Github user ash211 commented on a diff in the pull request:
https://github.com/apache/spark/pull/896#discussion_r13116488
--- Diff: docs/submitting-applications.md ---
@@ -0,0 +1,153 @@
+---
+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,
+to 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> \
+ ... # 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 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:
+
+<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.
--- End diff --
https://issues.apache.org/jira/browse/SPARK-1944
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
