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apache / spark / 1323ddcc17c27f8f3b5e4b2eaf5a41ec10212dfd
commit1323ddcc17c27f8f3b5e4b2eaf5a41ec10212dfd[log] [tgz]
authorgatorsmile <gatorsmile@gmail.com>Tue Apr 30 08:55:41 2019 -0700
committerDongjoon Hyun <dhyun@apple.com>Tue Apr 30 08:55:41 2019 -0700
tree490249ddb9934cd16070c027db93b0bef1371c04
parent3d49bd496e8abfda816beea03269cd4094f2ec52 [diff]
Revert "[SPARK-24601][SPARK-27051][BACKPORT][CORE] Update to Jackson 2.9.8

## What changes were proposed in this pull request?

This reverts commit 6f394a20bf49f67b4d6329a1c25171c8024a2fae.

In general, we need to be very cautious about the Jackson upgrade in the patch releases, especially when this upgrade could break the existing behaviors of the external packages or data sources, and generate different results after the upgrade. The external packages and data sources need to change their source code to keep the original behaviors. The upgrade requires more discussions before releasing it, I think.

In the previous PR https://github.com/apache/spark/pull/22071, we turned off `spark.master.rest.enabled` by default and added the following claim in our security doc:
> The Rest Submission Server and the MesosClusterDispatcher do not support authentication.  You should ensure that all network access to the REST API & MesosClusterDispatcher (port 6066 and 7077 respectively by default) are restricted to hosts that are trusted to submit jobs.

We need to understand whether this Jackson CVE applies to Spark. Before officially releasing it, we need more inputs from all of you. Currently, I would suggest to revert this upgrade from the upcoming 2.4.3 release, which is trying to fix the accidental default Scala version changes in pre-built artifacts.

## How was this patch tested?

N/A

Closes #24493 from gatorsmile/revert24418.

Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
11 files changed
tree: 490249ddb9934cd16070c027db93b0bef1371c04
  1. .github/
  2. assembly/
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  7. core/
  8. data/
  9. dev/
  10. docs/
  11. examples/
  12. external/
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  14. hadoop-cloud/
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  18. mllib/
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  20. project/
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  25. sbin/
  26. sql/
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  31. appveyor.yml
  32. CONTRIBUTING.md
  33. LICENSE
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  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.)

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.

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.