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https://issues.apache.org/jira/browse/SPARK-21928?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16172123#comment-16172123
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John Brock commented on SPARK-21928:
------------------------------------
[~irashid], thanks for taking a look. I see the same thing as that user -- the
executor gets stuck, causing the application to fail. Before I found the
workaround I mentioned above with spark.driver.extraClassPath and
spark.executor.extraClassPath, I was using speculation to kill off the hanging
tasks, although this wasn't always enough (e.g., if a stage got stuck before
reaching the speculation threshold), and sometimes caused long-running but
non-stuck tasks to be killed.
> ML LogisticRegression training occasionally produces
> java.lang.ClassNotFoundException when attempting to load custom Kryo
> registrator class
> -------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-21928
> URL: https://issues.apache.org/jira/browse/SPARK-21928
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.1.1, 2.2.0
> Reporter: John Brock
>
> I unfortunately can't reliably reproduce this bug; it happens only
> occasionally, when training a logistic regression model with very large
> datasets. The training will often proceed through several {{treeAggregate}}
> calls without any problems, and then suddenly workers will start running into
> this {{java.lang.ClassNotFoundException}}.
> After doing some debugging, it seems that whenever this error happens, Spark
> is trying to use the {{sun.misc.Launcher$AppClassLoader}} {{ClassLoader}}
> instance instead of the usual
> {{org.apache.spark.util.MutableURLClassLoader}}. {{MutableURLClassLoader}}
> can see my custom Kryo registrator, but the {{AppClassLoader}} instance can't.
> When this error does pop up, it's usually accompanied by the task seeming to
> hang, and I need to kill Spark manually.
> I'm running a Spark application in cluster mode via spark-submit, and I have
> a custom Kryo registrator. The JAR is built with {{sbt assembly}}.
> Exception message:
> {noformat}
> 17/08/29 22:39:04 ERROR TransportRequestHandler: Error opening block
> StreamChunkId{streamId=542074019336, chunkIndex=0} for request from
> /10.0.29.65:34332
> org.apache.spark.SparkException: Failed to register classes with Kryo
> at
> org.apache.spark.serializer.KryoSerializer.newKryo(KryoSerializer.scala:139)
> at
> org.apache.spark.serializer.KryoSerializerInstance.borrowKryo(KryoSerializer.scala:292)
> at
> org.apache.spark.serializer.KryoSerializerInstance.<init>(KryoSerializer.scala:277)
> at
> org.apache.spark.serializer.KryoSerializer.newInstance(KryoSerializer.scala:186)
> at
> org.apache.spark.serializer.SerializerManager.dataSerializeStream(SerializerManager.scala:169)
> at
> org.apache.spark.storage.BlockManager$$anonfun$dropFromMemory$3.apply(BlockManager.scala:1382)
> at
> org.apache.spark.storage.BlockManager$$anonfun$dropFromMemory$3.apply(BlockManager.scala:1377)
> at org.apache.spark.storage.DiskStore.put(DiskStore.scala:69)
> at
> org.apache.spark.storage.BlockManager.dropFromMemory(BlockManager.scala:1377)
> at
> org.apache.spark.storage.memory.MemoryStore.org$apache$spark$storage$memory$MemoryStore$$dropBlock$1(MemoryStore.scala:524)
> at
> org.apache.spark.storage.memory.MemoryStore$$anonfun$evictBlocksToFreeSpace$2.apply(MemoryStore.scala:545)
> at
> org.apache.spark.storage.memory.MemoryStore$$anonfun$evictBlocksToFreeSpace$2.apply(MemoryStore.scala:539)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.storage.memory.MemoryStore.evictBlocksToFreeSpace(MemoryStore.scala:539)
> at
> org.apache.spark.memory.StorageMemoryPool.acquireMemory(StorageMemoryPool.scala:92)
> at
> org.apache.spark.memory.StorageMemoryPool.acquireMemory(StorageMemoryPool.scala:73)
> at
> org.apache.spark.memory.StaticMemoryManager.acquireStorageMemory(StaticMemoryManager.scala:72)
> at
> org.apache.spark.storage.memory.MemoryStore.putBytes(MemoryStore.scala:147)
> at
> org.apache.spark.storage.BlockManager.maybeCacheDiskBytesInMemory(BlockManager.scala:1143)
> at
> org.apache.spark.storage.BlockManager.org$apache$spark$storage$BlockManager$$doGetLocalBytes(BlockManager.scala:594)
> at
> org.apache.spark.storage.BlockManager$$anonfun$getLocalBytes$2.apply(BlockManager.scala:559)
> at
> org.apache.spark.storage.BlockManager$$anonfun$getLocalBytes$2.apply(BlockManager.scala:559)
> at scala.Option.map(Option.scala:146)
> at
