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## What changes were proposed in this pull request? In optimizer, we try to evaluate the condition to see whether it's nullable or not, but some expressions are not evaluable, we should check that before evaluate it. ## How was this patch tested? Added regression tests. Author: Davies Liu <davies@databricks.com> Closes #15103 from davies/udf_join.
…sages This patch addresses a corner-case escaping bug where field names which contain special characters were unsafely interpolated into error message string literals in generated Java code, leading to compilation errors. This patch addresses these issues by using `addReferenceObj` to store the error messages as string fields rather than inline string constants. Author: Josh Rosen <joshrosen@databricks.com> Closes #15156 from JoshRosen/SPARK-17160.
## What changes were proposed in this pull request? Merge `MultinomialLogisticRegression` into `LogisticRegression` and remove `MultinomialLogisticRegression`. Marked as WIP because we should discuss the coefficients API in the model. See discussion below. JIRA: [SPARK-17163](https://issues.apache.org/jira/browse/SPARK-17163) ## How was this patch tested? Merged test suites and added some new unit tests. ## Design ### Switching between binomial and multinomial We default to automatically detecting whether we should run binomial or multinomial lor. We expose a new parameter called `family` which defaults to auto. When "auto" is used, we run normal binomial lor with pivoting if there are 1 or 2 label classes. Otherwise, we run multinomial. If the user explicitly sets the family, then we abide by that setting. In the case where "binomial" is set but multiclass lor is detected, we throw an error. ### coefficients/intercept model API (TODO) This is the biggest design point remaining, IMO. We need to decide how to store the coefficients and intercepts in the model, and in turn how to expose them via the API. Two important points: * We must maintain compatibility with the old API, i.e. we must expose `def coefficients: Vector` and `def intercept: Double` * There are two separate cases: binomial lr where we have a single set of coefficients and a single intercept and multinomial lr where we have `numClasses` sets of coefficients and `numClasses` intercepts. Some options: 1. **Store the binomial coefficients as a `2 x numFeatures` matrix.** This means that we would center the model coefficients before storing them in the model. The BLOR algorithm gives `1 * numFeatures` coefficients, but we would convert them to `2 x numFeatures` coefficients before storing them, effectively doubling the storage in the model. This has the advantage that we can make the code cleaner (i.e. less `if (isMultinomial) ... else ...`) and we don't have to reason about the different cases as much. It has the disadvantage that we double the storage space and we could see small regressions at prediction time since there are 2x the number of operations in the prediction algorithms. Additionally, we still have to produce the uncentered coefficients/intercept via the API, so we will have to either ALSO store the uncentered version, or compute it in `def coefficients: Vector` every time. 2. **Store the binomial coefficients as a `1 x numFeatures` matrix.** We still store the coefficients as a matrix and the intercepts as a vector. When users call `coefficients` we return them a `Vector` that is backed by the same underlying array as the `coefficientMatrix`, so we don't duplicate any data. At prediction time, we use the old prediction methods that are specialized for binary LOR. The benefits here are that we don't store extra data, and we won't see any regressions in performance. The cost of this is that we have separate implementations for predict methods in the binary vs multiclass case. The duplicated code is really not very high, but it's still a bit messy. If we do decide to store the 2x coefficients, we would likely want to see some performance tests to understand the potential regressions. **Update:** We have chosen option 2 ### Threshold/thresholds (TODO) Currently, when `threshold` is set we clear whatever value is in `thresholds` and when `thresholds` is set we clear whatever value is in `threshold`. [SPARK-11543](https://issues.apache.org/jira/browse/SPARK-11543) was created to prefer thresholds over threshold. We should decide if we should implement this behavior now or if we want to do it in a separate JIRA. **Update:** Let's leave it for a follow up PR ## Follow up * Summary model for multiclass logistic regression [SPARK-17139](https://issues.apache.org/jira/browse/SPARK-17139) * Thresholds vs threshold [SPARK-11543](https://issues.apache.org/jira/browse/SPARK-11543) Author: sethah <seth.hendrickson16@gmail.com> Closes #14834 from sethah/SPARK-17163.
## What changes were proposed in this pull request? This PR modifies StreamExecution such that it discards metadata for batches that have already been fully processed. I used the purge method that was added as part of SPARK-17235. This is based on work by frreiss in #15067, but fixed the test case along with some typos. ## How was this patch tested? A new test case in StreamingQuerySuite. The test case would fail without the changes in this pull request. Author: petermaxlee <petermaxlee@gmail.com> Author: frreiss <frreiss@us.ibm.com> Closes #15126 from petermaxlee/SPARK-17513.
…etadata" This reverts commit be9d57f.
