Wenbo Zhao created SPARK-24578: ---------------------------------- Summary: Reading remote cache block behavior changes and causes timeout issue Key: SPARK-24578 URL: https://issues.apache.org/jira/browse/SPARK-24578 Project: Spark Issue Type: Bug Components: Input/Output Affects Versions: 2.3.1, 2.3.0 Reporter: Wenbo Zhao
After Spark 2.3, we observed lots of errors like the following 18/06/15 20:59:42 ERROR TransportRequestHandler: Error sending result ChunkFetchSuccess\{streamChunkId=StreamChunkId{streamId=91672904003, chunkIndex=0}, buffer=org.apache.spark.storage.BlockManagerManagedBuffer@783a9324} to /172.22.18.7:60865; closing connection java.io.IOException: Broken pipe at sun.nio.ch.FileDispatcherImpl.write0(Native Method) at sun.nio.ch.SocketDispatcher.write(SocketDispatcher.java:47) at sun.nio.ch.IOUtil.writeFromNativeBuffer(IOUtil.java:93) at sun.nio.ch.IOUtil.write(IOUtil.java:65) at sun.nio.ch.SocketChannelImpl.write(SocketChannelImpl.java:471) at org.apache.spark.network.protocol.MessageWithHeader.writeNioBuffer(MessageWithHeader.java:156) at org.apache.spark.network.protocol.MessageWithHeader.copyByteBuf(MessageWithHeader.java:142) at org.apache.spark.network.protocol.MessageWithHeader.transferTo(MessageWithHeader.java:123) at io.netty.channel.socket.nio.NioSocketChannel.doWriteFileRegion(NioSocketChannel.java:355) at io.netty.channel.nio.AbstractNioByteChannel.doWrite(AbstractNioByteChannel.java:224) at io.netty.channel.socket.nio.NioSocketChannel.doWrite(NioSocketChannel.java:382) at io.netty.channel.AbstractChannel$AbstractUnsafe.flush0(AbstractChannel.java:934) at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.flush0(AbstractNioChannel.java:362) at io.netty.channel.AbstractChannel$AbstractUnsafe.flush(AbstractChannel.java:901) at io.netty.channel.DefaultChannelPipeline$HeadContext.flush(DefaultChannelPipeline.java:1321) at io.netty.channel.AbstractChannelHandlerContext.invokeFlush0(AbstractChannelHandlerContext.java:776) at io.netty.channel.AbstractChannelHandlerContext.invokeFlush(AbstractChannelHandlerContext.java:768) at io.netty.channel.AbstractChannelHandlerContext.flush(AbstractChannelHandlerContext.java:749) at io.netty.channel.ChannelOutboundHandlerAdapter.flush(ChannelOutboundHandlerAdapter.java:115) at io.netty.channel.AbstractChannelHandlerContext.invokeFlush0(AbstractChannelHandlerContext.java:776) at io.netty.channel.AbstractChannelHandlerContext.invokeFlush(AbstractChannelHandlerContext.java:768) at io.netty.channel.AbstractChannelHandlerContext.flush(AbstractChannelHandlerContext.java:749) at io.netty.channel.ChannelDuplexHandler.flush(ChannelDuplexHandler.java:117) at io.netty.channel.AbstractChannelHandlerContext.invokeFlush0(AbstractChannelHandlerContext.java:776) at io.netty.channel.AbstractChannelHandlerContext.invokeFlush(AbstractChannelHandlerContext.java:768) at io.netty.channel.AbstractChannelHandlerContext.flush(AbstractChannelHandlerContext.java:749) at io.netty.channel.DefaultChannelPipeline.flush(DefaultChannelPipeline.java:983) at io.netty.channel.AbstractChannel.flush(AbstractChannel.java:248) at io.netty.channel.nio.AbstractNioByteChannel$1.run(AbstractNioByteChannel.java:284) at io.netty.util.concurrent.AbstractEventExecutor.safeExecute(AbstractEventExecutor.java:163) at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:403) at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:463) at io.netty.util.concurrent.SingleThreadEventExecutor$5.run(SingleThreadEventExecutor.java:858) at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:138) Here is a small reproducible for a small cluster of 2 executors each with 8 cores (the memory of driver and executors are not a import factor here as long as it is big enough, say 10G). val n = 100000000 val df0 = sc.parallelize(1 to n).toDF val df = df0.withColumn("x0", rand()).withColumn("x0", rand() ).withColumn("x1", rand() ).withColumn("x2", rand() ).withColumn("x3", rand() ).withColumn("x4", rand() ).withColumn("x5", rand() ).withColumn("x6", rand() ).withColumn("x7", rand() ).withColumn("x8", rand() ).withColumn("x9", rand()) df.cache; df.count (1 to 10).toArray.par.map { i => println(i); df.groupBy("x1").agg(count("value")).show() } In the above example, we generated a random DataFrame of size around 7G; cache it and then did a parallel Dataframe operations by using `array.par.map`. Because of the parallel computation, with high possibility, some task will be scheduled to a host-2 where the task needs to read the cache block data from host-1. This will follow the code path of [https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L691] then try to transfer a big block (~ 600MB) of cache from host-1 to host-2. Often, this big transfer made the cluster suffer time out issue. We couldn't to reproduce the same issue in Spark 2.2.1. From the log of Spark 2.2.1, we found that 18/06/16 17:23:47 DEBUG BlockManager: Getting local block rdd_3_0 18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to acquire read lock for rdd_3_0 18/06/16 17:23:47 DEBUG BlockManager: Block rdd_3_0 was not found 18/06/16 17:23:47 DEBUG BlockManager: Getting remote block rdd_3_0 18/06/16 17:23:47 DEBUG BlockManager: Block rdd_3_0 not found 18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to put rdd_3_0 18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to acquire read lock for rdd_3_0 18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 trying to acquire write lock for rdd_3_0 18/06/16 17:23:47 TRACE BlockInfoManager: Task 0 acquired write lock for rdd_3_0 18/06/16 17:23:58 INFO MemoryStore: Block rdd_3_0 stored as values in memory (estimated size 538.2 MB, free 11.1 GB) That is, when a task is scheduled to a host-2 where it needs to read the cache block data from host-1, the endpoint of `master.getLocations(..)` ( see [https://github.com/apache/spark/blob/v2.2.1/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L622]) reports a remote cache block is not found and triggered the recompute. We have two questions here # what is the right behavior here, should we re-compute or should we transfer block from remote? # if we should transfer from remote, why the performance is so bad for cache block? -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org