> org.apache.spark.storage.BlockManager.getLocalBytes(BlockManager.scala:559)
> at
> org.apache.spark.storage.BlockManager.getBlockData(BlockManager.scala:353)
> at
> org.apache.spark.network.netty.NettyBlockRpcServer$$anonfun$1.apply(NettyBlockRpcServer.scala:61)
> at
> org.apache.spark.network.netty.NettyBlockRpcServer$$anonfun$1.apply(NettyBlockRpcServer.scala:60)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at
> scala.collection.convert.Wrappers$IteratorWrapper.next(Wrappers.scala:31)
> at
> org.apache.spark.network.server.OneForOneStreamManager.getChunk(OneForOneStreamManager.java:89)
> at
> org.apache.spark.network.server.TransportRequestHandler.processFetchRequest(TransportRequestHandler.java:125)
> at
> org.apache.spark.network.server.TransportRequestHandler.handle(TransportRequestHandler.java:103)
> at
> org.apache.spark.network.server.TransportChannelHandler.channelRead(TransportChannelHandler.java:118)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:357)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:343)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:336)
> at
> io.netty.handler.timeout.IdleStateHandler.channelRead(IdleStateHandler.java:287)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:357)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:343)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:336)
> at
> io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:102)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:357)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:343)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:336)
> at
> org.apache.spark.network.util.TransportFrameDecoder.channelRead(TransportFrameDecoder.java:85)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:357)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:343)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:336)
> at
> io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1294)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:357)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:343)
> at
> io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:911)
> at
> io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:131)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:643)
> 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)
> at
> io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: java.lang.ClassNotFoundException: com.foo.bar.MyKryoRegistrator
> at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
> at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
> at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331)
> at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
> at java.lang.Class.forName0(Native Method)
> at java.lang.Class.forName(Class.java:348)
> at
> org.apache.spark.serializer.KryoSerializer$$anonfun$newKryo$5.apply(KryoSerializer.scala:134)
> at
> org.apache.spark.serializer.KryoSerializer$$anonfun$newKryo$5.apply(KryoSerializer.scala:134)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
> at
> org.apache.spark.serializer.KryoSerializer.newKryo(KryoSerializer.scala:134)
> ... 60 more
> {noformat}
> My Spark session is created like so:
> {code:java}
> val spark = SparkSession.builder()
> .appName("FooBar")
> .config("spark.serializer",
> "org.apache.spark.serializer.KryoSerializer")
> .config("spark.kryoserializer.buffer.max", "2047m")
>
>
> .config("spark.kryo.registrator","com.foo.bar.MyKryoRegistrator")
> .config("spark.kryo.registrationRequired", "true")
> .config("spark.network.timeout", "3600s")
> .config("spark.driver.maxResultSize", "0")
> .config("spark.rdd.compress", "true")
> .config("spark.shuffle.spill", "true")
> .getOrCreate()
> {code}
> Here are the config options I'm passing to spark-submit:
> {noformat}
> --conf "spark.executor.heartbeatInterval=400s"
> --conf "spark.speculation=true"
> --conf "spark.speculation.multiplier=30"
> --conf "spark.speculation.quantile=0.95"
> --conf "spark.memory.useLegacyMode=true"
> --conf "spark.shuffle.memoryFraction=0.8"
> --conf "spark.storage.memoryFraction=0.2"
> --driver-java-options "-XX:+UseG1GC"
> {noformat}
> I was able to find a workaround: copy your application JAR to each of the
> machines in your cluster, and pass the JAR's path to {{spark-submit}} with:
> {noformat}
> --conf "spark.driver.extraClassPath=/path/to/sparklogisticregression.jar"
> --conf "spark.executor.extraClassPath=/path/to/sparklogisticregression.jar"
> {noformat}
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