RDD依赖关系

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发布于 2022-04-28 15:39:27

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发布于 2022-04-28 15:39:27

文章被收录于专栏：大数据共享

前言

RDD的五大特性

A list of partitions 一组分区：多个分区，在RDD中用分区的概念。
A function for computing each split 函数：每个(split/partitions)对应的计算逻辑
A list of dependencies on other RDDs 依赖关系：可对其他RDD有依赖关系，比如上一个RDD结果需要由下一个RDD进行处理。
Optionally, a Partitioner for key-value RDDs (e.g. to say that the RDD is hash-partitioned) 分区器：key-value型的RDD是根据哈希来分区的，类似于mapreduce当中的paritioner接口，控制Key分到哪个reduce。
Optionally, a list of preferred locations to compute each split on (e.g. block locations for an HDFS file) 优先位置：作用在每个分区上的优先位置。由spark自动分配

其中有一个就是 - A list of dependencies on other RDDs(依赖关系)

依赖关系的作用

当RDD运行出错时或造成数据丢失，可以根据依赖关系，重新计算并获取数据。

若rdd4运算过程中出现错误，它可以根据它的依赖关系，从头到尾再运行一遍。

查看血缘[了解即可]

所谓血缘，简单说就是，你的父亲是谁，你父类的父亲是谁，你父类的父亲的父亲又是谁。就相当于家里的族谱。通过族谱你可以知道，你的祖先是谁。在spark中可以通过toDebugString可以产线RDD的依赖关系线。

案例：通过wroldCount程序讲解说明源代码：方便对比后面的改动

  @Test
  def worldCount():Unit={
    //读取文件
    val lines=sc.textFile("file:///C:/Users/123456/Desktop/worldCount.txt",4)

    // 内容扁平化
    val worldList: RDD[String] = lines.flatMap(_.split(" "))

    // 内容分组
    val groupList: RDD[(String, Iterable[String])] = worldList.groupBy(s => s)

    // 统计单词数量
    val result=groupList.map(x=>(x._1,x._2.size))

    println(result.collect().toList)

  }

使用toDebugString 打印RDD之间的依赖线

  @Test
  def worldCount():Unit={
    //读取文件
    val lines=sc.textFile("file:///C:/Users/123456/Desktop/worldCount.txt",4)
    println("*"*50)
    println(lines.toDebugString)
    println("lines","-"*50)

    // 内容扁平化
    val worldList: RDD[String] = lines.flatMap(_.split(" "))
    println(worldList.toDebugString)
    println("worldList","-"*50)

    // 内容分组
    val groupList: RDD[(String, Iterable[String])] = worldList.groupBy(s => s)
    println(groupList.toDebugString)
    println("groupList","-"*50)

    // 统计单词数量
    val result=groupList.map(x=>(x._1,x._2.size))
    println(result.toDebugString)
    println("result","-"*50)

    println(result.collect().toList)

  }

结果：

**************************************************
(5) file:///C:/Users/123456/Desktop/worldCount.txt MapPartitionsRDD[1] at textFile at MapAndMapPartitions.scala:174 []
 |  file:///C:/Users/123456/Desktop/worldCount.txt HadoopRDD[0] at textFile at MapAndMapPartitions.scala:174 []
(lines,--------------------------------------------------)
(5) MapPartitionsRDD[2] at flatMap at MapAndMapPartitions.scala:180 []
 |  file:///C:/Users/123456/Desktop/worldCount.txt MapPartitionsRDD[1] at textFile at MapAndMapPartitions.scala:174 []
 |  file:///C:/Users/123456/Desktop/worldCount.txt HadoopRDD[0] at textFile at MapAndMapPartitions.scala:174 []
(worldList,--------------------------------------------------)
(5) ShuffledRDD[4] at groupBy at MapAndMapPartitions.scala:185 []
 +-(5) MapPartitionsRDD[3] at groupBy at MapAndMapPartitions.scala:185 []
    |  MapPartitionsRDD[2] at flatMap at MapAndMapPartitions.scala:180 []
    |  file:///C:/Users/123456/Desktop/worldCount.txt MapPartitionsRDD[1] at textFile at MapAndMapPartitions.scala:174 []
    |  file:///C:/Users/123456/Desktop/worldCount.txt HadoopRDD[0] at textFile at MapAndMapPartitions.scala:174 []
(groupList,--------------------------------------------------)
(5) MapPartitionsRDD[5] at map at MapAndMapPartitions.scala:190 []
 |  ShuffledRDD[4] at groupBy at MapAndMapPartitions.scala:185 []
 +-(5) MapPartitionsRDD[3] at groupBy at MapAndMapPartitions.scala:185 []
    |  MapPartitionsRDD[2] at flatMap at MapAndMapPartitions.scala:180 []
    |  file:///C:/Users/123456/Desktop/worldCount.txt MapPartitionsRDD[1] at textFile at MapAndMapPartitions.scala:174 []
    |  file:///C:/Users/123456/Desktop/worldCount.txt HadoopRDD[0] at textFile at MapAndMapPartitions.scala:174 []
(result,--------------------------------------------------)

