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apache / spark / 2be03d81cea34ab08c44426837260c22c67e092e
commit2be03d81cea34ab08c44426837260c22c67e092e[log] [tgz]
authorEmil Ejbyfeldt <eejbyfeldt@liveintent.com>Tue Oct 31 11:19:32 2023 +0800
committerWenchen Fan <wenchen@databricks.com>Tue Oct 31 11:19:32 2023 +0800
treed5f530a5a2f4d44e7f89a1d55654a65ad67f6add
parent311602a849a376dbf96d5b5d5bd10dabc119b7e0 [diff]
[SPARK-45592][SQL] Correctness issue in AQE with InMemoryTableScanExec

### What changes were proposed in this pull request?
Fixes correctness issue in 3.5.0. The problem seems to be that when AQEShuffleRead does a coalesced read it can return a HashPartitioning with the coalesced number of partitions. This causes a correctness bug as the partitioning is not compatible for joins with other HashPartitioning even though the number of partitions matches. This is resolved in this patch by introducing CoalescedHashPartitioning and making AQEShuffleRead return that instead.

The fix was suggested by cloud-fan

> AQEShuffleRead should probably return a different partitioning, e.g. CoalescedHashPartitioning. It still satisfies ClusterDistribution, so Aggregate is fine and there will be no shuffle. For joins, two CoalescedHashPartitionings are compatible if they have the same original partition number and coalesce boundaries, and CoalescedHashPartitioning is not compatible with HashPartitioning.

### Why are the changes needed?
Correctness bug.

### Does this PR introduce _any_ user-facing change?
Yes, fixed correctness issue.

### How was this patch tested?
New and existing unit test.

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

Closes #43435 from eejbyfeldt/SPARK-45592.

Authored-by: Emil Ejbyfeldt <eejbyfeldt@liveintent.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
6 files changed
tree: d5f530a5a2f4d44e7f89a1d55654a65ad67f6add
  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. .asf.yaml
  31. .gitattributes
  32. .gitignore
  33. appveyor.yml
  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/

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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.