[SPARK-48105][SS] Fix the race condition between state store unloading and snapshotting

### What changes were proposed in this pull request?

* When we close the hdfs state store, we should only remove the entry from `loadedMaps` rather than doing the active data cleanup. JVM GC should be able to help us GC those objects.
* we should wait for the maintenance thread to stop before unloading the providers.

### Why are the changes needed?

There are two race conditions between state store snapshotting and state store unloading which could result in query failure and potential data corruption.

Case 1:
1. the maintenance thread pool encounters some issues and call the [stopMaintenanceTask,](https://github.com/apache/spark/blob/d9d79a54a3cd487380039c88ebe9fa708e0dcf23/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStore.scala#L774) this function further calls [threadPool.stop.](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStore.scala#L587) However, this function doesn't wait for the stop operation to be completed and move to do the state store [unload and clear.](https://github.com/apache/spark/blob/d9d79a54a3cd487380039c88ebe9fa708e0dcf23/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStore.scala#L775-L778)
2. the provider unload will [close the state store](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStore.scala#L719-L721) which [clear the values of loadedMaps](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/HDFSBackedStateStoreProvider.scala#L353-L355) for HDFS backed state store.
3. if the not-yet-stop maintenance thread is still running and trying to do the snapshot, but the data in the underlying `HDFSBackedStateStoreMap` has been removed. if this snapshot process completes successfully, then we will write corrupted data and the following batches will consume this corrupted data.

Case 2:

1. In executor_1, the maintenance thread is going to do the snapshot for state_store_1, it retrieves the `HDFSBackedStateStoreMap` object from the loadedMaps, after this, the maintenance thread [releases the lock of the loadedMaps](https://github.com/apache/spark/blob/c6696cdcd611a682ebf5b7a183e2970ecea3b58c/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/HDFSBackedStateStoreProvider.scala#L750-L751).
2. state_store_1 is loaded in another executor, e.g. executor_2.
3. another state store, state_store_2, is loaded on executor_1 and [reportActiveStoreInstance](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStore.scala#L854-L871) to driver.
4. executor_1 does the [unload](https://github.com/apache/spark/blob/c6696cdcd611a682ebf5b7a183e2970ecea3b58c/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStore.scala#L713) for those no longer active state store which clears the data entries in the `HDFSBackedStateStoreMap`
5. the snapshotting thread is terminated and uploads the incomplete snapshot to cloud because the [iterator doesn't have next element](https://github.com/apache/spark/blob/c6696cdcd611a682ebf5b7a183e2970ecea3b58c/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/HDFSBackedStateStoreProvider.scala#L634) after doing the clear.
6. future batches are consuming the corrupted data.

