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https://issues.apache.org/jira/browse/SPARK-24578?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16517515#comment-16517515
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Attila Zsolt Piros commented on SPARK-24578:
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[~wbzhao] oh sorry I read your comment late, definitely without your help
(pointing to the right commit caused the problem) it would took much much
longer. If you like please create another PR, it is fine for me.
> 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: Spark Core
> Affects Versions: 2.3.0, 2.3.1
> Reporter: Wenbo Zhao
> Priority: Major
>
> After Spark 2.3, we observed lots of errors like the following in some of our
> production job
> {code:java}
> 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)
> {code}
>
> Here is a small reproducible for a small cluster of 2 executors (say host-1
> and host-2) each with 8 cores. Here, the memory of driver and executors are
> not an import factor here as long as it is big enough, say 20G.
> {code:java}
> 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() }
> {code}
>
> In the above example, we generate a random DataFrame of size around 7G; cache
> it and then perform a parallel DataFrame operations by using `array.par.map`.
> Because of the parallel computation, with high possibility, some task could
> be scheduled to a host-2 where it needs to read the cache block data from
> host-1. This follows the code path of
> [https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L691]
> and then tries to transfer a big block (~ 500MB) of cache block from host-1
> to host-2. Often, this big transfer makes the cluster suffer time out issue
> (it will retry 3 times, each with 120s timeout, and then do recompute to put
> the cache block into the local MemoryStore).
> We couldn't to reproduce the same issue in Spark 2.2.1. From the log of Spark
> 2.2.1, we found that
> {code:java}
> 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)
> {code}
> That is, when a task is scheduled to a host-2 where it needs to read the
> cache block rdd_3_0 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.
> -I believe this behavior change is introduced by this change set
> [https://github.com/apache/spark/commit/e1960c3d6f380b0dfbba6ee5d8ac6da4bc29a698#diff-2b643ea78c1add0381754b1f47eec132]-
>
> We have two questions here
> # what is the right behavior, 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?
>
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