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https://issues.apache.org/jira/browse/SPARK-24578?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16517416#comment-16517416
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Attila Zsolt Piros commented on SPARK-24578:
--------------------------------------------
I have written a small test I know it is a bit naive but I still thing it shows
us something:
[https://gist.github.com/attilapiros/730d67b62317d14f5fd0f6779adea245]
And the result is:
{noformat}
n = 500
duration221 = 2
duration221.new = 0
duration230 = 5242
duration230.new = 7
{noformat}
My guess the receiver timeouts and the sender is writing into a closed socket.
> 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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