Spark应用程序部署工具Spark Submit

　　

　　不多说，直接上干货！

 spark-submit在哪个位置

[spark@master ~]$ cd $SPARK_HOME/bin
[spark@master bin]$ pwd
/usr/local/spark/spark-1.6.-bin-hadoop2./bin
[spark@master bin]$ ll
total
-rwxr-xr-x.  spark spark  Feb    beeline
-rw-r--r--.  spark spark   Feb    beeline.cmd
-rw-r--r--.  spark spark  Feb    load-spark-env.cmd
-rw-r--r--.  spark spark  Feb    load-spark-env.sh
-rwxr-xr-x.  spark spark  Feb    pyspark
-rw-r--r--.  spark spark  Feb    pyspark2.cmd
-rw-r--r--.  spark spark  Feb    pyspark.cmd
-rwxr-xr-x.  spark spark  Feb    run-example
-rw-r--r--.  spark spark  Feb    run-example2.cmd
-rw-r--r--.  spark spark  Feb    run-example.cmd
-rwxr-xr-x.  spark spark  Feb    spark-class
-rw-r--r--.  spark spark  Feb    spark-class2.cmd
-rw-r--r--.  spark spark  Feb    spark-class.cmd
-rwxr-xr-x.  spark spark  Feb    sparkR
-rw-r--r--.  spark spark  Feb    sparkR2.cmd
-rw-r--r--.  spark spark   Feb    sparkR.cmd
-rwxr-xr-x.  spark spark  Feb    spark-shell
-rw-r--r--.  spark spark  Feb    spark-shell2.cmd
-rw-r--r--.  spark spark  Feb    spark-shell.cmd
-rwxr-xr-x.  spark spark  Feb    spark-sql
-rwxr-xr-x.  spark spark  Feb    spark-submit
-rw-r--r--.  spark spark  Feb    spark-submit2.cmd
-rw-r--r--.  spark spark  Feb    spark-submit.cmd
[spark@master bin]$ 

打包Spark application

　　将Spark application打成assemblyed jar。我们都知道，其实我们写好的一个Spark application，它除了spark本身的jar包和hdfs的jar包之外，它还有第三方其他的jar包对吧！所以，我们一般借助于maven或sbt的方式来打到最后的一个assemblyed jar。（同时，注意，只打包需要的依赖！！）

　　构建工具：

　　　　1.maven--maven-shade-plugin

　　　　请移步，

Spark编程环境搭建（基于Intellij IDEA的Ultimate版本）（包含Java和Scala版的WordCount）（博主强烈推荐)

　　　

　　　2.sbt

　　　　　这种方式，我不多说。个人偏爱好maven。

　　　3、更多方式见

IDEA里如何多种方式打jar包，然后上传到集群

　

使用spark-submit启动Spark application

　　$SPARK_HOME/bin/spark-submit \
　　--class <main-class> \
　　--master \
　　--deploy-mode \
　　--conf = \
　　... # other options  \
　　[application-arguments]

　　请移步，见

Spark on YARN简介与运行wordcount（master、slave1和slave2）（博主推荐）

Spark standalone简介与运行wordcount（master、slave1和slave2）

spark-submit usage

　　Usage: spark-submit [options] [app arguments]

　　Usage: spark-submit --kill [submission ID] --master [spark://...]

　　Usage: spark-submit --status [submission ID] --master [spark://...]

[spark@master ~]$ $SPARK_HOME/bin/spark-submit  
 
 或者  
 
[spark@master ~]$ $SPARK_HOME/bin/spark-submit --help

[spark@master ~]$ $SPARK_HOME/bin/spark-submit     或者     $SPARK_HOME/bin/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://...]

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.

  --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: ).

 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:  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: ).
  --queue QUEUE_NAME          The YARN queue to submit to (Default: "default").
  --num-executors NUM         Number of executors to launch (Default: ).
  --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.

[spark@master ~]$ 

spark-submit option—运行模式相关

　　设置Spark的运行模式，根据需求选择

　　典型的Master URL：

　　　　　　　　　　

　　　　　　注意：--deploy-mode不是spark on yarn专有

　　

　　典型的Master URL：

　　　　　　　　

spark-submit options—常规

　　　　　　　　

spark-submit options—classpath相关、driver、executor相关

　　　　　　　　　　

spark-submit options—资源、配置相关

　　　　　　　　　　

spark-submit options—YARN-only

　　以下options只有在Saprk on YARN模式下才有效

　　　　　　　　　　　　

spark-submit options—其他

　　　　　　　　

Advanced Dependency Management

　　依赖包分发方式

　　　　--jars

　　　　　　1.file—绝对路径，file:/xxxx

　　　　　  2.hdfs、http、https、ftp

　　　　　　3.local

　　　　--repositories、--packages

　　　　--py-files(仅限python app)

　　Clean up

　　　　Jars和files会被拷贝到每个executor的工作目录，需要定期清理：

　　　　　　Spark on yarn会自动清理（spark.yarn.preserve.staging.files设置为flase，默认就是false）

　　　　　　Spark standalone(spark.worker.cleanup.appDataTtl)