### What changes were proposed in this pull request?
```ChiSqSelector ``` depends on ```mllib.ChiSqSelectorModel``` to do the selection logic. Will remove the dependency in this PR.
### Why are the changes needed?
This PR is an intermediate PR. Removing ```ChiSqSelector``` dependency on ```mllib.ChiSqSelectorModel```. Next subtask will extract the common code between ```ChiSqSelector``` and ```FValueSelector``` and put in an abstract ```Selector```.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
New and existing tests
Closes#27841 from huaxingao/chisq.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR propose
1. Explicitly include xml-apis. xml-apis is already the part of xerces 2.12.0 (https://repo1.maven.org/maven2/xerces/xercesImpl/2.12.0/xercesImpl-2.12.0.pom). However, we're excluding it by setting `scope` to `test`. This seems causing `spark-shell`, built from Maven, to fail.
Seems like previously xml-apis wasn't reached for some reasons but after we upgrade, it seems requiring. Therefore, this PR proposes to include it.
2. Pins `xerces` version in SBT as well. Seems this dependency is resolved differently from Maven.
Note that Hadoop 3 does not looks requiring this as they replaced xerces as of [HDFS-12221](https://issues.apache.org/jira/browse/HDFS-12221).
### Why are the changes needed?
To make `spark-shell` working from Maven build, and uses the same xerces version.
### Does this PR introduce any user-facing change?
No, it's master only.
### How was this patch tested?
**1.**
```bash
./build/mvn -DskipTests -Psparkr -Phive clean package
./bin/spark-shell
```
Before:
```
Exception in thread "main" java.lang.NoClassDefFoundError: org/w3c/dom/ElementTraversal
at java.lang.ClassLoader.defineClass1(Native Method)
at java.lang.ClassLoader.defineClass(ClassLoader.java:763)
at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
at java.net.URLClassLoader.defineClass(URLClassLoader.java:468)
at java.net.URLClassLoader.access$100(URLClassLoader.java:74)
at java.net.URLClassLoader$1.run(URLClassLoader.java:369)
at java.net.URLClassLoader$1.run(URLClassLoader.java:363)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:362)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:349)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at org.apache.xerces.parsers.AbstractDOMParser.startDocument(Unknown Source)
at org.apache.xerces.xinclude.XIncludeHandler.startDocument(Unknown Source)
at org.apache.xerces.impl.dtd.XMLDTDValidator.startDocument(Unknown Source)
at org.apache.xerces.impl.XMLDocumentScannerImpl.startEntity(Unknown Source)
at org.apache.xerces.impl.XMLVersionDetector.startDocumentParsing(Unknown Source)
at org.apache.xerces.parsers.XML11Configuration.parse(Unknown Source)
at org.apache.xerces.parsers.XML11Configuration.parse(Unknown Source)
at org.apache.xerces.parsers.XMLParser.parse(Unknown Source)
at org.apache.xerces.parsers.DOMParser.parse(Unknown Source)
at org.apache.xerces.jaxp.DocumentBuilderImpl.parse(Unknown Source)
at javax.xml.parsers.DocumentBuilder.parse(DocumentBuilder.java:150)
at org.apache.hadoop.conf.Configuration.parse(Configuration.java:2482)
at org.apache.hadoop.conf.Configuration.parse(Configuration.java:2470)
at org.apache.hadoop.conf.Configuration.loadResource(Configuration.java:2541)
at org.apache.hadoop.conf.Configuration.loadResources(Configuration.java:2494)
at org.apache.hadoop.conf.Configuration.getProps(Configuration.java:2407)
at org.apache.hadoop.conf.Configuration.set(Configuration.java:1143)
at org.apache.hadoop.conf.Configuration.set(Configuration.java:1115)
at org.apache.spark.deploy.SparkHadoopUtil$.org$apache$spark$deploy$SparkHadoopUtil$$appendS3AndSparkHadoopHiveConfigurations(SparkHadoopUtil.scala:456)
at org.apache.spark.deploy.SparkHadoopUtil$.newConfiguration(SparkHadoopUtil.scala:427)
at org.apache.spark.deploy.SparkSubmit.$anonfun$prepareSubmitEnvironment$2(SparkSubmit.scala:342)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.deploy.SparkSubmit.prepareSubmitEnvironment(SparkSubmit.scala:342)
at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:871)
at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:203)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:90)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:1007)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:1016)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.ClassNotFoundException: org.w3c.dom.ElementTraversal
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:349)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 42 more
```
After:
```
...
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_202)
Type in expressions to have them evaluated.
Type :help for more information.
scala>
```
**2.**
```
./build/sbt dependencyTree -Phadoop-2.7 -Phive-2.3 -Phive-thriftserver -Phive
./build/sbt dependencyTree -Phadoop-3.2 -Phive-2.3 -Phive-thriftserver -Phive
```
Closes#27808 from HyukjinKwon/SPARK-30994.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Remove the cases for ```MissingTypesProblem```, ```InheritedNewAbstractMethodProblem```, ```DirectMissingMethodProblem``` and ```ReversedMissingMethodProblem```.
### Why are the changes needed?
After the changes, we don't have ```org.apache.spark.sql.sources.v2``` any more, so the only problem we can get is ```MissingClassProblem```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manually tested
Closes#27731 from huaxingao/spark-28998-followup.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
I found a few unnecessary MiMa excludes when auditing SQL binary incompatible changes.
### Why are the changes needed?
These MiMa excludes are not required any more, so remove.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manually tested
Closes#27729 from huaxingao/mima.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-30928
remove unnecessary MiMa excludes
### Why are the changes needed?
When auditing binary incompatible changes for 3.0, I found several MiMa excludes are not necessary, so remove these.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
run dev/mima to check
Closes#27696 from huaxingao/spark-mima.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.1.0-SNAPSHOT.
### Why are the changes needed?
N/A
### Does this PR introduce any user-facing change?
N/A
### How was this patch tested?
N/A
Closes#27698 from gatorsmile/updateVersion.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Fix for #27395
### What changes were proposed in this pull request?
The `allGather` method is added to the `BarrierTaskContext`. This method contains the same functionality as the `BarrierTaskContext.barrier` method; it blocks the task until all tasks make the call, at which time they may continue execution. In addition, the `allGather` method takes an input message. Upon returning from the `allGather` the task receives a list of all the messages sent by all the tasks that made the `allGather` call.
### Why are the changes needed?
There are many situations where having the tasks communicate in a synchronized way is useful. One simple example is if each task needs to start a server to serve requests from one another; first the tasks must find a free port (the result of which is undetermined beforehand) and then start making requests, but to do so they each must know the port chosen by the other task. An `allGather` method would allow them to inform each other of the port they will run on.
### Does this PR introduce any user-facing change?
Yes, an `BarrierTaskContext.allGather` method will be available through the Scala, Java, and Python APIs.
### How was this patch tested?
Most of the code path is already covered by tests to the `barrier` method, since this PR includes a refactor so that much code is shared by the `barrier` and `allGather` methods. However, a test is added to assert that an all gather on each tasks partition ID will return a list of every partition ID.
An example through the Python API:
```python
>>> from pyspark import BarrierTaskContext
>>>
>>> def f(iterator):
... context = BarrierTaskContext.get()
... return [context.allGather('{}'.format(context.partitionId()))]
...
>>> sc.parallelize(range(4), 4).barrier().mapPartitions(f).collect()[0]
[u'3', u'1', u'0', u'2']
```
Closes#27640 from sarthfrey/master.
Lead-authored-by: sarthfrey-db <sarth.frey@databricks.com>
Co-authored-by: sarthfrey <sarth.frey@gmail.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
- Add missing `since` annotation.
- Don't show classes under `org.apache.spark.sql.dynamicpruning` package in API docs.
- Fix the scope of `xxxExactNumeric` to remove it from the API docs.
### Why are the changes needed?
Avoid leaking APIs unintentionally in Spark 3.0.0.
### Does this PR introduce any user-facing change?
No. All these changes are to avoid leaking APIs unintentionally in Spark 3.0.0.
### How was this patch tested?
Manually generated the API docs and verified the above issues have been fixed.
Closes#27560 from xuanyuanking/SPARK-30809.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to throw exception by default when user use untyped UDF(a.k.a `org.apache.spark.sql.functions.udf(AnyRef, DataType)`).
And user could still use it by setting `spark.sql.legacy.useUnTypedUdf.enabled` to `true`.
### Why are the changes needed?
According to #23498, since Spark 3.0, the untyped UDF will return the default value of the Java type if the input value is null. For example, `val f = udf((x: Int) => x, IntegerType)`, `f($"x")` will return 0 in Spark 3.0 but null in Spark 2.4. And the behavior change is introduced due to Spark3.0 is built with Scala 2.12 by default.
As a result, this might change data silently and may cause correctness issue if user still expect `null` in some cases. Thus, we'd better to encourage user to use typed UDF to avoid this problem.
### Does this PR introduce any user-facing change?
Yeah. User will hit exception now when use untyped UDF.
### How was this patch tested?
Added test and updated some tests.
Closes#27488 from Ngone51/spark_26580_followup.
Lead-authored-by: yi.wu <yi.wu@databricks.com>
Co-authored-by: wuyi <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR tries #26710 (comment) way to fix the test.
### Why are the changes needed?
To make the tests pass.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Jenkins will test first, and then `on spark-branch-3.0-test-sbt-hadoop-2.7-hive-2.3` will test it out.
Closes#27513 from HyukjinKwon/test-SPARK-30756.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
(cherry picked from commit 8efe367a4e)
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
- Fix the scope of `Logging.initializeForcefully` so that it doesn't appear in subclasses' public methods. Right now, `sc.initializeForcefully(false, false)` is allowed to called.
- Don't show classes under `org.apache.spark.internal` package in API docs.
- Add missing `since` annotation.
- Fix the scope of `ArrowUtils` to remove it from the API docs.
### Why are the changes needed?
Avoid leaking APIs unintentionally in Spark 3.0.0.
### Does this PR introduce any user-facing change?
No. All these changes are to avoid leaking APIs unintentionally in Spark 3.0.0.
### How was this patch tested?
Manually generated the API docs and verified the above issues have been fixed.
Closes#27528 from zsxwing/audit-ss-apis.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
We should also expose it in documentation as we marked it as unstable API as of SPARK-30547
Note that, seems Javadoc -> Scaladoc doesn't work but this PR does not target to fix.
### Why are the changes needed?
To show the documentation of API.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually built the docs via `jykill serve` under `docs` directory:
Closes#27412 from HyukjinKwon/SPARK-30547.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds two pages to Web UI for Structured Streaming:
- "/streamingquery": Streaming Query Page, providing some aggregate information for running/completed streaming queries.
- "/streamingquery/statistics": Streaming Query Statistics Page, providing detailed information for streaming query, including `Input Rate`, `Process Rate`, `Input Rows`, `Batch Duration` and `Operation Duration`
### Why are the changes needed?
It helps users to better monitor Structured Streaming query.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
- new added and existing UTs
- manual test
Closes#26201 from uncleGen/SPARK-29543.
Lead-authored-by: uncleGen <hustyugm@gmail.com>
Co-authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Co-authored-by: Genmao Yu <hustyugm@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
### What changes were proposed in this pull request?
Remove ```numTrees``` in GBT in 3.0.0.
### Why are the changes needed?
Currently, GBT has
```
/**
* Number of trees in ensemble
*/
Since("2.0.0")
val getNumTrees: Int = trees.length
```
and
```
/** Number of trees in ensemble */
val numTrees: Int = trees.length
```
I think we should remove one of them. We deprecated it in 2.4.5 via https://github.com/apache/spark/pull/27352.
### Does this PR introduce any user-facing change?
Yes, remove ```numTrees``` in GBT in 3.0.0
### How was this patch tested?
existing tests
Closes#27330 from huaxingao/spark-numTrees.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Upgrade the version of Genjavadoc from 0.14 to 0.15.
### Why are the changes needed?
To enable to build for Scala 2.13.1.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
I confirmed there is no dependency error related to genjavadoc by manual build.
Also, I generated javadoc by `LANG=C build/sbt -Pkinesis-asl -Pyarn -Pkubernetes -Phive-thriftserver unidoc` for both code with/without this change and did `diff -r` target/javadoc.
