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[Java教程]Spark Rdd coalesce()方法和repartition()方法


在Spark的Rdd中,Rdd是分区的。

有时候需要重新设置Rdd的分区数量,比如Rdd的分区中,Rdd分区比较多,但是每个Rdd的数据量比较小,需要设置一个比较合理的分区。或者需要把Rdd的分区数量调大。还有就是通过设置一个Rdd的分区来达到设置生成的文件的数量。

有两种方法是可以重设Rdd的分区:分别是 coalesce()方法和repartition()。

 这两个方法有什么区别,看看源码就知道了:

 def coalesce(numPartitions: Int, shuffle: Boolean = false)(implicit ord: Ordering[T] = null)   : RDD[T] = withScope {  if (shuffle) {   /** Distributes elements evenly across output partitions, starting from a random partition. */   val distributePartition = (index: Int, items: Iterator[T]) => {    var position = (new Random(index)).nextInt(numPartitions)    items.map { t =>     // Note that the hash code of the key will just be the key itself. The HashPartitioner     // will mod it with the number of total partitions.     position = position + 1     (position, t)    }   } : Iterator[(Int, T)]   // include a shuffle step so that our upstream tasks are still distributed   new CoalescedRDD(    new ShuffledRDD[Int, T, T](mapPartitionsWithIndex(distributePartition),    new HashPartitioner(numPartitions)),    numPartitions).values  } else {   new CoalescedRDD(this, numPartitions)  } }

coalesce()方法的作用是返回指定一个新的指定分区的Rdd。

如果是生成一个窄依赖的结果,那么不会发生shuffle。比如:1000个分区被重新设置成10个分区,这样不会发生shuffle。

关于Rdd的依赖,这里提一下。Rdd的依赖分为两种:窄依赖和宽依赖。

窄依赖是指父Rdd的分区最多只能被一个子Rdd的分区所引用,即一个父Rdd的分区对应一个子Rdd的分区,或者多个父Rdd的分区对应一个子Rdd的分区。

而宽依赖就是宽依赖是指子RDD的分区依赖于父RDD的多个分区或所有分区,即存在一个父RDD的一个分区对应一个子RDD的多个分区。1个父RDD分区对应多个子RDD分区,这其中又分两种情况:1个父RDD对应所有子RDD分区(未经协同划分的Join)或者1个父RDD对应非全部的多个RDD分区(如groupByKey)。

如下图所示:map就是一种窄依赖,而join则会导致宽依赖

回到刚才的分区,如果分区的数量发生激烈的变化,如设置numPartitions = 1,这可能会造成运行计算的节点比你想象的要少,为了避免这个情况,可以设置shuffle=true,

那么这会增加shuffle操作。

关于这个分区的激烈的变化情况,比如分区数量从父Rdd的几千个分区设置成几个,有可能会遇到这么一个错误。

Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 77.0 failed 4 times, most recent failure: Lost task 1.3 in stage 77.0 (TID 6334, 192.168.8.61): java.io.IOException: Unable to acquire 16777216 bytes of memory    at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:351)    at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPageIfNecessary(UnsafeExternalSorter.java:332)    at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertKVRecord(UnsafeExternalSorter.java:461)    at org.apache.spark.sql.execution.UnsafeKVExternalSorter.insertKV(UnsafeKVExternalSorter.java:139)    at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.switchToSortBasedAggregation(TungstenAggregationIterator.scala:489)    at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:379)    at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.start(TungstenAggregationIterator.scala:622)    at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.org$apache$spark$sql$execution$aggregate$TungstenAggregate$$anonfun$$executePartition$1(TungstenAggregate.scala:110)    at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)    at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)    at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:64)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:63)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:63)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:99)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:87)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)    at org.apache.spark.rdd.CoalescedRDD$$anonfun$compute$1.apply(CoalescedRDD.scala:96)    at org.apache.spark.rdd.CoalescedRDD$$anonfun$compute$1.apply(CoalescedRDD.scala:95)    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)    at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:209)    at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:73)    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)    at org.apache.spark.scheduler.Task.run(Task.scala:88)    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)    at java.lang.Thread.run(Thread.java:744)

这个错误只要把shuffle设置成true即可解决。

当把父Rdd的分区数量增大时,比如Rdd的分区是100,设置成1000,如果shuffle为false,并不会起作用。

这时候就需要设置shuffle为true了,那么Rdd将在shuffle之后返回一个1000个分区的Rdd,数据分区方式默认是采用 hash partitioner。

最后来看看repartition()方法的源码:

 def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope {  coalesce(numPartitions, shuffle = true) }

从源码可以看出,repartition()方法就是coalesce()方法shuffle为true的情况。那么如果说只是要减少父Rdd的分区数量,并且要设置的分区数量并不是很激烈,可以考虑直接使用coalesce方法来避免执行shuffle操作,提高效率。

如有错误遗漏的地方,请不吝赐教,我必将改正。