org.apache.spark.SparkException: Can only zip RDDs with same number of elements in each partition

企业开发 2023-08-11 18:04:18 阅读次数: 0

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文章目录

问题描述：

在 Spark 中将两个RDD使用 zip 函数进行拉链时出现如下问题 org.apache.spark.SparkException: Can only zip RDDs with same number of elements in each partition

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net\sf\py4j\py4j\0.10.9\py4j-0.10.9.jar;D:\DevelopTools\apache-maven-3.9.0\maven-repo\org\apache\spark\spark-tags_2.12\3.0.0\spark-tags_2.12-3.0.0.jar;D:\DevelopTools\apache-maven-3.9.0\maven-repo\org\apache\commons\commons-crypto\1.0.0\commons-crypto-1.0.0.jar;D:\DevelopTools\apache-maven-3.9.0\maven-repo\org\spark-project\spark\unused\1.0.0\unused-1.0.0.jar com.guang.Test
2023-08-11 20:08:40,172{
    
    yy/MM/ddorg.apache.spark.SparkException: Can only zip RDDs with same number of elements in each partition
	at org.apache.spark.rdd.RDD$$anon$3.hasNext(RDD.scala:928)
	at scala.collection.Iterator.foreach(Iterator.scala:941)
	at scala.collection.Iterator.foreach$(Iterator.scala:941)
	at org.apache.spark.rdd.RDD$$anon$3.foreach(RDD.scala:924)
	at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
	at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
	at scala.collection.TraversableOnce.to(TraversableOnce.scala:315)
	at scala.collection.TraversableOnce.to$(TraversableOnce.scala:313)
	at org.apache.spark.rdd.RDD$$anon$3.to(RDD.scala:924)
	at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:307)
	at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:307)
	at org.apache.spark.rdd.RDD$$anon$3.toBuffer(RDD.scala:924)
	at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:294)
	at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:288)
	at org.apache.spark.rdd.RDD$$anon$3.toArray(RDD.scala:924)
	at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1004)
	at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2133)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:750)
2023-08-11 20:08:40,181{
    
    yy/MM/ddorg.apache.spark.SparkException: Can only zip RDDs with same number of elements in each partition
	at org.apache.spark.rdd.RDD$$anon$3.hasNext(RDD.scala:928)
	at scala.collection.Iterator.foreach(Iterator.scala:941)
	at scala.collection.Iterator.foreach$(Iterator.scala:941)
	at org.apache.spark.rdd.RDD$$anon$3.foreach(RDD.scala:924)
	at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
	at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
	at scala.collection.TraversableOnce.to(TraversableOnce.scala:315)
	at scala.collection.TraversableOnce.to$(TraversableOnce.scala:313)
	at org.apache.spark.rdd.RDD$$anon$3.to(RDD.scala:924)
	at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:307)
	at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:307)
	at org.apache.spark.rdd.RDD$$anon$3.toBuffer(RDD.scala:924)
	at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:294)
	at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:288)
	at org.apache.spark.rdd.RDD$$anon$3.toArray(RDD.scala:924)
	at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1004)
	at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2133)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:750)
2023-08-11 20:08:40,172{
    
    yy/MM/ddorg.apache.spark.SparkException: Can only zip RDDs with same number of elements in each partition
	at org.apache.spark.rdd.RDD$$anon$3.hasNext(RDD.scala:928)
	at scala.collection.Iterator.foreach(Iterator.scala:941)
	at scala.collection.Iterator.foreach$(Iterator.scala:941)
	at org.apache.spark.rdd.RDD$$anon$3.foreach(RDD.scala:924)
	at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
	at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
	at scala.collection.TraversableOnce.to(TraversableOnce.scala:315)
	at scala.collection.TraversableOnce.to$(TraversableOnce.scala:313)
	at org.apache.spark.rdd.RDD$$anon$3.to(RDD.scala:924)
	at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:307)
	at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:307)
	at org.apache.spark.rdd.RDD$$anon$3.toBuffer(RDD.scala:924)
	at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:294)
	at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:288)
	at org.apache.spark.rdd.RDD$$anon$3.toArray(RDD.scala:924)
	at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1004)
	at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2133)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:750)
2023-08-11 20:08:40,221{
    