Closes#27255 from sarutak/upgrade-genjavadoc.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is the second PR for the Stage Level Scheduling. This is adding in the necessary executor side changes:
1) executors to know what ResourceProfile they should be using
2) handle parsing the resource profile settings - these are not in the global configs
3) then reporting back to the driver what resource profile it was started with.
This PR adds all the piping for YARN to pass the information all the way to executors, but it just uses the default ResourceProfile (which is the global applicatino level configs).
At a high level these changes include:
1) adding a new --resourceProfileId option to the CoarseGrainedExecutorBackend
2) Add the ResourceProfile settings to new internal confs that gets passed into the Executor
3) Executor changes that use the resource profile id passed in to read the corresponding ResourceProfile confs and then parse those requests and discover resources as necessary
4) Executor registers to Driver with the Resource profile id so that the ExecutorMonitor can track how many executor with each profile are running
5) YARN side changes to show that passing the resource profile id and confs actually works. Just uses the DefaultResourceProfile for now.
I also removed a check from the CoarseGrainedExecutorBackend that used to check to make sure there were task requirements before parsing any custom resource executor requests. With the resource profiles this becomes much more expensive because we would then have to pass the task requests to each executor and the check was just a short cut and not really needed. It was much cleaner just to remove it.
Note there were some changes to the ResourceProfile, ExecutorResourceRequests, and TaskResourceRequests in this PR as well because I discovered some issues with things not being immutable. That api now look like:
val rpBuilder = new ResourceProfileBuilder()
val ereq = new ExecutorResourceRequests()
val treq = new TaskResourceRequests()
ereq.cores(2).memory("6g").memoryOverhead("2g").pysparkMemory("2g").resource("gpu", 2, "/home/tgraves/getGpus")
treq.cpus(2).resource("gpu", 2)
val resourceProfile = rpBuilder.require(ereq).require(treq).build
This makes is so that ResourceProfile is immutable and Spark can use it directly without worrying about the user changing it.
### Why are the changes needed?
These changes are needed for the executor to report which ResourceProfile they are using so that ultimately the dynamic allocation manager can use that information to know how many with a profile are running and how many more it needs to request. Its also needed to get the resource profile confs to the executor so that it can run the appropriate discovery script if needed.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Unit tests and manually on YARN.
Closes#26682 from tgravescs/SPARK-29306.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Make Regressors extend abstract class Regressor:
```AFTSurvivalRegression extends Estimator => extends Regressor```
```DecisionTreeRegressor extends Predictor => extends Regressor```
```FMRegressor extends Predictor => extends Regressor```
```GBTRegressor extends Predictor => extends Regressor```
```RandomForestRegressor extends Predictor => extends Regressor```
We will not make ```IsotonicRegression``` extend ```Regressor``` because it is tricky to handle both DoubleType and VectorType.
### Why are the changes needed?
Make class hierarchy consistent for all Regressors
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#27168 from huaxingao/spark-30377.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Make ```MultilayerPerceptronClassificationModel``` extend ```MultilayerPerceptronParams```
### Why are the changes needed?
Make ```MultilayerPerceptronClassificationModel``` extend ```MultilayerPerceptronParams``` to expose the training params, so user can see these params when calling ```extractParamMap```
### Does this PR introduce any user-facing change?
Yes. The ```MultilayerPerceptronParams``` such as ```seed```, ```maxIter``` ... are available in ```MultilayerPerceptronClassificationModel``` now
### How was this patch tested?
Manually tested ```MultilayerPerceptronClassificationModel.extractParamMap()``` to verify all the new params are there.
Closes#26838 from huaxingao/spark-30144.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
1, add new foreach-like methods: foreach/foreachNonZero
2, add iterator: iterator/activeIterator/nonZeroIterator
### Why are the changes needed?
see the [ticke](https://issues.apache.org/jira/browse/SPARK-30329) for details
foreach/foreachNonZero: for both convenience and performace (SparseVector.foreach should be faster than current traversal method)
iterator/activeIterator/nonZeroIterator: add the three iterators, so that we can futuremore add/change some impls based on those iterators for both ml and mllib sides, to avoid vector conversions.
### Does this PR introduce any user-facing change?
Yes, new methods are added
### How was this patch tested?
added testsuites
Closes#26982 from zhengruifeng/vector_iter.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
This reverts commit 709387d660.
See https://issues.apache.org/jira/browse/SPARK-27300?focusedCommentId=16990048&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-16990048 and previous mailing list discussions.
### What changes were proposed in this pull request?
Revert the addition of skeleton graph API modules for Spark 3.0.
### Why are the changes needed?
It does not appear that content will be added to the module for Spark 3, so I propose avoiding committing to the modules, which are no-ops now, in the upcoming major 3.0 release.
### Does this PR introduce any user-facing change?
No, the modules were not released.
### How was this patch tested?
Existing tests, but mostly N/A.
Closes#26928 from srowen/Revert27300.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR proposes to exclude Unidoc checking in Hive domain. We don't publish this as a part of Spark documentation (see also https://github.com/apache/spark/blob/master/docs/_plugins/copy_api_dirs.rb#L30) and most of them are copy of Hive thrift server so that we can officially use Hive 2.3 release.
It doesn't much make sense to check the documentation generation against another domain, and that we don't use in documentation publish.
### Why are the changes needed?
To avoid unnecessary computation.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
By Jenkins:
```
========================================================================
Building Spark
========================================================================
[info] Building Spark using SBT with these arguments: -Phadoop-2.7 -Phive-2.3 -Phive -Pmesos -Pkubernetes -Phive-thriftserver -Phadoop-cloud -Pkinesis-asl -Pspark-ganglia-lgpl -Pyarn test:package streaming-kinesis-asl-assembly/assembly
...
========================================================================
Building Unidoc API Documentation
========================================================================
[info] Building Spark unidoc using SBT with these arguments: -Phadoop-2.7 -Phive-2.3 -Phive -Pmesos -Pkubernetes -Phive-thriftserver -Phadoop-cloud -Pkinesis-asl -Pspark-ganglia-lgpl -Pyarn unidoc
...
[info] Main Java API documentation successful.
...
[info] Main Scala API documentation successful.
```
Closes#26800 from HyukjinKwon/do-not-merge.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Observable metrics are named arbitrary aggregate functions that can be defined on a query (Dataframe). As soon as the execution of a Dataframe reaches a completion point (e.g. finishes batch query or reaches streaming epoch) a named event is emitted that contains the metrics for the data processed since the last completion point.
A user can observe these metrics by attaching a listener to spark session, it depends on the execution mode which listener to attach:
- Batch: `QueryExecutionListener`. This will be called when the query completes. A user can access the metrics by using the `QueryExecution.observedMetrics` map.
- (Micro-batch) Streaming: `StreamingQueryListener`. This will be called when the streaming query completes an epoch. A user can access the metrics by using the `StreamingQueryProgress.observedMetrics` map. Please note that we currently do not support continuous execution streaming.
### Why are the changes needed?
This enabled observable metrics.
### Does this PR introduce any user-facing change?
Yes. It adds the `observe` method to `Dataset`.
### How was this patch tested?
- Added unit tests for the `CollectMetrics` logical node to the `AnalysisSuite`.
- Added unit tests for `StreamingProgress` JSON serialization to the `StreamingQueryStatusAndProgressSuite`.
- Added integration tests for streaming to the `StreamingQueryListenerSuite`.
- Added integration tests for batch to the `DataFrameCallbackSuite`.
Closes#26127 from hvanhovell/SPARK-29348.
Authored-by: herman <herman@databricks.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
This PR tries to fix flakiness in `HiveThriftServer2ListenerSuite` by using a dedicated JVM (after we switch to Hive 2.3 by default in PR builders). Likewise in 4a73bed318, there's no explicit evidence for this fix.
See https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/114653/testReport/org.apache.spark.sql.hive.thriftserver.ui/HiveThriftServer2ListenerSuite/_It_is_not_a_test_it_is_a_sbt_testing_SuiteSelector_/
```
sbt.ForkMain$ForkError: sbt.ForkMain$ForkError: java.lang.LinkageError: loader constraint violation: loader (instance of net/bytebuddy/dynamic/loading/MultipleParentClassLoader) previously initiated loading for a different type with name "org/apache/hive/service/ServiceStateChangeListener"
at org.mockito.codegen.HiveThriftServer2$MockitoMock$1974707245.<clinit>(Unknown Source)
at sun.reflect.GeneratedSerializationConstructorAccessor164.newInstance(Unknown Source)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.objenesis.instantiator.sun.SunReflectionFactoryInstantiator.newInstance(SunReflectionFactoryInstantiator.java:48)
at org.objenesis.ObjenesisBase.newInstance(ObjenesisBase.java:73)
at org.mockito.internal.creation.instance.ObjenesisInstantiator.newInstance(ObjenesisInstantiator.java:19)
at org.mockito.internal.creation.bytebuddy.SubclassByteBuddyMockMaker.createMock(SubclassByteBuddyMockMaker.java:47)
at org.mockito.internal.creation.bytebuddy.ByteBuddyMockMaker.createMock(ByteBuddyMockMaker.java:25)
at org.mockito.internal.util.MockUtil.createMock(MockUtil.java:35)
at org.mockito.internal.MockitoCore.mock(MockitoCore.java:62)
at org.mockito.Mockito.mock(Mockito.java:1908)
at org.mockito.Mockito.mock(Mockito.java:1880)
at org.apache.spark.sql.hive.thriftserver.ui.HiveThriftServer2ListenerSuite.createAppStatusStore(HiveThriftServer2ListenerSuite.scala:156)
at org.apache.spark.sql.hive.thriftserver.ui.HiveThriftServer2ListenerSuite.$anonfun$new$3(HiveThriftServer2ListenerSuite.scala:47)
at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
```
### Why are the changes needed?
To make test cases more robust.
### Does this PR introduce any user-facing change?
No (dev only).
### How was this patch tested?
Jenkins build.
Closes#26720 from shahidki31/mock.
Authored-by: shahid <shahidki31@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently, Apache Spark PR Builder using `hive-1.2` for `hadoop-2.7` and `hive-2.3` for `hadoop-3.2`. This PR aims to support
- `[test-hive1.2]` in PR builder
- `[test-hive2.3]` in PR builder to be consistent and independent of the default profile
- After this PR, all PR builders will use Hive 2.3 by default (because Spark uses Hive 2.3 by default as of c98e5eb339)
- Use default profile in AppVeyor build.
Note that this was reverted due to unexpected test failure at `ThriftServerPageSuite`, which was investigated in https://github.com/apache/spark/pull/26706 . This PR fixed it by letting it use their own forked JVM. There is no explicit evidence for this fix and it was just my speculation, and thankfully it fixed at least.
### Why are the changes needed?
This new tag allows us more flexibility.
### Does this PR introduce any user-facing change?
No. (This is a dev-only change.)
### How was this patch tested?
Check the Jenkins triggers in this PR.
Default:
```
========================================================================
Building Spark
========================================================================
[info] Building Spark using SBT with these arguments: -Phadoop-2.7 -Phive-2.3 -Phive-thriftserver -Pmesos -Pspark-ganglia-lgpl -Phadoop-cloud -Phive -Pkubernetes -Pkinesis-asl -Pyarn test:package streaming-kinesis-asl-assembly/assembly
```
`[test-hive1.2][test-hadoop3.2]`:
```
========================================================================
Building Spark
========================================================================
[info] Building Spark using SBT with these arguments: -Phadoop-3.2 -Phive-1.2 -Phadoop-cloud -Pyarn -Pspark-ganglia-lgpl -Phive -Phive-thriftserver -Pmesos -Pkubernetes -Pkinesis-asl test:package streaming-kinesis-asl-assembly/assembly
```
`[test-maven][test-hive-2.3]`:
```
========================================================================
Building Spark
========================================================================
[info] Building Spark using Maven with these arguments: -Phadoop-2.7 -Phive-2.3 -Pspark-ganglia-lgpl -Pyarn -Phive -Phadoop-cloud -Pkinesis-asl -Pmesos -Pkubernetes -Phive-thriftserver clean package -DskipTests
```
Closes#26710 from HyukjinKwon/SPARK-29991.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
support `modelType` `gaussian`
### Why are the changes needed?
current modelTypes do not support continuous data
### Does this PR introduce any user-facing change?
yes, add a `modelType` option
### How was this patch tested?
existing testsuites and added ones
Closes#26413 from zhengruifeng/gnb.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
This PR aims to add `io.netty.tryReflectionSetAccessible=true` to the testing configuration for JDK11 because this is an officially documented requirement of Apache Arrow.
