Spark Standalone Mode

In addition to running on the Mesos or YARN cluster managers, Spark also provides a simple standalone deploy mode. You can launch a standalone cluster either manually, by starting a master and workers by hand, or use our provided launch scripts. It is also possible to run these daemons on a single machine for testing.

Installing Spark Standalone to a Cluster

The easiest way to deploy Spark is by running the ./make-distribution.sh script to create a binary distribution. This distribution can be deployed to any machine with the Java runtime installed; there is no need to install Scala.

The recommended procedure is to deploy and start the master on one node first, get the master spark URL, then modify conf/spark-env.sh in the dist/ directory before deploying to all the other nodes.

Starting a Cluster Manually

You can start a standalone master server by executing:

./bin/start-master.sh

Once started, the master will print out a spark://HOST:PORT URL for itself, which you can use to connect workers to it, or pass as the “master” argument to SparkContext. You can also find this URL on the master’s web UI, which is http://localhost:8080 by default.

Similarly, you can start one or more workers and connect them to the master via:

./spark-class org.apache.spark.deploy.worker.Worker spark://IP:PORT

Once you have started a worker, look at the master’s web UI (http://localhost:8080 by default). You should see the new node listed there, along with its number of CPUs and memory (minus one gigabyte left for the OS).

Finally, the following configuration options can be passed to the master and worker:

Argument	Meaning
-i IP, --ip IP	IP address or DNS name to listen on
-p PORT, --port PORT	Port for service to listen on (default: 7077 for master, random for worker)
--webui-port PORT	Port for web UI (default: 8080 for master, 8081 for worker)
-c CORES, --cores CORES	Total CPU cores to allow Spark applicatons to use on the machine (default: all available); only on worker
-m MEM, --memory MEM	Total amount of memory to allow Spark applicatons to use on the machine, in a format like 1000M or 2G (default: your machine's total RAM minus 1 GB); only on worker
-d DIR, --work-dir DIR	Directory to use for scratch space and job output logs (default: SPARK_HOME/work); only on worker

Cluster Launch Scripts

To launch a Spark standalone cluster with the launch scripts, you need to create a file called conf/slaves in your Spark directory, which should contain the hostnames of all the machines where you would like to start Spark workers, one per line. The master machine must be able to access each of the slave machines via password-less ssh (using a private key). For testing, you can just put localhost in this file.

Once you’ve set up this file, you can launch or stop your cluster with the following shell scripts, based on Hadoop’s deploy scripts, and available in SPARK_HOME/bin:

• bin/start-master.sh - Starts a master instance on the machine the script is executed on.
• bin/start-slaves.sh - Starts a slave instance on each machine specified in the conf/slaves file.
• bin/start-all.sh - Starts both a master and a number of slaves as described above.
• bin/stop-master.sh - Stops the master that was started via the bin/start-master.sh script.
• bin/stop-slaves.sh - Stops the slave instances that were started via bin/start-slaves.sh.
• bin/stop-all.sh - Stops both the master and the slaves as described above.

Note that these scripts must be executed on the machine you want to run the Spark master on, not your local machine.

You can optionally configure the cluster further by setting environment variables in conf/spark-env.sh. Create this file by starting with the conf/spark-env.sh.template, and copy it to all your worker machines for the settings to take effect. The following settings are available:

Environment Variable	Meaning
SPARK_MASTER_IP	Bind the master to a specific IP address, for example a public one.
SPARK_MASTER_PORT	Start the master on a different port (default: 7077).
SPARK_MASTER_WEBUI_PORT	Port for the master web UI (default: 8080).
SPARK_WORKER_PORT	Start the Spark worker on a specific port (default: random).
SPARK_WORKER_DIR	Directory to run applications in, which will include both logs and scratch space (default: SPARK_HOME/work).
SPARK_WORKER_CORES	Total number of cores to allow Spark applications to use on the machine (default: all available cores).
SPARK_WORKER_MEMORY	Total amount of memory to allow Spark applications to use on the machine, e.g. 1000m, 2g (default: total memory minus 1 GB); note that each application's individual memory is configured using its spark.executor.memory property.
SPARK_WORKER_WEBUI_PORT	Port for the worker web UI (default: 8081).
SPARK_WORKER_INSTANCES	Number of worker instances to run on each machine (default: 1). You can make this more than 1 if you have have very large machines and would like multiple Spark worker processes. If you do set this, make sure to also set SPARK_WORKER_CORES explicitly to limit the cores per worker, or else each worker will try to use all the cores.
SPARK_DAEMON_MEMORY	Memory to allocate to the Spark master and worker daemons themselves (default: 512m).
SPARK_DAEMON_JAVA_OPTS	JVM options for the Spark master and worker daemons themselves (default: none).

Note: The launch scripts do not currently support Windows. To run a Spark cluster on Windows, start the master and workers by hand.

Connecting an Application to the Cluster

To run an application on the Spark cluster, simply pass the spark://IP:PORT URL of the master as to the SparkContext constructor.

To run an interactive Spark shell against the cluster, run the following command:

MASTER=spark://IP:PORT ./spark-shell

Note that if you are running spark-shell from one of the spark cluster machines, the spark-shell script will automatically set MASTER from the SPARK_MASTER_IP and SPARK_MASTER_PORT variables in conf/spark-env.sh.

You can also pass an option -c <numCores> to control the number of cores that spark-shell uses on the cluster.

Resource Scheduling

The standalone cluster mode currently only supports a simple FIFO scheduler across applications. However, to allow multiple concurrent users, you can control the maximum number of resources each application will acquire. By default, it will acquire all cores in the cluster, which only makes sense if you just run one application at a time. You can cap the number of cores using System.setProperty("spark.cores.max", "10") (for example). This value must be set before initializing your SparkContext.

Monitoring and Logging

Spark’s standalone mode offers a web-based user interface to monitor the cluster. The master and each worker has its own web UI that shows cluster and job statistics. By default you can access the web UI for the master at port 8080. The port can be changed either in the configuration file or via command-line options.

In addition, detailed log output for each job is also written to the work directory of each slave node (SPARK_HOME/work by default). You will see two files for each job, stdout and stderr, with all output it wrote to its console.

Running Alongside Hadoop

You can run Spark alongside your existing Hadoop cluster by just launching it as a separate service on the same machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically hdfs://<namenode>:9000/path, but you can find the right URL on your Hadoop Namenode’s web UI). Alternatively, you can set up a separate cluster for Spark, and still have it access HDFS over the network; this will be slower than disk-local access, but may not be a concern if you are still running in the same local area network (e.g. you place a few Spark machines on each rack that you have Hadoop on).