spark-SQL跑任务报错
错误信息如下
19/10/17 18:06:50 WARN YarnSchedulerBackend$YarnSchedulerEndpoint: Container marked as failed: container_e122_1568025476000_38356_01_000022 on host: node02. Exit status: 137. Diagnostics: Container killed on request. Exit code is 137
Container exited with a non-zero exit code 137
Killed by external signal
19/10/17 18:06:50 INFO BlockManagerMasterEndpoint: Trying to remove executor 21 from BlockManagerMaster.
19/10/17 18:06:50 INFO BlockManagerMaster: Removal of executor 21 requested
19/10/17 18:06:50 INFO YarnSchedulerBackend$YarnDriverEndpoint: Asked to remove non-existent executor 21
19/10/17 18:06:50 INFO TaskSetManager: Starting task 1.1 in stage 28.3 (TID 46, node02, executor 4, partition 1, NODE_LOCAL, 5066 bytes)
19/10/17 18:06:51 INFO BlockManagerInfo: Added broadcast_23_piece0 in memory on node02:27885 (size: 106.5 KB, free: 5.2 GB)
19/10/17 18:06:51 INFO MapOutputTrackerMasterEndpoint: Asked to send map output locations for shuffle 6 to xx.xx.xx.xx:30178
19/10/17 18:06:51 INFO MapOutputTrackerMaster: Size of output statuses for shuffle 6 is 166 bytes
19/10/17 18:06:51 WARN TaskSetManager: Lost task 1.1 in stage 28.3 (TID 46, node02, executor 4): FetchFailed(null, shuffleId=6, mapId=-1, reduceId=166, message=
org.apache.spark.shuffle.MetadataFetchFailedException: Missing an output location for shuffle 6
at org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$2.apply(MapOutputTracker.scala:697)
at org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$2.apply(MapOutputTracker.scala:693)
at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
at org.apache.spark.MapOutputTracker$.org$apache$spark$MapOutputTracker$$convertMapStatuses(MapOutputTracker.scala:693)
at org.apache.spark.MapOutputTracker.getMapSizesByExecutorId(MapOutputTracker.scala:147)
at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:49)
at org.apache.spark.sql.execution.ShuffledRowRDD.compute(ShuffledRowRDD.scala:165)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:89)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
)
上面的错误报完接着就是以下错误：
19/10/17 17:10:32 WARN TaskSetManager: Lost task 0.0 in stage 82.0 (TID 1855, node01, executor 5): FetchFailed(BlockManagerId(1, node01, 26969, None), shuffleId=13, mapId=1, reduceId=10, message=
org.apache.spark.shuffle.FetchFailedException: Failed to connect to hostname/xx.xx.xx.xx:26969
at org.apache.spark.storage.ShuffleBlockFetcherIterator.throwFetchFailedException(ShuffleBlockFetcherIterator.scala:513)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:444)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:61)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.sort_addToSorter$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at org.apache.spark.sql.execution.RowIteratorFromScala.advanceNext(RowIterator.scala:83)
at org.apache.spark.sql.execution.joins.SortMergeJoinScanner.advancedStreamed(SortMergeJoinExec.scala:776)
at org.apache.spark.sql.execution.joins.SortMergeJoinScanner.findNextOuterJoinRows(SortMergeJoinExec.scala:737)
at org.apache.spark.sql.execution.joins.OneSideOuterIterator.advanceStream(SortMergeJoinExec.scala:899)
at org.apache.spark.sql.execution.joins.OneSideOuterIterator.advanceNext(SortMergeJoinExec.scala:935)
at org.apache.spark.sql.execution.RowIteratorToScala.hasNext(RowIterator.scala:68)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$SingleDirectoryWriteTask.execute(FileFormatWriter.scala:313)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:256)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:254)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1371)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:259)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1.apply(FileFormatWriter.scala:189)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1.apply(FileFormatWriter.scala:188)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.io.IOException: Failed to connect to node01/xx.xx.xx.xx:26969
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:232)
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:182)
at org.apache.spark.network.netty.NettyBlockTransferService$$anon$1.createAndStart(NettyBlockTransferService.scala:97)
at org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:141)
at org.apache.spark.network.shuffle.RetryingBlockFetcher.lambda$initiateRetry$0(RetryingBlockFetcher.java:169)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
... 1 more
Caused by: io.netty.channel.AbstractChannel$AnnotatedConnectException: Connection refused: node01/xx.xx.xx.xx:26969
at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:717)
at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(NioSocketChannel.java:257)
at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.finishConnect(AbstractNioChannel.java:291)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:631)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:566)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:480)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:442)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:131)
... 2 more
)解决方案：
增加executor内存，把spark.executor.memory由10G调到20G就不出现这个异常了
原因分析
shuffle分为shuffle write和shuffle read两部分。
shuffle write的分区数由上一阶段的RDD分区数控制，shuffle read的分区数则是由Spark提供的一些参数控制。
shuffle write可以简单理解为类似于saveAsLocalDiskFile的操作，将计算的中间结果按某种规则临时放到各个executor所在的本地磁盘上。
shuffle read的时候数据的分区数则是由spark提供的一些参数控制。可以想到的是，如果这个参数值设置的很小，同时shuffle read的量很大，那么将会导致一个task需要处理的数据非常大。结果导致JVM crash，从而导致取shuffle数据失败，同时executor也丢失了，看到Failed to connect to host的错误，也就是executor lost的意思。有时候即使不会导致JVM crash也会造成长时间的gc。
解决思路
知道原因后问题就好解决了，主要从shuffle的数据量和处理shuffle数据的分区数两个角度入手。
- 减少shuffle数据
思考是否可以使用map side join或是broadcast join来规避shuffle的产生。
将不必要的数据在shuffle前进行过滤，比如原始数据有20个字段，只要选取需要的字段进行处理即可，将会减少一定的shuffle数据。 - SparkSQL和DataFrame的join,group by等操作
通过spark.sql.shuffle.partitions控制分区数，默认为200，根据shuffle的量以及计算的复杂度提高这个值。 - Rdd的join,groupBy,reduceByKey等操作
通过spark.default.parallelism控制shuffle read与reduce处理的分区数，默认为运行任务的core的总数（mesos细粒度模式为8个，local模式为本地的core总数），官方建议为设置成运行任务的core的2-3倍。 - 提高executor的内存
通过spark.executor.memory适当提高executor的memory值。
-是否存在数据倾斜的问题
空值是否已经过滤？异常数据（某个key数据特别大）是否可以单独处理？考虑改变数据分区规则