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apache / spark / 5e49665ac39b49b875d6970f93df59aedd830fa5
commit5e49665ac39b49b875d6970f93df59aedd830fa5[log] [tgz]
authorBhuwan Sahni <bhuwan.sahni@databricks.com>Wed May 08 09:20:01 2024 +0900
committerJungtaek Lim <kabhwan.opensource@gmail.com>Wed May 08 09:20:01 2024 +0900
tree8ff4d015f02d82a0a1f6758b0f7544d3f3fab344
parent3d9d1f3dc05a2825bf315c68fc4e4232354dbd00 [diff]
[SPARK-47960][SS] Allow chaining other stateful operators after transformWithState operator

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

This PR adds support to define event time column in the output dataset of `TransformWithState` operator. The new event time column will be used to evaluate watermark expressions in downstream operators.

1. Note that the transformWithState operator does not enforce that values generated by user's computation adhere to the watermark semantics. (no output rows are generated which have event time less than watermark).
2. Updated the watermark value passed in TimerInfo as evictionWatermark, rather than lateEventsWatermark.
3. Ensure that event time column can only be defined in output if a watermark has been defined previously.

### Why are the changes needed?

This change is required to support chaining of stateful operators after `transformWithState`. Event time column is required to evaluate watermark expressions in downstream stateful operators.

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

Yes. Adds a new version of transformWithState API which allows redefining the event time column.

### How was this patch tested?

Added unit test cases.

### Was this patch authored or co-authored using generative AI tooling?

No

Closes #45376 from sahnib/tws-chaining-stateful-operators.

Authored-by: Bhuwan Sahni <bhuwan.sahni@databricks.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
16 files changed
tree: 8ff4d015f02d82a0a1f6758b0f7544d3f3fab344
  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.