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apache / spark / 2cdf4eb7c20393e47709d02454356981df05040a
commit2cdf4eb7c20393e47709d02454356981df05040a[log] [tgz]
authorHyukjinKwon <gurwls223@apache.org>Mon May 18 14:35:02 2020 +0900
committerHyukjinKwon <gurwls223@apache.org>Mon May 18 14:56:24 2020 +0900
tree05c02df3d8c7c0b3725aca35262f566ee5105aa7
parent88e00c367bccf51a343abae2ef734fa028c46f7b [diff]
[SPARK-31746][YARN][TESTS] Show the actual error message in LocalityPlacementStrategySuite

This PR proposes to show the actual traceback when "handle large number of containers and tasks (SPARK-18750)" test fails in `LocalityPlacementStrategySuite`.

**It does not fully resolve the JIRA SPARK-31746 yet**. I tried to reproduce in my local by controlling the factors in the tests but I couldn't. I double checked the changes in SPARK-18750 are still valid.

This test is flaky for an unknown reason (see https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/122768/testReport/org.apache.spark.deploy.yarn/LocalityPlacementStrategySuite/handle_large_number_of_containers_and_tasks__SPARK_18750_/):

```
sbt.ForkMain$ForkError: org.scalatest.exceptions.TestFailedException: java.lang.StackOverflowError did not equal null
	at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:530)
	at org.scalatest.Assertions.newAssertionFailedException$(Assertions.scala:529)
	at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1560)
	at org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:503)
```

After this PR, it will help to investigate the root cause:

**Before**:

```
[info] - handle large number of containers and tasks (SPARK-18750) *** FAILED *** (824 milliseconds)
[info]   java.lang.StackOverflowError did not equal null (LocalityPlacementStrategySuite.scala:49)
[info]   org.scalatest.exceptions.TestFailedException:
[info]   at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:530)
[info]   at org.scalatest.Assertions.newAssertionFailedException$(Assertions.scala:529)
[info]   at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1560)
[info]   at org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:503)
[info]   at org.apache.spark.deploy.yarn.LocalityPlacementStrategySuite.$anonfun$new$1(LocalityPlacementStrategySuite.scala:49)
[info]   at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
[info]   at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
[info]   at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
[info]   at org.scalatest.Transformer.apply(Transformer.scala:22)
[info]   at org.scalatest.Transformer.apply(Transformer.scala:20)
[info]   at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:186)
[info]   at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:157)
[info]   at org.scalatest.FunSuiteLike.invokeWithFixture$1(FunSuiteLike.scala:184)
[info]   at org.scalatest.FunSuiteLike.$anonfun$runTest$1(FunSuiteLike.scala:196)
[info]   at org.scalatest.SuperEngine.runTestImpl(Engine.scala:286)
[info]   at org.scalatest.FunSuiteLike.runTest(FunSuiteLike.scala:196)
[info]   at org.scalatest.FunSuiteLike.runTest$(FunSuiteLike.scala:178)
...
```

**After**:

```
[info] - handle large number of containers and tasks (SPARK-18750) *** FAILED *** (825 milliseconds)
[info]   StackOverflowError should not be thrown; however, got:
[info]
[info]    java.lang.StackOverflowError
[info]   	at scala.collection.TraversableLike.$anonfun$filterImpl$1(TraversableLike.scala:256)
[info]   	at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info]   	at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info]   	at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info]   	at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info]   	at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info]   	at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info]   	at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info]   	at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info]   	at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info]   	at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info]   	at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info]   	at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info]   	at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
[info]   	at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
[info]   	at scala.collection.MapLike$MappedValues.$anonfun$foreach$3(MapLike.scala:256)
...

```

No, dev-only.

Manually tested by reverting https://github.com/apache/spark/commit/76db394f2baedc2c7b7a52c05314a64ec9068263 locally.

Closes #28566 from HyukjinKwon/SPARK-31746.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
(cherry picked from commit 3bf7bf99e96fab754679a4f3c893995263161341)
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
1 file changed
tree: 05c02df3d8c7c0b3725aca35262f566ee5105aa7
  1. .github/
  2. assembly/
  3. bin/
  4. build/
  5. common/
  6. conf/
  7. core/
  8. data/
  9. dev/
  10. docs/
  11. examples/
  12. external/
  13. graphx/
  14. hadoop-cloud/
  15. launcher/
  16. licenses/
  17. licenses-binary/
  18. mllib/
  19. mllib-local/
  20. project/
  21. python/
  22. R/
  23. repl/
  24. resource-managers/
  25. sbin/
  26. sql/
  27. streaming/
  28. tools/
  29. .gitattributes
  30. .gitignore
  31. appveyor.yml
  32. CONTRIBUTING.md
  33. LICENSE
  34. LICENSE-binary
  35. NOTICE
  36. NOTICE-binary
  37. pom.xml
  38. README.md
  39. scalastyle-config.xml
README.md

Apache Spark

Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

https://spark.apache.org/

Jenkins Build AppVeyor Build PySpark Coverage

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.

Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

./build/mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.)

More detailed documentation is available from the project site, at “Building Spark”.

For general development tips, including info on developing Spark using an IDE, see “Useful Developer Tools”.

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:

./bin/spark-shell

Try the following command, which should return 1,000,000,000:

scala> spark.range(1000 * 1000 * 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:

./bin/pyspark

And run the following command, which should also return 1,000,000,000:

>>> spark.range(1000 * 1000 * 1000).count()

Example Programs

Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

will run the Pi example locally.

You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, “yarn” to run on YARN, and “local” to run locally with one thread, or “local[N]” to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests can be run using:

./dev/run-tests

Please see the guidance on how to run tests for a module, or individual tests.

There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md

A Note About Hadoop Versions

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

Please refer to the build documentation at “Specifying the Hadoop Version and Enabling YARN” for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.

Configuration

Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.

Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.