## What changes were proposed in this pull request? The Scala version of `SparkContext` has a handy field called `uiWebUrl` that tells you which URL the SparkUI spawned by that instance lives at. This is often very useful because the value for `spark.ui.port` in the config is only a suggestion; if that port number is taken by another Spark instance on the same machine, Spark will just keep incrementing the port until it finds a free one. So, on a machine with a lot of running PySpark instances, you often have to start trying all of them one-by-one until you find your application name. Scala users have a way around this with `uiWebUrl` but Java and Python users do not. This pull request fixes this in the most straightforward way possible, simply propagating this field through the `JavaSparkContext` and into pyspark through the Java gateway. Please let me know if any additional documentation/testing is needed. ## How was this patch tested? Existing tests were run to make sure there were no regressions, and a binary distribution was created and tested manually for the correct value of `sc.uiWebPort` in a variety of circumstances. Author: Adrian Petrescu <apetresc@gmail.com> Closes #15000 from apetresc/pyspark-uiweburl.
…iews ### What changes were proposed in this pull request? - When the permanent tables/views do not exist but the temporary view exists, the expected error should be `NoSuchTableException` for partition-related ALTER TABLE commands. However, it always reports a confusing error message. For example, ``` Partition spec is invalid. The spec (a, b) must match the partition spec () defined in table '`testview`'; ``` - When the permanent tables/views do not exist but the temporary view exists, the expected error should be `NoSuchTableException` for `ALTER TABLE ... UNSET TBLPROPERTIES`. However, it reports a missing table property. For example, ``` Attempted to unset non-existent property 'p' in table '`testView`'; ``` - When `ANALYZE TABLE` is called on a view or a temporary view, we should issue an error message. However, it reports a strange error: ``` ANALYZE TABLE is not supported for Project ``` - When inserting into a temporary view that is generated from `Range`, we will get the following error message: ``` assertion failed: No plan for 'InsertIntoTable Range (0, 10, step=1, splits=Some(1)), false, false +- Project [1 AS 1#20] +- OneRowRelation$ ``` This PR is to fix the above four issues. ### How was this patch tested? Added multiple test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #15054 from gatorsmile/tempViewDDL.
## What changes were proposed in this pull request? Hive confs in hive-site.xml will be loaded in `hadoopConf`, so we should use `hadoopConf` in `InsertIntoHiveTable` instead of `SessionState.conf` ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #14634 from cloud-fan/bug.
…ataLog in FileStreamSource ## What changes were proposed in this pull request? Current `metadataLog` in `FileStreamSource` will add a checkpoint file in each batch but do not have the ability to remove/compact, which will lead to large number of small files when running for a long time. So here propose to compact the old logs into one file. This method is quite similar to `FileStreamSinkLog` but simpler. ## How was this patch tested? Unit test added. Author: jerryshao <sshao@hortonworks.com> Closes #13513 from jerryshao/SPARK-15698.
…for cached relation." This reverts commit 39e2bad because of the problem mentioned at https://issues.apache.org/jira/browse/SPARK-17549?focusedCommentId=15505060&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15505060 Author: Yin Huai <yhuai@databricks.com> Closes #15157 from yhuai/revert-SPARK-17549.
Currently, the code is just swallowing exceptions, and not really checking whether the auth information was being recorded properly. Fix both problems, and also avoid tests inadvertently affecting other tests by modifying the shared config variable (by making it not shared). Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #15161 from vanzin/SPARK-17611.
## What changes were proposed in this pull request? This PR modifies StreamExecution such that it discards metadata for batches that have already been fully processed. I used the purge method that was added as part of SPARK-17235. This is a resubmission of 15126, which was based on work by frreiss in #15067, but fixed the test case along with some typos. ## How was this patch tested? A new test case in StreamingQuerySuite. The test case would fail without the changes in this pull request. Author: petermaxlee <petermaxlee@gmail.com> Closes #15166 from petermaxlee/SPARK-17513-2.
## What changes were proposed in this pull request? This PR is to fix the code style errors before 2.0.1 release. ## How was this patch tested? Manual. Before: ``` ./dev/lint-java Using `mvn` from path: /usr/local/bin/mvn Checkstyle checks failed at following occurrences: [ERROR] src/main/java/org/apache/spark/network/client/TransportClient.java:[153] (sizes) LineLength: Line is longer than 100 characters (found 107). [ERROR] src/main/java/org/apache/spark/network/client/TransportClient.java:[196] (sizes) LineLength: Line is longer than 100 characters (found 108). [ERROR] src/main/java/org/apache/spark/network/client/TransportClient.java:[239] (sizes) LineLength: Line is longer than 100 characters (found 115). [ERROR] src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:[119] (sizes) LineLength: Line is longer than 100 characters (found 107). [ERROR] src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:[129] (sizes) LineLength: Line is longer than 100 characters (found 104). [ERROR] src/main/java/org/apache/spark/network/util/LevelDBProvider.java:[124,11] (modifier) ModifierOrder: 'static' modifier out of order with the JLS suggestions. [ERROR] src/main/java/org/apache/spark/network/util/TransportConf.java:[26] (regexp) RegexpSingleline: No trailing whitespace allowed. [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java:[33] (sizes) LineLength: Line is longer than 100 characters (found 110). [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java:[38] (sizes) LineLength: Line is longer than 100 characters (found 110). [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java:[43] (sizes) LineLength: Line is longer than 100 characters (found 106). [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java:[48] (sizes) LineLength: Line is longer than 100 characters (found 110). [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java:[0] (misc) NewlineAtEndOfFile: File does not end with a newline. [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java:[67] (sizes) LineLength: Line is longer than 100 characters (found 106). [ERROR] src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java:[200] (regexp) RegexpSingleline: No trailing whitespace allowed. [ERROR] src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java:[309] (regexp) RegexpSingleline: No trailing whitespace allowed. [ERROR] src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java:[332] (regexp) RegexpSingleline: No trailing whitespace allowed. [ERROR] src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java:[348] (regexp) RegexpSingleline: No trailing whitespace allowed. ``` After: ``` ./dev/lint-java Using `mvn` from path: /usr/local/bin/mvn Checkstyle checks passed. ``` Author: Weiqing Yang <yangweiqing001@gmail.com> Closes #15170 from Sherry302/fixjavastyle.