### Does this PR introduce _any_ user-facing change?

No

### How was this patch tested?
```
[info] Run completed in 2 minutes, 55 seconds.
[info] Total number of tests run: 153
[info] Suites: completed 1, aborted 0
[info] Tests: succeeded 153, failed 0, canceled 0, ignored 0, pending 0
[info] All tests passed.
[success] Total time: 271 s (04:31), completed May 2, 2024, 6:26:33 PM
```
before this change

```
[info] - state store unload/close happens during the maintenance *** FAILED *** (648 milliseconds)
[info]   Vector("a1", "a10", "a11", "a12", "a13", "a14", "a15", "a16", "a17", "a18", "a19", "a2", "a20", "a3", "a4", "a5", "a6", "a7", "a8", "a9") did not equal ArrayBuffer("a8") (StateStoreSuite.scala:414)
[info]   Analysis:
[info]   Vector1(0: "a1" -> "a8", 1: "a10" -> , 2: "a11" -> , 3: "a12" -> , 4: "a13" -> , 5: "a14" -> , 6: "a15" -> , 7: "a16" -> , 8: "a17" -> , 9: "a18" -> , 10: "a19" -> , 11: "a2" -> , 12: "a20" -> , 13: "a3" -> , 14: "a4" -> , 15: "a5" -> , 16: "a6" -> , 17: "a7" -> , 18: "a8" -> , 19: "a9" -> )
[info]   org.scalatest.exceptions.TestFailedException:
[info]   at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:472)
[info]   at org.scalatest.Assertions.newAssertionFailedException$(Assertions.scala:471)
[info]   at org.scalatest.Assertions$.newAssertionFailedException(Assertions.scala:1231)
[info]   at org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:1295)
[info]   at org.apache.spark.sql.execution.streaming.state.StateStoreSuite.$anonfun$new$39(StateStoreSuite.scala:414)
[info]   at org.apache.spark.sql.execution.streaming.state.StateStoreSuiteBase.tryWithProviderResource(StateStoreSuite.scala:1663)
[info]   at org.apache.spark.sql.execution.streaming.state.StateStoreSuite.$anonfun$new$38(StateStoreSuite.scala:394)
18:32:09.694 WARN org.apache.spark.sql.execution.streaming.state.StateStoreSuite:

===== POSSIBLE THREAD LEAK IN SUITE o.a.s.sql.execution.streaming.state.StateStoreSuite, threads: ForkJoinPool.commonPool-worker-1 (daemon=true) =====
[info]   at org.scalatest.enablers.Timed$$anon$1.timeoutAfter(Timed.scala:127)
[info]   at org.scalatest.concurrent.TimeLimits$.failAfterImpl(TimeLimits.scala:282)
[info]   at org.scalatest.concurrent.TimeLimits.failAfter(TimeLimits.scala:231)
[info]   at org.scalatest.concurrent.TimeLimits.failAfter$(TimeLimits.scala:230)
[info]   at org.apache.spark.SparkFunSuite.failAfter(SparkFunSuite.scala:69)
[info]   at org.apache.spark.SparkFunSuite.$anonfun$test$2(SparkFunSuite.scala:155)
[info]   at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
[info]   at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
[info]   at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
[info]   at org.scalatest.Transformer.apply(Transformer.scala:22)
[info]   at org.scalatest.Transformer.apply(Transformer.scala:20)
[info]   at org.scalatest.funsuite.AnyFunSuiteLike$$anon$1.apply(AnyFunSuiteLike.scala:226)
[info]   at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:227)
[info]   at org.scalatest.funsuite.AnyFunSuiteLike.invokeWithFixture$1(AnyFunSuiteLike.scala:224)
[info]   at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTest$1(AnyFunSuiteLike.scala:236)
[info]   at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
[info]   at org.scalatest.funsuite.AnyFunSuiteLike.runTest(AnyFunSuiteLike.scala:236)
[info]   at org.scalatest.funsuite.AnyFunSuiteLike.runTest$(AnyFunSuiteLike.scala:218)
[info]   at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(SparkFunSuite.scala:69)
[info]   at org.scalatest.BeforeAndAfterEach.runTest(BeforeAndAfterEach.scala:234)
[info]   at org.scalatest.BeforeAndAfterEach.runTest$(BeforeAndAfterEach.scala:227)
[info]   at org.apache.spark.sql.execution.streaming.state.StateStoreSuite.org$scalatest$BeforeAndAfter$$super$runTest(StateStoreSuite.scala:90)
[info]   at org.scalatest.BeforeAndAfter.runTest(BeforeAndAfter.scala:213)
[info]   at org.scalatest.BeforeAndAfter.runTest$(BeforeAndAfter.scala:203)
