用spark-submit启动程序

时间：2023-03-09 13:39:06
用spark-submit启动程序

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

用spark-submit启动程序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

用spark-submit启动程序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: ).

 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: ).

                              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  cores

./bin/spark-submit \

  --class org.apache.spark.examples.SparkPi \

  --master local[] \

  /path/to/examples.jar \

# 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  \

  /path/to/examples.jar \

# 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  \

  /path/to/examples.jar \

# 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  \

  /path/to/examples.jar \

# 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 \

# 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  \

  http://path/to/examples.jar \

# 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  \

  http://path/to/examples.jar \

用spark-submit启动程序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 YARNcluster 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.

相关文章