Question

我对以下代码提出的问题很少：

val input1 = rawinput.map(_.split("\t")).map(x=>(x(6).trim(),x)).sortByKey()
val input2 = input1.map(x=> x._2.mkString("\t"))
val x0 = input2.map(_.split("\t")).map(x => (x(6),x(0))
val x1 = input2.map(_.split("\t")).map(x => (x(6),x(1))
val x2 = input2.map(_.split("\t")).map(x => (x(6),x(2))
val x3 = input2.map(_.split("\t")).map(x => (x(6),x(3))
val x4 = input2.map(_.split("\t")).map(x => (x(6),x(4))
val x5 = input2.map(_.split("\t")).map(x => (x(6),x(5))
val x6 = input2.map(_.split("\t")).map(x => (x(6),x(6))
val x = x0 union x1 union x2 union  x3 union x4 union x5 union x6


<pre>
**Lineage Graph:**
(7) UnionRDD[25] at union at rddCustUtil.scala:78 []
|  UnionRDD[24] at union at rddCustUtil.scala:78 []
|  UnionRDD[23] at union at rddCustUtil.scala:78 []
|  UnionRDD[22] at union at rddCustUtil.scala:78 []
|  UnionRDD[21] at union at rddCustUtil.scala:78 []
|  UnionRDD[20] at union at rddCustUtil.scala:78 []
|  MapPartitionsRDD[7] at map at rddCustUtil.scala:43 []
|  MapPartitionsRDD[6] at map at rddCustUtil.scala:43 []
|  MapPartitionsRDD[5] at map at rddCustUtil.scala:40 []
|  ShuffledRDD[4] at sortByKey at rddCustUtil.scala:38 []
+-(1) MapPartitionsRDD[3] at map at rddCustUtil.scala:38 []
    |  MapPartitionsRDD[2] at map at rddCustUtil.scala:38 []
    |  /Data/ MapPartitionsRDD[1] at textFile at rddCustUtil.scala:35 []
    |  /Data/ HadoopRDD[0] at textFile at rddCustUtil.scala:35 []
|  MapPartitionsRDD[9] at map at rddCustUtil.scala:48 []
|  MapPartitionsRDD[8] at map at rddCustUtil.scala:48 []
|  MapPartitionsRDD[5] at map at rddCustUtil.scala:40 []
|  ShuffledRDD[4] at sortByKey at rddCustUtil.scala:38 []
+-(1) MapPartitionsRDD[3] at map at rddCustUtil.scala:38 []
    |  MapPartitionsRDD[2] at map at rddCustUtil.scala:38 []
    |  /Data/ MapPartitionsRDD[1] at textFile at rddCustUtil.scala:35 []
    |  /Data/ HadoopRDD[0] at textFile at rddCustUtil.scala:35 []
|  MapPartitionsRDD[11] at map at rddCustUtil.scala:53 []
|  MapPartitionsRDD[10] at map at rddCustUtil.scala:53 []
|  MapPartitionsRDD[5] at map at rddCustUtil.scala:40 []
|  ShuffledRDD[4] at sortByKey at rddCustUtil.scala:38 []
+-(1) MapPartitionsRDD[3] at map at rddCustUtil.scala:38 []
    |  MapPartitionsRDD[2] at map at rddCustUtil.scala:38 []
    |  /Data/ MapPartitionsRDD[1] at textFile at rddCustUtil.scala:35 []
    |  /Data/ HadoopRDD[0] at textFile at rddCustUtil.scala:35 []
|  MapPartitionsRDD[13] at map at rddCustUtil.scala:58 []
|  MapPartitionsRDD[12] at map at rddCustUtil.scala:58 []
|  MapPartitionsRDD[5] at map at rddCustUtil.scala:40 []
|  ShuffledRDD[4] at sortByKey at rddCustUtil.scala:38 []
+-(1) MapPartitionsRDD[3] at map at rddCustUtil.scala:38 []
    |  MapPartitionsRDD[2] at map at rddCustUtil.scala:38 []
    |  /Data/ MapPartitionsRDD[1] at textFile at rddCustUtil.scala:35 []
    |  /Data/ HadoopRDD[0] at textFile at rddCustUtil.scala:35 []
|  MapPartitionsRDD[15] at map at rddCustUtil.scala:63 []
|  MapPartitionsRDD[14] at map at rddCustUtil.scala:63 []
|  MapPartitionsRDD[5] at map at rddCustUtil.scala:40 []
|  ShuffledRDD[4] at sortByKey at rddCustUtil.scala:38 []
+-(1) MapPartitionsRDD[3] at map at rddCustUtil.scala:38 []
    |  MapPartitionsRDD[2] at map at rddCustUtil.scala:38 []
    |  /Data/ MapPartitionsRDD[1] at textFile at rddCustUtil.scala:35 []
    |  /Data/ HadoopRDD[0] at textFile at rddCustUtil.scala:35 []
|  MapPartitionsRDD[17] at map at rddCustUtil.scala:68 []
|  MapPartitionsRDD[16] at map at rddCustUtil.scala:68 []
|  MapPartitionsRDD[5] at map at rddCustUtil.scala:40 []
|  ShuffledRDD[4] at sortByKey at rddCustUtil.scala:38 []
+-(1) MapPartitionsRDD[3] at map at rddCustUtil.scala:38 []
    |  MapPartitionsRDD[2] at map at rddCustUtil.scala:38 []
    |  /Data/ MapPartitionsRDD[1] at textFile at rddCustUtil.scala:35 []
    |  /Data/ HadoopRDD[0] at textFile at rddCustUtil.scala:35 []
|  MapPartitionsRDD[19] at map at rddCustUtil.scala:73 []
|  MapPartitionsRDD[18] at map at rddCustUtil.scala:73 []
|  MapPartitionsRDD[5] at map at rddCustUtil.scala:40 []
|  ShuffledRDD[4] at sortByKey at rddCustUtil.scala:38 []
+-(1) MapPartitionsRDD[3] at map at rddCustUtil.scala:38 []
    |  MapPartitionsRDD[2] at map at rddCustUtil.scala:38 []
    |  /Data/ MapPartitionsRDD[1] at textFile at rddCustUtil.scala:35 []
    |  /Data/ HadoopRDD[0] at textFile at rddCustUtil.scala:35 []
</pre>