…k log get configuration issue ## What changes were proposed in this pull request? This issue was introduced in the previous commit of SPARK-15698. Mistakenly change the way to get configuration back to original one, so here with the follow up PR to revert them up. ## How was this patch tested? N/A Ping zsxwing , please review again, sorry to bring the inconvenience. Thanks a lot. Author: jerryshao <sshao@hortonworks.com> Closes #15173 from jerryshao/SPARK-15698-follow.
## What changes were proposed in this pull request? While reading source code of CORE and SQL core, I found some minor errors in comments such as extra space, missing blank line and grammar error. I fixed these minor errors and might find more during my source code study. ## How was this patch tested? Manually build Author: wm624@hotmail.com <wm624@hotmail.com> Closes #15151 from wangmiao1981/mem.
…ding files recursively ## What changes were proposed in this pull request? Users would like to add a directory as dependency in some cases, they can use ```SparkContext.addFile``` with argument ```recursive=true``` to recursively add all files under the directory by using Scala. But Python users can only add file not directory, we should also make it supported. ## How was this patch tested? Unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15140 from yanboliang/spark-17585.
… Word2VecModel
## What changes were proposed in this pull request?
The code in `Word2VecModel.findSynonyms` to choose the vocabulary elements with the highest similarity to the query vector currently sorts the collection of similarities for every vocabulary element. This involves making multiple copies of the collection of similarities while doing a (relatively) expensive sort. It would be more efficient to find the best matches by maintaining a bounded priority queue and populating it with a single pass over the vocabulary, and that is exactly what this patch does.
## How was this patch tested?
This patch adds no user-visible functionality and its correctness should be exercised by existing tests. To ensure that this approach is actually faster, I made a microbenchmark for `findSynonyms`:
```
object W2VTiming {
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.mllib.feature.Word2VecModel
def run(modelPath: String, scOpt: Option[SparkContext] = None) {
val sc = scOpt.getOrElse(new SparkContext(new SparkConf(true).setMaster("local[*]").setAppName("test")))
val model = Word2VecModel.load(sc, modelPath)
val keys = model.getVectors.keys
val start = System.currentTimeMillis
for(key <- keys) {
model.findSynonyms(key, 5)
model.findSynonyms(key, 10)
model.findSynonyms(key, 25)
model.findSynonyms(key, 50)
}
val finish = System.currentTimeMillis
println("run completed in " + (finish - start) + "ms")
}
}
```
I ran this test on a model generated from the complete works of Jane Austen and found that the new approach was over 3x faster than the old approach. (If the `num` argument to `findSynonyms` is very close to the vocabulary size, the new approach will have less of an advantage over the old one.)
Author: William Benton <willb@redhat.com>
Closes #15150 from willb/SPARK-17595.
…ult on double value
## What changes were proposed in this pull request?
Remainder(%) expression's `eval()` returns incorrect result when the dividend is a big double. The reason is that Remainder converts the double dividend to decimal to do "%", and that lose precision.
This bug only affects the `eval()` that is used by constant folding, the codegen path is not impacted.
### Before change
```
scala> -5083676433652386516D % 10
res2: Double = -6.0
scala> spark.sql("select -5083676433652386516D % 10 as a").show
+---+
| a|
+---+
|0.0|
+---+
```
### After change
```
scala> spark.sql("select -5083676433652386516D % 10 as a").show
+----+
| a|
+----+
|-6.0|
+----+
```
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes #15171 from clockfly/SPARK-17617.
… exist ## What changes were proposed in this pull request? The `ListingFileCatalog` lists files given a set of resolved paths. If a folder is deleted at any time between the paths were resolved and the file catalog can check for the folder, the Spark job fails. This may abruptly stop long running StructuredStreaming jobs for example. Folders may be deleted by users or automatically by retention policies. These cases should not prevent jobs from successfully completing. ## How was this patch tested? Unit test in `FileCatalogSuite` Author: Burak Yavuz <brkyvz@gmail.com> Closes #15153 from brkyvz/SPARK-17599.
