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Posted to issues@spark.apache.org by "Liyin Tang (JIRA)" <ji...@apache.org> on 2016/03/25 17:17:25 UTC
[jira] [Issue Comment Deleted] (SPARK-14105) Serialization issue
for MessageAndMetadata in KafkaRDD
[ https://issues.apache.org/jira/browse/SPARK-14105?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Liyin Tang updated SPARK-14105:
-------------------------------
Comment: was deleted
(was: Avoid serializing MessageAndMetadata.
Easy workaround:
{code}
val messageHandler = (mmd: MessageAndMetadata[String, String]) => (mmd.key(), mmd.message())
{code})
> Serialization issue for MessageAndMetadata in KafkaRDD
> ------------------------------------------------------
>
> Key: SPARK-14105
> URL: https://issues.apache.org/jira/browse/SPARK-14105
> Project: Spark
> Issue Type: Bug
> Components: Streaming
> Affects Versions: 1.5.2, 1.6.1
> Reporter: Liyin Tang
> Attachments: Screenshot 2016-03-23 09.04.51.png
>
>
> When using DISK or Memory to persistent KafkaDirectInputStream, it will serialize the FetchResponse into blocks. The FetchResponse contains the ByteBufferMessageSet where each Kafka Message is just one slice of the underlying ByteBuffer.
> When serializing the KafkaRDDIterator, it seems like the entire underlying ByteBuffer in ByteBufferMessageSet will be serialized for each and every message. This will cause block size easily exceeds 2G, and lead to "java.lang.OutOfMemoryError: Requested array size exceeds VM limit" or "FileChannelImpl.map -> exceeds Integer.MAX_VALUE:"
> The consumer fetch is the default value (1M). I tried to reduce fetch size, but it will cause other errors like errRanOutBeforeEnd.
> Here is the min code to reproduce this issue. This example just tries to demonstrate the bug, not the actual code we run.
> {code}
> // create source stream object
> val ssc = new StreamingContext(sparkConf, Seconds(intervalSeconds))
> // Create topic, kafkaParams, messageHandler and offsetnRanges
> val topicsSet: Set[String] = "flog".split(",").toSet
> val kafkaParams = Map[String, String]("metadata.broker.list" -> KafkaCluster.MAIN.getKafkaConnString)
> val messageHandler = (mmd: MessageAndMetadata[String, String]) => mmd
> val topicPartitionOffsetRange = KafkaOffsetsUtil.getKafkaOffsets(
> KafkaCluster.MAIN,
> topicsSet.toList.asJava,
> kafka.api.OffsetRequest.LatestTime).toMap.mapValues(Long2long).take(10)
> // Create an DStream
> val inputStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder, MessageAndMetadata[String, String]](
> ssc,
> kafkaParams,
> topicPartitionOffsetRange,
> messageHandler)
> // Apply window function
> inputStream.window(Seconds(slidingWindowInterval), Seconds(intervalSeconds)).foreachRDD(rdd => rdd.count())
> ssc.start()
> ssc.awaitTermination()
> {code}
> Here are exceptions I got for both Memory and Disk persistent.
> Memory Persistent:
> {code}
> 16/03/23 15:34:44 ERROR SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[Executor task launch worker-9,5,main]
> java.lang.OutOfMemoryError: Requested array size exceeds VM limit
> at java.util.Arrays.copyOf(Arrays.java:3236)
> at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:113)
> at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
> at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:140)
> at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)
> at java.io.BufferedOutputStream.write(BufferedOutputStream.java:126)
> at com.esotericsoftware.kryo.io.Output.flush(Output.java:155)
> at com.esotericsoftware.kryo.io.Output.require(Output.java:135)
> at com.esotericsoftware.kryo.io.Output.writeBytes(Output.java:220)
> at com.esotericsoftware.kryo.io.Output.writeBytes(Output.java:206)
> at com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ByteArraySerializer.write(DefaultArraySerializers.java:29)
> at com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ByteArraySerializer.write(DefaultArraySerializers.java:18)
> at com.esotericsoftware.kryo.Kryo.writeObjectOrNull(Kryo.java:549)
> at com.esotericsoftware.kryo.serializers.FieldSerializer$ObjectField.write(FieldSerializer.java:570)
> at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:213)
> at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:501)
> at com.esotericsoftware.kryo.serializers.FieldSerializer$ObjectField.write(FieldSerializer.java:564)
> at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:213)
> at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:501)
> at com.esotericsoftware.kryo.serializers.FieldSerializer$ObjectField.write(FieldSerializer.java:564)
> at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:213)
> at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:568)
> at org.apache.spark.serializer.KryoSerializationStream.writeObject(KryoSerializer.scala:158)
> at org.apache.spark.serializer.SerializationStream.writeAll(Serializer.scala:153)
> at org.apache.spark.storage.BlockManager.dataSerializeStream(BlockManager.scala:1190)
> at org.apache.spark.storage.BlockManager.dataSerialize(BlockManager.scala:1199)
> at org.apache.spark.storage.MemoryStore.putArray(MemoryStore.scala:132)
> at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:793)
> at org.apache.spark.storage.BlockManager.putArray(BlockManager.scala:669)
> at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:175)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:262)
> {code}
> Disk Persistent:
> {code}
> 16/03/23 17:04:00 INFO TaskSetManager: Lost task 42.1 in stage 2.0 (TID 974) on executor i-2878ceb3.inst.aws.airbnb.com: java.lang.RuntimeException (java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
> at sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:836)
> at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2.apply(DiskStore.scala:125)
> at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2.apply(DiskStore.scala:113)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1206)
> at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:127)
> at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:134)
> at org.apache.spark.storage.BlockManager.doGetLocal(BlockManager.scala:512)
> at org.apache.spark.storage.BlockManager.getBlockData(BlockManager.scala:302)
> {code}
>
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