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apache / spark / refs/heads/branch-0.8
commit62b3158b80c14668b16476818d72f1296b06da66[log] [tgz]
authorRaymond Liu <raymond.liu@intel.com>Tue Feb 11 22:39:48 2014 -0800
committerAaron Davidson <aaron@databricks.com>Tue Feb 11 22:48:48 2014 -0800
tree38193f5a09bfc9d45e29151097b42521c366296d
parentc89b71ac761dc1eea32435eadc0621f739e96098 [diff]
Merge pull request #583 from colorant/zookeeper.

Minor fix for ZooKeeperPersistenceEngine to use configured working dir

Author: Raymond Liu <raymond.liu@intel.com>

Closes #583 and squashes the following commits:

91b0609 [Raymond Liu] Minor fix for ZooKeeperPersistenceEngine to use configured working dir

(cherry picked from commit 68b2c0d02dbdca246ca686b871c06af53845d5b5)
Signed-off-by: Aaron Davidson <aaron@databricks.com>

Conflicts:
	core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala
1 file changed
tree: 38193f5a09bfc9d45e29151097b42521c366296d
  1. assembly/
  2. bagel/
  3. bin/
  4. conf/
  5. core/
  6. docker/
  7. docs/
  8. ec2/
  9. examples/
  10. mllib/
  11. new-yarn/
  12. project/
  13. python/
  14. repl/
  15. repl-bin/
  16. sbt/
  17. streaming/
  18. tools/
  19. yarn/
  20. .gitignore
  21. CHANGES.txt
  22. kmeans_data.txt
  23. LICENSE
  24. lr_data.txt
  25. make-distribution.sh
  26. NOTICE
  27. pagerank_data.txt
  28. pom.xml
  29. pyspark
  30. pyspark.cmd
  31. pyspark2.cmd
  32. README.md
  33. run-example
  34. run-example.cmd
  35. run-example2.cmd
  36. spark-class
  37. spark-class.cmd
  38. spark-class2.cmd
  39. spark-executor
  40. spark-shell
  41. spark-shell.cmd
README.md

Apache Spark

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

Online Documentation

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

Building

Spark requires Scala 2.9.3 (Scala 2.10 is not yet supported). The project is built using Simple Build Tool (SBT), which is packaged with it. To build Spark and its example programs, run:

sbt/sbt assembly

Once you've built Spark, the easiest way to start using it is the shell:

./spark-shell

Or, for the Python API, the Python shell (./pyspark).

Spark also comes with several sample programs in the examples directory. To run one of them, use ./run-example <class> <params>. For example:

./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.

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 2.0.X, 2.1.X, 2.2.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 2.2.0 with YARN
$ 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.

Apache Incubator Notice

Apache Spark is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.

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.