…tive Rate (FPR) test ## What changes were proposed in this pull request? Univariate feature selection works by selecting the best features based on univariate statistical tests. False Positive Rate (FPR) is a popular univariate statistical test for feature selection. We add a chiSquare Selector based on False Positive Rate (FPR) test in this PR, like it is implemented in scikit-learn. http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection ## How was this patch tested? Add Scala ut Author: Peng, Meng <peng.meng@intel.com> Closes #14597 from mpjlu/fprChiSquare.
## What changes were proposed in this pull request? This PR fixes an issue when Bucketizer is called to handle a dataset containing NaN value. Sometimes, null value might also be useful to users, so in these cases, Bucketizer should reserve one extra bucket for NaN values, instead of throwing an illegal exception. Before: ``` Bucketizer.transform on NaN value threw an illegal exception. ``` After: ``` NaN values will be grouped in an extra bucket. ``` ## How was this patch tested? New test cases added in `BucketizerSuite`. Signed-off-by: VinceShieh <vincent.xieintel.com> Author: VinceShieh <vincent.xie@intel.com> Closes #14858 from VinceShieh/spark-17219.
…expanding buffer as default for maxCharsPerColumn option in CSV ## What changes were proposed in this pull request? This PR includes the changes below: 1. Upgrade Univocity library from 2.1.1 to 2.2.1 This includes some performance improvement and also enabling auto-extending buffer in `maxCharsPerColumn` option in CSV. Please refer the [release notes](https://github.com/uniVocity/univocity-parsers/releases). 2. Remove useless `rowSeparator` variable existing in `CSVOptions` We have this unused variable in [CSVOptions.scala#L127](https://github.com/apache/spark/blob/29952ed096fd2a0a19079933ff691671d6f00835/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala#L127) but it seems possibly causing confusion that it actually does not care of `\r\n`. For example, we have an issue open about this, [SPARK-17227](https://issues.apache.org/jira/browse/SPARK-17227), describing this variable. This variable is virtually not being used because we rely on `LineRecordReader` in Hadoop which deals with only both `\n` and `\r\n`. 3. Set the default value of `maxCharsPerColumn` to auto-expending. We are setting 1000000 for the length of each column. It'd be more sensible we allow auto-expending rather than fixed length by default. To make sure, using `-1` is being described in the release note, [2.2.0](https://github.com/uniVocity/univocity-parsers/releases/tag/v2.2.0). ## How was this patch tested? N/A Author: hyukjinkwon <gurwls223@gmail.com> Closes #15138 from HyukjinKwon/SPARK-17583.
## What changes were proposed in this pull request? - TaskState and ExecutorState expose isFailed and isFinished functions. It can be useful to add test coverage for different states. Currently, Other enums do not expose any functions so this PR aims just these two enums. - `private` access modifier is added for Finished Task States Set - A minor doc change is added. ## How was this patch tested? New Unit tests are added and run locally. Author: erenavsarogullari <erenavsarogullari@gmail.com> Closes #15143 from erenavsarogullari/SPARK-17584.
…uery to define CTE ## What changes were proposed in this pull request? We substitute logical plan with CTE definitions in the analyzer rule CTESubstitution. A CTE definition can be used in the logical plan for multiple times, and its analyzed logical plan should be the same. We should not analyze CTE definitions multiple times when they are reused in the query. By analyzing CTE definitions before substitution, we can support defining CTE in subquery. ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #15146 from viirya/cte-analysis-once.
…shed This patch updates the `kinesis-asl-assembly` build to prevent that module from being published as part of Maven releases and snapshot builds. The `kinesis-asl-assembly` includes classes from the Kinesis Client Library (KCL) and Kinesis Producer Library (KPL), both of which are licensed under the Amazon Software License and are therefore prohibited from being distributed in Apache releases. Author: Josh Rosen <joshrosen@databricks.com> Closes #15167 from JoshRosen/stop-publishing-kinesis-assembly.
## What changes were proposed in this pull request? Update error handling for Cholesky decomposition to provide a little more info when input is singular. ## How was this patch tested? New test case; jenkins tests. Author: Sean Owen <sowen@cloudera.com> Closes #15177 from srowen/SPARK-11918.
…ess.
The goal of this feature is to allow the Spark driver to run in an
isolated environment, such as a docker container, and be able to use
the host's port forwarding mechanism to be able to accept connections
from the outside world.
The change is restricted to the driver: there is no support for achieving
the same thing on executors (or the YARN AM for that matter). Those still
need full access to the outside world so that, for example, connections
can be made to an executor's block manager.
The core of the change is simple: add a new configuration that tells what's
the address the driver should bind to, which can be different than the address
it advertises to executors (spark.driver.host). Everything else is plumbing
the new configuration where it's needed.
To use the feature, the host starting the container needs to set up the
driver's port range to fall into a range that is being forwarded; this
required the block manager port to need a special configuration just for
the driver, which falls back to the existing spark.blockManager.port when
not set. This way, users can modify the driver settings without affecting
the executors; it would theoretically be nice to also have different
retry counts for driver and executors, but given that docker (at least)
allows forwarding port ranges, we can probably live without that for now.
