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apache / spark / 92e650c9ccab7a5f4aa25af2d6c0d6052dfe576b
commit92e650c9ccab7a5f4aa25af2d6c0d6052dfe576b[log] [tgz]
authorCheng Pan <chengpan@apache.org>Thu Dec 12 20:47:18 2024 +0800
committeryangjie01 <yangjie01@baidu.com>Thu Dec 12 20:47:18 2024 +0800
tree9357dd96cd76394cb9ebdf11cc15d358264161a8
parente97580a904dab438b2b5a51e49db63602dc45592 [diff]
[SPARK-50545][CORE][SQL][3.5] `AccessControlException` should be thrown even if `ignoreCorruptFiles` is enabled

Cherry-pick https://github.com/apache/spark/issues/49143 to branch-3.5

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

`AccessControlException` extends `IOException` but we should not treat it as a data corruption issue.

This is similar to SPARK-50483 which handles `BlockMissingException` in the same way.

```
2024-12-11 06:29:05 WARN HadoopRDD: Skipped the rest content in the corrupted file: hdfs://hadoop-master1.orb.local:8020/warehouse/region/part-00000-2dc8a6f6-8cea-4652-8ba1-762c1b65e2b4-c000:192+192
org.apache.hadoop.security.AccessControlException: Permission denied: user=hive, access=READ, inode="/warehouse/region/part-00000-2dc8a6f6-8cea-4652-8ba1-762c1b65e2b4-c000":kyuubi.hadoop:hadoop:-rw-------
	at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.check(FSPermissionChecker.java:506)
```
<img width="1462" alt="image" src="https://github.com/user-attachments/assets/d3a64578-90c6-49bb-b92f-7c5c71451a9b">

### Why are the changes needed?

Avoid data issue if `ignoreCorruptFiles` is enabled when `AccessControlException` occurred.

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

Yes.

### How was this patch tested?

Manual test.

Task fails with `org.apache.hadoop.security.AccessControlException` even with `spark.sql.files.ignoreCorruptFiles=true` and `spark.files.ignoreCorruptFiles=true` <img width="1477" alt="image" src="https://github.com/user-attachments/assets/373ad5fc-15f5-486f-aba3-53b7f7af3b13">

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

No.

Closes #49162 from pan3793/SPARK-50545-3.5.

Authored-by: Cheng Pan <chengpan@apache.org>
Signed-off-by: yangjie01 <yangjie01@baidu.com>
4 files changed
tree: 9357dd96cd76394cb9ebdf11cc15d358264161a8
  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/

GitHub Actions Build AppVeyor 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 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.