用spark-submit启动程序

来源：http://spark.apache.org/docs/latest/submitting-applications.html

提交程序常用的一些选项

./bin/spark-submit 
  --class <main-class> 
  --master <master-url> 
  --deploy-mode <deploy-mode> 
  --conf <key>=<value> 
  ... # other options
  <application-jar> 
  [application-arguments]

--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 on the worker nodes (cluster) or locally as an external client (client) (default: client) †
--conf: Arbitrary（任意的） Spark configuration property in key=value format. For values that contain spaces wrap “key=value” in quotes (as shown).（有空格的value用双引号引起来）
application-jar: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster,（jar的路径必须是全局可见的，即所有结点可见） 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

有一些选项是为特定的集群管理使用的，可通过--help来查看：

[root@node02 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://...]
Usage: spark-submit run-example [options] example-class [example args]

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.
This argument does not work with --principal / --keytab.

--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: 1).

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: 1 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: 1).
--queue QUEUE_NAME The YARN queue to submit to (Default: "default").
--num-executors NUM Number of executors to launch (Default: 2).
If dynamic allocation is enabled, the initial number of
executors will be at least NUM.
--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.

eg.

# 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 
  --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://207.184.161.138:7077 
  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://207.184.161.138:7077 
  --deploy-mode cluster 
  --supervise 
  --executor-memory 20G 
  --total-executor-cores 100 
  http://path/to/examples.jar 
  1000

# Run on a Kubernetes cluster in cluster deploy mode
./bin/spark-submit 
  --class org.apache.spark.examples.SparkPi 
  --master k8s://xx.yy.zz.ww:443 
  --deploy-mode cluster 
  --executor-memory 20G 
  --num-executors 50 
  http://path/to/examples.jar 
  1000

View Code

Master URLs

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[K,F]`	Run Spark locally with K worker threads and F maxFailures (see spark.task.maxFailures for an explanation of this variable)
`local[*]`	Run Spark locally with as many worker threads as logical cores on your machine.
`local[*,F]`	Run Spark locally with as many worker threads as logical cores on your machine and F maxFailures.
`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.
`spark://HOST1:PORT1,HOST2:PORT2`	Connect to the given Spark standalone cluster with standby masters with Zookeeper. The list must have all the master hosts in the high availability cluster set up with Zookeeper. The port must be whichever each master is configured to use, which is 7077 by default.
`yarn`	Connect to a YARN cluster in `client` or `cluster` mode depending on the value of `--deploy-mode`. The cluster location will be found based on the `HADOOP_CONF_DIR` or `YARN_CONF_DIR` variable.