1.3 集羣資源管理

spark-submit幫助

[centos@s101 ~/myspark]$ 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.

core : 使用worker節點的全部內核,內核進行物理檢測。
memory : 內存使用1g內存，內存不進行物理檢測。

 

--driver-memory 2g //控制driver堆內存，默認1g
--executor-memory MEM //每一個executor的內存，默認 1G.

[standalone + cluster]
--driver-cores NUM //控制driver的內核數

[Spark standalone和 Mesos]
--total-executor-cores NUM //用於全部executor的總的內核數

[spark standalone | yarn]
--executor-cores //每一個執行器的內核數，yarn模式是1，standalone是全部可能內核。

[YARN-only]
--driver-cores NUM //driver內核數，只用於cluster模式(Default: 1).
--num-executors NUM //啓動的執行器個數(Default: 2).

模式standalone

[spark/conf/spark-env.sh]
export JAVA_HOME=/soft/jdk

# 每一個worker使用的內核數
export SPARK_WORKER_CORES=2
#每一個worker使用內存數
export SPARK_WORKER_MEMORY=2g
#是否能夠在一個節點啓動幾個worker進程
export SPARK_WORKER_INSTANCES=2
#master和worker進程自己的內存數
export SPARK_DAEMON_MEMORY=200m

配置完分發

//啓動spark-shell，s102-s104各兩個worker，每一個worker啓動四個CoarseGrainedExecutorBackend
spark-shell --master spark://s101:7077 --driver-memory 2g --executor-memory 1g --driver-cores 2 --executor-cores 1