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apache / spark / refs/heads/branch-1.0-jdbc
commit9caf3a9659821a3b4fd4394c9f4134adff9caf88[log] [tgz]
authorHossein <hossein@databricks.com>Sat Jul 26 01:04:56 2014 -0700
committerMatei Zaharia <matei@databricks.com>Sat Jul 26 01:05:05 2014 -0700
tree3f6c43e8a5376a705e336aa1e92781369e0aa21a
parent1d4103a09ec63dec50a64d9a31e76b8f203b601a [diff]
[SPARK-2696] Reduce default value of spark.serializer.objectStreamReset

The current default value of spark.serializer.objectStreamReset is 10,000.
When trying to re-partition (e.g., to 64 partitions) a large file (e.g., 500MB), containing 1MB records, the serializer will cache 10000 x 1MB x 64 ~= 640 GB which will cause out of memory errors.

This patch sets the default value to a more reasonable default value (100).

Author: Hossein <hossein@databricks.com>

Closes #1595 from falaki/objectStreamReset and squashes the following commits:

650a935 [Hossein] Updated documentation
1aa0df8 [Hossein] Reduce default value of spark.serializer.objectStreamReset

(cherry picked from commit 66f26a4610aede57322cb7e193a50aecb6c57d22)
Signed-off-by: Matei Zaharia <matei@databricks.com>
2 files changed
tree: 3f6c43e8a5376a705e336aa1e92781369e0aa21a
  1. assembly/
  2. bagel/
  3. bin/
  4. conf/
  5. core/
  6. data/
  7. dev/
  8. docker/
  9. docs/
  10. ec2/
  11. examples/
  12. external/
  13. extras/
  14. graphx/
  15. mllib/
  16. project/
  17. python/
  18. repl/
  19. sbin/
  20. sbt/
  21. sql/
  22. streaming/
  23. tools/
  24. yarn/
  25. .gitignore
  26. .rat-excludes
  27. .travis.yml
  28. CHANGES.txt
  29. LICENSE
  30. make-distribution.sh
  31. NOTICE
  32. pom.xml
  33. README.md
  34. scalastyle-config.xml
  35. tox.ini
README.md

Apache Spark

Lightning-Fast Cluster Computing - http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project webpage at http://spark.apache.org/documentation.html. This README file only contains basic setup instructions.

Building Spark

Spark is built on Scala 2.10. To build Spark and its example programs, run:

./sbt/sbt assembly

(You do not need to do this if you downloaded a pre-built package.)

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-cluster” or “yarn-client” 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:

./sbt/sbt test

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. You can change the version by setting the SPARK_HADOOP_VERSION environment when building Spark.

For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions without YARN, use:

# Apache Hadoop 1.2.1
$ SPARK_HADOOP_VERSION=1.2.1 sbt/sbt assembly

# Cloudera CDH 4.2.0 with MapReduce v1
$ SPARK_HADOOP_VERSION=2.0.0-mr1-cdh4.2.0 sbt/sbt assembly

For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with YARN, also set SPARK_YARN=true:

# Apache Hadoop 2.0.5-alpha
$ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly

# Cloudera CDH 4.2.0 with MapReduce v2
$ SPARK_HADOOP_VERSION=2.0.0-cdh4.2.0 SPARK_YARN=true sbt/sbt assembly

# Apache Hadoop 2.2.X and newer
$ SPARK_HADOOP_VERSION=2.2.0 SPARK_YARN=true sbt/sbt assembly

When developing a Spark application, specify the Hadoop version by adding the “hadoop-client” artifact to your project‘s dependencies. For example, if you’re using Hadoop 1.2.1 and build your application using SBT, add this entry to libraryDependencies:

"org.apache.hadoop" % "hadoop-client" % "1.2.1"

If your project is built with Maven, add this to your POM file's <dependencies> section:

<dependency>
  <groupId>org.apache.hadoop</groupId>
  <artifactId>hadoop-client</artifactId>
  <version>1.2.1</version>
</dependency>

Configuration

Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.

Contributing to Spark

Contributions via GitHub pull requests are gladly accepted from their original author. Along with any pull requests, please state that the contribution is your original work and that you license the work to the project under the project‘s open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project’s open source license and warrant that you have the legal authority to do so.