Apache Arrow community documented this requirement at `0.15.0` ([ARROW-6206](https://github.com/apache/arrow/pull/5078)).
> #### For java 9 or later, should set "-Dio.netty.tryReflectionSetAccessible=true".
> This fixes `java.lang.UnsupportedOperationException: sun.misc.Unsafe or java.nio.DirectByteBuffer.(long, int) not available`. thrown by netty.
### Why are the changes needed?
After ARROW-3191, Arrow Java library requires the property `io.netty.tryReflectionSetAccessible` to be set to true for JDK >= 9. After https://github.com/apache/spark/pull/26133, JDK11 Jenkins job seem to fail.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-3.2-jdk-11/676/
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-3.2-jdk-11/677/
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-3.2-jdk-11/678/
```scala
Previous exception in task:
sun.misc.Unsafe or java.nio.DirectByteBuffer.<init>(long, int) not available

io.netty.util.internal.PlatformDependent.directBuffer(PlatformDependent.java:473)

io.netty.buffer.NettyArrowBuf.getDirectBuffer(NettyArrowBuf.java:243)

io.netty.buffer.NettyArrowBuf.nioBuffer(NettyArrowBuf.java:233)

io.netty.buffer.ArrowBuf.nioBuffer(ArrowBuf.java:245)

org.apache.arrow.vector.ipc.message.ArrowRecordBatch.computeBodyLength(ArrowRecordBatch.java:222)

```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass the Jenkins with JDK11.
Closes#26552 from dongjoon-hyun/SPARK-ARROW-JDK11.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
SPARK-29397 added new interfaces for creating driver and executor
plugins. These were added in a new, more isolated package that does
not pollute the main o.a.s package.
The old interface is now redundant. Since it's a DeveloperApi and
we're about to have a new major release, let's remove it instead of
carrying more baggage forward.
Closes#26390 from vanzin/SPARK-29399.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to upgrade ASM to 7.2.
- https://issues.apache.org/jira/browse/XBEAN-322 (Upgrade to ASM 7.2)
- https://asm.ow2.io/versions.html
### Why are the changes needed?
This will bring the following patches.
- 317875: Infinite loop when parsing invalid method descriptor
- 317873: Add support for RET instruction in AdviceAdapter
- 317872: Throw an exception if visitFrame used incorrectly
- add support for Java 14
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass the Jenkins with the existing UTs.
Closes#26373 from dongjoon-hyun/SPARK-29729.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch addresses CI build issue on sbt Hadoop-3.2 Jenkins job: SparkSQLEnvSuite are failing. Looks like the reason of test failure is the test checks registered listeners from active SparkSession which could be interfered with other test suites running concurrently. If we isolate test suite the problem should be gone.
### Why are the changes needed?
CI builds for "spark-master-test-sbt-hadoop-3.2" are failing.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
I've run the single test suite with below command and it passed 3 times sequentially:
```
build/sbt "hive-thriftserver/testOnly *.SparkSQLEnvSuite" -Phadoop-3.2 -Phive-thriftserver
```
so we expect the test suite will pass if we isolate the test suite.
Closes#26342 from HeartSaVioR/SPARK-29604-FOLLOWUP.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
1, add shared param `relativeError`
2, `Imputer`/`RobusterScaler`/`QuantileDiscretizer` extend `HasRelativeError`
### Why are the changes needed?
It makes sense to expose RelativeError to end users, since it controls both the precision and memory overhead.
`QuantileDiscretizer` had already added this param, while other algs not yet.
### Does this PR introduce any user-facing change?
yes, new param is added in `Imputer`/`RobusterScaler`
### How was this patch tested?
existing testsutes
Closes#26305 from zhengruifeng/add_relative_err.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
This add `typesafe` bintray repo for `sbt-mima-plugin`.
### Why are the changes needed?
Since Oct 21, the following plugin causes [Jenkins failures](https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-branch-2.4-test-sbt-hadoop-2.6/611/console
) due to the missing jar.
- `branch-2.4`: `sbt-mima-plugin:0.1.17` is missing.
- `master`: `sbt-mima-plugin:0.3.0` is missing.
These versions of `sbt-mima-plugin` seems to be removed from the old repo.
```
$ rm -rf ~/.ivy2/
$ build/sbt scalastyle test:scalastyle
...
[warn] ::::::::::::::::::::::::::::::::::::::::::::::
[warn] :: UNRESOLVED DEPENDENCIES ::
[warn] ::::::::::::::::::::::::::::::::::::::::::::::
[warn] :: com.typesafe#sbt-mima-plugin;0.1.17: not found
[warn] ::::::::::::::::::::::::::::::::::::::::::::::
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Check `GitHub Action` linter result. This PR should pass. Or, manual check.
(Note that Jenkins PR builder didn't fail until now due to the local cache.)
Closes#26217 from dongjoon-hyun/SPARK-29560.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
We added a TaskContext.resources() api, but I realized this is returning a scala Map which is not ideal for access from Java. Here I add a resourcesJMap function which returns a java.util.Map to make it easily accessible from Java.
### Why are the changes needed?
Java API access
### Does this PR introduce any user-facing change?
<!--
If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible.
If no, write 'No'.
-->
Yes, new TaskContext function to access from Java
### How was this patch tested?
<!--
If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible.
If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future.
If tests were not added, please describe why they were not added and/or why it was difficult to add.
-->
new unit test
Closes#26083 from tgravescs/SPARK-29417.
Lead-authored-by: Thomas Graves <tgraves@ngvpn01-168-221.dyn.scz.us.nvidia.com>
Co-authored-by: Thomas Graves <tgraves@TGRAVES-MLT.local>
Co-authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
### What changes were proposed in this pull request?
This PR aims to specify the JDK8 default configurations `-XX:+UseParallelGC -XX:-UseDynamicNumberOfGCThreads` explicitly. As we see in this PR [here](https://github.com/apache/spark/pull/25966/files#diff-12b89b7ee67c63c2254b749c8f8d0694R10), this will make the comparison between JDK8 and JDK11 easier by removing a misleading regression.
**NOTE THAT THESE JVM CONFS ARE ONLY FOR BENCHMARK COMPARISON, NOT FOR A PRODUCTION**
### Why are the changes needed?
There exists many JVM-level changes between JDK8 and JDK11. For example, the followings are notable changes and it turns out that especially (1) and (2) shows a misleading regression in our micro-benchmark environment because our microbenchmark uses small VM memory.
1. [JEP 248: Make G1 the Default Garbage Collector](https://bugs.openjdk.java.net/browse/JDK-8073273) **JDK9+**
2. [Enable UseDynamicNumberOfGCThreads by default](https://bugs.openjdk.java.net/browse/JDK-8198547) **JDK11+**
3. [Change default value of HeapSizePerGCThread](https://bugs.openjdk.java.net/browse/JDK-8200417) **JDK11+**
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This is a test-only JVM configuration change. Manually, run the benchmark.
Closes#25966 from dongjoon-hyun/SPARK-29282.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Copy any "spark.hive.foo=bar" spark properties into hadoop conf as "hive.foo=bar"
### Why are the changes needed?
Providing spark side config entry for hive configurations.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
UT.
Closes#25661 from WeichenXu123/add_hive_conf.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
After the newly added shuffle block fetching protocol in #24565, we can keep this work by extending the FetchShuffleBlocks message.
### What changes were proposed in this pull request?
In this patch, we achieve the indeterminate shuffle rerun by reusing the task attempt id(unique id within an application) in shuffle id, so that each shuffle write attempt has a different file name. For the indeterministic stage, when the stage resubmits, we'll clear all existing map status and rerun all partitions.
All changes are summarized as follows:
- Change the mapId to mapTaskAttemptId in shuffle related id.
- Record the mapTaskAttemptId in MapStatus.
- Still keep mapId in ShuffleFetcherIterator for fetch failed scenario.
- Add the determinate flag in Stage and use it in DAGScheduler and the cleaning work for the intermediate stage.
### Why are the changes needed?
This is a follow-up work for #22112's future improvment[1]: `Currently we can't rollback and rerun a shuffle map stage, and just fail.`
Spark will rerun a finished shuffle write stage while meeting fetch failures, currently, the rerun shuffle map stage will only resubmit the task for missing partitions and reuse the output of other partitions. This logic is fine in most scenarios, but for indeterministic operations(like repartition), multiple shuffle write attempts may write different data, only rerun the missing partition will lead a correctness bug. So for the shuffle map stage of indeterministic operations, we need to support rolling back the shuffle map stage and re-generate the shuffle files.
### Does this PR introduce any user-facing change?
Yes, after this PR, the indeterminate stage rerun will be accepted by rerunning the whole stage. The original behavior is aborting the stage and fail the job.
### How was this patch tested?
- UT: Add UT for all changing code and newly added function.
- Manual Test: Also providing a manual test to verify the effect.
```
import scala.sys.process._
import org.apache.spark.TaskContext
val determinateStage0 = sc.parallelize(0 until 1000 * 1000 * 100, 10)
val indeterminateStage1 = determinateStage0.repartition(200)
val indeterminateStage2 = indeterminateStage1.repartition(200)
val indeterminateStage3 = indeterminateStage2.repartition(100)
val indeterminateStage4 = indeterminateStage3.repartition(300)
val fetchFailIndeterminateStage4 = indeterminateStage4.map { x =>
if (TaskContext.get.attemptNumber == 0 && TaskContext.get.partitionId == 190 &&
TaskContext.get.stageAttemptNumber == 0) {
throw new Exception("pkill -f -n java".!!)
}
x
}
val indeterminateStage5 = fetchFailIndeterminateStage4.repartition(200)
val finalStage6 = indeterminateStage5.repartition(100).collect().distinct.length
```
It's a simple job with multi indeterminate stage, it will get a wrong answer while using old Spark version like 2.2/2.3, and will be killed after #22112. With this fix, the job can retry all indeterminate stage as below screenshot and get the right result.
Closes#25620 from xuanyuanking/SPARK-25341-8.27.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR upgrade Scala to **2.12.10**.
Release notes:
- Fix regression in large string interpolations with non-String typed splices
- Revert "Generate shallower ASTs in pattern translation"
- Fix regression in classpath when JARs have 'a.b' entries beside 'a/b'
- Faster compiler: 5–10% faster since 2.12.8
- Improved compatibility with JDK 11, 12, and 13
- Experimental support for build pipelining and outline type checking
More details:
https://github.com/scala/scala/releases/tag/v2.12.10https://github.com/scala/scala/releases/tag/v2.12.9
## How was this patch tested?
Existing tests
Closes#25404 from wangyum/SPARK-28683.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This proposes to improve Spark instrumentation by adding a hook for user-defined metrics, extending Spark’s Dropwizard/Codahale metrics system.
The original motivation of this work was to add instrumentation for S3 filesystem access metrics by Spark job. Currently, [[ExecutorSource]] instruments HDFS and local filesystem metrics. Rather than extending the code there, we proposes with this JIRA to add a metrics plugin system which is of more flexible and general use.
Context: The Spark metrics system provides a large variety of metrics, see also , useful to monitor and troubleshoot Spark workloads. A typical workflow is to sink the metrics to a storage system and build dashboards on top of that.
Highlights:
- The metric plugin system makes it easy to implement instrumentation for S3 access by Spark jobs.
- The metrics plugin system allows for easy extensions of how Spark collects HDFS-related workload metrics. This is currently done using the Hadoop Filesystem GetAllStatistics method, which is deprecated in recent versions of Hadoop. Recent versions of Hadoop Filesystem recommend using method GetGlobalStorageStatistics, which also provides several additional metrics. GetGlobalStorageStatistics is not available in Hadoop 2.7 (had been introduced in Hadoop 2.8). Using a metric plugin for Spark would allow an easy way to “opt in” using such new API calls for those deploying suitable Hadoop versions.
