| commit | e63783a23f49fafc5d9f464ecfd107d19bd87787 | [log] [tgz] |
|---|---|---|
| author | Patrick Wendell <patrick@databricks.com> | Wed Jun 03 23:35:16 2015 -0700 |
| committer | Patrick Wendell <patrick@databricks.com> | Wed Jun 03 23:35:16 2015 -0700 |
| tree | 8144be53ebe69a71fc4cef2b35c7a3744da2f897 | |
| parent | 2f03fc17c3511bc6ef0965c3371d6a3789853486 [diff] |
[MAINTENANCE] Closes #2854 This commit exists to close a pull request on github.
Lightning-Fast Cluster Computing - http://spark.apache.org/
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.
Spark requires Scala 2.10. The project is built using Simple Build Tool (SBT), which can be obtained here. If SBT is installed we will use the system version of sbt otherwise we will attempt to download it automatically. 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:
./bin/spark-shell
Or, for the Python API, the Python shell (./bin/pyspark).
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.
Testing first requires Building Spark. Once Spark is built, tests can be run using:
./sbt/sbt test
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>
Please refer to the Configuration guide in the online documentation for an overview on how to configure 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.