    yy/MM/ddException in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 15 in stage 0.0 failed 1 times, most recent failure: Lost task 15.0 in stage 0.0 (TID 15, DESKTOP-3S19428, executor driver): org.apache.spark.SparkException: Can only zip RDDs with same number of elements in each partition
	at org.apache.spark.rdd.RDD$$anon$3.hasNext(RDD.scala:928)
	at scala.collection.Iterator.foreach(Iterator.scala:941)
	at scala.collection.Iterator.foreach$(Iterator.scala:941)
	at org.apache.spark.rdd.RDD$$anon$3.foreach(RDD.scala:924)
	at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
	at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
	at scala.collection.TraversableOnce.to(TraversableOnce.scala:315)
	at scala.collection.TraversableOnce.to$(TraversableOnce.scala:313)
	at org.apache.spark.rdd.RDD$$anon$3.to(RDD.scala:924)
	at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:307)
	at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:307)
	at org.apache.spark.rdd.RDD$$anon$3.toBuffer(RDD.scala:924)
	at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:294)
	at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:288)
	at org.apache.spark.rdd.RDD$$anon$3.toArray(RDD.scala:924)
	at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1004)
	at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2133)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:750)

Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2023)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:1972)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:1971)
	at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
	at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1971)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:950)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:950)
	at scala.Option.foreach(Option.scala:407)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:950)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2203)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2152)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2141)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:752)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2093)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2114)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2133)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2158)
	at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1004)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:388)
	at org.apache.spark.rdd.RDD.collect(RDD.scala:1003)
	at com.guang.Test$.main(Test.scala:22)
	at com.guang.Test.main(Test.scala)
Caused by: org.apache.spark.SparkException: Can only zip RDDs with same number of elements in each partition
	at org.apache.spark.rdd.RDD$$anon$3.hasNext(RDD.scala:928)
	at scala.collection.Iterator.foreach(Iterator.scala:941)
	at scala.collection.Iterator.foreach$(Iterator.scala:941)
	at org.apache.spark.rdd.RDD$$anon$3.foreach(RDD.scala:924)
	at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
	at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
	at scala.collection.TraversableOnce.to(TraversableOnce.scala:315)
	at scala.collection.TraversableOnce.to$(TraversableOnce.scala:313)
	at org.apache.spark.rdd.RDD$$anon$3.to(RDD.scala:924)
	at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:307)
	at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:307)
	at org.apache.spark.rdd.RDD$$anon$3.toBuffer(RDD.scala:924)
	at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:294)
	at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:288)
	at org.apache.spark.rdd.RDD$$anon$3.toArray(RDD.scala:924)
	at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1004)
	at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2133)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:750)

原因分析：

这个错误是由于在尝试使用 zip 操作将元素数量不同的 RDD 进行合并时引起的。zip 操作要求两个 RDD 在每个分区中具有相同数量的元素，以便能够一一对应地进行合并。

在这里插入图片描述
从源码中我们可以看到首先遍历两个集合的迭代器，如果两个集合都有元素，那么模式匹配都为true，如果都没有元素，则匹配为false，如果不符合上述情况，则为红线情况，报出元素个数不一致的异常。

使用 zip 进行拉链前提需要两个 RDD 的分区数一致同时每个分区内的元素个数一致，否则就会出现问题。

解决方案：

对于解决方法我们需要保证我们的RDD的分区数和每个分区数的元素个数一致就好了：

val v1: RDD[Int] = sc.makeRDD(List(1, 2, 3, 4), 2)
val v2: RDD[Int] = sc.makeRDD(List(1, 2, 3, 4), 2)

v1.zip(v2).collect().foreach(println)

>>>
(1,1)
(2,2)
(3,3)
(4,4)

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