Lightning-Fast Cluster Computing - http://www.spark-project.org/
You can find the latest Spark documentation, including a programming guide, on the project wiki at http://github.com/mesos/spark/wiki. This file only contains basic setup instructions.
Spark requires Scala 2.8. This version has been tested with 2.8.1.final.
Experimental support for Scala 2.9 is available in the
The project is built using Simple Build Tool (SBT), which is packaged with it. To build Spark and its example programs, run:
sbt/sbt update compile
To run Spark, you will need to have Scala's bin in your
PATH, or you
will need to set the
SCALA_HOME environment variable to point to where
you've installed Scala. Scala must be accessible through one of these
methods on Mesos slave nodes as well as on the master.
To run one of the examples, use
./run <class> <params>. For example:
./run spark.examples.SparkLR local
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
<host> parameter that is the Mesos master
to connect to. This can be a Mesos URL, or "local" to run locally with one
thread, or "local[N]" to run locally with N threads.
Spark can be configured through two files:
java-opts, you can add flags to be passed to the JVM when running Spark.
spark-env.sh, you can set any environment variables you wish to be available
when running Spark programs, such as
SCALA_HOME, etc. There are also
several Spark-specific variables you can set:
SPARK_CLASSPATH: Extra entries to be added to the classpath, separated by ":".
SPARK_MEM: Memory for Spark to use, in the format used by java's
-Xmxoption (for example,
-Xmx200mmeans 200 MB,
-Xmx1gmeans 1 GB, etc).
SPARK_LIBRARY_PATH: Extra entries to add to
java.library.pathfor locating shared libraries.
SPARK_JAVA_OPTS: Extra options to pass to JVM.
spark-env.sh must be a shell script (it must be executable and start
#! header to specify the shell to use).