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apache / spark / 7733510d0403625c41710d7e79f810117aac2ced
commit7733510d0403625c41710d7e79f810117aac2ced[log] [tgz]
authorLiang-Chi Hsieh <viirya@gmail.com>Fri May 07 09:07:57 2021 -0700
committerLiang-Chi Hsieh <viirya@gmail.com>Fri May 07 09:09:25 2021 -0700
treef3b68a8204e26ceef2ed09fbf1c31d74153d80a3
parenteaf0553990be7261b669bd2fbb9b92873e257276 [diff]
[SPARK-35288][SQL] StaticInvoke should find the method without exact argument classes match

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

This patch proposes to make StaticInvoke able to find method with given method name even the parameter types do not exactly match to argument classes.

### Why are the changes needed?

Unlike `Invoke`, `StaticInvoke` only tries to get the method with exact argument classes. If the calling method's parameter types are not exactly matched with the argument classes, `StaticInvoke` cannot find the method.

`StaticInvoke` should be able to find the method under the cases too.

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

Yes. `StaticInvoke` can find a method even the argument classes are not exactly matched.

### How was this patch tested?

Unit test.

Closes #32413 from viirya/static-invoke.

Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
(cherry picked from commit 33fbf5647b4a5587c78ac51339c0cbc9d70547a4)
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
2 files changed
tree: f3b68a8204e26ceef2ed09fbf1c31d74153d80a3
  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 fast and general cluster computing system for Big Data. 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 Spark Streaming for stream processing.

http://spark.apache.org/

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 1000:

scala> sc.parallelize(1 to 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 1000:

>>> sc.parallelize(range(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.