spark-submit 主要参数详细说明及Standalone集群最佳实践

文章目录

1. 前言

部署提交应用到 spark 集群，可能会用到

spark-submit

工具，鉴于网上的博客质量残差不齐，且有很多完全是无效且错误的配置，没有搞明白诸如

--total-executor-cores

、

--executor-cores

、

--num-executors

的关系和区别。因此有必要结合官网文档 submitting-applications 详细记录一下参数的含义。

ps：Spark Standalone 集群安装流程参考这里

2. 参数说明

一般的用法是：

spark-submit [option] xx.jar/xx.py

详细说明如下：

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 formatfor 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).
  --archives ARCHIVES         Comma-separated list of archives to be extracted into the
                              working directory of each executor.

  --conf, -cPROP=VALUE       Arbitrary Spark configuration property.
  --properties-file FILE      Path to a file from which to load extra properties. If not
                              specified, this will lookfor 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 Connect only:
   --remote CONNECT_URL       URL to connect to the server for Spark Connect, e.g.,
                              sc://host:port. --master and --deploy-mode cannot be set
                              together with this option. This option is experimental, and
                              might change between minor releases.

 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.

 Spark standalone, Mesos or K8s with cluster deploy mode only:
  --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, YARN and Kubernetes only:
  --executor-cores NUM        Number of cores used by each executor. (Default: 1in
                              YARN and K8S modes, or all available cores on the worker
                              in standalone mode).

 Spark on YARN and Kubernetes only:
  --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.
  --principal PRINCIPAL       Principal to be used to login to KDC.
  --keytab KEYTAB             The full path to the file that contains the keytab for the
                              principal specified above.

 Spark on YARN only:
  --queue QUEUE_NAME          The YARN queue to submit to (Default: "default").

我把一些主要的参数列举一下：

• --master MASTER_URL ，其中 MASTER_URL 可选如下： - local，启1个work线程本地运行应用程序- local[K]，启K个work线程本地运行应用程序- local[K,F]，启K个work线程本地运行应用程序，且运行中最大容忍F次失败次数- local[*]，尽可能多启动cpu逻辑线程本地运行应用程序- local[*,F]，尽可能多启动cpu逻辑线程本地运行应用程序，且运行中最大容忍F次失败次数- local-cluster[N,C,M]，仅用于单元测试，它在一个JVM中模拟一个分布式集群，其中有N个工作线程，每个工作线程有C个内核，每个工作进程有M MiB的内存。- spark://host:port，连接standalone集群的master节点，端口默认7077- spark://HOST1:PORT1,HOST2:PORT2，连接带有Zookeeper备份的standalone集群的master节点。该列表必须使用Zookeeper设置高可用性集群中的所有主主机，端口默认7077。- mesos://host:port，连接 Mesos 集群，端口默认5050- yarn，连接 YARN 集群，此外--deploy-mode参数决定了是client还是cluster模式- k8s://https://host:port 连接 K8s 集群，此外--deploy-mode参数决定了是client还是cluster模式
• --deploy-mode 可选cluster及client。cluster：在work节点上部署driver。client：作为外部client在本地部署driver，默认是client
• --driver-memory MEM 分配driver的内存，默认1024M
• --executor-memory MEM 分配每个executor的内存，默认1G
• --driver-cores NUM driver 可以使用的核数，默认1。注意仅在cluster模式下有效
• --total-executor-cores NUM 所有的executor总共的核数。注意仅在Spark standalone 以及 Mesos下生效
• --executor-cores NUM 每个executor可以使用的核数，默认1。注意仅在 Spark standalone, YARN以及Kubernetes下生效
• --num-executors NUM executor启动的数量，默认2。注意仅在Spark on YARN 以及 Kubernetes下生效

3. Standalone集群最佳实践

因为Spark Standalone集群下

--num-executors NUM

参数不生效，而且如果你没有用

--deploy-mode=cluster

，那么

--driver-cores NUM

参数也是不生效的，那么一种可行的提交参数：

spark-submit 
--master spark://master:7077 
--name spark-app
--total-executor-cores={集群机器数}*{一台机器的逻辑核数-1}
--executor-cores={一台机器的逻辑核数-1}
--executor-memory={一台机器的内存-3GB}
xxx.py

例如，Spark Standalone集群有3台机器，每台机器 cpu 逻辑核数是16，每台机器的内存是16GB，那么可以如下提交：

spark-submit 
--master spark://master:7077 
--name spark-app
--total-executor-cores=45
--executor-cores=15
--executor-memory=13GB
xxx.py

当然，

--executor-memory

可以根据实际情况去调整，先大致看一下有多少空闲的内存：

free-h

查看 cpu 逻辑核数：

cat /proc/cpuinfo|grep"processor"|wc-l

然后再调整大小～

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