你能否解释一下，由于显示7个ShuffledRDD [4]，将会执行多少个shuffle stage？
您能否在DAG流程下给我详细说明？
此操作费用昂贵吗？

Answer 1

将执行多少个shuffle阶段

实际上，对数据进行排序所需的随机播放发生了7次，因为Spark的评估是懒惰的并且按需运行，除非缓存，否则将重新计算DAG中需要它的每个分支。要解决此问题（并且可能更快地进行此计算），您可以在多次使用之前缓存（或更常见地，持久）input2 ：

val input1 = rawinput.map(_.split("\t")).map(x=>(x(6).trim(),x)).sortByKey()
val input2 = input1.map(x=> x._2.mkString("\t")).cache()
// continue as before

您能否在DAG流程下给我详细说明

使用以下计算“单独”计算每个x_ RDD：

+-(1) MapPartitionsRDD[3] at map at rddCustUtil.scala:38 []
    |  MapPartitionsRDD[2] at map at rddCustUtil.scala:38 []
    |  /Data/ MapPartitionsRDD[1] at textFile at rddCustUtil.scala:35 []
    |  /Data/ HadoopRDD[0] at textFile at rddCustUtil.scala:35 []
|  MapPartitionsRDD[9] at map at rddCustUtil.scala:48 []
|  MapPartitionsRDD[8] at map at rddCustUtil.scala:48 []
|  MapPartitionsRDD[5] at map at rddCustUtil.scala:40 []
|  ShuffledRDD[4] at sortByKey at rddCustUtil.scala:38 []

其中显示了从textFile创建rawinput的计算，然后是排序和三个map操作。

然后，你有6个联合操作来取消这7个RDD。

此操作费用昂贵吗？

是，似乎就是这样。如上所述，缓存可以使它更快 - 但有一种更好的方法来实现这一点 - 而不将RDD分成许多单独的：

val x = rawinput.map(_.split("\t"))
  .keyBy(_(6).trim()) // extract key
  .flatMap{ case (k, arr) => arr.take(7).zipWithIndex.map((k, _)) } // flatMap into (key, (value, index))
  .sortBy { case (k, (_, index)) => (index, k) } // sort by index first, key second
  .map    { case (k, (value, _)) => (k, value) } // remove index, it was just used for sorting

这将执行单个shuffle操作，并且不需要持久保存数据。 DAG看起来像这样：

(4) MapPartitionsRDD[9] at map at Test.scala:75 []
 |  MapPartitionsRDD[8] at sortBy at Test.scala:74 []
 |  ShuffledRDD[7] at sortBy at Test.scala:74 []
 +-(4) MapPartitionsRDD[4] at sortBy at Test.scala:74 []
    |  MapPartitionsRDD[3] at flatMap at Test.scala:73 []
    |  MapPartitionsRDD[2] at keyBy at Test.scala:72 []
    |  MapPartitionsRDD[1] at map at Test.scala:71 []
    |  ParallelCollectionRDD[0] at parallelize at Test.scala:64 []

如何解释Apache Spark RDD Lineage Graph？

1 个答案: