SPARK提交job的几种模式

常见语法：

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

几个常见的例子

# 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 clusterexport 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 Spark Standalone cluster./bin/spark-submit \  --master spark://207.184.161.138:7077 \  examples/src/main/python/pi.py \  1000

master的几种方式

client与cluster有什么区别

client就是driver就在本机上运行，提交后不能退出程序。
cluster是driver也在集群上，本机只是提交一个任务，提交任务后就可以关闭窗口了。

spark-submit完整的提交文档

hadoop@gdc-nn01-logtest:~/spark$ bin/spark-submitUsage: spark-submit [options]  [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.  --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: 512M).  --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: 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).  --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.15/07/22 11:03:25 INFO util.Utils: Shutdown hook called