- We also have the use case of adding Hadoop filesystem monitoring for a custom Hadoop compliant filesystem in use in our organization (EOS using the XRootD protocol). The metrics plugin infrastructure makes this easy to do. Others may have similar use cases.
- More generally, this method makes it straightforward to plug in Filesystem and other metrics to the Spark monitoring system. Future work on plugin implementation can address extending monitoring to measure usage of external resources (OS, filesystem, network, accelerator cards, etc), that maybe would not normally be considered general enough for inclusion in Apache Spark code, but that can be nevertheless useful for specialized use cases, tests or troubleshooting.
Implementation:
The proposed implementation extends and modifies the work on Executor Plugin of SPARK-24918. Additionally, this is related to recent work on extending Spark executor metrics, such as SPARK-25228.
As discussed during the review, the implementaiton of this feature modifies the Developer API for Executor Plugins, such that the new version is incompatible with the original version in Spark 2.4.
## How was this patch tested?
This modifies existing tests for ExecutorPluginSuite to adapt them to the API changes. In addition, the new funtionality for registering pluginMetrics has been manually tested running Spark on YARN and K8S clusters, in particular for monitoring S3 and for extending HDFS instrumentation with the Hadoop Filesystem “GetGlobalStorageStatistics” metrics. Executor metric plugin example and code used for testing are available, for example at: https://github.com/cerndb/SparkExecutorPluginsCloses#24901 from LucaCanali/executorMetricsPlugin.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
- Remove SQLContext.createExternalTable and Catalog.createExternalTable, deprecated in favor of createTable since 2.2.0, plus tests of deprecated methods
- Remove HiveContext, deprecated in 2.0.0, in favor of `SparkSession.builder.enableHiveSupport`
- Remove deprecated KinesisUtils.createStream methods, plus tests of deprecated methods, deprecate in 2.2.0
- Remove deprecated MLlib (not Spark ML) linear method support, mostly utility constructors and 'train' methods, and associated docs. This includes methods in LinearRegression, LogisticRegression, Lasso, RidgeRegression. These have been deprecated since 2.0.0
- Remove deprecated Pyspark MLlib linear method support, including LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD
- Remove 'runs' argument in KMeans.train() method, which has been a no-op since 2.0.0
- Remove deprecated ChiSqSelector isSorted protected method
- Remove deprecated 'yarn-cluster' and 'yarn-client' master argument in favor of 'yarn' and deploy mode 'cluster', etc
Notes:
- I was not able to remove deprecated DataFrameReader.json(RDD) in favor of DataFrameReader.json(Dataset); the former was deprecated in 2.2.0, but, it is still needed to support Pyspark's .json() method, which can't use a Dataset.
- Looks like SQLContext.createExternalTable was not actually deprecated in Pyspark, but, almost certainly was meant to be? Catalog.createExternalTable was.
- I afterwards noted that the toDegrees, toRadians functions were almost removed fully in SPARK-25908, but Felix suggested keeping just the R version as they hadn't been technically deprecated. I'd like to revisit that. Do we really want the inconsistency? I'm not against reverting it again, but then that implies leaving SQLContext.createExternalTable just in Pyspark too, which seems weird.
- I *kept* LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD, RidgeRegressionWithSGD in Pyspark, though deprecated, as it is hard to remove them (still used by StreamingLogisticRegressionWithSGD?) and they are not fully removed in Scala. Maybe should not have been deprecated.
### Why are the changes needed?
Deprecated items are easiest to remove in a major release, so we should do so as much as possible for Spark 3. This does not target items deprecated 'recently' as of Spark 2.3, which is still 18 months old.
### Does this PR introduce any user-facing change?
Yes, in that deprecated items are removed from some public APIs.
### How was this patch tested?
Existing tests.
Closes#25684 from srowen/SPARK-28980.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Add HasNumFeatures in the scala side, with `1<<18` as the default value
### Why are the changes needed?
HasNumFeatures is already added in the py side, it is reasonable to keep them in sync.
I don't find other similar place.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing testsuites
Closes#25671 from zhengruifeng/add_HasNumFeatures.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Delete the incorrect method `def setWeightCol(value: Double): this.type = set(threshold, value)` in `LinearSVCModel`
### Why are the changes needed?
`LinearSVCModel` should not provide this setter, moreover, this method is wrongly defined.
### Does this PR introduce any user-facing change?
yes, a public method is removed
### How was this patch tested?
existing suites
Closes#25510 from zhengruifeng/linearsvc_model_set_weightcol.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fixes a vulnerability from the GitHub Security Advisory Database:
_Moderate severity vulnerability that affects com.puppycrawl.tools:checkstyle_
Checkstyle prior to 8.18 loads external DTDs by default, which can potentially lead to denial of service attacks or the leaking of confidential information.
https://github.com/checkstyle/checkstyle/issues/6474
Affected versions: < 8.18
## How was this patch tested?
Ran checkstyle locally.
Closes#25432 from Fokko/SPARK-28713.
Authored-by: Fokko Driesprong <fokko@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This patch tries to keep consistency whenever UTF-8 charset is needed, as using `StandardCharsets.UTF_8` instead of using "UTF-8". If the String type is needed, `StandardCharsets.UTF_8.name()` is used.
This change also brings the benefit of getting rid of `UnsupportedEncodingException`, as we're providing `Charset` instead of `String` whenever possible.
This also changes some private Catalyst helper methods to operate on encodings as `Charset` objects rather than strings.
## How was this patch tested?
Existing unit tests.
Closes#25335 from HeartSaVioR/SPARK-28601.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Currently, PythonBroadcast may delete its data file while a python worker still needs it. This happens because PythonBroadcast overrides the `finalize()` method to delete its data file. So, when GC happens and no references on broadcast variable, it may trigger `finalize()` to delete
data file. That's also means, data under python Broadcast variable couldn't be deleted when `unpersist()`/`destroy()` called but relys on GC.
In this PR, we removed the `finalize()` method, and map the PythonBroadcast data file to a BroadcastBlock(which has the same broadcast id with the broadcast variable who wrapped this PythonBroadcast) when PythonBroadcast is deserializing. As a result, the data file could be deleted just like other pieces of the Broadcast variable when `unpersist()`/`destroy()` called and do not rely on GC any more.
## How was this patch tested?
Added a Python test, and tested manually(verified create/delete the broadcast block).
Closes#25262 from Ngone51/SPARK-28486.
Authored-by: wuyi <ngone_5451@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes to make Javadoc in org.apache.spark.shuffle.api visible.
## How was this patch tested?
Manually built the doc and checked:
Closes#25323 from HyukjinKwon/SPARK-28568.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Prior to this change, in an executor, on each heartbeat, memory metrics are polled and sent in the heartbeat. The heartbeat interval is 10s by default. With this change, in an executor, memory metrics can optionally be polled in a separate poller at a shorter interval.
For each executor, we use a map of (stageId, stageAttemptId) to (count of running tasks, executor metric peaks) to track what stages are active as well as the per-stage memory metric peaks. When polling the executor memory metrics, we attribute the memory to the active stage(s), and update the peaks. In a heartbeat, we send the per-stage peaks (for stages active at that time), and then reset the peaks. The semantics would be that the per-stage peaks sent in each heartbeat are the peaks since the last heartbeat.
We also keep a map of taskId to memory metric peaks. This tracks the metric peaks during the lifetime of the task. The polling thread updates this as well. At end of a task, we send the peak metric values in the task result. In case of task failure, we send the peak metric values in the `TaskFailedReason`.
We continue to do the stage-level aggregation in the EventLoggingListener.
For the driver, we still only poll on heartbeats. What the driver sends will be the current values of the metrics in the driver at the time of the heartbeat. This is semantically the same as before.
## How was this patch tested?
Unit tests. Manually tested applications on an actual system and checked the event logs; the metrics appear in the SparkListenerTaskEnd and SparkListenerStageExecutorMetrics events.
Closes#23767 from wypoon/wypoon_SPARK-26329.
Authored-by: Wing Yew Poon <wypoon@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
I remove the deprecate `ImageSchema.readImages`.
Move some useful methods from class `ImageSchema` into class `ImageFileFormat`.
In pyspark, I rename `ImageSchema` class to be `ImageUtils`, and keep some useful python methods in it.
## How was this patch tested?
UT.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#25245 from WeichenXu123/remove_image_schema.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Right now `Error` is not sent to `QueryExecutionListener.onFailure`. If there is any `Error` (such as `AssertionError`) when running a query, `QueryExecutionListener.onFailure` cannot be triggered.
This PR changes `onFailure` to accept a `Throwable` instead.
## How was this patch tested?
Jenkins
Closes#25292 from zsxwing/fix-QueryExecutionListener.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This patch proposes moving all Trigger implementations to `Triggers.scala`, to avoid exposing these implementations to the end users and let end users only deal with `Trigger.xxx` static methods. This fits the intention of deprecation of `ProcessingTIme`, and we agree to move others without deprecation as this patch will be shipped in major version (Spark 3.0.0).
## How was this patch tested?
UTs modified to work with newly introduced class.
Closes#24996 from HeartSaVioR/SPARK-28199.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Track tasks separately for each stage attempt (instead of tracking by stage), and do NOT reset the numRunningTasks to 0 on StageCompleted.
In the case of stage retry, the `taskEnd` event from the zombie stage sometimes makes the number of `totalRunningTasks` negative, which will causes the job to get stuck.
Similar problem also exists with `stageIdToTaskIndices` & `stageIdToSpeculativeTaskIndices`.
If it is a failed `taskEnd` event of the zombie stage, this will cause `stageIdToTaskIndices` or `stageIdToSpeculativeTaskIndices` to remove the task index of the active stage, and the number of `totalPendingTasks` will increase unexpectedly.
## How was this patch tested?
unit test properly handle task end events from completed stages
Closes#24497 from cxzl25/fix_stuck_job_follow_up.
Authored-by: sychen <sychen@ctrip.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
This PR introduces the necessary Maven modules for the new [Spark Graph](https://issues.apache.org/jira/browse/SPARK-25994) feature for Spark 3.0.
* `spark-graph` is a parent module that users depend on to get all graph functionalities (Cypher and Graph Algorithms)
* `spark-graph-api` defines the [Property Graph API](https://docs.google.com/document/d/1Wxzghj0PvpOVu7XD1iA8uonRYhexwn18utdcTxtkxlI) that is being shared between Cypher and Algorithms
* `spark-cypher` contains a Cypher query engine implementation
Both, `spark-graph-api` and `spark-cypher` depend on Spark SQL.
Note, that the Maven module for Graph Algorithms is not part of this PR and will be introduced in https://issues.apache.org/jira/browse/SPARK-27302
A PoC for a running Cypher implementation can be found in this WIP PR https://github.com/apache/spark/pull/24297
## How was this patch tested?
Pass the Jenkins with all profiles and manually build and check the followings.
```
$ ls assembly/target/scala-2.12/jars/spark-cypher*
assembly/target/scala-2.12/jars/spark-cypher_2.12-3.0.0-SNAPSHOT.jar
$ ls assembly/target/scala-2.12/jars/spark-graph* | grep -v graphx
assembly/target/scala-2.12/jars/spark-graph-api_2.12-3.0.0-SNAPSHOT.jar
assembly/target/scala-2.12/jars/spark-graph_2.12-3.0.0-SNAPSHOT.jar
```
Closes#24490 from s1ck/SPARK-27300.
Lead-authored-by: Martin Junghanns <martin.junghanns@neotechnology.com>
Co-authored-by: Max Kießling <max@kopfueber.org>
Co-authored-by: Martin Junghanns <martin.junghanns@neo4j.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Move methods that implement v2 catalog operations to CatalogV2Util so they can be used in #24768.
## How was this patch tested?
Behavior is validated by existing tests.
Closes#24813 from rdblue/SPARK-27964-add-catalog-v2-util.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Currently we are in a strange status that, some data source v2 interfaces(catalog related) are in sql/catalyst, some data source v2 interfaces(Table, ScanBuilder, DataReader, etc.) are in sql/core.
