spark/docs/configuration.md

layout	title
global	Spark Configuration

Spark provides three main locations to configure the system:

• The conf/spark-env.sh script, in which you can set environment variables that affect how the JVM is launched, such as, most notably, the amount of memory per JVM.
• Java system properties, which control internal configuration parameters and can be set either programmatically (by calling System.setProperty before creating a SparkContext) or through the SPARK_JAVA_OPTS environment variable in spark-env.sh.
• Logging configuration, which is done through log4j.properties.

Environment Variables in spark-env.sh

Spark determines how to initialize the JVM on worker nodes, or even on the local node when you run spark-shell, by running the conf/spark-env.sh script in the directory where it is installed. This script does not exist by default in the Git repository, but but you can create it by copying conf/spark-env.sh.template. Make sure that you make the copy executable.

Inside spark-env.sh, you can set the following 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 any 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 most important things to set first will be SCALA_HOME, without which spark-shell cannot run, and MESOS_NATIVE_LIBRARY if running on Mesos. The next setting 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).

System Properties

To set a system property for configuring Spark, you need to either pass it with a -D flag to the JVM (for example java -Dspark.cores.max=5 MyProgram) or call System.setProperty in your code before creating your Spark context, as follows:

{% highlight scala %} System.setProperty("spark.cores.max", "5") val sc = new SparkContext(...) {% endhighlight %}

Most of the configurable system properties control internal settings that have reasonable default values. However, there are at least four properties that you will commonly want to control:

Property Name	Default	Meaning
spark.serializer	spark.JavaSerializer	Class to use for serializing objects that will be sent over the network or need to be cached in serialized form. The default of Java serialization works with any Serializable Java object but is quite slow, so we recommend using spark.KryoSerializer and configuring Kryo serialization when speed is necessary. Can be any subclass of spark.Serializer).
spark.kryo.registrator	(none)	If you use Kryo serialization, set this class to register your custom classes with Kryo. You need to set it to a class that extends spark.KryoRegistrator). See the tuning guide for more details.
spark.local.dir	/tmp	Directory to use for "scratch" space in Spark, including map output files and RDDs that get stored on disk. This should be on a fast, local disk in your system.
spark.cores.max	(infinite)	When running on a standalone deploy cluster or a Mesos cluster in "coarse-grained" sharing mode, how many CPU cores to request at most. The default will use all available cores.

Apart from these, the following properties are also available, and may be useful in some situations:

Property Name	Default	Meaning
spark.mesos.coarse	false	If set to "true", runs over Mesos clusters in "coarse-grained" sharing mode, where Spark acquires one long-lived Mesos task on each machine instead of one Mesos task per Spark task. This gives lower-latency scheduling for short queries, but leaves resources in use for the whole duration of the Spark job.
spark.default.parallelism	8	Default number of tasks to use for distributed shuffle operations (groupByKey, reduceByKey, etc) when not set by user.
spark.blockManager.compress	false	Set to "true" to have Spark compress map output files, RDDs that get cached on disk, and RDDs that get cached in serialized form. Generally a good idea when dealing with large datasets, but might add some CPU overhead.
spark.broadcast.compress	false	Set to "true" to have Spark compress broadcast variables before sending them. Generally a good idea when broadcasting large values.
spark.storage.memoryFraction	0.66	Fraction of Java heap to use for Spark's memory cache. This should not be larger than the "old" generation of objects in the JVM, which by default is given 2/3 of the heap, but you can increase it if you configure your own old generation size.
spark.blockManager.parallelFetches	4	Number of map output files to fetch concurrently from each reduce task.
spark.closure.serializer	spark.JavaSerializer	Serializer class to use for closures. Generally Java is fine unless your distributed functions (e.g. map functions) reference large objects in the driver program.
spark.kryoserializer.buffer.mb	32	Maximum object size to allow within Kryo (the library needs to create a buffer at least as large as the largest single object you'll serialize). Increase this if you get a "buffer limit exceeded" exception inside Kryo. Note that there will be one buffer per core on each worker.
spark.broadcast.factory	spark.broadcast. HttpBroadcastFactory	Which broadcast implementation to use.
spark.locality.wait	3000	Number of milliseconds to wait to launch a data-local task before giving up and launching it in a non-data-local location. You should increase this if your tasks are long and you are seeing poor data locality, but the default generally works well.
spark.master.host	(local hostname)	Hostname for the master to listen on (it will bind to this hostname's IP address).
spark.master.port	(random)	Port for the master to listen on.

Configuring Logging

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.