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Spark-Submit 参数说明

spark-submit 详细参数说明


前言

部分初级开发者需要使用 Spark-submit 提交 spark 作业到 yarn 上,经常问些参数设置的问题,
基于此需求梳理下

说明

在命令行输入 spark-submit -h,可以看到 spark-submit 的所用参数如下

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$ bin/spark-submit -h
Usage: spark-submit [options] <app jar | python file | R 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,
k8s://https://host:port, or local (Default: 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 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. File paths of these files
in executors can be accessed via SparkFiles.get(fileName).

--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.

Cluster deploy mode only:
--driver-cores NUM Number of cores used by the driver, only in cluster mode
(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:
--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.

相关意义如下:

参数名 默认值 参数说明
–master local[*] master 的地址,提交任务到哪里执行
–deploy-mode client 在本地 (client) 启动 driver 或在 cluster 上启动,默认是 client
–class - 应用程序的主类,仅针对 java 或 scala 应用
–name - 应用程序的名称
–jars - 用逗号分隔的本地 jar 包,设置后,这些 jar 将包含在 driver 和 executor 的 classpath 下
–packages - 包含在 driver 和 executor 的 classpath 中的 jar 的 maven 坐标
–exclude-packages - 为了避免冲突 而指定不包含的 package
–repositories - 逗号分隔的其他远程存储库列表,用于搜索用–packages给定的maven坐标
–conf PROP=VALUE - 指定 spark 配置属性的值,例如 -conf spark.executor.extraJavaOptions=”-XX:MaxPermSize=256m”
–properties-file - 加载的配置文件,默认为 conf/spark-defaults.conf
–driver-memory 1024M Driver内存
–driver-cores 1 Driver 的核数。只能在 client 模式下使用
–driver-java-options - 传给 driver 的额外的 Java 选项
–driver-library-path - 传给 driver 的额外的库路径
–driver-class-path - 传给 driver 的额外的类路径
–total-executor-cores - 所有 executor 总共的核数。仅仅在 mesos 或者 standalone 下使用
–num-executors 2 启动的 executor 数量。在 yarn 下使用
–executor-core 1 每个 executor 的核数。在 yarn 或者 standalone下使用
–executor-memory 1G 每个 executor 的内存
–queue “default” 提交到 Yarn 上队列

举例

将官方的 example 包使用 client 模式提交到 Yarn 上

spark-submit --class org.apache.spark.examples.SparkPi --master yarn --deploy-mode client --driver-memory 4g --num-executors 2 --executor-memory 2g --executor-cores 2 spark-examples*.jar 10

client 模式输出结果,会在控制台展示

使用 cluster 模式只需将 --deploy-mode 参数换成 cluster,使用 cluster 模式,控制台上面是没有输出结果的

需要利用 Yarn 来查看

  • yarn logs -applicationId {applicationId} >> yarnApp.log
  • Yarn Web UI

遇到问题

Spark代码中设置appName在client模式和cluster模式中不一样问题


参考链接