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。
有这两种方法是可以重设RDD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,               partitionCoalescer: Option[PartitionCoalescer] = Option.empty)              (implicit ord: Ordering[T] = null)      : RDD[T] = withScope {    require(numPartitions > 0, s"Number of partitions ($numPartitions) must be positive.")    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,        partitionCoalescer).values    } else {      new CoalescedRDD(this, numPartitions, partitionCoalescer)    }  }

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，从而提高并行度，但这会增加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$$anonfun$doExecute$1.org$apache$spark$sql$execution$aggregate$TungstenAggregateanonfunexecutePartition$1(TungstenAggregate.scala:110)          at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$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的分区数量增大是，比如RDD的分区是100，设置成1000，如果shuffle为false，那就不会起作用。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hashpartitioner。
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。