullyQualifiedName(), usageReport, 100, 100, authHeaders); // a put request updates the data again @@ -221,7 +221,7 @@ void put_validUsageForTables_200_OK() throws HttpResponseException { // and monthly usage percentile rank is correct. // Publish usage for DAYS_OF_USAGE number of days starting from today - String today = RestUtil.DATE_FORMAT.format(new Date()); // today + String today = RestUtil.DATE_FORMAT.format(LocalDate.now()); // today // Add table usages of each table - 0, 1 to TABLE_COUNT - 1 to get database usage final int dailyDatabaseUsageCount = TABLE_COUNT * (TABLE_COUNT - 1) / 2; @@ -348,7 +348,7 @@ void put_validUsageForTables_200_OK() throws HttpResponseException { public DailyCount usageReport() { Random random = new Random(); - String today = RestUtil.DATE_FORMAT.format(new Date()); + String today = RestUtil.DATE_FORMAT.format(LocalDate.now()); return new DailyCount().withCount(random.nextInt(100)).withDate(today); } From 93ef52398ebcae95cf97bb3b41d871998ea5b5b3 Mon Sep 17 00:00:00 2001 From: RounakDhillon <162090200+RounakDhillon@users.noreply.github.com> Date: Wed, 20 Nov 2024 14:22:39 +0530 Subject: [PATCH 03/38] Docs: Metapilot Updation to Collate AI (#18704) Co-authored-by: Rounak Dhillon --- .../v1.5.x/Metapilot/how-to-use-metapilot.md | 110 ------------------ .../content/v1.5.x/Metapilot/index.md | 85 -------------- .../content/v1.5.x/collate-menu.md | 8 +- .../v1.5.x/collateai/how-to-use-collate-ai.md | 110 ++++++++++++++++++ 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openmetadata-docs/images/v1.6/metapilot/setting-up-metapilot-3.png delete mode 100644 openmetadata-docs/images/v1.6/metapilot/setting-up-metapilot-4.png diff --git a/openmetadata-docs/content/v1.5.x/Metapilot/how-to-use-metapilot.md b/openmetadata-docs/content/v1.5.x/Metapilot/how-to-use-metapilot.md deleted file mode 100644 index 3bf1b2a56350..000000000000 --- a/openmetadata-docs/content/v1.5.x/Metapilot/how-to-use-metapilot.md +++ /dev/null @@ -1,110 +0,0 @@ ---- -title: How to Use MetaPilot -slug: /metapilot/how-to-use-metapilot -collate: true ---- - -# How to Use MetaPilot - -## 1. Setting Up MetaPilot -- Navigate to **Settings > Applications** in the Collate platform. - -{% image -src="/images/v1.5/metapilot/setting-up-metapilot-1.png" -alt="setting up metapilot" -caption="Navigate to Settings > Applications" -/%} - -- Install MetaPilot by following the on-screen instructions. - -{% image -src="/images/v1.5/metapilot/setting-up-metapilot-2.png" -alt="Install MetaPilot" -caption="Install MetaPilot" -/%} - -- Select the databases for which you want MetaPilot to automatically generate descriptions. You can configure multiple databases and set a default database for the MetaPilot chatbot to work with. - -{% image -src="/images/v1.5/metapilot/setting-up-metapilot-3.png" -alt="automatically generate descriptions" -caption="automatically generate descriptions" -/%} - -- **Scheduling**: Schedule MetaPilot to run regularly, automatically generating metadata at predefined intervals (e.g., weekly). - -{% image -src="/images/v1.5/metapilot/setting-up-metapilot-4.png" -alt="Schedule MetaPilot" -caption="Schedule MetaPilot" -/%} - -## 2. Using the MetaPilot Chatbot -- The MetaPilot chatbot icon appears on every page after installation. - -{% image -src="/images/v1.5/metapilot/using-metapilot-chatbot-1.png" -alt="chatbot icon" -caption="chatbot icon" -/%} - -- Interact with the chatbot by typing natural language questions. For example: - - “Show me sales data for Q1.” - - “What is the average revenue per customer?” - -- MetaPilot will generate the corresponding SQL query and provide a detailed explanation of the query logic. - -{% image -src="/images/v1.5/metapilot/using-metapilot-chatbot-2.png" -alt="natural language questions" -caption="natural language questions" -/%} - -- Users can refine queries by providing further instructions, and the chatbot will adjust the SQL query accordingly. - -## 3. Optimizing and Fixing SQL Queries - -- The **Metadata Usage** workflows will ingest the queries being run against the tables. You can see how long each query has been executed in the **Queries** tab. - -{% image -src="/images/v1.5/metapilot/fixing-sql-queries-1.png" -alt="metadata usage workflows" -caption="metadata usage workflows" -/%} - -- If a query runs inefficiently, ask the chatbot to optimize it by typing: “Optimize this query.” - -{% image -src="/images/v1.5/metapilot/fixing-sql-queries-3.png" -alt="Optimize this query" -caption="Optimize this query" -/%} - -- MetaPilot will return a more efficient version of the SQL query, which you can then copy and execute in your database. - -- If the query contains errors or isn’t functioning correctly, ask MetaPilot: “Can you fix this query?” - -- MetaPilot will correct the query and provide a working version. - -{% image -src="/images/v1.5/metapilot/fixing-sql-queries-4.png" -alt="Can you fix this query" -caption="Can you fix this query" -/%} - -## 4. Reviewing Generated Metadata -- Once MetaPilot generates descriptions for tables and columns, navigate to the **database view** to review the metadata. -- You can accept or reject each suggestion individually or choose to accept all suggestions in bulk. - -{% image -src="/images/v1.5/metapilot/reviewing-generated-metadata.png" -alt="reviewing Generated Metadata" -caption="reviewing Generated Metadata" -/%} - -- MetaPilot allows you to document entire datasets in a matter of minutes, significantly reducing the manual effort required to maintain metadata. - -## Best Practices -- **Regular Scheduling**: Schedule MetaPilot to run at regular intervals to ensure your metadata is always up-to-date, especially when dealing with frequently changing datasets. -- **Leverage the Chatbot for Query Writing**: Encourage both technical and non-technical users to use the MetaPilot chatbot for SQL query generation. It simplifies complex query writing and ensures accurate results. -- **Optimize Queries Regularly**: Monitor the performance of your queries and use MetaPilot to optimize them, especially when working with large datasets or queries that require complex joins and filters. diff --git a/openmetadata-docs/content/v1.5.x/Metapilot/index.md b/openmetadata-docs/content/v1.5.x/Metapilot/index.md deleted file mode 100644 index 43f031cf4e0a..000000000000 --- a/openmetadata-docs/content/v1.5.x/Metapilot/index.md +++ /dev/null @@ -1,85 +0,0 @@ ---- -title: Metapilot -slug: /metapilot -collate: true ---- - -# Metapilot Technical Documentation - -{% youtube videoId="6glMYLzxNqk" start="0:00" end="04:20" width="560px" height="315px" /%} - -MetaPilot is an AI-powered tool within Collate that simplifies and enhances metadata management. By integrating generative AI, MetaPilot assists users in automating the documentation of data assets, writing and optimizing SQL queries, and interacting with data through natural language. This first-of-its-kind data copilot improves productivity by automating tedious tasks and providing intelligent insights into your data environment. - -## Key Features - -- **Automated Data Documentation**: Automatically generates metadata descriptions for tables and columns, saving time and effort for data owners and stewards. -- **Natural Language SQL Query Generation**: Allows users to interact with MetaPilot through a chatbot to generate SQL queries by simply asking questions in plain English. -- **SQL Query Optimization and Fixing**: Capable of optimizing and troubleshooting SQL queries to improve their performance and efficiency. - -## Why MetaPilot is Useful - -### Metadata Management Challenges - -Managing metadata across multiple data assets can be overwhelming due to the influx of new data and changing team dynamics. MetaPilot addresses these challenges by: - -- Automating metadata description generation. -- Simplifying the creation and optimization of SQL queries. -- Reducing manual effort and enhancing data quality. - -### Time-Saving Features - -Documenting thousands of tables manually is tedious and time-consuming. MetaPilot automates metadata generation, allowing data teams to focus on high-value tasks and ensuring that data assets are consistently documented and understood across the organization. - -## Use Cases - -### 1. Automatic Data Asset Documentation - -{% image -src="/images/v1.5/metapilot/reviewing-generated-metadata.png" -alt="Automatic Data Asset Documentation" -caption="Auto Generate data Asset Documentation" -/%} - -- **Problem**: Manually creating metadata descriptions for large datasets is labor-intensive and error-prone. -- **Solution**: MetaPilot’s generative AI automates the process, analyzing database structures and suggesting accurate descriptions for tables and columns. -- **How It Works**: After configuring MetaPilot, it scans the database schema and generates metadata descriptions automatically. Users can review these descriptions and approve or reject them in bulk or individually. -- **Benefit**: Streamlines the documentation process, ensuring consistent and up-to-date metadata across all datasets. - -### 2. Natural Language SQL Query Generation - -{% image -src="/images/v1.5/metapilot/using-metapilot-chatbot-2.png" -alt="Natural Language SQL Query Generation" -caption="Natural Language SQL Query Generation" -/%} - -- **Problem**: Non-technical users often struggle with writing SQL queries to extract insights from databases. -- **Solution**: MetaPilot’s chatbot allows users to ask questions in natural language, generating SQL queries and providing explanations to help extract the required data quickly and easily. -- **How It Works**: Users can click on the MetaPilot chatbot widget and ask questions like "Show me sales data from last quarter." MetaPilot generates the corresponding SQL query and explains its logic. -- **Benefit**: Democratizes data access by enabling users of all technical levels to interact with data without deep SQL knowledge. - -### 3. SQL Query Optimization and Troubleshooting - -{% image -src="/images/v1.5/metapilot/fixing-sql-queries-3.png" -alt="SQL Query Optimization and Troubleshooting" -caption="SQL Query Optimization" -/%} - -- **Problem**: SQL queries can become complex and inefficient, leading to performance issues and increased costs. -- **Solution**: MetaPilot optimizes inefficient queries to improve performance, saving both time and resources. -- **How It Works**: If a query runs too long, users can request MetaPilot to optimize