lines 的依赖关系

(5) file:///C:/Users/123456/Desktop/worldCount.txt MapPartitionsRDD[1] at textFile at MapAndMapPartitions.scala:174 []
 |  file:///C:/Users/123456/Desktop/worldCount.txt HadoopRDD[0] at textFile at MapAndMapPartitions.scala:174 []

RDD(lines)需要依赖HadoopRDD；MapPartitionsRDD 就是lines本身这个RDD; 这一步操作，完成了从文件中读取数据，

worldList 的依赖关系：它的父RDD就是lines，所以需要依赖MapPartitionsRDD，同时也会继承父RDD的依赖。

(5) MapPartitionsRDD[2] at flatMap at MapAndMapPartitions.scala:180 []
 |  file:///C:/Users/123456/Desktop/worldCount.txt MapPartitionsRDD[1] at textFile at MapAndMapPartitions.scala:174 []
 |  file:///C:/Users/123456/Desktop/worldCount.txt HadoopRDD[0] at textFile at MapAndMapPartitions.scala:174 []

result 的依赖关系：中间的依赖关系都是这样，所以就省略了，到了result这个RDD，除了继承它的父RDD外，也会把它父RDD之前的依赖关系，都会继承下来。

(5) MapPartitionsRDD[5] at map at MapAndMapPartitions.scala:190 []
 |  ShuffledRDD[4] at groupBy at MapAndMapPartitions.scala:185 []
 +-(5) MapPartitionsRDD[3] at groupBy at MapAndMapPartitions.scala:185 []
    |  MapPartitionsRDD[2] at flatMap at MapAndMapPartitions.scala:180 []
    |  file:///C:/Users/123456/Desktop/worldCount.txt MapPartitionsRDD[1] at textFile at MapAndMapPartitions.scala:174 []
    |  file:///C:/Users/123456/Desktop/worldCount.txt HadoopRDD[0] at textFile at MapAndMapPartitions.scala:174 []

总结：一整个job中所有rdd的链条

子RDD 会有父类的所有依赖关系，父RDD不会有子类的依赖关系。
每一层依赖都有一个序列号，序号越小，表示关系依赖越深。就像族谱中的排名，往往在最前面或最后的，都是时间关系线很久的先辈。
序号为0表示最顶级的RDD依赖。

依赖关系

依赖关系: 是指两个RDD的关系

spark RDD依赖关系分为两种:

宽依赖：有shuffle的称之为宽依赖【如果父RDD一个分区的数据被子RDD多个分区所使用】
窄依赖：没有shuffle的称之为窄依赖【如果父RDD一个分区的数据只被子RDD一个分区所使用】

依旧时上面的案例

  @Test
  def worldCount():Unit={
    //读取文件
    val lines=sc.textFile("file:///C:/Users/123456/Desktop/worldCount.txt",4)
    println("*"*50)
    println(lines.dependencies)
    println("lines","-"*50)

    // 内容扁平化
    val worldList: RDD[String] = lines.flatMap(_.split(" "))
    println(worldList.dependencies)
    println("worldList","-"*50)

    // 内容分组
    val groupList: RDD[(String, Iterable[String])] = worldList.groupBy(s => s)
    println(groupList.dependencies)
    println("groupList","-"*50)

