spark job分析

spark job

spark job提交

三级调度框架,
DagSch,计算stage，提交阶段，将stage映射成taskset，提交taskset给tasksch。
TaskSch
BackendSch

setMaster("local[n]")

n表示使用n个线程模拟的spark集群下的worker数据。
默认是1，n称为并发度。
textFile("..." , m)，m是分区数，默认收到并发度的影响。
1. local
	new LocalSchedulerBackend(sc.getConf, scheduler, 1)
2. local[4] / local[*]
	* 是cpu内核数
	def localCpuCount: Int = Runtime.getRuntime.availableProcessors()
	val threadCount = if (threads == "*") localCpuCount else threads.toInt
3. local[m,n]
	m是并发度，n是重试次数。
	local[N, cores, memory]

4. spark://(.*)

RPC

remote procedure call ,远程过程调用。socket编程。

TaskScheduler

任务调度器接口,spark中只有一种实现TaskSchedulerImpl。

textFile()

加载文件时，可指定最小分区数，但最小分区数有默认值。

def textFile(path: String,minPartitions: Int = defaultMinPartitions)
//默认最小分区不会超过2
def defaultMinPartitions: Int = math.min(defaultParallelism, 2)

//spark上下文的默认并发度 = 任务调度器的默认并发度
def SparkContext.defaultParallelism: Int = {taskScheduler.defaultParallelism }

//任务调度器的默认并发度 = 后台调度器的默认并发度
override def taskScheduler.defaultParallelism(): Int = backend.defaultParallelism()

1.LocalScheduclerBackend
	//local  本地后台调度器，读取默认并发度配置属性，若没有，则采用cpu内核数作为默认并发度。
	new LocalSchedulerBackend(sc.getConf, scheduler, 1)
	//local[*|n],取出指定的内核数
	new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
	//local[N,cores,memory],跟local[n]
	new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)

	//
	override def defaultParallelism(): Int = scheduler.conf.getInt("spark.default.parallelism", totalCores)

2.CoarseGrainedSchedulerBackend
	粗粒度后台调度器
3.StandaloneSchedulerBackend
	独立模式后台调度器 , 继承与CoarseGrainedSchedulerBackend.

spark最小分区数计算

sc.textFile( ..., n) ;
HadoopRDD -> MapPaqrtitionRDD.
rdd.partitions.length ;

Sparkjob在集群模式下，分两步走

1.创建SparkContext对象时，在spark master中注册应用.分配资源,在worker节点启动Executor进程。

spark集群默认的资源使用

core	:	使用worker节点的所有内核,内核进行物理检测。
memory	:	内存使用1g内存，内存不进行物理检测。

修改默认值
[spark/conf/spark-env.sh]
...
# 每个worker使用的内核数
export SPARK_WORKER_CORES=6

#每个worker使用内存数
export SPARK_WORKER_MEMORY=6g

#是否可以在一个节点启动几个worker进程
export SPARK_WORKER_INSTANCES=2

#master和worker进程本身的内存数
export SPARK_DAEMON_MEMORY=200m

[修改完之后分发]
xsync.sh spark-env.sh

Spark job的资源控制

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://...]
Usage: spark-submit run-example [options] example-class [example args]

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.

  c

  --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).
							  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.

--driver-memory	2g			//控制driver堆内存，默认1g
--executor-memory MEM       //每个executor的内存，默认 1G.

[standalone + cluster]
--driver-cores NUM			//控制driver的内核数

[Spark standalone和 Mesos]
--total-executor-cores NUM  //用于所有executor的总的内核数

[spark standalone | yarn]
--executor-cores			//每个执行器的内核数，yarn模式是1，standalone是所有可能内核。

[YARN-only]
--driver-cores NUM          //driver内核数，只用于cluster模式(Default: 1).
--num-executors NUM         //启动的执行器个数(Default: 2).

-- 每个执行器分配4个核
spark-shell --master spark://s201:7077 --driver-memory 800m --executor-memory 800m --executor-cores 10

--
spark-shell --master spark://s201:7077 --executor-memory 3g --executor-cores 4 --total-executor-cores 16

-- yarn模式下指定资源
spark-submit --master yarn --deploy-mode client --class TempAggDemoScala_GroupByKey --executor-memory 1g --executor-cores 2 --num-executors 4 myspark.jar 1000
spark-submit --master yarn --deploy-mode cluster --class TempAggDemoScala_GroupByKey --executor-memory 1g --executor-cores 2 --num-executors 4 myspark.jar 1000