it. MetaPilot provides a more efficient SQL query version that can be implemented immediately. -- **Benefit**: Enhances query performance, reduces costs associated with inefficient queries, and speeds up data processing. - -### 4. Fixing SQL Queries - -{% image -src="/images/v1.5/metapilot/fixing-sql-queries-4.png" -alt="Fixing SQL Queries" -caption="Fixing SQL Queries" -/%} - -- **Problem**: Complex queries can cause issues even for SQL experts. -- **Solution**: MetaPilot can fix problematic SQL queries, ensuring they run correctly and efficiently. -- **How It Works**: Users can ask MetaPilot, "Can you fix this query for me?" It analyzes the query, detects issues, and returns a corrected version ready for use. -- **Benefit**: Simplifies writing and maintaining queries, allowing data teams to focus on analysis rather than troubleshooting. diff --git a/openmetadata-docs/content/v1.5.x/collate-menu.md b/openmetadata-docs/content/v1.5.x/collate-menu.md index 85dde6fa6345..9ebb2f0cf46e 100644 --- a/openmetadata-docs/content/v1.5.x/collate-menu.md +++ b/openmetadata-docs/content/v1.5.x/collate-menu.md @@ -1897,8 +1897,8 @@ site_menu: url: /sdk/java - category: SDK / Go url: /sdk/go - - category: Metapilot - url: /metapilot - - category: Metapilot / How to Use MetaPilot - url: /metapilot/how-to-use-metapilot + - category: Collate AI + url: /collateai + - category: Collate AI / How to Use Collate AI + url: /collateai/how-to-use-collate-ai --- diff --git a/openmetadata-docs/content/v1.5.x/collateai/how-to-use-collate-ai.md b/openmetadata-docs/content/v1.5.x/collateai/how-to-use-collate-ai.md new file mode 100644 index 000000000000..7ca5bafe529e --- /dev/null +++ b/openmetadata-docs/content/v1.5.x/collateai/how-to-use-collate-ai.md @@ -0,0 +1,110 @@ +--- +title: How to Use Collate AI +slug: /collateai/how-to-use-collate-ai +collate: true +--- + +# How to Use Collate AI + +## 1. Setting Up Collate AI +- Navigate to **Settings > Applications** in the Collate platform. + +{% image +src="/images/v1.5/collate-ai/setting-up-collate-ai-1.png" +alt="setting up Collate AI" +caption="Navigate to Settings > Applications" +/%} + +- Install Collate AI by following the on-screen instructions. + +{% image +src="/images/v1.5/collate-ai/setting-up-collate-ai-2.png" +alt="Install Collate AI" +caption="Install Collate AI" +/%} + +- Configure and add filters based on criteria such as owner, schema, database, service, and more. You can also combine multiple conditions to meet your specific requirements. + +{% image +src="/images/v1.5/collate-ai/setting-up-collate-ai-3.png" +alt="automatically generate descriptions" +caption="automatically generate descriptions" +/%} + +- **Scheduling**: Schedule Collate AI to run regularly, automatically generating metadata at predefined intervals (e.g., weekly). + +{% image +src="/images/v1.5/collate-ai/setting-up-collate-ai-4.png" +alt="Schedule Collate AI" +caption="Schedule Collate AI" +/%} + +## 2. Using the Collate AI Chatbot +- The Collate AI chatbot icon appears on every page after installation. + +{% image +src="/images/v1.5/collate-ai/using-collate-ai-chatbot-1.png" +alt="chatbot icon" +caption="chatbot icon" +/%} + +- Interact with the chatbot by typing natural language questions. For example: + - “Show me sales data for Q1.” + - “What is the average revenue per customer?” + +- Collate AI will generate the corresponding SQL query and provide a detailed explanation of the query logic. + +{% image +src="/images/v1.5/collate-ai/using-collate-ai-chatbot-2.png" +alt="natural language questions" +caption="natural language questions" +/%} + +- Users can refine queries by providing further instructions, and the chatbot will adjust the SQL query accordingly. + +## 3. Optimizing and Fixing SQL Queries + +- The **Metadata Usage** workflows will ingest the queries being run against the tables. You can see how long each query has been executed in the **Queries** tab. + +{% image +src="/images/v1.5/collate-ai/fixing-sql-queries-1.png" +alt="metadata usage workflows" +caption="metadata usage workflows" +/%} + +- If a query runs inefficiently, ask the chatbot to optimize it by typing: “Optimize this query.” + +{% image +src="/images/v1.5/collate-ai/fixing-sql-queries-3.png" +alt="Optimize this query" +caption="Optimize this query" +/%} + +- Collate AI will return a more efficient version of the SQL query, which you can then copy and execute in your database. + +- If the query contains errors or isn’t functioning correctly, ask Collate AI: “Can you fix this query?” + +- Collate AI will correct the query and provide a working version. + +{% image +src="/images/v1.5/collate-ai/fixing-sql-queries-4.png" +alt="Can you fix this query" +caption="Can you fix this query" +/%} + +## 4. Reviewing Generated Metadata +- Once Collate AI generates descriptions for tables and columns, navigate to the **database view** to review the metadata. +- You can accept or reject each suggestion individually or choose to accept all suggestions in bulk. + +{% image +src="/images/v1.5/collate-ai/reviewing-generated-metadata.png" +alt="reviewing Generated Metadata" +caption="reviewing Generated Metadata" +/%} + +- Collate AI allows you to document entire datasets