I don't see a reason to keep data source v2 API in 2 modules. If we should pick one module, I think sql/catalyst is the one to go.
Catalyst module already has some user-facing stuff like DataType, Row, etc. And we have to update `Analyzer` and `SessionCatalog` to support the new catalog plugin, which needs to be in the catalyst module.
This PR can solve the problem we have in https://github.com/apache/spark/pull/24246
## How was this patch tested?
existing tests
Closes#24416 from cloud-fan/move.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR adds support to schedule tasks with extra resource requirements (eg. GPUs) on executors with available resources. It also introduce a new method `TaskContext.resources()` so tasks can access available resource addresses allocated to them.
## How was this patch tested?
* Added new end-to-end test cases in `SparkContextSuite`;
* Added new test case in `CoarseGrainedSchedulerBackendSuite`;
* Added new test case in `CoarseGrainedExecutorBackendSuite`;
* Added new test case in `TaskSchedulerImplSuite`;
* Added new test case in `TaskSetManagerSuite`;
* Updated existing tests.
Closes#24374 from jiangxb1987/gpu.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
For the below three thread configuration items applied to both driver and executor,
spark.rpc.io.serverThreads
spark.rpc.io.clientThreads
spark.rpc.netty.dispatcher.numThreads,
we separate them to driver specifics and executor specifics.
spark.driver.rpc.io.serverThreads < - > spark.executor.rpc.io.serverThreads
spark.driver.rpc.io.clientThreads < - > spark.executor.rpc.io.clientThreads
spark.driver.rpc.netty.dispatcher.numThreads < - > spark.executor.rpc.netty.dispatcher.numThreads
Spark reads these specifics first and fall back to the common configurations.
## How was this patch tested?
We ran the SimpleMap app without shuffle for benchmark purpose to test Spark's scalability in HPC with omini-path NIC which has higher bandwidth than normal ethernet NIC.
Spark's base version is 2.4.0.
Spark ran in the Standalone mode. Driver was in a standalone node.
After the separation, the performance is improved a lot in 256 nodes and 512 nodes. see below test results of SimpleMapTask before and after the enhancement. You can view the tables in the [JIRA](https://issues.apache.org/jira/browse/SPARK-26632) too.
ds: spark.driver.rpc.io.serverThreads
dc: spark.driver.rpc.io.clientThreads
dd: spark.driver.rpc.netty.dispatcher.numThreads
ed: spark.executor.rpc.netty.dispatcher.numThreads
time: Overall Time (s)
old time: Overall Time without Separation (s)
**Before:**
nodes | ds | dc | dd | ed | time
-- |-- | -- | -- | -- | --
128 nodes | 8 | 8 | 8 | 8 | 108
256 nodes | 8 | 8 | 8 | 8 | 196
512 nodes | 8 | 8 | 8 | 8 | 377
**After:**
nodes | ds | dc | dd | ed | time | improvement
-- | -- | -- | -- | -- | -- | --
128 nodes | 15 | 15 | 10 | 30 | 107 | 0.9%
256 nodes | 12 | 15 | 10 | 30 | 159 | 18.8%
512 nodes | 12 | 15 | 10 | 30 | 283 | 24.9%
Closes#23560 from zjf2012/thread_conf_separation.
Authored-by: jiafu.zhang@intel.com <jiafu.zhang@intel.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Fixed the `spark-<version>-yarn-shuffle.jar` artifact packaging to shade the native netty libraries:
- shade the `META-INF/native/libnetty_*` native libraries when packagin
the yarn shuffle service jar. This is required as netty library loader
derives that based on shaded package name.
- updated the `org/spark_project` shade package prefix to `org/sparkproject`
(i.e. removed underscore) as the former breaks the netty native lib loading.
This was causing the yarn external shuffle service to fail
when spark.shuffle.io.mode=EPOLL
## How was this patch tested?
Manual tests
Closes#24502 from amuraru/SPARK-27610_master.
Authored-by: Adi Muraru <amuraru@adobe.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Kind of related to https://github.com/gatorsmile/spark/pull/5 - let's update genjavadoc to see if it generates fewer spurious javadoc errors to begin with.
## How was this patch tested?
Existing docs build
Closes#24443 from srowen/genjavadoc013.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/24395. This PR update `plugins.sbt`, too.
## How was this patch tested?
Pass the Jenkins.
Closes#24444 from dongjoon-hyun/SPARK-ASM71-2.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
The test time of `HiveClientVersions` is around 3.5 minutes.
This PR is to add it into the parallel test suite list. To make sure there is no colliding warehouse location, we can change the warehouse path to a temporary directory.
## How was this patch tested?
Unit test
Closes#24404 from gengliangwang/parallelTestFollowUp.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This patch modifies SparkBuild so that the largest / slowest test suites (or collections of suites) can run in their own forked JVMs, allowing them to be run in parallel with each other. This opt-in / whitelisting approach allows us to increase parallelism without having to fix a long-tail of flakiness / brittleness issues in tests which aren't performance bottlenecks.
See comments in SparkBuild.scala for information on the details, including a summary of why we sometimes opt to run entire groups of tests in a single forked JVM .
The time of full new pull request test in Jenkins is reduced by around 53%:
before changes: 4hr 40min
after changes: 2hr 13min
## How was this patch tested?
Unit test
Closes#24373 from gengliangwang/parallelTest.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The API docs should not include the "org.apache.spark.util.kvstore" package because they are internal private APIs. See the doc link: https://spark.apache.org/docs/latest/api/java/org/apache/spark/util/kvstore/LevelDB.html
## How was this patch tested?
N/A
Closes#24386 from gatorsmile/rmDoc.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Remove deprecated / no-op mllib.KMeans getRuns, setRuns
mllib.KMeans has getRuns, setRuns methods which haven't done anything since Spark 2.1. They're deprecated, and no-ops, and should be removed for Spark 3.
## How was this patch tested?
Existing tests.
Closes#24320 from srowen/SPARK-27410.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Remove Scala 2.11 support in build files and docs, and in various parts of code that accommodated 2.11. See some targeted comments below.
## How was this patch tested?
Existing tests.
Closes#23098 from srowen/SPARK-26132.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
LEGACY_DRIVER_IDENTIFIER and its reference are removed.
corresponding references test are updated.
## How was this patch tested?
tested UT test cases
Closes#24026 from shivusondur/newjira2.
Authored-by: shivusondur <shivusondur@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Use the sbt maven plugin option`antlr4TreatWarningsAsErrors` to make sure the warnings are treated as errors while generating the parser. In the absence of it, we may inadvertently introduce problems while making grammar changes. Please refer to PR-23897 to know more about the context. We made a change in [pr-23925](https://github.com/apache/spark/pull/23925) which handled only the maven build.
In this PR, we handle the sbt build. I had submitted [PR-23](https://github.com/ihji/sbt-antlr4/pull/23) to enhance the sbt-antlr plugin to make is possible to pass the error on warning option.
## How was this patch tested?
Force an warning in the grammar file to check if the build fails. Then remove the warning to verify the build succeeds.
Closes#24060 from dilipbiswal/sbt_build_antlr.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
`Saveable` interface introduces `formatVersion` which is protected and it is used nowhere. So the PR proposes to remove it.
## How was this patch tested?
existing tests
Closes#22830 from mgaido91/SPARK-25838.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The java version here means versions of javac source, javac target, scalac target. They could be consolidated as a single version (currently 1.8)
| |javac|scalac |
|------|-----|---------|
|source|1.8 |2.12/2.11|
|target|1.8 |1.8 |
The current issues are as follows
* Maven build defines a single property (`java.version`) to specify java version while SBT build defines different properties for javac (`javacJVMVersion`) and scalac (`scalacJVMVersion`). SBT build should use a single property as Maven build does.
* Furthermore, it's better for SBT build to refer to `java.version` defined by Maven build. This is possible since we've already been using sbt-pom-reader.
## How was this patch tested?
Tested locally.
```
build/mvn clean compile
build/sbt clean compile
```
Closes#23724 from seancxmao/specify-java-version-once.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently ANTLR v4 versions used by Maven and SBT are slightly different. Maven uses `4.7.1` while SBT uses `4.7`.
* Maven(`pom.xml`): `<antlr4.version>4.7.1</antlr4.version>`
* SBT(`project/SparkBuild.scala`): `antlr4Version in Antlr4 := "4.7"`
We should make Maven and SBT use a single version. Furthermore we'd better specify antlr4 version in one place to avoid mismatch between Maven and SBT in the future.
This PR lets SBT use antlr4 version specified in Maven POM file, rather than specify its own antlr4 version. This is in the same as how `hadoop.version` is specified in `project/SparkBuild.scala`
## How was this patch tested?
Test locally.
After run `sbt compile`, Java files generated by ANTLR are located at:
```
sql/catalyst/target/scala-2.12/src_managed/main/antlr4/org/apache/spark/sql/catalyst/parser/*.java
```
These Java files have a comment at the head. We can see now SBT uses ANTLR `4.7.1`.
```
// Generated from .../spark/sql/catalyst/src/main/antlr4/org/apache/spark/sql/catalyst/parser/SqlBase.g4 by ANTLR 4.7.1
```
Closes#23713 from seancxmao/antlr4-version-consistent.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This patch proposes adding a new configuration on SHS: custom executor log URL pattern. This will enable end users to replace executor logs to other than RM provide, like external log service, which enables to serve executor logs when NodeManager becomes unavailable in case of YARN.
End users can build their own of custom executor log URLs with pre-defined patterns which would be vary on each resource manager. This patch adds some patterns to YARN resource manager. (For others, there's even no executor log url available so cannot define patterns as well.)
Please refer the doc change as well as added UTs in this patch to see how to set up the feature.
## How was this patch tested?
Added UT, as well as manual test with YARN cluster
Closes#23260 from HeartSaVioR/SPARK-26311.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This takes over #19621 to add multi-column support to StringIndexer:
1. Supports encoding multiple columns.
2. Previously, when specifying `frequencyDesc` or `frequencyAsc` as `stringOrderType` param in `StringIndexer`, in case of equal frequency, the order of strings is undefined. After this change, the strings with equal frequency are further sorted alphabetically.
## How was this patch tested?
Added tests.
Closes#20146 from viirya/SPARK-11215.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
There are ugly provided dependencies inside core for the following:
* Hive
* Kafka
In this PR I've extracted them out. This PR contains the following:
* Token providers are now loaded with service loader
* Hive token provider moved to hive project
* Kafka token provider extracted into a new project
## How was this patch tested?
Existing + newly added unit tests.
Additionally tested on cluster.
Closes#23499 from gaborgsomogyi/SPARK-26254.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This change exposes the `df` (document frequency) as a public val along with the number of documents (`m`) as part of the IDF model.
* The document frequency is returned as an `Array[Long]`
* If the minimum document frequency is set, this is considered in the df calculation. If the count is less than minDocFreq, the df is 0 for such terms
* numDocs is not very required. But it can be useful, if we plan to provide a provision in future for user to give their own idf function, instead of using a default (log((1+m)/(1+df))). In such cases, the user can provide a function taking input of `m` and `df` and returning the idf value
* Pyspark changes
## How was this patch tested?
The existing test case was edited to also check for the document frequency values.
I am not very good with python or pyspark. I have committed and run tests based on my understanding. Kindly let me know if I have missed anything
Reviewer request: mengxr zjffdu yinxusen
Closes#23549 from purijatin/master.
Authored-by: Jatin Puri <purijatin@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
I know that yarn provided all hadoop configurations. But I guess it may be fine that the historyserver unify all configuration in it. It will be convenient for us to debug some problems.
## How was this patch tested?
Closes#23486 from deshanxiao/spark-26457.
Lead-authored-by: xiaodeshan <xiaodeshan@xiaomi.com>
Co-authored-by: deshanxiao <42019462+deshanxiao@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Increase test memory to avoid OOM in TimSort-related tests.
## How was this patch tested?
Existing tests.
Closes#23425 from srowen/SPARK-26306.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The PR adds the `trainingCost` value to the `BisectingKMeansSummary`, in order to expose the information retrievable by running `computeCost` on the training dataset. This fills the gap with `KMeans` implementation.