Because of the nature of the feature it's kinda hard to add unit tests;
I just added a simple one to make sure the configuration works.
This was tested with a docker image running spark-shell with the following
command:
docker blah blah blah \
-p 38000-38100:38000-38100 \
[image] \
spark-shell \
--num-executors 3 \
--conf spark.shuffle.service.enabled=false \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.driver.host=[host's address] \
--conf spark.driver.port=38000 \
--conf spark.driver.blockManager.port=38020 \
--conf spark.ui.port=38040
Running on YARN; verified the driver works, executors start up and listen
on ephemeral ports (instead of using the driver's config), and that caching
and shuffling (without the shuffle service) works. Clicked through the UI
to make sure all pages (including executor thread dumps) worked. Also tested
apps without docker, and ran unit tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes #15120 from vanzin/SPARK-4563.
## What changes were proposed in this pull request? In TaskResultGetter, enqueueFailedTask currently deserializes the result as a TaskEndReason. But the type is actually more specific, its a TaskFailedReason. This just leads to more blind casting later on – it would be more clear if the msg was cast to the right type immediately, so method parameter types could be tightened. ## How was this patch tested? Existing unit tests via jenkins. Note that the code was already performing a blind-cast to a TaskFailedReason before in any case, just in a different spot, so there shouldn't be any behavior change. Author: Imran Rashid <irashid@cloudera.com> Closes #15181 from squito/SPARK-17623.
…s cluster mode ## What changes were proposed in this pull request? Yarn and mesos cluster mode support remote python path (HDFS/S3 scheme) by their own mechanism, it is not necessary to check and format the python when running on these modes. This is a potential regression compared to 1.6, so here propose to fix it. ## How was this patch tested? Unit test to verify SparkSubmit arguments, also with local cluster verification. Because of lack of `MiniDFSCluster` support in Spark unit test, there's no integration test added. Author: jerryshao <sshao@hortonworks.com> Closes #15137 from jerryshao/SPARK-17512.
…ion faster ## What changes were proposed in this pull request? While getting the batch for a `FileStreamSource` in StructuredStreaming, we know which files we must take specifically. We already have verified that they exist, and have committed them to a metadata log. When creating the FileSourceRelation however for an incremental execution, the code checks the existence of every single file once again! When you have 100,000s of files in a folder, creating the first batch takes 2 hours+ when working with S3! This PR disables that check ## How was this patch tested? Added a unit test to `FileStreamSource`. Author: Burak Yavuz <brkyvz@gmail.com> Closes #15122 from brkyvz/SPARK-17569.
## What changes were proposed in this pull request? There are 4 listLeafFiles-related functions in Spark: - ListingFileCatalog.listLeafFiles (which calls HadoopFsRelation.listLeafFilesInParallel if the number of paths passed in is greater than a threshold; if it is lower, then it has its own serial version implemented) - HadoopFsRelation.listLeafFiles (called only by HadoopFsRelation.listLeafFilesInParallel) - HadoopFsRelation.listLeafFilesInParallel (called only by ListingFileCatalog.listLeafFiles) It is actually very confusing and error prone because there are effectively two distinct implementations for the serial version of listing leaf files. As an example, SPARK-17599 updated only one of the code path and ignored the other one. This code can be improved by: - Move all file listing code into ListingFileCatalog, since it is the only class that needs this. - Keep only one function for listing files in serial. ## How was this patch tested? This change should be covered by existing unit and integration tests. I also moved a test case for HadoopFsRelation.shouldFilterOut from HadoopFsRelationSuite to ListingFileCatalogSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #15235 from petermaxlee/SPARK-17661.
…rialization ## What changes were proposed in this pull request? Value classes were unsupported because catalyst data types were obtained through reflection on erased types, which would resolve to a value class' wrapped type and hence lead to unavailable methods during code generation. E.g. the following class ```scala case class Foo(x: Int) extends AnyVal ``` would be seen as an `int` in catalyst and will cause instance cast failures when generated java code tries to treat it as a `Foo`. This patch simply removes the erasure step when getting data types for catalyst. ## How was this patch tested? Additional tests in `ExpressionEncoderSuite`. Author: Jakob Odersky <jakob@odersky.com> Closes #15284 from jodersky/value-classes.
…ceIndexLabel. ## What changes were proposed in this pull request? Follow-up work of #13675, add Python API for ```RFormula forceIndexLabel```. ## How was this patch tested? Unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15430 from yanboliang/spark-15957-python.
## What changes were proposed in this pull request? speculationEnabled and DATASOURCE_OUTPUTPATH seem like just dead code. ## How was this patch tested? Tests should fail if they are not dead code. Author: Reynold Xin <rxin@databricks.com> Closes #15477 from rxin/SPARK-17927.
## What changes were proposed in this pull request? This patch does a few changes to the file structure of data sources: - Break fileSourceInterfaces.scala into multiple pieces (HadoopFsRelation, FileFormat, OutputWriter) - Move ParquetOutputWriter into its own file I created this as a separate patch so it'd be easier to review my future PRs that focus on refactoring this internal logic. This patch only moves code around, and has no logic changes. ## How was this patch tested? N/A - should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #15473 from rxin/SPARK-17925.
