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apache / spark / c4df12cc884cddefcfcf8324b4d7b9349fb4f6a0
commitc4df12cc884cddefcfcf8324b4d7b9349fb4f6a0[log] [tgz]
authorTom van Bussel <tom.vanbussel@databricks.com>Tue May 07 17:51:43 2024 +0900
committerHyukjin Kwon <gurwls223@apache.org>Tue May 07 17:51:43 2024 +0900
treef3aa958d4736e170e228f28e4428544a9ac3976f
parent7f8ef96cea27d52d0bdda3808c6c48534dcd8567 [diff]
[SPARK-48113][CONNECT] Allow Plugins to integrate with Spark Connect

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

This PR enables Spark Connect plugins to process `Relation`s and `Expression`s using `SparkConnectPlanner` on the server side and to create `DataFrame`s using `Relation`s on the client side. This has been difficult due to the shading of the protobuf libraries, which meant that Java classes generated for the plugin's Protobuf message usage different base classes than the Java classes for Spark's protobuf messages. This was previously worked around by adding methods that accept a byte array (containing a serialized protobuf message) instead of a protobuf message. Unfortunately, this was not scalable as a lot of methods (such as `DataTypeProtoConverter.toCatalystType`) did not have this alternative. Luckily, we can avoid having to do this as the generated Java classes are not shaded and can still be used from plugins. Instead, plugins should serialize their own version of the generated Java classes for Spark Connect's protobuf messages and then parse them using Spark's (using the `ConnectProtoUtils` added in this PR).

### Why are the changes needed?

Enable the development of Spark Connect plugins.

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

No

### How was this patch tested?

Existing tests

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

No

Closes #46364 from tomvanbussel/SPARK-48113.

Authored-by: Tom van Bussel <tom.vanbussel@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
10 files changed
tree: f3aa958d4736e170e228f28e4428544a9ac3976f
  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.