spark-instrumented-optimizer/docs/submitting-applications.md

layout	title
global	Submitting Applications

The spark-submit script in Spark's bin directory is used to launch applications on a cluster. It can use all of Spark's supported cluster managers through a uniform interface so you don't have to configure your application specially for each one.

Bundling Your Application's Dependencies

If your code depends on other projects, you will need to package them alongside your application in order to distribute the code to a Spark cluster. To do this, to create an assembly jar (or "uber" jar) containing your code and its dependencies. Both sbt and Maven have assembly plugins. When creating assembly jars, list Spark and Hadoop as provided dependencies; these need not be bundled since they are provided by the cluster manager at runtime. Once you have an assembled jar you can call the bin/spark-submit script as shown here while passing your jar.

For Python, you can use the --py-files argument of spark-submit to add .py, .zip or .egg files to be distributed with your application. If you depend on multiple Python files we recommend packaging them into a .zip or .egg.

Launching Applications with spark-submit

Once a user application is bundled, it can be launched using the bin/spark-submit script. This script takes care of setting up the classpath with Spark and its dependencies, and can support different cluster managers and deploy modes that Spark supports:

{% highlight bash %} ./bin/spark-submit
--class --master
--deploy-mode
... # other options
[application-arguments] {% endhighlight %}

Some of the commonly used options are:

• --class: The entry point for your application (e.g. org.apache.spark.examples.SparkPi)
• --master: The master URL for the cluster (e.g. spark://23.195.26.187:7077)
• --deploy-mode: Whether to deploy your driver program within the cluster or run it locally as an external client (either cluster or client)
• application-jar: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster, for instance, an hdfs:// path or a file:// path that is present on all nodes.
• application-arguments: Arguments passed to the main method of your main class, if any

For Python applications, simply pass a .py file in the place of <application-jar> instead of a JAR, and add Python .zip, .egg or .py files to the search path with --py-files.

To enumerate all options available to spark-submit run it with --help. Here are a few examples of common options:

{% highlight bash %}

Run application locally on 8 cores

./bin/spark-submit
--class org.apache.spark.examples.SparkPi
--master local[8]
/path/to/examples.jar
100

Run on a Spark standalone cluster

./bin/spark-submit
--class org.apache.spark.examples.SparkPi
--master spark://207.184.161.138:7077
--executor-memory 20G
--total-executor-cores 100
/path/to/examples.jar
1000

Run on a YARN cluster

export HADOOP_CONF_DIR=XXX ./bin/spark-submit
--class org.apache.spark.examples.SparkPi
--master yarn-cluster \ # can also be yarn-client for client mode --executor-memory 20G
--num-executors 50
/path/to/examples.jar
1000

Run a Python application on a cluster

./bin/spark-submit
--master spark://207.184.161.138:7077
examples/src/main/python/pi.py
1000 {% endhighlight %}

Master URLs

The master URL passed to Spark can be in one of the following formats:

Master URL	Meaning
local	Run Spark locally with one worker thread (i.e. no parallelism at all).
local[K]	Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine).
local[*]	Run Spark locally with as many worker threads as logical cores on your machine.
spark://HOST:PORT	Connect to the given Spark standalone cluster master. The port must be whichever one your master is configured to use, which is 7077 by default.
mesos://HOST:PORT	Connect to the given Mesos cluster. The port must be whichever one your is configured to use, which is 5050 by default. Or, for a Mesos cluster using ZooKeeper, use mesos://zk://....
yarn-client	Connect to a YARN cluster in client mode. The cluster location will be found based on the HADOOP_CONF_DIR variable.
yarn-cluster	Connect to a YARN cluster in cluster mode. The cluster location will be found based on HADOOP_CONF_DIR.

Loading Configuration from a File

The spark-submit script can load default Spark configuration values from a properties file and pass them on to your application. By default it will read options from conf/spark-defaults.conf in the Spark directory. For more detail, see the section on loading default configurations.

Loading default Spark configurations this way can obviate the need for certain flags to spark-submit. For instance, if the spark.master property is set, you can safely omit the --master flag from spark-submit. In general, configuration values explicitly set on a SparkConf take the highest precedence, then flags passed to spark-submit, then values in the defaults file.

If you are ever unclear where configuration options are coming from, you can print out fine-grained debugging information by running spark-submit with the --verbose option.

Advanced Dependency Management

When using spark-submit, the application jar along with any jars included with the --jars option will be automatically transferred to the cluster. Spark uses the following URL scheme to allow different strategies for disseminating jars:

• file: - Absolute paths and file:/ URIs are served by the driver's HTTP file server, and every executor pulls the file from the driver HTTP server.
• hdfs:, http:, https:, ftp: - these pull down files and JARs from the URI as expected
• local: - a URI starting with local:/ is expected to exist as a local file on each worker node. This means that no network IO will be incurred, and works well for large files/JARs that are pushed to each worker, or shared via NFS, GlusterFS, etc.

Note that JARs and files are copied to the working directory for each SparkContext on the executor nodes. This can use up a significant amount of space over time and will need to be cleaned up. With YARN, cleanup is handled automatically, and with Spark standalone, automatic cleanup can be configured with the spark.worker.cleanup.appDataTtl property.

For python, the equivalent --py-files option can be used to distribute .egg, .zip and .py libraries to executors.

More Information

Once you have deployed your application, the cluster mode overview describes the components involved in distributed execution, and how to monitor and debug applications.