执行命令pyspark2 –help结果如下:

–master MASTER_URL spark://host:port, mesos://host:port, yarn, or local. #master选项用于指定使用集群还是单台主机进行处理数据
–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). #使用集群还是客服端部署,缺省是客户端。
–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. #jar包的路径
–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. #添加python应用到 PYTHONPATH ,用逗号分隔的列表,支持.zip,.egg或者.py为后缀的文件,

–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. #配置属性。

–driver-memory MEM Memory for driver (e.g. 1000M, 2G) (Default: 1024M). #用于指定使用驱动内存的大小
–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). #这里是执行内存的大小

–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)

–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.


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