…instead of hive client ## What changes were proposed in this pull request? `HiveExternalCatalog` should be the only interface to talk to the hive metastore. In `MetastoreRelation` we can just use `ExternalCatalog` instead of `HiveClient` to interact with hive metastore, and add missing API in `ExternalCatalog`. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #15460 from cloud-fan/relation.
…load ## What changes were proposed in this pull request? Since ```ml.evaluation``` has supported save/load at Scala side, supporting it at Python side is very straightforward and easy. ## How was this patch tested? Add python doctest. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13194 from yanboliang/spark-15402.
## What changes were proposed in this pull request? Add BisectingKMeansSummary ## How was this patch tested? unit test Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #12394 from zhengruifeng/biKMSummary.
…nd SelectPercentile because of DoF difference ## What changes were proposed in this pull request? For feature selection method ChiSquareSelector, it is based on the ChiSquareTestResult.statistic (ChiSqure value) to select the features. It select the features with the largest ChiSqure value. But the Degree of Freedom (df) of ChiSqure value is different in Statistics.chiSqTest(RDD), and for different df, you cannot base on ChiSqure value to select features. So we change statistic to pValue for SelectKBest and SelectPercentile ## How was this patch tested? change existing test Author: Peng <peng.meng@intel.com> Closes #15444 from mpjlu/chisqure-bug.
## What changes were proposed in this pull request? Spark-submit support jar url with http protocol. However, if the url contains any query strings, `worker.DriverRunner.downloadUserJar()` method will throw "Did not see expected jar" exception. This is because this method checks the existance of a downloaded jar whose name contains query strings. This is a problem when your jar is located on some web service which requires some additional information to retrieve the file. This pr just removes query strings before checking jar existance on worker. ## How was this patch tested? For now, you can only test this patch by manual test. * Deploy a spark cluster locally * Make sure apache httpd service is on * Save an uber jar, e.g spark-job.jar under `/var/www/html/` * Use http://localhost/spark-job.jar?param=1 as jar url when running `spark-submit` * Job should be launched Author: invkrh <invkrh@gmail.com> Closes #15420 from invkrh/spark-17855.
## What changes were proposed in this pull request? This pr adds some test cases for statistics: case sensitive column names, non ascii column names, refresh table, and also improves some documentation. ## How was this patch tested? add test cases Author: wangzhenhua <wangzhenhua@huawei.com> Closes #15360 from wzhfy/colStats2.
Change is too trivial to file a JIRA. Author: Dhruve Ashar <dhruveashar@gmail.com> Closes #15485 from dhruve/master.
## What changes were proposed in this pull request? Minor typo fix ## How was this patch tested? Existing unit tests on Jenkins Author: Andrew Ash <andrew@andrewash.com> Closes #15486 from ash211/patch-8.
## What changes were proposed in this pull request? Ignoring the flaky test introduced in #15307 https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/1736/testReport/junit/org.apache.spark.sql.streaming/StreamingQueryListenerSuite/single_listener__check_trigger_statuses/ Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #15491 from tdas/metrics-flaky-test.
…eights. ## What changes were proposed in this pull request? The sample weight testing for logistic regressions is not robust. Logistic regression suite already has many test cases comparing results to R glmnet. Since both libraries support sample weights, we should use sample weights in the test to increase coverage for sample weighting. This patch doesn't really add any code and makes the testing more complete. Also fixed some errors with the R code that was referenced in the test suit. Changed `standardization=T` to `standardize=T` since the former is invalid. ## How was this patch tested? Existing unit tests are modified. No non-test code is touched. Author: sethah <seth.hendrickson16@gmail.com> Closes #15488 from sethah/logreg_weight_tests.
…eating Hive Serde Tables
## What changes were proposed in this pull request?
Make sure the hive.default.fileformat is used to when creating the storage format metadata.
Output
``` SQL
scala> spark.sql("SET hive.default.fileformat=orc")
res1: org.apache.spark.sql.DataFrame = [key: string, value: string]
scala> spark.sql("CREATE TABLE tmp_default(id INT)")
res2: org.apache.spark.sql.DataFrame = []
```
Before
```SQL
scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println)
..
[# Storage Information,,]
[SerDe Library:,org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe,]
[InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,]
[OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,]
[Compressed:,No,]
[Storage Desc Parameters:,,]
[ serialization.format,1,]
```
After
```SQL
scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println)
..
[# Storage Information,,]
[SerDe Library:,org.apache.hadoop.hive.ql.io.orc.OrcSerde,]
[InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,]
[OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,]
[Compressed:,No,]
[Storage Desc Parameters:,,]
[ serialization.format,1,]
```
## How was this patch tested?
Added new tests to HiveDDLCommandSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes #15190 from dilipbiswal/orc.
… when Creating Hive Serde Tables" This reverts commit 7ab8624.