in a matter of minutes, significantly reducing the manual effort required to maintain metadata. + +## Best Practices +- **Regular Scheduling**: Schedule Collate AI to run at regular intervals to ensure your metadata is always up-to-date, especially when dealing with frequently changing datasets. +- **Leverage the Chatbot for Query Writing**: Encourage both technical and non-technical users to use the Collate AI chatbot for SQL query generation. It simplifies complex query writing and ensures accurate results. +- **Optimize Queries Regularly**: Monitor the performance of your queries and use Collate AI to optimize them, especially when working with large datasets or queries that require complex joins and filters. diff --git a/openmetadata-docs/content/v1.5.x/collateai/index.md b/openmetadata-docs/content/v1.5.x/collateai/index.md new file mode 100644 index 000000000000..5cf23c1c1796 --- /dev/null +++ b/openmetadata-docs/content/v1.5.x/collateai/index.md @@ -0,0 +1,85 @@ +--- +title: Collate AI +slug: /collateai +collate: true +--- + +# Collate AI Technical Documentation + +{% youtube videoId="6glMYLzxNqk" start="0:00" end="04:20" width="560px" height="315px" /%} + +Collate AI is an AI-powered tool within Collate that simplifies and enhances metadata management. By integrating generative AI, Collate AI assists users in automating the documentation of data assets, writing and optimizing SQL queries, and interacting with data through natural language. This first-of-its-kind data copilot improves productivity by automating tedious tasks and providing intelligent insights into your data environment. + +## Key Features + +- **Automated Data Documentation**: Automatically generates metadata descriptions for tables and columns, saving time and effort for data owners and stewards. +- **Natural Language SQL Query Generation**: Allows users to interact with Collate AI through a chatbot to generate SQL queries by simply asking questions in plain English. +- **SQL Query Optimization and Fixing**: Capable of optimizing and troubleshooting SQL queries to improve their performance and efficiency. + +## Why Collate AI is Useful + +### Metadata Management Challenges + +Managing metadata across multiple data assets can be overwhelming due to the influx of new data and changing team dynamics. Collate AI addresses these challenges by: + +- Automating metadata description generation. +- Simplifying the creation and optimization of SQL queries. +- Reducing manual effort and enhancing data quality. + +### Time-Saving Features + +Documenting thousands of tables manually is tedious and time-consuming. Collate AI automates metadata generation, allowing data teams to focus on high-value tasks and ensuring that data assets are consistently documented and understood across the organization. + +## Use Cases + +### 1. Automatic Data Asset Documentation + +{% image +src="/images/v1.5/Collate AI/reviewing-generated-metadata.png" +alt="Automatic Data Asset Documentation" +caption="Auto Generate data Asset Documentation" +/%} + +- **Problem**: Manually creating metadata descriptions for large datasets is labor-intensive and error-prone. +- **Solution**: Collate AI’s generative AI automates the process, analyzing database structures and suggesting accurate descriptions for tables and columns. +- **How It Works**: After configuring Collate AI, it scans the database schema and generates metadata descriptions automatically. Users can review these descriptions and approve or reject them in bulk or individually. +- **Benefit**: Streamlines the documentation process, ensuring consistent and up-to-date metadata across all datasets. + +### 2. Natural Language SQL Query Generation + +{% image +src="/images/v1.5/collate-ai/using-collate-ai-chatbot-2.png" +alt="Natural Language SQL Query Generation" +caption="Natural Language SQL Query Generation" +/%} + +- **Problem**: Non-technical users often struggle with writing SQL queries to extract insights from databases. +- **Solution**: Collate AI’s chatbot allows users to ask questions in natural language, generating SQL queries and providing explanations to help extract the required data quickly and easily. +- **How It Works**: Users can click on the Collate AI chatbot widget and ask questions like "Show me sales data from last quarter." Collate AI generates the corresponding SQL query and explains its logic. +- **Benefit**: Democratizes data access by enabling users of all technical levels to interact with data without deep SQL knowledge. + +### 3. SQL Query Optimization and Troubleshooting + +{% image +src="/images/v1.5/collate-ai/fixing-sql-queries-3.png" +alt="SQL Query Optimization and Troubleshooting" +caption="SQL Query Optimization" +/%} + +- **Problem**: SQL queries can become complex and inefficient, leading to performance issues and increased costs. +- **Solution**: Collate AI optimizes inefficient queries to improve performance, saving both time and resources. +- **How It Works**: If a query runs too long, users can request Collate AI to optimize it. Collate AI provides a more efficient SQL query version that can be implemented immediately. +- **Benefit**: Enhances query performance, reduces costs associated with inefficient