[info]   at org.apache.spark.sql.execution.streaming.state.StateStoreSuite.runTest(StateStoreSuite.scala:90)
[info]   at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTests$1(AnyFunSuiteLike.scala:269)
[info]   at org.scalatest.SuperEngine.$anonfun$runTestsInBranch$1(Engine.scala:413)
[info]   at scala.collection.immutable.List.foreach(List.scala:334)
[info]   at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401)
[info]   at org.scalatest.SuperEngine.runTestsInBranch(Engine.scala:396)
[info]   at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:475)
[info]   at org.scalatest.funsuite.AnyFunSuiteLike.runTests(AnyFunSuiteLike.scala:269)
[info]   at org.scalatest.funsuite.AnyFunSuiteLike.runTests$(AnyFunSuiteLike.scala:268)
[info]   at org.scalatest.funsuite.AnyFunSuite.runTests(AnyFunSuite.scala:1564)
[info]   at org.scalatest.Suite.run(Suite.scala:1114)
[info]   at org.scalatest.Suite.run$(Suite.scala:1096)
[info]   at org.scalatest.funsuite.AnyFunSuite.org$scalatest$funsuite$AnyFunSuiteLike$$super$run(AnyFunSuite.scala:1564)
[info]   at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$run$1(AnyFunSuiteLike.scala:273)
[info]   at org.scalatest.SuperEngine.runImpl(Engine.scala:535)
[info]   at org.scalatest.funsuite.AnyFunSuiteLike.run(AnyFunSuiteLike.scala:273)
[info]   at org.scalatest.funsuite.AnyFunSuiteLike.run$(AnyFunSuiteLike.scala:272)
[info]   at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:69)
[info]   at org.scalatest.BeforeAndAfterAll.liftedTree1$1(BeforeAndAfterAll.scala:213)
[info]   at org.scalatest.BeforeAndAfterAll.run(BeforeAndAfterAll.scala:210)
[info]   at org.scalatest.BeforeAndAfterAll.run$(BeforeAndAfterAll.scala:208)
[info]   at org.apache.spark.sql.execution.streaming.state.StateStoreSuite.org$scalatest$BeforeAndAfter$$super$run(StateStoreSuite.scala:90)
[info]   at org.scalatest.BeforeAndAfter.run(BeforeAndAfter.scala:273)
[info]   at org.scalatest.BeforeAndAfter.run$(BeforeAndAfter.scala:271)
[info]   at org.apache.spark.sql.execution.streaming.state.StateStoreSuite.run(StateStoreSuite.scala:90)
[info]   at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:321)
[info]   at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:517)
[info]   at sbt.ForkMain$Run.lambda$runTest$1(ForkMain.java:414)
[info]   at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
[info]   at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
[info]   at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
[info]   at java.base/java.lang.Thread.run(Thread.java:840)
[info] Run completed in 2 seconds, 4 milliseconds.
[info] Total number of tests run: 1
[info] Suites: completed 1, aborted 0
[info] Tests: succeeded 0, failed 1, canceled 0, ignored 0, pending 0
[info] *** 1 TEST FAILED ***

```

### Was this patch authored or co-authored using generative AI tooling?
No

Closes #46351 from huanliwang-db/race.

Authored-by: Huanli Wang <huanli.wang@databricks.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
4 files changed
tree: c9aa374a1e9a0f0c9aa797752a1bc732a26b1ea5
  1. .github/
  2. assembly/
  3. bin/
  4. binder/
  5. build/
  6. common/
  7. conf/
  8. connector/
  9. core/
  10. data/
  11. dev/
  12. docs/
  13. examples/
  14. graphx/
  15. hadoop-cloud/
  16. launcher/
  17. licenses/
  18. licenses-binary/
  19. mllib/
  20. mllib-local/
  21. project/
  22. python/
  23. R/
  24. repl/
  25. resource-managers/
  26. sbin/
  27. sql/
  28. streaming/
  29. tools/
  30. ui-test/
  31. .asf.yaml
  32. .gitattributes
  33. .gitignore
  34. CONTRIBUTING.md
  35. LICENSE
  36. LICENSE-binary
  37. NOTICE
  38. NOTICE-binary
  39. pom.xml
  40. README.md
  41. 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, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

https://spark.apache.org/

GitHub Actions Build PySpark Coverage PyPI Downloads

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 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.