## What changes were proposed in this pull request? We are trying to resolve the attribute in sort by pulling up some column for grandchild into child, but that's wrong when the child is Distinct, because the added column will change the behavior of Distinct, we should not do that. ## How was this patch tested? Added regression test. Author: Davies Liu <davies@databricks.com> Closes #15489 from davies/order_distinct.
[SPARK-11905](https://issues.apache.org/jira/browse/SPARK-11905) added support for `persist`/`cache` for `Dataset`. However, there is no user-facing API to check if a `Dataset` is cached and if so what the storage level is. This PR adds `getStorageLevel` to `Dataset`, analogous to `RDD.getStorageLevel`. Updated `DatasetCacheSuite`. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13780 from MLnick/ds-storagelevel. Signed-off-by: Michael Armbrust <michael@databricks.com>
Currently pyspark can only call the builtin java UDF, but can not call custom java UDF. It would be better to allow that. 2 benefits: * Leverage the power of rich third party java library * Improve the performance. Because if we use python UDF, python daemons will be started on worker which will affect the performance. Author: Jeff Zhang <zjffdu@apache.org> Closes #9766 from zjffdu/SPARK-11775.
## What changes were proposed in this pull request? This patch graduates a list of Spark SQL APIs and mark them stable. The following are marked stable: Dataset/DataFrame - functions, since 1.3 - ColumnName, since 1.3 - DataFrameNaFunctions, since 1.3.1 - DataFrameStatFunctions, since 1.4 - UserDefinedFunction, since 1.3 - UserDefinedAggregateFunction, since 1.5 - Window and WindowSpec, since 1.4 Data sources: - DataSourceRegister, since 1.5 - RelationProvider, since 1.3 - SchemaRelationProvider, since 1.3 - CreatableRelationProvider, since 1.3 - BaseRelation, since 1.3 - TableScan, since 1.3 - PrunedScan, since 1.3 - PrunedFilteredScan, since 1.3 - InsertableRelation, since 1.3 The following are kept experimental / evolving: Data sources: - CatalystScan (tied to internal logical plans so it is not stable by definition) Structured streaming: - all classes (introduced new in 2.0 and will likely change) Dataset typed operations (introduced in 1.6 and 2.0 and might change, although probability is low) - all typed methods on Dataset - KeyValueGroupedDataset - o.a.s.sql.expressions.javalang.typed - o.a.s.sql.expressions.scalalang.typed - methods that return typed Dataset in SparkSession We should discuss more whether we want to mark Dataset typed operations stable in 2.1. ## How was this patch tested? N/A - just annotation changes. Author: Reynold Xin <rxin@databricks.com> Closes #15469 from rxin/SPARK-17900.
## What changes were proposed in this pull request? Add a crossJoin function to the DataFrame API similar to that in Scala. Joins with no condition (cartesian products) must be specified with the crossJoin API ## How was this patch tested? Added python tests to ensure that an AnalysisException if a cartesian product is specified without crossJoin(), and that cartesian products can execute if specified via crossJoin() (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request. Author: Srinath Shankar <srinath@databricks.com> Closes #15493 from srinathshankar/crosspython.
…d to answer a query (This PR addresses https://issues.apache.org/jira/browse/SPARK-16980.) ## What changes were proposed in this pull request? In a new Spark session, when a partitioned Hive table is converted to use Spark's `HadoopFsRelation` in `HiveMetastoreCatalog`, metadata for every partition of that table are retrieved from the metastore and loaded into driver memory. In addition, every partition's metadata files are read from the filesystem to perform schema inference. If a user queries such a table with predicates which prune that table's partitions, we would like to be able to answer that query without consulting partition metadata which are not involved in the query. When querying a table with a large number of partitions for some data from a small number of partitions (maybe even a single partition), the current conversion strategy is highly inefficient. I suspect this scenario is not uncommon in the wild. In addition to being inefficient in running time, the current strategy is inefficient in its use of driver memory. When the sum of the number of partitions of all tables loaded in a driver reaches a certain level (somewhere in the tens of thousands), their cached data exhaust all driver heap memory in the default configuration. I suspect this scenario is less common (in that not too many deployments work with tables with tens of thousands of partitions), however this does illustrate how large the memory footprint of this metadata can be. With tables with hundreds or thousands of partitions, I would expect the `HiveMetastoreCatalog` table cache to represent a significant portion of the driver's heap space. This PR proposes an alternative approach. Basically, it makes four changes: 1. It adds a new method, `listPartitionsByFilter` to the Catalyst `ExternalCatalog` trait which returns the partition metadata for a given sequence of partition pruning predicates. 1. It refactors the `FileCatalog` type hierarchy to include a new `TableFileCatalog` to efficiently return files only for partitions matching a sequence of partition pruning predicates. 1. It removes partition loading and caching from `HiveMetastoreCatalog`. 