从物理执行的角度透视spark Job

时间:2022-09-05 08:49:09

  

本博文主要内容:

  1、再次思考pipeline

  2、窄依赖物理执行内幕

  3、宽依赖物理执行内幕

  4、Job提交流程

一:再次思考pipeline

  即使采用pipeline的方式,函数f对依赖的RDD中的数据的操作也会有2种方式:

    1:f(record), f作用于集合的每一条记录,每次只作用于一条记录。

    2、f(redord), f一次性作用于集合的全部数据。

Spark采用的是第一种方式,原因:

    1、spark无需等待,可以最大化的使用集群计算资源。

    2、减少OOM的发生

    3、最大化的有利于开发

    4、可以精准的控制每一个Partition本身(Dependency)及内部的计算(computer)

    5、基于lineage的算子流动函数式编程,节省了中间结果的产生,并可以最快的恢复

  疑问:会不会增加网络通信?当然不会, 因为在pipeline!

二: 思考Spark Job 具体的物理执行

  Spark Application 里面可以产生1个或者多个job,例如spark-shell默认启动的时候内部就没有Job,只是作为资源的分配程序,可以在spark-shell里面写代码

产生若干个Job,普通程序中一般而言可以有不同的Action,每一个Action一般也就触发一个/job.

  Spark 是 MapReduce思想的一种更加精致和高效的实现,MapReduce有很多具体不同的实现,例如Hadoop 的Mapreduce基本计算流程如下

:首先是以JVM为对象的并发 执行Mapper,Mapper中map的执行会产生输出数据,输出数据会经过Partition指定的规则放在Local FileSystem中,然后

经由Shuffle、 sort、Aggreate变成Reducer中的reduce的输入,执行reduce产生最终的执行结果:Hadoop Mapreduce执行的流程虽然很简单,但是过于死板,尤其

在构造复杂算法(迭代)的时候非常不利于算法的实现。且执行效率极为低下。

  Spark算法构造和物理执行是最基本核心算法:最大化pipeline!

  基于Pipeline的思想,数据被使用的时候才开始计算,从数据流动的角度来讲,是数据流动到计算的位置!!!实际上从逻辑的角度来看, 是算子在数据上流动。

  从算法构建的角度而言:肯定是算子作用于数据,所以是算子在数据上流动,方便算法的构建!

  从物理执行的角度而言:是数据流动到计算的位置。方便系统最为高效的运行!

  对于pipeline而言,数据计算的位置就是每个Stage中最后的RDD, 一个震撼人心的内幕真想就是:每个Stage中除了最后一个RDD 算子是真实的外,前面的算子都是假的。

由于计算的Lazy特性,导致计算从后往前回溯形成Computing  Chain,导致的结果就是需要首先计算出具体一个Stage内部左侧的RDD中本次计算依赖的Partiton。

  

  三:窄依赖的物理执行内幕

  1、 一个Stage内部的RDD都是窄依赖,窄依赖计算本身是逻辑上看是从Stage内部最左侧的RDD开始立即计算的,根据Computing Chain,数据

从一个计算步骤流动到下一个结算步骤,以此类推(算的时候从前往后), 直到计算到Stage内部的最后一个RDD产生计算结果。

  Computiing Chain 的构建是从后往前回溯构建而成,而实际的物理计算则是让数据从前往后在算子上流动,直到流动到不能在流动位置才开始计算下一个Record。这就导致一个美好的结果,后面的RDD 对前面RDD的依赖虽然是Partition级别数据集合的依赖,但是并不需要父RDD把partition中所有Records计算完毕才整体往后流动数据进行计算,这就极大的 提高了计算速率!

从物理执行的角度透视spark Job

/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/ package org.apache.spark.rdd import scala.reflect.ClassTag import org.apache.spark.{Partition, TaskContext} /**
* An RDD that applies the provided function to every partition of the parent RDD.
*/
private[spark] class MapPartitionsRDD[U: ClassTag, T: ClassTag](
prev: RDD[T],
f: (TaskContext, Int, Iterator[T]) => Iterator[U], // (TaskContext, partition index, iterator)
preservesPartitioning: Boolean = false)
extends RDD[U](prev) { override val partitioner = if (preservesPartitioning) firstParent[T].partitioner else None override def getPartitions: Array[Partition] = firstParent[T].partitions override def compute(split: Partition, context: TaskContext): Iterator[U] =
f(context, split.index, firstParent[T].iterator(split, context))
}