queries, and speeds up data processing. + +### 4. Fixing SQL Queries + +{% image +src="/images/v1.5/collate-ai/fixing-sql-queries-4.png" +alt="Fixing SQL Queries" +caption="Fixing SQL Queries" +/%} + +- **Problem**: Complex queries can cause issues even for SQL experts. +- **Solution**: Collate AI can fix problematic SQL queries, ensuring they run correctly and efficiently. +- **How It Works**: Users can ask Collate AI, "Can you fix this query for me?" It analyzes the query, detects issues, and returns a corrected version ready for use. +- **Benefit**: Simplifies writing and maintaining queries, allowing data teams to focus on analysis rather than troubleshooting. diff --git a/openmetadata-docs/content/v1.6.x-SNAPSHOT/Metapilot/how-to-use-metapilot.md b/openmetadata-docs/content/v1.6.x-SNAPSHOT/Metapilot/how-to-use-metapilot.md deleted file mode 100644 index e78793cca165..000000000000 --- a/openmetadata-docs/content/v1.6.x-SNAPSHOT/Metapilot/how-to-use-metapilot.md +++ /dev/null @@ -1,110 +0,0 @@ ---- -title: How to Use MetaPilot -slug: /metapilot/how-to-use-metapilot -collate: true ---- - -# How to Use MetaPilot - -## 1. Setting Up MetaPilot -- Navigate to **Settings > Applications** in the Collate platform. - -{% image -src="/images/v1.6/metapilot/setting-up-metapilot-1.png" -alt="setting up metapilot" -caption="Navigate to Settings > Applications" -/%} - -- Install MetaPilot by following the on-screen instructions. - -{% image -src="/images/v1.6/metapilot/setting-up-metapilot-2.png" -alt="Install MetaPilot" -caption="Install MetaPilot" -/%} - -- Select the databases for which you want MetaPilot to automatically generate descriptions. You can configure multiple databases and set a default database for the MetaPilot chatbot to work with. - -{% image -src="/images/v1.6/metapilot/setting-up-metapilot-3.png" -alt="automatically generate descriptions" -caption="automatically generate descriptions" -/%} - -- **Scheduling**: Schedule MetaPilot to run regularly, automatically generating metadata at predefined intervals (e.g., weekly). - -{% image -src="/images/v1.6/metapilot/setting-up-metapilot-4.png" -alt="Schedule MetaPilot" -caption="Schedule MetaPilot" -/%} - -## 2. Using the MetaPilot Chatbot -- The MetaPilot chatbot icon appears on every page after installation. - -{% image -src="/images/v1.6/metapilot/using-metapilot-chatbot-1.png" -alt="chatbot icon" -caption="chatbot icon" -/%} - -- Interact with the chatbot by typing natural language questions. For example: - - “Show me sales data for Q1.” - - “What is the average revenue per customer?” - -- MetaPilot will generate the corresponding SQL query and provide a detailed explanation of the query logic. - -{% image -src="/images/v1.6/metapilot/using-metapilot-chatbot-2.png" -alt="natural language questions" -caption="natural language questions" -/%} - -- Users can refine queries by providing further instructions, and the chatbot will adjust the SQL query accordingly. - -## 3. Optimizing and Fixing SQL Queries - -- The **Metadata Usage** workflows will ingest the queries being run against the tables. You can see how long each query has been executed in the **Queries** tab. - -{% image -src="/images/v1.6/metapilot/fixing-sql-queries-1.png" -alt="metadata usage workflows" -caption="metadata usage workflows" -/%} - -- If a query runs inefficiently, ask the chatbot to optimize it by typing: “Optimize this query.” - -{% image -src="/images/v1.6/metapilot/fixing-sql-queries-3.png" -alt="Optimize this query" -caption="Optimize this query" -/%} - -- MetaPilot will return a more efficient version of the SQL query, which you can then copy and execute in your database. - -- If the query contains errors or isn’t functioning correctly, ask MetaPilot: “Can you fix this query?” - -- MetaPilot will correct the query and provide a working version. - -{% image -src="/images/v1.6/metapilot/fixing-sql-queries-4.png" -alt="Can you fix this query" -caption="Can you fix this query" -/%} - -## 4. Reviewing Generated Metadata -- Once MetaPilot generates descriptions for tables and columns, navigate to the **database view** to review the metadata. -- You can accept or reject each suggestion individually or choose to accept all suggestions in bulk. - -{% image -src="/images/v1.6/metapilot/reviewing-generated-metadata.png" -alt="reviewing Generated Metadata" -caption="reviewing Generated Metadata" -/%} - -- MetaPilot allows you to document entire datasets in a matter of minutes, significantly reducing the manual effort required to maintain metadata. - -## Best Practices -- **Regular Scheduling**: Schedule MetaPilot to run at regular intervals to ensure your metadata is always up-to-date, especially when dealing with frequently changing datasets. -- **Leverage the Chatbot for Query Writing**: Encourage both technical and non-technical users to use the MetaPilot chatbot for SQL query generation. It simplifies complex query writing and ensures accurate results. -- **Optimize Queries Regularly**: Monitor the performance of your queries and use MetaPilot to optimize them, especially when working with large datasets or queries that require complex joins and filters. diff --git a/openmetadata-docs/content/v1.6.x-SNAPSHOT/Metapilot/index.md