Spark Configuration

Spark is configured primarily through the conf/spark-env.sh script. This script doesn't exist in the Git repository, but you can create it by copying conf/spark-env.sh.template. Make sure the script is executable.

Inside this script, you can set several environment variables:

• SCALA_HOME to point to your Scala installation.
• MESOS_NATIVE_LIBRARY if you are running on a Mesos cluster.
• SPARK_MEM to set the amount of memory used per node (this should be in the same format as the JVM's -Xmx option, e.g. 300m or 1g)
• SPARK_JAVA_OPTS to add JVM options. This includes system properties that you'd like to pass with -D.
• SPARK_CLASSPATH to add elements to Spark's classpath.
• SPARK_LIBRARY_PATH to add search directories for native libraries.

The spark-env.sh script is executed both when you submit jobs with run, when you start the interpreter with spark-shell, and on each worker node on a Mesos cluster to set up the environment for that worker.

The most important thing to set first will probably be the memory (SPARK_MEM). Make sure you set it high enough to be able to run your job but lower than the total memory on the machines (leave at least 1 GB for the operating system).

Logging Configuration

Spark uses log4j for logging. You can configure it by adding a log4j.properties file in the conf directory. One way to start is to copy the existing log4j.properties.template located there.