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

---
layout: global
title: 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](cluster-overview.html#cluster-manager-types)
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,
create an assembly jar (or "uber" jar) containing your code and its dependencies. Both
[sbt](https://github.com/sbt/sbt-assembly) and
[Maven](http://maven.apache.org/plugins/maven-shade-plugin/)
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 <main-class>
  --master <master-url> \
  --deploy-mode <deploy-mode> \
  --conf <key>=<value> \
  ... # other options
  <application-jar> \
  [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](#master-urls) for the cluster (e.g. `spark://23.195.26.187:7077`)
* `--deploy-mode`: Whether to deploy your driver on the worker nodes (`cluster`) or locally as an external client (`client`) (default: `client`) <b> &#8224; </b>
* `--conf`: Arbitrary Spark configuration property in key=value format. For values that contain spaces wrap "key=value" in quotes (as shown).
* `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

<b>&#8224;</b> A common deployment strategy is to submit your application from a gateway machine
that is
physically co-located with your worker machines (e.g. Master node in a standalone EC2 cluster).
In this setup, `client` mode is appropriate. In `client` mode, the driver is launched directly
within the `spark-submit` process which acts as a *client* to the cluster. The input and
output of the application is attached to the console. Thus, this mode is especially suitable
for applications that involve the REPL (e.g. Spark shell).

Alternatively, if your application is submitted from a machine far from the worker machines (e.g.
locally on your laptop), it is common to use `cluster` mode to minimize network latency between
the drivers and the executors. Note that `cluster` mode is currently not supported for
Mesos clusters or Python applications.

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`.

There are a few options available that are specific to the
[cluster manager](#cluster-overview.html#cluster-manager-types) that is being used.
For example, with a [Spark Standalone](#spark-standalone) cluster with `cluster` deploy mode,
you can also specify `--supervise` to make sure that the driver is automatically restarted if it
fails with non-zero exit code. To enumerate all such 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 in client deploy mode
./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 Spark Standalone cluster in cluster deploy mode with supervise
./bin/spark-submit \
  --class org.apache.spark.examples.SparkPi \
  --master spark://207.184.161.138:7077 \
  --deploy-mode cluster
  --supervise
  --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 Spark Standalone 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:

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


# Loading Configuration from a File

The `spark-submit` script can load default [Spark configuration values](configuration.html) 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](configuration.html#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](cluster-overview.html) describes
the components involved in distributed execution, and how to monitor and debug applications.