b/openmetadata-docs/content/v1.6.x-SNAPSHOT/Metapilot/index.md deleted file mode 100644 index 310dc0dbfb09..000000000000 --- a/openmetadata-docs/content/v1.6.x-SNAPSHOT/Metapilot/index.md +++ /dev/null @@ -1,85 +0,0 @@ ---- -title: Metapilot -slug: /metapilot -collate: true ---- - -# Metapilot Technical Documentation - -{% youtube videoId="6glMYLzxNqk" start="0:00" end="04:20" width="560px" height="315px" /%} - -MetaPilot is an AI-powered tool within Collate that simplifies and enhances metadata management. By integrating generative AI, MetaPilot assists users in automating the documentation of data assets, writing and optimizing SQL queries, and interacting with data through natural language. This first-of-its-kind data copilot improves productivity by automating tedious tasks and providing intelligent insights into your data environment. - -## Key Features - -- **Automated Data Documentation**: Automatically generates metadata descriptions for tables and columns, saving time and effort for data owners and stewards. -- **Natural Language SQL Query Generation**: Allows users to interact with MetaPilot through a chatbot to generate SQL queries by simply asking questions in plain English. -- **SQL Query Optimization and Fixing**: Capable of optimizing and troubleshooting SQL queries to improve their performance and efficiency. - -## Why MetaPilot is Useful - -### Metadata Management Challenges - -Managing metadata across multiple data assets can be overwhelming due to the influx of new data and changing team dynamics. MetaPilot addresses these challenges by: - -- Automating metadata description generation. -- Simplifying the creation and optimization of SQL queries. -- Reducing manual effort and enhancing data quality. - -### Time-Saving Features - -Documenting thousands of tables manually is tedious and time-consuming. MetaPilot automates metadata generation, allowing data teams to focus on high-value tasks and ensuring that data assets are consistently documented and understood across the organization. - -## Use Cases - -### 1. Automatic Data Asset Documentation - -{% image -src="/images/v1.6/metapilot/reviewing-generated-metadata.png" -alt="Automatic Data Asset Documentation" -caption="Auto Generate data Asset Documentation" -/%} - -- **Problem**: Manually creating metadata descriptions for large datasets is labor-intensive and error-prone. -- **Solution**: MetaPilot’s generative AI automates the process, analyzing database structures and suggesting accurate descriptions for tables and columns. -- **How It Works**: After configuring MetaPilot, it scans the database schema and generates metadata descriptions automatically. Users can review these descriptions and approve or reject them in bulk or individually. -- **Benefit**: Streamlines the documentation process, ensuring consistent and up-to-date metadata across all datasets. - -### 2. Natural Language SQL Query Generation - -{% image -src="/images/v1.6/metapilot/using-metapilot-chatbot-2.png" -alt="Natural Language SQL Query Generation" -caption="Natural Language SQL Query Generation" -/%} - -- **Problem**: Non-technical users often struggle with writing SQL queries to extract insights from databases. -- **Solution**: MetaPilot’s chatbot allows users to ask questions in natural language, generating SQL queries and providing explanations to help extract the required data quickly and easily. -- **How It Works**: Users can click on the MetaPilot chatbot widget and ask questions like "Show me sales data from last quarter." MetaPilot generates the corresponding SQL query and explains its logic. -- **Benefit**: Democratizes data access by enabling users of all technical levels to interact with data without deep SQL knowledge. - -### 3. SQL Query Optimization and Troubleshooting - -{% image -src="/images/v1.6/metapilot/fixing-sql-queries-3.png" -alt="SQL Query Optimization and Troubleshooting" -caption="SQL Query Optimization" -/%} - -- **Problem**: SQL queries can become complex and inefficient, leading to performance issues and increased costs. -- **Solution**: MetaPilot optimizes inefficient queries to improve performance, saving both time and resources. -- **How It Works**: If a query runs too long, users can request MetaPilot to optimize it. MetaPilot provides a more efficient SQL query version that can be implemented immediately. -- **Benefit**: Enhances query performance, reduces costs associated with inefficient queries, and speeds up data processing. - -### 4. Fixing SQL Queries - -{% image -src="/images/v1.6/metapilot/fixing-sql-queries-4.png" -alt="Fixing SQL Queries" -caption="Fixing SQL Queries" -/%} - -- **Problem**: Complex queries can cause issues even for SQL experts. -- **Solution**: MetaPilot can fix problematic SQL queries, ensuring they run correctly and efficiently. -- **How It Works**: Users can ask MetaPilot, "Can you fix this query for me?" It analyzes the query, detects issues, and returns a corrected version ready for use. -- **Benefit**: Simplifies writing and maintaining queries, allowing data teams to focus on analysis rather than troubleshooting. diff --git a/openmetadata-docs/content/v1.6.x-SNAPSHOT/collate-ai/how-to-use-collate-ai.md