Spark on YARN资源申请

1.spark submit参数

$ ./bin/spark-submit --class path.to.your.Class --master yarn --deploy-mode cluster [options] <app jar> [app options]

Multiple versions of Spark are installed but SPARK_MAJOR_VERSION is not set
Spark1 will be picked by default
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.
                              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).
  --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.

For example:

$ ./bin/spark-submit --class org.apache.spark.examples.SparkPi \
    --master yarn \
    --deploy-mode cluster \
    --driver-memory 4g \
    --executor-memory 2g \
    --executor-cores 1 \
    --queue thequeue \
    lib/spark-examples*.jar \
    10

In client mode:

$ ./bin/spark-shell --master yarn --deploy-mode client

 

最近一直测试spark程序，对spark-submit的参数进行了调节。

通过上面图片可以看到，总共有7个节点，总共的VCores为133，总共内存为1.49TB，有3个application在运行，2个分别为spark thrift server和spark2 thrift server，1个为我提交的任务，可以看出我提交的任务占用了81个cpu VCores（1个为application master）。

提交命令：

time spark-submit --master yarn-client --driver-memory 10g --executor-memory 10g --num-executors 80 --class com.test.test.ByHour ~/cs0308/quickstart-SNAPSHOT.jar /test/origin/20170306_02 /test/result/02

其中

--num-executors 80           80个executor（默认是2个）

--executor-memory 10g        exector内存大小（默认1g）

--driver-memory 10g          Driver程序使用内存大小

--executor-cores             每个executor使用的内核数，默认为1

发现在设置过程中运行起来还有剩余的资源，但是若把参数直接设成80以上，程序一运行就直接报错，程序刚运行时申请资源会超过设置参数，然后就会下降80（设置参数）+1个cpu，1个为Application Master。这个是跑了1.2T大小的gz文件，跑完为4.6T的txt文件。
同样的程序在第二天跑的时候，发现老报sockettimeoutexception，重启集群（没有程序在运行）后，再一次运行同样的命令发现正常了（用的HDP的集群）。


具体spark-submit参数说明： https://my.oschina.net/u/140462/blog/519409