    // 统计单词数量
    val result=groupList.map(x=>(x._1,x._2.size))
    println(result.dependencies)
    println("result","-"*50)

    println(result.collect().toList)
  }

结果

**************************************************
List(org.apache.spark.OneToOneDependency@623ebac7)
(lines,--------------------------------------------------)
List(org.apache.spark.OneToOneDependency@3dd31157)
(worldList,--------------------------------------------------)
List(org.apache.spark.ShuffleDependency@34b9eb03)
(groupList,--------------------------------------------------)
List(org.apache.spark.OneToOneDependency@606f81b5)
(result,--------------------------------------------------)

**************************************************
(5) file:///C:/Users/123456/Desktop/worldCount.txt MapPartitionsRDD[1] at textFile at MapAndMapPartitions.scala:174 []
 |  file:///C:/Users/123456/Desktop/worldCount.txt HadoopRDD[0] at textFile at MapAndMapPartitions.scala:174 []
(lines,--------------------------------------------------)
(5) MapPartitionsRDD[2] at flatMap at MapAndMapPartitions.scala:180 []
 |  file:///C:/Users/123456/Desktop/worldCount.txt MapPartitionsRDD[1] at textFile at MapAndMapPartitions.scala:174 []
 |  file:///C:/Users/123456/Desktop/worldCount.txt HadoopRDD[0] at textFile at MapAndMapPartitions.scala:174 []
(worldList,--------------------------------------------------)
(5) ShuffledRDD[4] at groupBy at MapAndMapPartitions.scala:185 []
 +-(5) MapPartitionsRDD[3] at groupBy at MapAndMapPartitions.scala:185 []
    |  MapPartitionsRDD[2] at flatMap at MapAndMapPartitions.scala:180 []
    |  file:///C:/Users/123456/Desktop/worldCount.txt MapPartitionsRDD[1] at textFile at MapAndMapPartitions.scala:174 []
    |  file:///C:/Users/123456/Desktop/worldCount.txt HadoopRDD[0] at textFile at MapAndMapPartitions.scala:174 []
(groupList,--------------------------------------------------)
(5) MapPartitionsRDD[5] at map at MapAndMapPartitions.scala:190 []
 |  ShuffledRDD[4] at groupBy at MapAndMapPartitions.scala:185 []
 +-(5) MapPartitionsRDD[3] at groupBy at MapAndMapPartitions.scala:185 []
    |  MapPartitionsRDD[2] at flatMap at MapAndMapPartitions.scala:180 []
    |  file:///C:/Users/123456/Desktop/worldCount.txt MapPartitionsRDD[1] at textFile at MapAndMapPartitions.scala:174 []
    |  file:///C:/Users/123456/Desktop/worldCount.txt HadoopRDD[0] at textFile at MapAndMapPartitions.scala:174 []
(result,--------------------------------------------------)

注意到没有：RDD('groupList') 是一个宽依赖(ShuffledRDD)，会进行一次shuffle(通过ShuffledRDD可以看出来；其他都是窄依赖(OneToOneDependency)。

依赖(Dependency)的分类

spark只有两种依赖宽依赖(WideDependence)，窄依赖(NarrowDependency)

宽依赖(WideDependence)：只有一个

ShuffleDependency‘：父对子（一对多），一个父亲多个孩子

窄依赖(NarrowDependency)：有三个

PruneDependency ：外部无法使用，所以不讲
OneToOneDependency：一对一的依赖关系，如;RDD1依赖RDD2
RangeDependency：子对父（一个还是有多个干爹），如;RDD1依赖RDD2，同时依赖于RDD3

宽依赖，窄依赖的作用

主要用于进行shuffle切分的

最后

血统: 一个job中rdd先后顺序的链条

如何查看血统: rdd.toDebugString

依赖: 两个RDD的关系

查了两个RDD的依赖关系: rdd.dependencys
RDD的依赖关系分为两种: 宽依赖: 有shuffle的称之为宽依赖窄依赖: 没有shuffle的称之为窄依赖

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spark

编程算法

javascript

本文系转载，前往查看

如有侵权，请联系 cloudcommunity@tencent.com 删除。

spark

编程算法

javascript

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