b/openmetadata-docs/content/v1.6.x-SNAPSHOT/collate-ai/how-to-use-collate-ai.md new file mode 100644 index 000000000000..3140d3dd609f --- /dev/null +++ b/openmetadata-docs/content/v1.6.x-SNAPSHOT/collate-ai/how-to-use-collate-ai.md @@ -0,0 +1,110 @@ +--- +title: How to Use Collate AI +slug: /collateai/how-to-use-collate-ai +collate: true +--- + +# How to Use Collate AI + +## 1. Setting Up Collate AI +- Navigate to **Settings > Applications** in the Collate platform. + +{% image +src="/images/v1.6/collate-ai/setting-up-collate-ai-1.png" +alt="setting up Collate AI" +caption="Navigate to Settings > Applications" +/%} + +- Install Collate AI by following the on-screen instructions. + +{% image +src="/images/v1.6/collate-ai/setting-up-collate-ai-2.png" +alt="Install Collate AI" +caption="Install Collate AI" +/%} + +- Configure and add filters based on criteria such as owner, schema, database, service, and more. You can also combine multiple conditions to meet your specific requirements. + +{% image +src="/images/v1.6/collate-ai/setting-up-collate-ai-3.png" +alt="automatically generate descriptions" +caption="automatically generate descriptions" +/%} + +- **Scheduling**: Schedule Collate AI to run regularly, automatically generating metadata at predefined intervals (e.g., weekly). + +{% image +src="/images/v1.6/collate-ai/setting-up-collate-ai-4.png" +alt="Schedule Collate AI" +caption="Schedule Collate AI" +/%} + +## 2. Using the Collate AI Chatbot +- The Collate AI chatbot icon appears on every page after installation. + +{% image +src="/images/v1.6/collate-ai/using-collate-ai-chatbot-1.png" +alt="chatbot icon" +caption="chatbot icon" +/%} + +- Interact with the chatbot by typing natural language questions. For example: + - “Show me sales data for Q1.” + - “What is the average revenue per customer?” + +- Collate AI will generate the corresponding SQL query and provide a detailed explanation of the query logic. + +{% image +src="/images/v1.6/collate-ai/using-collate-ai-chatbot-2.png" +alt="natural language questions" +caption="natural language questions" +/%} + +- Users can refine queries by providing further instructions, and the chatbot will adjust the SQL query accordingly. + +## 3. Optimizing and Fixing SQL Queries + +- The **Metadata Usage** workflows will ingest the queries being run against the tables. You can see how long each query has been executed in the **Queries** tab. + +{% image +src="/images/v1.6/collate-ai/fixing-sql-queries-1.png" +alt="metadata usage workflows" +caption="metadata usage workflows" +/%} + +- If a query runs inefficiently, ask the chatbot to optimize it by typing: “Optimize this query.” + +{% image +src="/images/v1.6/collate-ai/fixing-sql-queries-3.png" +alt="Optimize this query" +caption="Optimize this query" +/%} + +- Collate AI will return a more efficient version of the SQL query, which you can then copy and execute in your database. + +- If the query contains errors or isn’t functioning correctly, ask Collate AI: “Can you fix this query?” + +- Collate AI will correct the query and provide a working version. + +{% image +src="/images/v1.6/collate-ai/fixing-sql-queries-4.png" +alt="Can you fix this query" +caption="Can you fix this query" +/%} + +## 4. Reviewing Generated Metadata +- Once Collate AI generates descriptions for tables and columns, navigate to the **database view** to review the metadata. +- You can accept or reject each suggestion individually or choose to accept all suggestions in bulk. + +{% image +src="/images/v1.6/collate-ai/reviewing-generated-metadata.png" +alt="reviewing Generated Metadata" +caption="reviewing Generated Metadata" +/%} + +- Collate AI allows you to document entire datasets in a matter of minutes, significantly reducing the manual effort required to maintain metadata. + +## Best Practices +- **Regular Scheduling**: Schedule Collate AI to run at regular intervals to ensure your metadata is always up-to-date, especially when dealing with frequently changing datasets. +- **Leverage the Chatbot for Query Writing**: Encourage both technical and non-technical users to use the Collate AI chatbot for SQL query generation. It simplifies complex query writing and ensures accurate results. +- **Optimize Queries Regularly**: Monitor the performance of your queries and use Collate AI to optimize them, especially when working with large datasets or queries that require complex joins and filters. diff --git a/openmetadata-docs/content/v1.6.x-SNAPSHOT/collate-ai/index.md b/openmetadata-docs/content/v1.6.x-SNAPSHOT/collate-ai/index.md new file mode 100644 index 000000000000..221f5d5828f3 --- /dev/null +++ b/openmetadata-docs/content/v1.6.x-SNAPSHOT/collate-ai/index.md @@ -0,0 +1,85 @@ +--- +title: Collate AI +slug: /collateai +collate: true +--- + +# Collate AI Technical Documentation + +{% youtube videoId="6glMYLzxNqk" start="0:00" end="04:20" width="560px" height="315px" /%} + +Collate AI is an AI-powered tool within Collate that simplifies and enhances metadata management. By integrating generative AI, Collate AI assists users in automating the documentation of data assets, writing and optimizing SQL queries, and interacting with data through natural language. This first-of-its-kind data