## How was this patch tested?
improved UTs
Closes#22764 from mgaido91/SPARK-25765.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
A followup of https://github.com/apache/spark/pull/23178 , to keep binary compability by using abstract class.
## How was this patch tested?
Manual test. I created a simple app with Spark 2.4
```
object TryUDF {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().appName("test").master("local[*]").getOrCreate()
import spark.implicits._
val f1 = udf((i: Int) => i + 1)
println(f1.deterministic)
spark.range(10).select(f1.asNonNullable().apply($"id")).show()
spark.stop()
}
}
```
When I run it with current master, it fails with
```
java.lang.IncompatibleClassChangeError: Found interface org.apache.spark.sql.expressions.UserDefinedFunction, but class was expected
```
When I run it with this PR, it works
Closes#23351 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Multiple SparkContexts are discouraged and it has been warning for last 4 years, see SPARK-4180. It could cause arbitrary and mysterious error cases, see SPARK-2243.
Honestly, I didn't even know Spark still allows it, which looks never officially supported, see SPARK-2243.
I believe It should be good timing now to remove this configuration.
## How was this patch tested?
Each doc was manually checked and manually tested:
```
$ ./bin/spark-shell --conf=spark.driver.allowMultipleContexts=true
...
scala> new SparkContext()
org.apache.spark.SparkException: Only one SparkContext should be running in this JVM (see SPARK-2243).The currently running SparkContext was created at:
org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:939)
...
org.apache.spark.SparkContext$.$anonfun$assertNoOtherContextIsRunning$2(SparkContext.scala:2435)
at scala.Option.foreach(Option.scala:274)
at org.apache.spark.SparkContext$.assertNoOtherContextIsRunning(SparkContext.scala:2432)
at org.apache.spark.SparkContext$.markPartiallyConstructed(SparkContext.scala:2509)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:80)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:112)
... 49 elided
```
Closes#23311 from HyukjinKwon/SPARK-26362.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The evaluators BinaryClassificationEvaluator, RegressionEvaluator, and MulticlassClassificationEvaluator and the corresponding metrics classes BinaryClassificationMetrics, RegressionMetrics and MulticlassMetrics should use sample weight data.
I've closed the PR: https://github.com/apache/spark/pull/16557
as recommended in favor of creating three pull requests, one for each of the evaluators (binary/regression/multiclass) to make it easier to review/update.
The updates to the regression metrics were based on (and updated with new changes based on comments):
https://issues.apache.org/jira/browse/SPARK-11520
("RegressionMetrics should support instance weights")
but the pull request was closed as the changes were never checked in.
## How was this patch tested?
I added tests to the metrics class.
Closes#17085 from imatiach-msft/ilmat/regression-evaluate.
Authored-by: Ilya Matiach <ilmat@microsoft.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
SBT 0.13.14 ~ 1.1.1 has a bug on accessing `java.util.Base64.getDecoder` with JDK9+. It's fixed at 1.1.2 and backported to [0.13.18 (released on Nov 28th)](https://github.com/sbt/sbt/releases/tag/v0.13.18). This PR aims to update SBT.
## How was this patch tested?
Pass the Jenkins with the building and existing tests.
Closes#23270 from dongjoon-hyun/SPARK-26317.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
1. Implement `SQLShuffleWriteMetricsReporter` on the SQL side as the customized `ShuffleWriteMetricsReporter`.
2. Add shuffle write metrics to `ShuffleExchangeExec`, and use these metrics to create corresponding `SQLShuffleWriteMetricsReporter` in shuffle dependency.
3. Rework on `ShuffleMapTask` to add new class named `ShuffleWriteProcessor` which control shuffle write process, we use sql shuffle write metrics by customizing a ShuffleWriteProcessor on SQL side.
## How was this patch tested?
Add UT in SQLMetricsSuite.
Manually test locally, update screen shot to document attached in JIRA.
Closes#23207 from xuanyuanking/SPARK-26193.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Adds a new method to SparkAppHandle called getError which returns
the exception (if present) that caused the underlying Spark app to
fail.
New tests added to SparkLauncherSuite for the new method.
Closes#21849Closes#23221 from vanzin/SPARK-24243.
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
docker-image-tool.sh requires explicit argument to create the python
image now; do that from the sbt integration tests target too.
Closes#23172 from vanzin/SPARK-25957.followup.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
It's a bad idea to use case class as public API, as it has a very wide surface. For example, the `copy` method, its fields, the companion object, etc.
For a particular case, `UserDefinedFunction`. It has a private constructor, and I believe we only want users to access a few methods:`apply`, `nullable`, `asNonNullable`, etc.
However, all its fields, and `copy` method, and the companion object are public unexpectedly. As a result, we made many tricks to work around the binary compatibility issues.
This PR proposes to only make interfaces public, and hide implementations behind with a private class. Now `UserDefinedFunction` is a pure trait, and the concrete implementation is `SparkUserDefinedFunction`, which is private.
Changing class to interface is not binary compatible(but source compatible), so 3.0 is a good chance to do it.
This is the first PR to go with this direction. If it's accepted, I'll create a umbrella JIRA and fix all the public case classes.
## How was this patch tested?
existing tests.
Closes#23178 from cloud-fan/udf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is the first step of the data source v2 API refactor [proposal](https://docs.google.com/document/d/1uUmKCpWLdh9vHxP7AWJ9EgbwB_U6T3EJYNjhISGmiQg/edit?usp=sharing)
It adds the new API for batch read, without removing the old APIs, as they are still needed for streaming sources.
More concretely, it adds
1. `TableProvider`, works like an anonymous catalog
2. `Table`, represents a structured data set.
3. `ScanBuilder` and `Scan`, a logical represents of data source scan
4. `Batch`, a physical representation of data source batch scan.
## How was this patch tested?
existing tests
Closes#23086 from cloud-fan/refactor-batch.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
We have deprecated `OneHotEncoder` at Spark 2.3.0 and introduced `OneHotEncoderEstimator`. At 3.0.0, we remove deprecated `OneHotEncoder` and rename `OneHotEncoderEstimator` to `OneHotEncoder`.
TODO: According to ML migration guide, we need to keep `OneHotEncoderEstimator` as an alias after renaming. This is not done at this patch in order to facilitate review.
## How was this patch tested?
Existing tests.
Closes#23100 from viirya/remove_one_hot_encoder.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
The "build context" for a docker image - basically the whole contents of the
current directory where "docker" is invoked - can be huge in a dev build,
easily breaking a couple of gigs.
Doing that copy 3 times during the build of docker images severely slows
down the process.
This patch creates a smaller build context - basically mimicking what the
make-distribution.sh script does, so that when building the docker images,
only the necessary bits are in the current directory. For PySpark and R that
is optimized further, since those images are built based on the previously
built Spark main image.
In my current local clone, the dir size is about 2G, but with this script
the "context" sent to docker is about 250M for the main image, 1M for the
pyspark image and 8M for the R image. That speeds up the image builds
considerably.
I also snuck in a fix to the k8s integration test dependencies in the sbt
build, so that the examples are properly built (without having to do it
manually).
Closes#23019 from vanzin/SPARK-26025.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This is the write side counterpart to https://github.com/apache/spark/pull/23105
## How was this patch tested?
No behavior change expected, as it is a straightforward refactoring. Updated all existing test cases.
Closes#23106 from rxin/SPARK-26141.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request?
The PR removes the deprecated method `computeCost` of `KMeans`.
## How was this patch tested?
NA
Closes#22875 from mgaido91/SPARK-25867.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The setter methods are deprecated since 2.1 for the models of regression and classification using trees. The deprecation was stating that the method would have been removed in 3.0. Hence the PR removes the deprecated method.
## How was this patch tested?
NA
Closes#23093 from mgaido91/SPARK-26127.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Update many plugins we use to the latest version, especially MiMa, which entails excluding some new errors on old changes.
## How was this patch tested?
N/A
Closes#23087 from srowen/Plugins.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The build has a lot of deprecation warnings. Some are new in Scala 2.12 and Java 11. We've fixed some, but I wanted to take a pass at fixing lots of easy miscellaneous ones here.
They're too numerous and small to list here; see the pull request. Some highlights:
- `BeanInfo` is deprecated in 2.12, and BeanInfo classes are pretty ancient in Java. Instead, case classes can explicitly declare getters
- Eta expansion of zero-arg methods; foo() becomes () => foo() in many cases
- Floating-point Range is inexact and deprecated, like 0.0 to 100.0 by 1.0
- finalize() is finally deprecated (just needs to be suppressed)
- StageInfo.attempId was deprecated and easiest to remove here
I'm not now going to touch some chunks of deprecation warnings:
- Parquet deprecations
- Hive deprecations (particularly serde2 classes)
- Deprecations in generated code (mostly Thriftserver CLI)
- ProcessingTime deprecations (we may need to revive this class as internal)
- many MLlib deprecations because they concern methods that may be removed anyway
- a few Kinesis deprecations I couldn't figure out
- Mesos get/setRole, which I don't know well
- Kafka/ZK deprecations (e.g. poll())
- Kinesis
- a few other ones that will probably resolve by deleting a deprecated method
## How was this patch tested?
Existing tests, including manual testing with the 2.11 build and Java 11.
Closes#23065 from srowen/SPARK-26090.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Our `GBTClassifier` supports only `variance` impurity. But unfortunately, its `impurity` param by default contains the value `gini`: it is not even modifiable by the user and it differs from the actual impurity used, which is `variance`. This issue does not limit to a wrong value returned for it if the user queries by `getImpurity`, but it also affect the load of a saved model, as its `impurityStats` are created as `gini` (since this is the value stored for the model impurity) which leads to wrong `featureImportances` in model loaded from saved ones.
The PR changes the `impurity` param used to one which allows only the value `variance`.
## How was this patch tested?
modified UT
Closes#22986 from mgaido91/SPARK-25959.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR makes Spark's default Scala version as 2.12, and Scala 2.11 will be the alternative version. This implies that Scala 2.12 will be used by our CI builds including pull request builds.
We'll update the Jenkins to include a new compile-only jobs for Scala 2.11 to ensure the code can be still compiled with Scala 2.11.
## How was this patch tested?
existing tests
Closes#22967 from dbtsai/scala2.12.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Since Spark 2.4.0 is already in maven repo, we can Bump previousSparkVersion in MimaBuild.scala to be 2.4.0.
Note that, seems we forgot to do it for branch 2.4, so this PR also updates MimaExcludes.scala
## How was this patch tested?
N/A
Closes#22977 from cloud-fan/mima.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add `Locale.ROOT` to all internal calls to String `toLowerCase`, `toUpperCase`
## How was this patch tested?
existing tests
Closes#22975 from zhengruifeng/Tokenizer_Locale.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
- Remove some AccumulableInfo .apply() methods
- Remove non-label-specific multiclass precision/recall/fScore in favor of accuracy
- Remove toDegrees/toRadians in favor of degrees/radians (SparkR: only deprecated)
- Remove approxCountDistinct in favor of approx_count_distinct (SparkR: only deprecated)
- Remove unused Python StorageLevel constants
- Remove Dataset unionAll in favor of union
- Remove unused multiclass option in libsvm parsing
- Remove references to deprecated spark configs like spark.yarn.am.port
- Remove TaskContext.isRunningLocally
- Remove ShuffleMetrics.shuffle* methods
- Remove BaseReadWrite.context in favor of session
- Remove Column.!== in favor of =!=
- Remove Dataset.explode
- Remove Dataset.registerTempTable
- Remove SQLContext.getOrCreate, setActive, clearActive, constructors
Not touched yet
- everything else in MLLib
- HiveContext
- Anything deprecated more recently than 2.0.0, generally
## How was this patch tested?
Existing tests
Closes#22921 from srowen/SPARK-25908.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
The integration tests can now be run in sbt if the right profile
is enabled, using the "test" task under the respective project.
This avoids having to fall back to maven to run the tests, which
invalidates all your compiled stuff when you go back to sbt, making
development way slower than it should.
There's also a task to run the tests directly without refreshing
the docker images, which is helpful if you just made a change to
the submission code which should not affect the code in the images.
