1.RDD 血缘关系

在这里插入图片描述
依赖关系：两个相邻RDD之间的关系
血缘关系：多个连续的RDD的依赖关系

2.RDD血缘关系的演示

下图演示了RDD的血缘关系：
在这里插入图片描述

RDD是不会保存数据的，但是每个RDD会保存自己的血缘关系；
血缘关系的意义：因为RDD不保存数据，一旦计算失败了，不能从上一个RDD重新计算，必须重头计算，那么RDD必须要知道数据源在哪里，血缘关系就用于追溯数据源，提高了容错性

血缘关系演示

package SparkCore._04_血缘关系

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

/**
 * yatolovefantasy
 * 2021-10-08-21:12
 */
object wordcount {
  def main(args: Array[String]): Unit = {

    val conf: SparkConf = new SparkConf().setAppName("wc").setMaster("local[*]")
    val sc = new SparkContext(conf)

    val fileRDD: RDD[String] = sc.textFile("SparkCore/target/classes/wc.txt")
    println(fileRDD.toDebugString)
    println("***********************")

    val words: RDD[String] = fileRDD.flatMap(_.split(" "))
    println(words.toDebugString)
    println("***********************")

    val wordToOne: RDD[(String, Int)] = words.map((_, 1))
    println(wordToOne.toDebugString)
    println("***********************")

    val wordToSum: RDD[(String, Int)] = wordToOne.reduceByKey(_ + _)
    println(wordToSum.toDebugString)
    println("***********************")
    
    val tuples: Array[(String, Int)] = wordToSum.collect()
    tuples.foreach(println)


    //todo 3.关闭链接
    sc.stop()

  }
}

执行结果：

(2) SparkCore/target/classes/wc.txt MapPartitionsRDD[1] at textFile at wordcount.scala:17 []
 |  SparkCore/target/classes/wc.txt HadoopRDD[0] at textFile at wordcount.scala:17 []
***********************
(2) MapPartitionsRDD[2] at flatMap at wordcount.scala:21 []
 |  SparkCore/target/classes/wc.txt MapPartitionsRDD[1] at textFile at wordcount.scala:17 []
 |  SparkCore/target/classes/wc.txt HadoopRDD[0] at textFile at wordcount.scala:17 []
***********************
(2) MapPartitionsRDD[3] at map at wordcount.scala:25 []
 |  MapPartitionsRDD[2] at flatMap at wordcount.scala:21 []
 |  SparkCore/target/classes/wc.txt MapPartitionsRDD[1] at textFile at wordcount.scala:17 []
 |  SparkCore/target/classes/wc.txt HadoopRDD[0] at textFile at wordcount.scala:17 []
***********************
(2) ShuffledRDD[4] at reduceByKey at wordcount.scala:29 []
 +-(2) MapPartitionsRDD[3] at map at wordcount.scala:25 []
    |  MapPartitionsRDD[2] at flatMap at wordcount.scala:21 []
    |  SparkCore/target/classes/wc.txt MapPartitionsRDD[1] at textFile at wordcount.scala:17 []
    |  SparkCore/target/classes/wc.txt HadoopRDD[0] at textFile at wordcount.scala:17 []
***********************
(hive,1)
(mapreduce,1)
(flink,1)
(spark,1)
(hadoop,2)

Process finished with exit code 0

从上面可以看到一个RDD所经过的算子
并且可以看到这个算子是否有shuffle
±：表示依赖断开，也就是经历了shuffle
(1) 表示分区

3.RDD依赖关系演示

package SparkCore._04_血缘关系

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

/**
 * yatolovefantasy
 * 2021-10-08-21:12
 */
object wordcountdep {
  def main(args: Array[String]): Unit = {
    //todo 1. 建立和Spark框架的链接
    val conf: SparkConf = new SparkConf().setAppName("wc").setMaster("local[*]")
    val sc = new SparkContext(conf)

    //todo 2.业务逻辑处理
    val fileRDD: RDD[String] = sc.textFile("SparkCore/target/classes/wc.txt")
    println(fileRDD.dependencies)
    println("***********************")

    val words: RDD[String] = fileRDD.flatMap(_.split(" "))
    println(words.dependencies)
    println("***********************")

    val wordToOne: RDD[(String, Int)] = words.map((_, 1))
    println(wordToOne.dependencies)
    println("***********************")

    val wordToSum: RDD[(String, Int)] = wordToOne.reduceByKey(_ + _)
    println(wordToSum.dependencies)
    println("***********************")

    val tuples: Array[(String, Int)] = wordToSum.collect()
    tuples.foreach(println)


    //todo 3.关闭链接
    sc.stop()

