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apache / spark / 596f680ea37c8fae77d2ba29d79cbc9339a04ca9
commit596f680ea37c8fae77d2ba29d79cbc9339a04ca9[log] [tgz]
authorjackylee-ch <lijunqing@baidu.com>Fri Jul 12 18:15:56 2024 +0800
committerKent Yao <yao@apache.org>Fri Jul 12 18:16:22 2024 +0800
tree9df707bc702df547b378ebf1d0181d83d8501461
parentb15a8725b25c0b3f78efcccfb1f69e8d7fbd9a72 [diff]
[SPARK-48845][SQL] GenericUDF catch exceptions from children

### What changes were proposed in this pull request?
This pr is trying to fix the syntax issues with GenericUDF since 3.5.0. The problem arose from DeferredObject currently passing a value instead of a function, which prevented users from catching exceptions in GenericUDF, resulting in semantic differences.

Here is an example case we encountered. Originally, the semantics were that udf_exception would throw an exception, while udf_catch_exception could catch the exception and return a null value. However, currently, any exception encountered by udf_exception will cause the program to fail.
```
select udf_catch_exception(udf_exception(col1)) from table
```

### Why are the changes needed?
For before Spark 3.5, we directly made the GenericUDF's DeferredObject lazy and evaluated the children in `function.evaluate(deferredObjects)`.
Now, we would run the children's code first. If an exception is thrown, we would make it lazy to GenericUDF's DeferredObject.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Newly added UT.

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

Closes #47268 from jackylee-ch/generic_udf_catch_exception_from_child_func.

Lead-authored-by: jackylee-ch <lijunqing@baidu.com>
Co-authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Kent Yao <yao@apache.org>
(cherry picked from commit 236d95738b6e50bc9ec54955e86d01b6dcf11c0e)
Signed-off-by: Kent Yao <yao@apache.org>
5 files changed
tree: 9df707bc702df547b378ebf1d0181d83d8501461
  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 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.