copilot improves productivity by automating tedious tasks and providing intelligent insights into your data environment. + +## Key Features + +- **Automated Data Documentation**: Automatically generates metadata descriptions for tables and columns, saving time and effort for data owners and stewards. +- **Natural Language SQL Query Generation**: Allows users to interact with Collate AI through a chatbot to generate SQL queries by simply asking questions in plain English. +- **SQL Query Optimization and Fixing**: Capable of optimizing and troubleshooting SQL queries to improve their performance and efficiency. + +## Why Collate AI is Useful + +### Metadata Management Challenges + +Managing metadata across multiple data assets can be overwhelming due to the influx of new data and changing team dynamics. Collate AI addresses these challenges by: + +- Automating metadata description generation. +- Simplifying the creation and optimization of SQL queries. +- Reducing manual effort and enhancing data quality. + +### Time-Saving Features + +Documenting thousands of tables manually is tedious and time-consuming. Collate AI automates metadata generation, allowing data teams to focus on high-value tasks and ensuring that data assets are consistently documented and understood across the organization. + +## Use Cases + +### 1. Automatic Data Asset Documentation + +{% image +src="/images/v1.6/Collate AI/reviewing-generated-metadata.png" +alt="Automatic Data Asset Documentation" +caption="Auto Generate data Asset Documentation" +/%} + +- **Problem**: Manually creating metadata descriptions for large datasets is labor-intensive and error-prone. +- **Solution**: Collate AI’s generative AI automates the process, analyzing database structures and suggesting accurate descriptions for tables and columns. +- **How It Works**: After configuring Collate AI, it scans the database schema and generates metadata descriptions automatically. Users can review these descriptions and approve or reject them in bulk or individually. +- **Benefit**: Streamlines the documentation process, ensuring consistent and up-to-date metadata across all datasets. + +### 2. Natural Language SQL Query Generation + +{% image +src="/images/v1.6/collate-ai/using-collate-ai-chatbot-2.png" +alt="Natural Language SQL Query Generation" +caption="Natural Language SQL Query Generation" +/%} + +- **Problem**: Non-technical users often struggle with writing SQL queries to extract insights from databases. +- **Solution**: Collate AI’s chatbot allows users to ask questions in natural language, generating SQL queries and providing explanations to help extract the required data quickly and easily. +- **How It Works**: Users can click on the Collate AI chatbot widget and ask questions like "Show me sales data from last quarter." Collate AI generates the corresponding SQL query and explains its logic. +- **Benefit**: Democratizes data access by enabling users of all technical levels to interact with data without deep SQL knowledge. + +### 3. SQL Query Optimization and Troubleshooting + +{% image +src="/images/v1.6/collate-ai/fixing-sql-queries-3.png" +alt="SQL Query Optimization and Troubleshooting" +caption="SQL Query Optimization" +/%} + +- **Problem**: SQL queries can become complex and inefficient, leading to performance issues and increased costs. +- **Solution**: Collate AI optimizes inefficient queries to improve performance, saving both time and resources. +- **How It Works**: If a query runs too long, users can request Collate AI to optimize it. Collate AI provides a more efficient SQL query version that can be implemented immediately. +- **Benefit**: Enhances query performance, reduces costs associated with inefficient queries, and speeds up data processing. + +### 4. Fixing SQL Queries + +{% image +src="/images/v1.6/collate-ai/fixing-sql-queries-4.png" +alt="Fixing SQL Queries" +caption="Fixing SQL Queries" +/%} + +- **Problem**: Complex queries can cause issues even for SQL experts. +- **Solution**: Collate AI can fix problematic SQL queries, ensuring they run correctly and efficiently. +- **How It Works**: Users can ask Collate AI, "Can you fix this query for me?" It analyzes the query, detects issues, and returns a corrected version ready for use. +- **Benefit**: Simplifies writing and maintaining queries, allowing data teams to focus on analysis rather than troubleshooting. diff --git a/openmetadata-docs/content/v1.6.x-SNAPSHOT/collate-menu.md b/openmetadata-docs/content/v1.6.x-SNAPSHOT/collate-menu.md index 4ac4e6605cd0..ded28af367f0 100644 --- a/openmetadata-docs/content/v1.6.x-SNAPSHOT/collate-menu.md +++ b/openmetadata-docs/content/v1.6.x-SNAPSHOT/collate-menu.md @@ -1911,9 +1911,8 @@ site_menu: - category: SDK / Go url: /sdk/go - - category: Metapilot - url: /metapilot - - category: Metapilot / How to Use MetaPilot - url: /metapilot/how-to-use-metapilot + - category: Collate AI + url: /collateai + - category: Collate AI / How to Use Collate AI + url: /collateai/how-to-use-collate-ai --- diff --git a/openmetadata-docs/images/v1.5/metapilot/fixing-sql-queries-1.png b/openmetadata-docs/images/v1.5/collate-ai/fixing-sql-queries-1.png 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