四: 宽依赖物理执行内幕

  提示:写代码的时候尽量减少宽依赖

  必须等待依赖的父Stage中的最后一个RDD把全部数据彻底计算完成,才能够经过shuffle来计算当前的Stage

  遇到 shuffle级别的就是形成stage

  所有依赖父Stage,是拿所有Stage的数据还是拿一部分数据:拿一部分数据,算一部分。

  计算数据是从Dependency来的;

  spark作业提交都是触发Action

源码分析类:

    ShuffleDependency  
    --->
MapPartitionRDD   override def compute(split : org.apache.spark.Partition, context : org.apache.spark.TaskContext) : scala.Iterator[U]
     -->
   RDD  (count,--runJob-onReceive-doOnReceive)

  

==========宽依赖物理执行内幕 ============

必须等到依赖的父Stage中的最后一个RDD把全部数据彻底计算完毕才能够经过shuffle来计算当前的Stage。

这样写代码的时候尽量避免宽依赖!!!

/**
 * Implemented by subclasses to return how this RDD depends on parent RDDs. This method will only
 * be called once, so it is safe to implement a time-consuming computation in it.
 */
protected def getDependencies: Seq[Dependency[_]] = deps

compute负责接受父Stage的数据流,计算出record

五、Job提交流程

  

==========Job提交流程 ============

作业提交,触发Action

/**
* Run a function on a given set of partitions in an RDD and pass the results to the given
* handler function. This is the main entry point for all actions in Spark.
*/
def runJob[T, U: ClassTag](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
resultHandler: (Int, U) => Unit): Unit = {
if (stopped.get()) {
throw new IllegalStateException("SparkContext has been shutdown")
}
val callSite = getCallSite
val cleanedFunc = clean(func)
logInfo("Starting job: " + callSite.shortForm)
if (conf.getBoolean("spark.logLineage", false)) {
logInfo("RDD‘s recursive dependencies:\n" + rdd.toDebugString)
}
dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, resultHandler, localProperties.get)
progressBar.foreach(_.finishAll())
rdd.doCheckpoint()
} /**
* Run an action job on the given RDD and pass all the results to the resultHandler function as
* they arrive.
*
* @param rdd target RDD to run tasks on
* @param func a function to run on each partition of the RDD
* @param partitions set of partitions to run on; some jobs may not want to compute on all
* partitions of the target RDD, e.g. for operations like first()
* @param callSite where in the user program this job was called
* @param resultHandler callback to pass each result to
* @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
*
* @throws Exception when the job fails
*/
def runJob[T, U](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
callSite: CallSite,
resultHandler: (Int, U) => Unit,
properties: Properties): Unit = {
val start = System.nanoTime
val waiter = submitJob(rdd, func, partitions, callSite, resultHandler, properties)
waiter.awaitResult() match {
case JobSucceeded =>
logInfo("Job %d finished: %s, took %f s".format
(waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
case JobFailed(exception: Exception) =>
logInfo("Job %d failed: %s, took %f s".format
(waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
// SPARK-8644: Include user stack trace in exceptions coming from DAGScheduler.
val callerStackTrace = Thread.currentThread().getStackTrace.tail
exception.setStackTrace(exception.getStackTrace ++ callerStackTrace)
throw exception
}
} /**
* Submit an action job to the scheduler.
*
* @param rdd target RDD to run tasks on
* @param func a function to run on each partition of the RDD
* @param partitions set of partitions to run on; some jobs may not want to compute on all
* partitions of the target RDD, e.g. for operations like first()
* @param callSite where in the user program this job was called
* @param resultHandler callback to pass each result to
* @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
*
* @return a JobWaiter object that can be used to block until the job finishes executing
* or can be used to cancel the job.
*
* @throws IllegalArgumentException when partitions ids are illegal
*/
def submitJob[T, U](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
callSite: CallSite,
resultHandler: (Int, U) => Unit,
properties: Properties): JobWaiter[U] = {
// Check to make sure we are not launching a task on a partition that does not exist.
val maxPartitions = rdd.partitions.length
partitions.find(p => p >= maxPartitions || p < 0).foreach { p =>
throw new IllegalArgumentException(
"Attempting to access a non-existent partition: " + p + ". " +
"Total number of partitions: " + maxPartitions)
} val jobId = nextJobId.getAndIncrement()
if (partitions.size == 0) {
// Return immediately if the job is running 0 tasks
return new JobWaiter[U](this, jobId, 0, resultHandler)
} assert(partitions.size > 0)
val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler)
eventProcessLoop.post(JobSubmitted(
jobId, rdd, func2, partitions.toArray, callSite, waiter,
SerializationUtils.clone(properties)))
waiter
}

作业:

  写一下我理解中的spark job物理执行。

感谢下面的博主:

http://feiweihy.blog.51cto.com/6389397/1743588