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Former GraphX development repository. GraphX has been merged into Apache Spark; please submit pull requests there.
Scala Java Python Shell Other

[SPARK-1357 (fix)] remove empty line after :: DeveloperApi/Experiment…

…al ::

Remove empty line after :: DeveloperApi/Experimental :: in comments to make the original doc show up in the preview of the generated html docs. Thanks @andrewor14 !

Author: Xiangrui Meng <meng@databricks.com>

Closes #373 from mengxr/api and squashes the following commits:

9c35bdc [Xiangrui Meng] remove the empty line after :: DeveloperApi/Experimental ::
latest commit 0adc932add
Xiangrui Meng mengxr authored pwendell committed
Failed to load latest commit information.
assembly SPARK-1314: Use SPARK_HIVE to determine if we include Hive in packaging
bagel Spark 1271: Co-Group and Group-By should pass Iterable[X]
bin SPARK-1445: compute-classpath should not print error if lib_managed n…
conf Revert "[SPARK-1150] fix repo location in create script"
core SPARK-1407 drain event queue before stopping event logger
data moved user scripts to bin folder
dev SPARK-1431: Allow merging conflicting pull requests
docker [SPARK-1342] Scala 2.10.4
docs SPARK-1093: Annotate developer and experimental API's
ec2 SPARK-1156: allow user to login into a cluster without slaves
examples SPARK-1407 drain event queue before stopping event logger
external SPARK-1352 - Comment style single space before ending */ check.
extras Spark 1271: Co-Group and Group-By should pass Iterable[X]
graphx SPARK-1093: Annotate developer and experimental API's
mllib [SPARK-1357 (fix)] remove empty line after :: DeveloperApi/Experiment…
project Spark-939: allow user jars to take precedence over spark jars
python Spark 1271: Co-Group and Group-By should pass Iterable[X]
repl Spark-939: allow user jars to take precedence over spark jars
sbin SPARK-1286: Make usage of spark-env.sh idempotent
sbt [SQL] Un-ignore a test that is now passing.
sql SPARK-1093: Annotate developer and experimental API's
streaming Spark 1271: Co-Group and Group-By should pass Iterable[X]
tools SPARK-1093: Annotate developer and experimental API's
yarn Remove extra semicolon in import statement and unused import in Appli…
.gitignore SPARK-1336 Reducing the output of run-tests script.
.rat-excludes Spark-939: allow user jars to take precedence over spark jars
.travis.yml Cut down the granularity of travis tests.
LICENSE Merge the old sbt-launch-lib.bash with the new sbt-launcher jar downl…
NOTICE [SPARK-1212] Adding sparse data support and update KMeans
README.md Removed reference to incubation in README.md.
make-distribution.sh fix path for jar, make sed actually work on OSX
pom.xml SPARK-1433: Upgrade Mesos dependency to 0.17.0
scalastyle-config.xml SPARK-1096, a space after comment start style checker.

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 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.

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.

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