The sbt tasks currently are not very customizable; there's some
very minor things you can set in the sbt shell itself, but otherwise
it's hardcoded to run on minikube.
I also had to make some slight adjustments to the IT code itself,
mostly to remove assumptions about the existing harness.
Tested on sbt and maven.
Closes#22909 from vanzin/SPARK-25897.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Remove JavaSparkContextVarargsWorkaround
## How was this patch tested?
Existing tests.
Closes#22729 from srowen/SPARK-25737.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Remove deprecated accumulator v1
## How was this patch tested?
Existing tests.
Closes#22730 from srowen/SPARK-16775.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The SQL execution listener framework was created from scratch(see https://github.com/apache/spark/pull/9078). It didn't leverage what we already have in the spark listener framework, and one major problem is, the listener runs on the spark execution thread, which means a bad listener can block spark's query processing.
This PR re-implements the SQL execution listener framework. Now `ExecutionListenerManager` is just a normal spark listener, which watches the `SparkListenerSQLExecutionEnd` events and post events to the
user-provided SQL execution listeners.
## How was this patch tested?
existing tests.
Closes#22674 from cloud-fan/listener.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Remove Kafka 0.8 integration
## How was this patch tested?
Existing tests, build scripts
Closes#22703 from srowen/SPARK-25705.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fix old oversight in API: Java `flatMapValues` needs a `FlatMapFunction`
## How was this patch tested?
Existing tests.
Closes#22690 from srowen/SPARK-19287.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Removes all vestiges of Flume in the build, for Spark 3.
I don't think this needs Jenkins config changes.
## How was this patch tested?
Existing tests.
Closes#22692 from srowen/SPARK-25598.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Remove SnappyOutputStreamWrapper and other workaround now that new Snappy fixes these.
See also https://github.com/apache/spark/pull/21176 and comments it links to.
## How was this patch tested?
Existing tests
Closes#22691 from srowen/SPARK-24109.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is the same as #22492 but for master branch. Revert SPARK-14681 to avoid API breaking changes.
cc: WeichenXu123
## How was this patch tested?
Existing unit tests.
Closes#22618 from mengxr/SPARK-25321.master.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.0.0-SNAPSHOT.
## How was this patch tested?
N/A
Closes#22606 from gatorsmile/bump3.0.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In the dev list, we can still discuss whether the next version is 2.5.0 or 3.0.0. Let us first bump the master branch version to `2.5.0-SNAPSHOT`.
## How was this patch tested?
N/A
Closes#22426 from gatorsmile/bumpVersionMaster.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
Add new executor level memory metrics (JVM used memory, on/off heap execution memory, on/off heap storage memory, on/off heap unified memory, direct memory, and mapped memory), and expose via the executors REST API. This information will help provide insight into how executor and driver JVM memory is used, and for the different memory regions. It can be used to help determine good values for spark.executor.memory, spark.driver.memory, spark.memory.fraction, and spark.memory.storageFraction.
## What changes were proposed in this pull request?
An ExecutorMetrics class is added, with jvmUsedHeapMemory, jvmUsedNonHeapMemory, onHeapExecutionMemory, offHeapExecutionMemory, onHeapStorageMemory, and offHeapStorageMemory, onHeapUnifiedMemory, offHeapUnifiedMemory, directMemory and mappedMemory. The new ExecutorMetrics is sent by executors to the driver as part of the Heartbeat. A heartbeat is added for the driver as well, to collect these metrics for the driver.
The EventLoggingListener store information about the peak values for each metric, per active stage and executor. When a StageCompleted event is seen, a StageExecutorsMetrics event will be logged for each executor, with peak values for the stage.
The AppStatusListener records the peak values for each memory metric.
The new memory metrics are added to the executors REST API.
## How was this patch tested?
New unit tests have been added. This was also tested on our cluster.
Author: Edwina Lu <edlu@linkedin.com>
Author: Imran Rashid <irashid@cloudera.com>
Author: edwinalu <edwina.lu@gmail.com>
Closes#21221 from edwinalu/SPARK-23429.2.
## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/22259 .
Scala case class has a wide surface: apply, unapply, accessors, copy, etc.
In https://github.com/apache/spark/pull/22259 , we change the type of `UserDefinedFunction.inputTypes` from `Option[Seq[DataType]]` to `Option[Seq[Schema]]`. This breaks backward compatibility.
This PR changes the type back, and use a `var` to keep the new nullable info.
## How was this patch tested?
N/A
Closes#22319 from cloud-fan/revert.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
I made one pass over barrier APIs added to Spark 2.4 and updates some scopes and docs. I will update Python docs once Scala doc was reviewed.
One major issue is that `BarrierTaskContext` implements `TaskContextImpl` that exposes some public methods. And internally there were several direct references to `TaskContextImpl` methods instead of `TaskContext`. This PR moved some methods from `TaskContextImpl` to `TaskContext`, remaining package private, and used delegate methods to avoid inheriting `TaskContextImp` and exposing unnecessary APIs.
TODOs:
- [x] scala doc
- [x] python doc (#22261 ).
Closes#22240 from mengxr/SPARK-25248.
Authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
Improve build for Scala 2.12. Current build for sbt fails on the subproject `repl`:
```
[info] Compiling 6 Scala sources to /Users/rendong/wdi/spark/repl/target/scala-2.12/classes...
[error] /Users/rendong/wdi/spark/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoopInterpreter.scala:80: overriding lazy value importableSymbolsWithRenames in class ImportHandler of type List[(this.intp.global.Symbol, this.intp.global.Name)];
[error] lazy value importableSymbolsWithRenames needs `override' modifier
[error] lazy val importableSymbolsWithRenames: List[(Symbol, Name)] = {
[error] ^
[warn] /Users/rendong/wdi/spark/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala:53: variable addedClasspath in class ILoop is deprecated (since 2.11.0): use reset, replay or require to update class path
[warn] if (addedClasspath != "") {
[warn] ^
[warn] /Users/rendong/wdi/spark/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala:54: variable addedClasspath in class ILoop is deprecated (since 2.11.0): use reset, replay or require to update class path
[warn] settings.classpath append addedClasspath
[warn] ^
[warn] two warnings found
[error] one error found
[error] (repl/compile:compileIncremental) Compilation failed
[error] Total time: 93 s, completed 2018-9-3 10:07:26
```
## How was this patch tested?
```
./dev/change-scala-version.sh 2.12
## For Maven
./build/mvn -Pscala-2.12 [mvn commands]
## For SBT
sbt -Dscala.version=2.12.6
```
Closes#22310 from sadhen/SPARK-25298.
Authored-by: Darcy Shen <sadhen@zoho.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The PR adds the lift measure to Association rules.
## How was this patch tested?
existing and modified UTs
Closes#22236 from mgaido91/SPARK-10697.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Alternative take on https://github.com/apache/spark/pull/22063 that does not introduce udfInternal.
Resolve issue with inferring func types in 2.12 by instead using info captured when UDF is registered -- capturing which types are nullable (i.e. not primitive)
## How was this patch tested?
Existing tests.
Closes#22259 from srowen/SPARK-25044.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
When replicating large cached RDD blocks, it can be helpful to replicate
them as a stream, to avoid using large amounts of memory during the
transfer. This also allows blocks larger than 2GB to be replicated.
Added unit tests in DistributedSuite. Also ran tests on a cluster for
blocks > 2gb.
Closes#21451 from squito/clean_replication.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
When starting spark-shell from Mac terminal (MacOS High Sirra Version 10.13.6), Getting exception
[ERROR] Failed to construct terminal; falling back to unsupported
java.lang.NumberFormatException: For input string: "0x100"
at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
at java.lang.Integer.parseInt(Integer.java:580)
at java.lang.Integer.valueOf(Integer.java:766)
at jline.internal.InfoCmp.parseInfoCmp(InfoCmp.java:59)
at jline.UnixTerminal.parseInfoCmp(UnixTerminal.java:242)
at jline.UnixTerminal.<init>(UnixTerminal.java:65)
at jline.UnixTerminal.<init>(UnixTerminal.java:50)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at java.lang.Class.newInstance(Class.java:442)
at jline.TerminalFactory.getFlavor(TerminalFactory.java:211)
This issue is due a jline defect : https://github.com/jline/jline2/issues/281, which is fixed in Jline 2.14.4, bumping up JLine version in spark to version >= Jline 2.14.4 will fix the issue
## How was this patch tested?
No new UT/automation test added, after upgrade to latest Jline version 2.14.6, manually tested spark shell features
Closes#22130 from vinodkc/br_UpgradeJLineVersion.
Authored-by: Vinod KC <vinod.kc.in@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In MultilayerPerceptronClassifier, we use RDD operation to encode labels for now. I think we should use ML's OneHotEncoderEstimator/Model to do the encoding.
## How was this patch tested?
Existing tests.
Closes#20232 from viirya/SPARK-23042.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Added the number of iterations in `ClusteringSummary`. This is an helpful information in evaluating how to eventually modify the parameters in order to get a better model.
## How was this patch tested?
modified existing UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20701 from mgaido91/SPARK-23528.
## What changes were proposed in this pull request?
Apache Avro (https://avro.apache.org) is a popular data serialization format. It is widely used in the Spark and Hadoop ecosystem, especially for Kafka-based data pipelines. Using the external package https://github.com/databricks/spark-avro, Spark SQL can read and write the avro data. Making spark-Avro built-in can provide a better experience for first-time users of Spark SQL and structured streaming. We expect the built-in Avro data source can further improve the adoption of structured streaming.
The proposal is to inline code from spark-avro package (https://github.com/databricks/spark-avro). The target release is Spark 2.4.
[Built-in AVRO Data Source In Spark 2.4.pdf](https://github.com/apache/spark/files/2181511/Built-in.AVRO.Data.Source.In.Spark.2.4.pdf)
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21742 from gengliangwang/export_avro.
## What changes were proposed in this pull request?
During SPARK-24418 (Upgrade Scala to 2.11.12 and 2.12.6), we upgrade `jline` version together. So, `mvn` works correctly. However, `sbt` brings old jline library and is hitting `NoSuchMethodError` in `master` branch, see https://github.com/apache/spark/pull/21495#issuecomment-401560826. This overrides jline version in SBT to make sbt build work.
## How was this patch tested?
Manually test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21692 from viirya/SPARK-24715.
These changes allow an RPCHandler to receive an upload as a stream of
data, without having to buffer the entire message in the FrameDecoder.
The primary use case is for replicating large blocks. By itself, this change is adding dead-code that is not being used -- it is a step towards SPARK-24296.
Added unit tests for handling streaming data, including successfully sending data, and failures in reading the stream with concurrent requests.
Summary of changes:
* Introduce a new UploadStream RPC which is sent to push a large payload as a stream (in contrast, the pre-existing StreamRequest and StreamResponse RPCs are used for pull-based streaming).
* Generalize RpcHandler.receive() to support requests which contain streams.
* Generalize StreamInterceptor to handle both request and response messages (previously it only handled responses).
* Introduce StdChannelListener to abstract away common logging logic in ChannelFuture listeners.
Author: Imran Rashid <irashid@cloudera.com>
Closes#21346 from squito/upload_stream.
## What changes were proposed in this pull request?
Fix java checkstyle failure of kubernetes-integration-tests
## How was this patch tested?
Checked manually on my local environment.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#21545 from jiangxb1987/k8s-checkstyle.
Apply the suggestion on the bug to fix source links. Tested with
the 2.3.1 release docs.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#21521 from vanzin/SPARK-23732.
## What changes were proposed in this pull request?
Upgrade SBT to 0.13.17 with Scala 2.10.7 for JDK9+
## How was this patch tested?
Existing tests
Author: DB Tsai <d_tsai@apple.com>
Closes#21458 from dbtsai/sbt.
## What changes were proposed in this pull request?
This PR proposes to check Java lint via SBT for Jenkins. It uses the SBT wrapper for checkstyle.
I manually tested. If we build the codes once, running this script takes 2 mins at maximum in my local:
Test codes:
```
Checkstyle failed at following occurrences:
[error] Checkstyle error found in /.../spark/core/src/test/java/test/org/apache/spark/JavaAPISuite.java:82: Line is longer than 100 characters (found 103).