1. It adds a new Catalyst optimizer rule, `PruneFileSourcePartitions`, which applies a plan's partition-pruning predicates to prune out unnecessary partition files from a `HadoopFsRelation`'s underlying file catalog. The net effect is that when a query over a partitioned Hive table is planned, the analyzer retrieves the table metadata from `HiveMetastoreCatalog`. As part of this operation, the `HiveMetastoreCatalog` builds a `HadoopFsRelation` with a `TableFileCatalog`. It does not load any partition metadata or scan any files. The optimizer prunes-away unnecessary table partitions by sending the partition-pruning predicates to the relation's `TableFileCatalog `. The `TableFileCatalog` in turn calls the `listPartitionsByFilter` method on its external catalog. This queries the Hive metastore, passing along those filters. As a bonus, performing partition pruning during optimization leads to a more accurate relation size estimate. This, along with c481bdf, can lead to automatic, safe application of the broadcast optimization in a join where it might previously have been omitted. ## Open Issues 1. This PR omits partition metadata caching. I can add this once the overall strategy for the cold path is established, perhaps in a future PR. 1. This PR removes and omits partitioned Hive table schema reconciliation. As a result, it fails to find Parquet schema columns with upper case letters because of the Hive metastore's case-insensitivity. This issue may be fixed by #14750, but that PR appears to have stalled. ericl has contributed to this PR a workaround for Parquet wherein schema reconciliation occurs at query execution time instead of planning. Whether ORC requires a similar patch is an open issue. 1. This PR omits an implementation of `listPartitionsByFilter` for the `InMemoryCatalog`. 1. This PR breaks parquet log output redirection during query execution. I can work around this by running `Class.forName("org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$")` first thing in a Spark shell session, but I haven't figured out how to fix this properly. ## How was this patch tested? The current Spark unit tests were run, and some ad-hoc tests were performed to validate that only the necessary partition metadata is loaded. Author: Michael Allman <michael@videoamp.com> Author: Eric Liang <ekl@databricks.com> Author: Eric Liang <ekhliang@gmail.com> Closes #14690 from mallman/spark-16980-lazy_partition_fetching.
## What changes were proposed in this pull request?
### Before:
```scala
SparkSession.builder()
.master("local")
.appName("Word Count")
.config("spark.some.config.option", "some-value").
.getOrCreate()
```
### After:
```scala
SparkSession.builder()
.master("local")
.appName("Word Count")
.config("spark.some.config.option", "some-value")
.getOrCreate()
```
There was one unexpected dot!
Author: Jun Kim <i2r.jun@gmail.com>
Closes #15498 from tae-jun/SPARK-17953.
…cutors ## What changes were proposed in this pull request? Restructure the code and implement two new task assigner. PackedAssigner: try to allocate tasks to the executors with least available cores, so that spark can release reserved executors when dynamic allocation is enabled. BalancedAssigner: try to allocate tasks to the executors with more available cores in order to balance the workload across all executors. By default, the original round robin assigner is used. We test a pipeline, and new PackedAssigner save around 45% regarding the reserved cpu and memory with dynamic allocation enabled. ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Both unit test in TaskSchedulerImplSuite and manual tests in production pipeline. Author: Zhan Zhang <zhanzhang@fb.com> Closes #15218 from zhzhan/packed-scheduler.
…ross executors" This reverts commit ed14633. The patch merged had obvious quality and documentation issue. The idea is useful, and we should work towards improving its quality and merging it in again.
…ark Thrift Server ## What changes were proposed in this pull request? Currently, Spark Thrift Server ignores the default database in URI. This PR supports that like the following. ```sql $ bin/beeline -u jdbc:hive2://localhost:10000 -e "create database testdb" $ bin/beeline -u jdbc:hive2://localhost:10000/testdb -e "create table t(a int)" $ bin/beeline -u jdbc:hive2://localhost:10000/testdb -e "show tables" ... +------------+--------------+--+ | tableName | isTemporary | +------------+--------------+--+ | t | false | +------------+--------------+--+ 1 row selected (0.347 seconds) $ bin/beeline -u jdbc:hive2://localhost:10000 -e "show tables" ... +------------+--------------+--+ | tableName | isTemporary | +------------+--------------+--+ +------------+--------------+--+ No rows selected (0.098 seconds) ``` ## How was this patch tested? Manual. Note: I tried to add a test case for this, but I cannot found a suitable testsuite for this. I'll add the testcase if some advice is given. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15399 from dongjoon-hyun/SPARK-17819.
### What changes were proposed in this pull request? Just document the impact of `spark.sql.debug`: When enabling the debug, Spark SQL internal table properties are not filtered out; however, some related DDL commands (e.g., Analyze Table and CREATE TABLE LIKE) might not work properly. ### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #15494 from gatorsmile/addDocForSQLDebug.
## What changes were proposed in this pull request?
Added a `prettyname` for current_database function.
## How was this patch tested?
Manually.
Before:
```
scala> sql("select current_database()").show
+-----------------+
|currentdatabase()|
+-----------------+
| default|
+-----------------+
```
After:
```
scala> sql("select current_database()").show
+------------------+
|current_database()|
+------------------+
| default|
+------------------+
```
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes #15506 from weiqingy/prettyName.
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