[spark@master ~]$ cd $SPARK_HOME/bin

[spark@master bin]$ pwd

/usr/local/spark/spark-1.6.-bin-hadoop2./bin

[spark@master bin]$ ll

total

-rwxr-xr-x.  spark spark  Feb    beeline

-rw-r--r--.  spark spark   Feb    beeline.cmd

-rw-r--r--.  spark spark  Feb    load-spark-env.cmd

-rw-r--r--.  spark spark  Feb    load-spark-env.sh

-rwxr-xr-x.  spark spark  Feb    pyspark

-rw-r--r--.  spark spark  Feb    pyspark2.cmd

-rw-r--r--.  spark spark  Feb    pyspark.cmd

-rwxr-xr-x.  spark spark  Feb    run-example

-rw-r--r--.  spark spark  Feb    run-example2.cmd

-rw-r--r--.  spark spark  Feb    run-example.cmd

-rwxr-xr-x.  spark spark  Feb    spark-class

-rw-r--r--.  spark spark  Feb    spark-class2.cmd

-rw-r--r--.  spark spark  Feb    spark-class.cmd

-rwxr-xr-x.  spark spark  Feb    sparkR

-rw-r--r--.  spark spark  Feb    sparkR2.cmd

-rw-r--r--.  spark spark   Feb    sparkR.cmd

-rwxr-xr-x.  spark spark  Feb    spark-shell

-rw-r--r--.  spark spark  Feb    spark-shell2.cmd

-rw-r--r--.  spark spark  Feb    spark-shell.cmd

-rwxr-xr-x.  spark spark  Feb    spark-sql

-rwxr-xr-x.  spark spark  Feb    spark-submit

-rw-r--r--.  spark spark  Feb    spark-submit2.cmd

-rw-r--r--.  spark spark  Feb    spark-submit.cmd

[spark@master bin]$ 

　　$SPARK_HOME/bin/spark-submit \

　　--class <main-class> \

　　--master \

　　--deploy-mode \

　　--conf = \

　　... # other options  \

　　[application-arguments]

[spark@master ~]$ $SPARK_HOME/bin/spark-submit  
 

 或者  
 

[spark@master ~]$ $SPARK_HOME/bin/spark-submit --help

[spark@master ~]$ $SPARK_HOME/bin/spark-submit     或者     $SPARK_HOME/bin/spark-submit --help

Usage: spark-submit [options] <app jar | python file> [app arguments]

Usage: spark-submit --kill [submission ID] --master [spark://...]

Usage: spark-submit --status [submission ID] --master [spark://...]

Options:

  --master MASTER_URL         spark://host:port, mesos://host:port, yarn, or local.

  --deploy-mode DEPLOY_MODE   Whether to launch the driver program locally ("client") or

                              on one of the worker machines inside the cluster ("cluster")

                              (Default: client).

  --class CLASS_NAME          Your application's main class (for Java / Scala apps).

  --name NAME                 A name of your application.

  --jars JARS                 Comma-separated list of local jars to include on the driver

                              and executor classpaths.

  --packages                  Comma-separated list of maven coordinates of jars to include

                              on the driver and executor classpaths. Will search the local

                              maven repo, then maven central and any additional remote

                              repositories given by --repositories. The format for the

                              coordinates should be groupId:artifactId:version.

  --exclude-packages          Comma-separated list of groupId:artifactId, to exclude while

                              resolving the dependencies provided in --packages to avoid

                              dependency conflicts.

  --repositories              Comma-separated list of additional remote repositories to

                              search for the maven coordinates given with --packages.

  --py-files PY_FILES         Comma-separated list of .zip, .egg, or .py files to place

                              on the PYTHONPATH for Python apps.

  --files FILES               Comma-separated list of files to be placed in the working

                              directory of each executor.

  --conf PROP=VALUE           Arbitrary Spark configuration property.

  --properties-file FILE      Path to a file from which to load extra properties. If not

                              specified, this will look for conf/spark-defaults.conf.

  --driver-memory MEM         Memory for driver (e.g. 1000M, 2G) (Default: 1024M).

  --driver-java-options       Extra Java options to pass to the driver.

  --driver-library-path       Extra library path entries to pass to the driver.

  --driver-class-path         Extra class path entries to pass to the driver. Note that

                              jars added with --jars are automatically included in the

                              classpath.

  --executor-memory MEM       Memory per executor (e.g. 1000M, 2G) (Default: 1G).

  --proxy-user NAME           User to impersonate when submitting the application.

  --help, -h                  Show this help message and exit

  --verbose, -v               Print additional debug output

  --version,                  Print the version of current Spark

 Spark standalone with cluster deploy mode only:

  --driver-cores NUM          Cores for driver (Default: ).

 Spark standalone or Mesos with cluster deploy mode only:

  --supervise                 If given, restarts the driver on failure.

  --kill SUBMISSION_ID        If given, kills the driver specified.

  --status SUBMISSION_ID      If given, requests the status of the driver specified.

 Spark standalone and Mesos only:

  --total-executor-cores NUM  Total cores for all executors.

 Spark standalone and YARN only:

  --executor-cores NUM        Number of cores per executor. (Default:  in YARN mode,

                              or all available cores on the worker in standalone mode)

 YARN-only:

  --driver-cores NUM          Number of cores used by the driver, only in cluster mode

                              (Default: ).

  --queue QUEUE_NAME          The YARN queue to submit to (Default: "default").

  --num-executors NUM         Number of executors to launch (Default: ).

  --archives ARCHIVES         Comma separated list of archives to be extracted into the

                              working directory of each executor.

  --principal PRINCIPAL       Principal to be used to login to KDC, while running on

                              secure HDFS.

  --keytab KEYTAB             The full path to the file that contains the keytab for the

                              principal specified above. This keytab will be copied to

                              the node running the Application Master via the Secure

                              Distributed Cache, for renewing the login tickets and the

                              delegation tokens periodically.

[spark@master ~]$