2019独角兽企业重金招聘Python工程师标准>>> 
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：
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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。