Spark RDD分区2G限制

问题现象

遇到这个问题时，spark日志会报如下的日志

片段1：

15/04/16 14:13:03 WARN scheduler.TaskSetManager: Lost task 19.0 in stage 6.0 (TID 120, 10.215.149.47): java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
at sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:828)
at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:123)
at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:132)
at org.apache.spark.storage.BlockManager.doGetLocal(BlockManager.scala:517)
at org.apache.spark.storage.BlockManager.getLocal(BlockManager.scala:432)
at org.apache.spark.storage.BlockManager.get(BlockManager.scala:618)
at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:146)
at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70)

片段2：

15/04/16 14:19:45 INFO scheduler.TaskSetManager: Starting task 20.2 in stage 6.0 (TID 146, 10.196.151.213, PROCESS_LOCAL, 1666 bytes)15/04/16 14:19:45 INFO scheduler.TaskSetManager: Lost task 20.2 in stage 6.0 (TID 146) on executor 10.196.151.213: java.lang.IllegalArgumentException (Size exceeds Integer.MAX_VALUE) [duplicate 1]15/04/16 14:19:45 INFO scheduler.TaskSetManager: Starting task 20.3 in stage 6.0 (TID 147, 10.196.151.213, PROCESS_LOCAL, 1666 bytes)15/04/16 14:19:45 INFO scheduler.TaskSetManager: Lost task 20.3 in stage 6.0 (TID 147) on executor 10.196.151.213: java.lang.IllegalArgumentException (Size exceeds Integer.MAX_VALUE) [duplicate 2]15/04/16 14:19:45 ERROR scheduler.TaskSetManager: Task 20 in stage 6.0 failed 4 times; aborting job15/04/16 14:19:45 INFO cluster.YarnClusterScheduler: Cancelling stage 615/04/16 14:19:45 INFO cluster.YarnClusterScheduler: Stage 6 was cancelled15/04/16 14:19:45 INFO scheduler.DAGScheduler: Job 6 failed: collectAsMap at DecisionTree.scala:653, took 239.760845 s15/04/16 14:19:45 ERROR yarn.ApplicationMaster: User class threw exception: Job aborted due to stage failure: Task 20 in stage 6.0 failed 4 times, most recent failure: Lost task 20.3 in stage 6.0 (TID 147, 10.196.151.213): java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUEat sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:828)

异常就是某个partition的数据量超过了Integer.MAX_VALUE（2147483647 = 2GB）

解决方法

手动设置RDD的分区数量。当前使用的Spark默认RDD分区是18个，后来手动设置为1000个，上面这个问题就迎刃而解了。可以在RDD加载后，使用RDD.repartition(numPart:Int)函数重新设置分区数量。

为什么2G限制

目前spark社区对这个限制有很多讨（tu）论（cao），spark官方团队已经注意到了这个问题，但是直到1.2版本，这个问题还是没有解决。因为牵涉到整个RDD的实现框架，所以改进成本相当大!

下面是一些相关的资料，有兴趣的读者可以进一步的阅读:

• 2GB limit in spark for blocks
• create LargeByteBuffer abstraction for eliminating 2GB limit on blocks
• Why does Spark RDD partition has 2GB limit for HDFS
• 抛异常的java代码:FileChannelImpl.java

个人思（yu）考（jian）

这个限制有一定合理性。因为RDD中partition的操作是并发执行的，如果partition量过少，导致并发数过少，会限制计算效率。所以，基于这个限制，spark应用程序开发者会主动扩大partition数量，也就是加大并发量，最终提高计算性能。

转载自：https://www.cnblogs.com/bourneli/p/4456109.html