[error] 1 issue(s) found in Checkstyle report: /.../spark/core/target/checkstyle-test-report.xml
[error] Checkstyle error found in /.../spark/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java:84: Line is longer than 100 characters (found 115).
[error] 1 issue(s) found in Checkstyle report: /.../spark/sql/hive/target/checkstyle-test-report.xml
...
```
Main codes:
```
Checkstyle failed at following occurrences:
[error] Checkstyle error found in /.../spark/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/InputPartition.java:39: Line is longer than 100 characters (found 104).
[error] Checkstyle error found in /.../spark/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/InputPartitionReader.java:26: Line is longer than 100 characters (found 110).
[error] Checkstyle error found in /.../spark/sql/core/src/main/java/org/apache/spark/sql/sources/v2/reader/InputPartitionReader.java:30: Line is longer than 100 characters (found 104).
...
```
## How was this patch tested?
Manually tested. Jenkins build should test this.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21399 from HyukjinKwon/SPARK-22269.
## What changes were proposed in this pull request?
The ultimate goal is for listeners to onTaskEnd to receive metrics when a task is killed intentionally, since the data is currently just thrown away. This is already done for ExceptionFailure, so this just copies the same approach.
## How was this patch tested?
Updated existing tests.
This is a rework of https://github.com/apache/spark/pull/17422, all credits should go to noodle-fb
Author: Xianjin YE <advancedxy@gmail.com>
Author: Charles Lewis <noodle@fb.com>
Closes#21165 from advancedxy/SPARK-20087.
## What changes were proposed in this pull request?
Add fit with validation set to spark.ml GBT
## How was this patch tested?
Will add later.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#21129 from WeichenXu123/gbt_fit_validation.
## What changes were proposed in this pull request?
We save ML's user-supplied params and default params as one entity in metadata. During loading the saved models, we set all the loaded params into created ML model instances as user-supplied params.
It causes some problems, e.g., if we strictly disallow some params to be set at the same time, a default param can fail the param check because it is treated as user-supplied param after loading.
The loaded default params should not be set as user-supplied params. We should save ML default params separately in metadata.
For backward compatibility, when loading metadata, if it is a metadata file from previous Spark, we shouldn't raise error if we can't find the default param field.
## How was this patch tested?
Pass existing tests and added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20633 from viirya/save-ml-default-params.
The current in-process launcher implementation just calls the SparkSubmit
object, which, in case of errors, will more often than not exit the JVM.
This is not desirable since this launcher is meant to be used inside other
applications, and that would kill the application.
The change turns SparkSubmit into a class, and abstracts aways some of
the functionality used to print error messages and abort the submission
process. The default implementation uses the logging system for messages,
and throws exceptions for errors. As part of that I also moved some code
that doesn't really belong in SparkSubmit to a better location.
The command line invocation of spark-submit now uses a special implementation
of the SparkSubmit class that overrides those behaviors to do what is expected
from the command line version (print to the terminal, exit the JVM, etc).
A lot of the changes are to replace calls to methods such as "printErrorAndExit"
with the new API.
As part of adding tests for this, I had to fix some small things in the
launcher option parser so that things like "--version" can work when
used in the launcher library.
There is still code that prints directly to the terminal, like all the
Ivy-related code in SparkSubmitUtils, and other areas where some re-factoring
would help, like the CommandLineUtils class, but I chose to leave those
alone to keep this change more focused.
Aside from existing and added unit tests, I ran command line tools with
a bunch of different arguments to make sure messages and errors behave
like before.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20925 from vanzin/SPARK-22941.
## What changes were proposed in this pull request?
The PR adds the option to specify a distance measure in BisectingKMeans. Moreover, it introduces the ability to use the cosine distance measure in it.
## How was this patch tested?
added UTs + existing UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20600 from mgaido91/SPARK-23412.
## What changes were proposed in this pull request?
In this PR StorageStatus is made to private and simplified a bit moreover SparkContext.getExecutorStorageStatus method is removed. The reason of keeping StorageStatus is that it is usage from SparkContext.getRDDStorageInfo.
Instead of the method SparkContext.getExecutorStorageStatus executor infos are extended with additional memory metrics such as usedOnHeapStorageMemory, usedOffHeapStorageMemory, totalOnHeapStorageMemory, totalOffHeapStorageMemory.
## How was this patch tested?
By running existing unit tests.
Author: “attilapiros” <piros.attila.zsolt@gmail.com>
Author: Attila Zsolt Piros <2017933+attilapiros@users.noreply.github.com>
Closes#20546 from attilapiros/SPARK-20659.
## What changes were proposed in this pull request?
Bump previousSparkVersion in MimaBuild.scala to be 2.2.0 and add the missing exclusions to `v23excludes` in `MimaExcludes`. No item can be un-excluded in `v23excludes`.
## How was this patch tested?
The existing tests.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20264 from gatorsmile/bump22.
## What changes were proposed in this pull request?
This patch bumps the master branch version to `2.4.0-SNAPSHOT`.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20222 from gatorsmile/bump24.
## What changes were proposed in this pull request?
stageAttemptId added in TaskContext and corresponding construction modification
## How was this patch tested?
Added a new test in TaskContextSuite, two cases are tested:
1. Normal case without failure
2. Exception case with resubmitted stages
Link to [SPARK-22897](https://issues.apache.org/jira/browse/SPARK-22897)
Author: Xianjin YE <advancedxy@gmail.com>
Closes#20082 from advancedxy/SPARK-22897.
## What changes were proposed in this pull request?
Basic continuous execution, supporting map/flatMap/filter, with commits and advancement through RPC.
## How was this patch tested?
new unit-ish tests (exercising execution end to end)
Author: Jose Torres <jose@databricks.com>
Closes#19984 from jose-torres/continuous-impl.
Prevents Scala 2.12 scaladoc from blowing up attempting to parse java comments.
## What changes were proposed in this pull request?
Adds -no-java-comments to docs/scalacOptions under Scala 2.12. Also
moves scaladoc configs out of the TestSettings and into the standard sharedSettings
section in SparkBuild.scala.
## How was this patch tested?
SBT_OPTS=-Dscala-2.12 sbt
++2.12.4
tags/publishLocal
Author: Erik LaBianca <erik.labianca@gmail.com>
Closes#20042 from easel/scaladoc-212.
## What changes were proposed in this pull request?
Moves the -Xlint:unchecked flag in the sbt build configuration from Compile to (Compile, compile) scope, allowing publish and publishLocal commands to work.
## How was this patch tested?
Successfully published the spark-launcher subproject from within sbt successfully, where it fails without this patch.
Author: Erik LaBianca <erik.labianca@gmail.com>
Closes#20040 from easel/javadoc-xlint.
## What changes were proposed in this pull request?
MLlib ```LinearRegression``` supports _huber_ loss addition to _leastSquares_ loss. The huber loss objective function is:
Refer Eq.(6) and Eq.(8) in [A robust hybrid of lasso and ridge regression](http://statweb.stanford.edu/~owen/reports/hhu.pdf). This objective is jointly convex as a function of (w, σ) ∈ R × (0,∞), we can use L-BFGS-B to solve it.
The current implementation is a straight forward porting for Python scikit-learn [```HuberRegressor```](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html). There are some differences:
* We use mean loss (```lossSum/weightSum```), but sklearn uses total loss (```lossSum```).
* We multiply the loss function and L2 regularization by 1/2. It does not affect the result if we multiply the whole formula by a factor, we just keep consistent with _leastSquares_ loss.
So if fitting w/o regularization, MLlib and sklearn produce the same output. If fitting w/ regularization, MLlib should set ```regParam``` divide by the number of instances to match the output of sklearn.
## How was this patch tested?
Unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19020 from yanboliang/spark-3181.
The main goal of this change is to allow multiple cluster-mode
submissions from the same JVM, without having them end up with
mixed configuration. That is done by extending the SparkApplication
trait, and doing so was reasonably trivial for standalone and
mesos modes.
For YARN mode, there was a complication. YARN used a "SPARK_YARN_MODE"
system property to control behavior indirectly in a whole bunch of
places, mainly in the SparkHadoopUtil / YarnSparkHadoopUtil classes.
Most of the changes here are removing that.
Since we removed support for Hadoop 1.x, some methods that lived in
YarnSparkHadoopUtil can now live in SparkHadoopUtil. The remaining
methods don't need to be part of the class, and can be called directly
from the YarnSparkHadoopUtil object, so now there's a single
implementation of SparkHadoopUtil.
There were two places in the code that relied on SPARK_YARN_MODE to
make decisions about YARN-specific functionality, and now explicitly check
the master from the configuration for that instead:
* fetching the external shuffle service port, which can come from the YARN
configuration.
* propagation of the authentication secret using Hadoop credentials. This also
was cleaned up a little to not need so many methods in `SparkHadoopUtil`.
With those out of the way, actually changing the YARN client
to extend SparkApplication was easy.
Tested with existing unit tests, and also by running YARN apps
with auth and kerberos both on and off in a real cluster.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19631 from vanzin/SPARK-22372.
The only remaining use of this class was the SparkStatusTracker, which
was modified to use the new status store. The test code to wait for
executors was moved to TestUtils and now uses the SparkStatusTracker API.
Indirectly, ConsoleProgressBar also uses this data. Because it has
some lower latency requirements, a shortcut to efficiently get the
active stages from the active listener was added to the AppStateStore.
Now that all UI code goes through the status store to get its data,
the FsHistoryProvider can be cleaned up to only replay event logs
when needed - that is, when there is no pre-existing disk store for
the application.
As part of this change I also modified the streaming UI to read the needed
data from the store, which was missed in the previous patch that made
JobProgressListener redundant.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19750 from vanzin/SPARK-20650.
## What changes were proposed in this pull request?
This is a stripped down version of the `KubernetesClusterSchedulerBackend` for Spark with the following components:
- Static Allocation of Executors
- Executor Pod Factory
- Executor Recovery Semantics
It's step 1 from the step-wise plan documented [here](https://github.com/apache-spark-on-k8s/spark/issues/441#issuecomment-330802935).
This addition is covered by the [SPIP vote](http://apache-spark-developers-list.1001551.n3.nabble.com/SPIP-Spark-on-Kubernetes-td22147.html) which passed on Aug 31 .
## How was this patch tested?
- The patch contains unit tests which are passing.
- Manual testing: `./build/mvn -Pkubernetes clean package` succeeded.
- It is a **subset** of the entire changelist hosted in http://github.com/apache-spark-on-k8s/spark which is in active use in several organizations.
- There is integration testing enabled in the fork currently [hosted by PepperData](spark-k8s-jenkins.pepperdata.org:8080) which is being moved over to RiseLAB CI.
- Detailed documentation on trying out the patch in its entirety is in: https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html
cc rxin felixcheung mateiz (shepherd)
k8s-big-data SIG members & contributors: mccheah ash211 ssuchter varunkatta kimoonkim erikerlandson liyinan926 tnachen ifilonenko
Author: Yinan Li <liyinan926@gmail.com>
Author: foxish <ramanathana@google.com>
Author: mcheah <mcheah@palantir.com>
Closes#19468 from foxish/spark-kubernetes-3.
## What changes were proposed in this pull request?
Set `-ea` and `-Xss4m` consistently for tests, to fix in particular:
```
OrderingSuite:
...
- GenerateOrdering with ShortType
*** RUN ABORTED ***
java.lang.StackOverflowError:
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:370)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
at org.codehaus.janino.CodeContext.flowAnalysis(CodeContext.java:541)
...
```
## How was this patch tested?
Existing tests. Manually verified it resolves the StackOverflowError this intends to resolve.
Author: Sean Owen <sowen@cloudera.com>
Closes#19820 from srowen/SPARK-22607.
## What changes were proposed in this pull request?
We add a parameter whether to collect the full model list when CrossValidator/TrainValidationSplit training (Default is NOT), avoid the change cause OOM)
- Add a method in CrossValidatorModel/TrainValidationSplitModel, allow user to get the model list
- CrossValidatorModelWriter add a “option”, allow user to control whether to persist the model list to disk (will persist by default).
- Note: when persisting the model list, use indices as the sub-model path
## How was this patch tested?
Test cases added.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19208 from WeichenXu123/expose-model-list.
