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apache / spark / adb5255add24aeb85da5b799773f8eca5edacf7c
commitadb5255add24aeb85da5b799773f8eca5edacf7c[log] [tgz]
authorAlessandro Bellina <abellina@gmail.com>Thu Aug 22 00:39:36 2019 -0700
committerDongjoon Hyun <dhyun@apple.com>Thu Aug 22 00:39:36 2019 -0700
treeadd3f30265307404178a6809fffb81d63db9ef32
parent5ce02fc087ac21abca0e8bae6ba36b9ef66f3c52 [diff]
[SPARK-26895][CORE][2.3] prepareSubmitEnvironment should be called within doAs for proxy users

This also includes the follow-up fix by Hyukjin Kwon:
[SPARK-26895][CORE][FOLLOW-UP] Uninitializing log after `prepareSubmitEnvironment` in SparkSubmit

`prepareSubmitEnvironment` performs globbing that will fail in the case where a proxy user (`--proxy-user`) doesn't have permission to the file. This is a bug also with 2.3, so we should backport, as currently you can't launch an application that for instance is passing a file under `--archives`, and that file is owned by the target user.

The solution is to call `prepareSubmitEnvironment` within a doAs context if proxying.

Manual tests running with `--proxy-user` and `--archives`, before and after, showing that the globbing is successful when the resource is owned by the target user.

I've looked at writing unit tests, but I am not sure I can do that cleanly (perhaps with a custom FileSystem). Open to ideas.

Closes #25517 from vanzin/SPARK-26895-2.3.

Lead-authored-by: Alessandro Bellina <abellina@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Co-authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
1 file changed
tree: add3f30265307404178a6809fffb81d63db9ef32
  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. .travis.yml
  32. appveyor.yml
  33. CONTRIBUTING.md
  34. LICENSE
  35. NOTICE
  36. pom.xml
  37. README.md
  38. 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.)

You can build Spark using more than one thread by using the -T option with Maven, see “Parallel builds in Maven 3”. 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.

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” 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.