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,
create an assembly jar (or “uber” jar) containing your code and its dependencies. Both
have assembly plugins. When creating assembly jars, list Spark and Hadoop
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
script as shown here while passing your jar.
For Python, you can use the
--py-files argument of
spark-submit to add
files to be distributed with your application. If you depend on multiple Python files we recommend
packaging them into a
Launching Applications with spark-submit
Once a user application is bundled, it can be launched using the
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:
./bin/spark-submit \ --class <main-class> \ --master <master-url> \ --deploy-mode <deploy-mode> \ --conf <key>=<value> \ ... # other options <application-jar> \ [application-arguments]
Some of the commonly used options are:
--class: The entry point for your application (e.g.
--master: The master URL for the cluster (e.g.
--deploy-mode: Whether to deploy your driver on the worker nodes (
cluster) or locally as an external client (
--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
† A common deployment strategy is to submit your application from a gateway machine
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
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. Currently, standalone mode does not support cluster mode for Python
For Python applications, simply pass a
.py file in the place of
<application-jar> instead of a JAR,
and add Python
.py files to the search path with
There are a few options available that are specific to the
cluster manager that is being used.
For example, with a 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
run it with
--help. Here are a few examples of common options:
# Run application locally on 8 cores ./bin/spark-submit \ --class org.apache.spark.examples.SparkPi \ --master local \ /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://188.8.131.52:7077 \ --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://184.108.40.206:7077 \ --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 \ --deploy-mode cluster \ # can be 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://220.127.116.11:7077 \ examples/src/main/python/pi.py \ 1000 # Run on a Mesos cluster in cluster deploy mode with supervise ./bin/spark-submit \ --class org.apache.spark.examples.SparkPi \ --master mesos://18.104.22.168:7077 \ --deploy-mode cluster \ --supervise \ --executor-memory 20G \ --total-executor-cores 100 \ http://path/to/examples.jar \ 1000
The master URL passed to Spark can be in one of the following formats:
| ||Run Spark locally with one worker thread (i.e. no parallelism at all).|
| ||Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine).|
| ||Run Spark locally with as many worker threads as logical cores on your machine.|
| ||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.|
| || 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 |
| || Connect to a YARN cluster in
Loading Configuration from a File
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
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
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
Advanced Dependency Management
spark-submit, the application jar along with any jars included with the
will be automatically transferred to the cluster. URLs supplied after
--jars must be separated by commas. That list is included on the driver and executor classpaths. Directory expansion does not work with
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
Users may also include any other dependencies by supplying a comma-delimited list of maven coordinates
--packages. All transitive dependencies will be handled when using this command. Additional
repositories (or resolvers in SBT) can be added in a comma-delimited fashion with the flag
(Note that credentials for password-protected repositories can be supplied in some cases in the repository URI,
such as in
https://user:password@host/.... Be careful when supplying credentials this way.)
These commands can be used with
spark-submit to include Spark Packages.
For Python, the equivalent
--py-files option can be used to distribute
Once you have deployed your application, the cluster mode overview describes the components involved in distributed execution, and how to monitor and debug applications.