  }
}

执行结果：

"D:\development software\Java\jdk1.8.0_281\bin\java.exe" "-javaagent:D:\development software\IDEA\IntelliJ IDEA 2021.2\lib\idea_rt.jar=9900:D:\development software\IDEA\IntelliJ IDEA 2021.2\bin" -Dfile.encoding=UTF-8 -classpath "D:\development software\Java\jdk1.8.0_281\jre\lib\charsets.jar;D:\development software\Java\jdk1.8.0_281\jre\lib\deploy.jar;D:\development software\Java\jdk1.8.0_281\jre\lib\ext\access-bridge-64.jar;D:\development software\Java\jdk1.8.0_281\jre\lib\ext\cldrdata.jar;D:\development software\Java\jdk1.8.0_281\jre\lib\ext\dnsns.jar;D:\development software\Java\jdk1.8.0_281\jre\lib\ext\jaccess.jar;D:\development software\Java\jdk1.8.0_281\jre\lib\ext\jfxrt.jar;D:\development software\Java\jdk1.8.0_281\jre\lib\ext\localedata.jar;D:\development software\Java\jdk1.8.0_281\jre\lib\ext\nashorn.jar;D:\development software\Java\jdk1.8.0_281\jre\lib\ext\sunec.jar;D:\development software\Java\jdk1.8.0_281\jre\lib\ext\sunjce_provider.jar;D:\development 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software\RepMaven\com\fasterxml\jackson\module\jackson-module-paranamer\2.10.0\jackson-module-paranamer-2.10.0.jar;D:\development software\RepMaven\org\apache\ivy\ivy\2.4.0\ivy-2.4.0.jar;D:\development software\RepMaven\oro\oro\2.0.8\oro-2.0.8.jar;D:\development software\RepMaven\net\razorvine\pyrolite\4.30\pyrolite-4.30.jar;D:\development software\RepMaven\net\sf\py4j\py4j\0.10.9\py4j-0.10.9.jar;D:\development software\RepMaven\org\apache\spark\spark-tags_2.12\3.0.0\spark-tags_2.12-3.0.0.jar;D:\development software\RepMaven\org\apache\commons\commons-crypto\1.0.0\commons-crypto-1.0.0.jar;D:\development software\RepMaven\org\spark-project\spark\unused\1.0.0\unused-1.0.0.jar" SparkCore._04_血缘关系.wordcountdep
List(org.apache.spark.OneToOneDependency@4d41ba0f)
***********************
List(org.apache.spark.OneToOneDependency@59072e9d)
***********************
List(org.apache.spark.OneToOneDependency@2924f1d8)
***********************
List(org.apache.spark.ShuffleDependency@7dffda8b)
***********************
(hive,1)
(mapreduce,1)
(flink,1)
(spark,1)
(hadoop,2)

Process finished with exit code 0

（1）oneToOneDependency
在这里插入图片描述

oneTooneDependency又叫做窄依赖
窄依赖表示每一个父(上游)RDD的Partition最多被子（下游）RDD的一个Partition使用，窄依赖我们形象的比喻为独生子女。

（2）shuffleDependency

shuffleDependency又叫做宽依赖
宽依赖表示同一个父（上游）RDD的Partition被多个子（下游）RDD的Partition依赖，会引起Shuffle，总结：宽依赖我们形象的比喻为多生。

4.stage&partition&task

4.1Task数量和分区的关系

（1）窄依赖
窄依赖数据分区到分区，task个数不变，还是n个task
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（2）宽依赖
宽依赖前后的Task数量会改变，shuffle前task数量等于分区数n，shuffle后task数量等于分区数m，一共n+m个task

4.2 阶段的划分

对于宽依赖来说，下游RDD分区的数据是经过上游RDD各个分区数据打乱重组的，因此，上游RDD必须每个分区的数据都准备好，下游RDD才能进行运算
对于窄依赖来说，分区之间可以相互独立运行，分区1不需要等分区2，因此不需要阶段划分

DAG（Directed Acyclic Graph）有向无环图是由点和线组成的拓扑图形，该图形具有方向，不会闭环。例如，DAG记录了RDD的转换过程和任务的阶段。

阶段和shuffle有必然的联系

4.3 阶段划分源码

（1）从行动算子进去

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一直到

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(1) 根据触发行动算子的RDD创建ResultStage
(2) 然后由此RDD沿着依赖往前追溯，如果是shuffleDependency，就会创建ShuffleMapStage
(3) 结论就是：
① 阶段数量= shuffle依赖数量+1，这个1其实指的就是ResultStage，因为没有shuffle依赖，也会有ResultStage；
② ResultStage永远只有一个，就是最后需要执行的stage。

4.4 RDD 任务划分

四个概念：Application、Job、Stage和Task

Application：初始化一个SparkContext即生成一个Application；
Job：一个Action算子就会生成一个Job；
Stage：Stage等于宽依赖(ShuffleDependency)的个数加1；
task：

（1）一个Stage阶段中，最后一个RDD的分区个数就是当前Stage的Task的个数。
（2）一个作业(Application)的task个数就是所有stage的task个数总和

Application->Job->Stage->Task每一层都是1对n的关系。

一个Application可以有多个行动算子
一个job有>=0数量的shuffle依赖
一个stage中最后的Rdd有>=1的分区，一个分区对应一个任务

4.5 Task的数量

没有阶段划分，任务数量怎么来的
①submitStage(finalStage)
②val missing = getMissingParaentStage(stage)
③submitMissingTasks(stage,jobId.get) 提交没有上一阶段的tasks
④ Seq[Task]:当前阶段中所有的task
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1.Task集合的来源：
匹配当前阶段的类型(说明不同阶段的任务是不一样的)

2. 从上面的截图中可以看出partitionsToCompute的map算子里面创建Task，因为map是一一映射，所以task数量取决于partitionsToCompute

结论：
Job.numPartitions来自于阶段中最后一个RDD的分区个数，所以一个阶段中的Task数量=当前阶段中最后一个RDD的分区个数。