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Matei Zaharia authored
This PR updates spark-submit to allow submitting Python scripts (currently only with deploy-mode=client, but that's all that was supported before) and updates the PySpark code to properly find various paths, etc. One significant change is that we assume we can always find the Python files either from the Spark assembly JAR (which will happen with the Maven assembly build in make-distribution.sh) or from SPARK_HOME (which will exist in local mode even if you use sbt assembly, and should be enough for testing). This means we no longer need a weird hack to modify the environment for YARN.

This patch also updates the Python worker manager to run python with -u, which means unbuffered output (send it to our logs right away instead of waiting a while after stuff was written); this should simplify debugging.

In addition, it fixes https://issues.apache.org/jira/browse/SPARK-1709, setting the main class from a JAR's Main-Class attribute if not specified by the user, and fixes a few help strings and style issues in spark-submit.

In the future we may want to make the `pyspark` shell use spark-submit as well, but it seems unnecessary for 1.0.

Author: Matei Zaharia <matei@databricks.com>

Closes #664 from mateiz/py-submit and squashes the following commits:

15e9669 [Matei Zaharia] Fix some uses of path.separator property
051278c [Matei Zaharia] Small style fixes
0afe886 [Matei Zaharia] Add license headers
4650412 [Matei Zaharia] Add pyFiles to PYTHONPATH in executors, remove old YARN stuff, add tests
15f8e1e [Matei Zaharia] Set PYTHONPATH in PythonWorkerFactory in case it wasn't set from outside
47c0655 [Matei Zaharia] More work to make spark-submit work with Python:
d4375bd [Matei Zaharia] Clean up description of spark-submit args a bit and add Python ones
951a5d93
History

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

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 org.apache.spark.examples.SparkLR local[2]

will run the Logistic Regression example locally on 2 CPUs.

Each of the example programs prints usage help if no params are given.

All of the Spark samples take a <master> parameter that is the cluster URL to connect to. This can be a mesos:// or spark:// URL, or "local" to run locally with one thread, or "local[N]" to run locally with N threads.

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.