Spark Configuration

Spark provides three main locations to configure the system:

Environment Variables

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/ 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/ Make sure that you make the copy executable.

Inside, you must set at least the following two variables:

In addition, there are four other variables that control execution. These can be set either in or in each job’s driver program, because they will automatically be propagated to workers from the driver. For a multi-user environment, we recommend setting the in the driver program instead of, so that different user jobs can use different amounts of memory, JVM options, etc.

Note that if you do set these in, they will override the values set by user programs, which is undesirable; you can choose to have set them only if the user program hasn’t, as follows:

if [ -z "$SPARK_MEM" ] ; then

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:

System.setProperty("spark.cores.max", "5")
val sc = new SparkContext(...)

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 NameDefaultMeaning
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. It can also be a comma-separated list of multiple directories.
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 NameDefaultMeaning
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. 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.shuffle.compress true Whether to compress map output files. Generally a good idea.
spark.broadcast.compress true Whether to compress broadcast variables before sending them. Generally a good idea.
spark.rdd.compress false Whether to compress serialized RDD partitions (e.g. for StorageLevel.MEMORY_ONLY_SER). Can save substantial space at the cost of some extra CPU time.
spark.reducer.maxMbInFlight 48 Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. Since each output requires us to create a buffer to receive it, this represents a fixed memory overhead per reduce task, so keep it small unless you have a large amount of memory.
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.worker.timeout 60 Number of seconds after which the standalone deploy master considers a worker lost if it receives no heartbeats.
spark.akka.frameSize 10 Maximum message size to allow in "control plane" communication (for serialized tasks and task results), in MB. Increase this if your tasks need to send back large results to the master (e.g. using collect() on a large dataset).
spark.akka.threads 4 Number of actor threads to use for communication. Can be useful to increase on large clusters when the master has a lot of CPU cores.
spark.akka.timeout 20 Communication timeout between Spark nodes, in seconds. (local hostname) Hostname or IP address for the master to listen on.
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 file in the conf directory. One way to start is to copy the existing located there.