OutOfMemoryError：Spark中的Java堆空间和内存变量

时间：2015-08-17 13:11:45

标签： scala apache-spark

我一直在尝试执行scala程序，输出似乎总是这样：

15/08/17 14:13:14 ERROR util.Utils: uncaught error in thread SparkListenerBus, stopping SparkContext
java.lang.OutOfMemoryError: Java heap space
at java.lang.AbstractStringBuilder.<init>(AbstractStringBuilder.java:64)
at java.lang.StringBuilder.<init>(StringBuilder.java:97)
at com.fasterxml.jackson.core.util.TextBuffer.contentsAsString(TextBuffer.java:339)
at com.fasterxml.jackson.core.io.SegmentedStringWriter.getAndClear(SegmentedStringWriter.java:83)
at com.fasterxml.jackson.databind.ObjectMapper.writeValueAsString(ObjectMapper.java:2344)
at org.json4s.jackson.JsonMethods$class.compact(JsonMethods.scala:32)
at org.json4s.jackson.JsonMethods$.compact(JsonMethods.scala:44)
at org.apache.spark.scheduler.EventLoggingListener$$anonfun$logEvent$1.apply(EventLoggingListener.scala:143)
at org.apache.spark.scheduler.EventLoggingListener$$anonfun$logEvent$1.apply(EventLoggingListener.scala:143)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.EventLoggingListener.logEvent(EventLoggingListener.scala:143)
at org.apache.spark.scheduler.EventLoggingListener.onJobStart(EventLoggingListener.scala:169)
at org.apache.spark.scheduler.SparkListenerBus$class.onPostEvent(SparkListenerBus.scala:34)
at org.apache.spark.scheduler.LiveListenerBus.onPostEvent(LiveListenerBus.scala:31)
at org.apache.spark.scheduler.LiveListenerBus.onPostEvent(LiveListenerBus.scala:31)
at org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:56)
at org.apache.spark.util.AsynchronousListenerBus.postToAll(AsynchronousListenerBus.scala:37)
at org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(AsynchronousListenerBus.scala:79)
at org.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1215)
at org.apache.spark.util.AsynchronousListenerBus$$anon$1.run(AsynchronousListenerBus.scala:63)

或者像这样

15/08/19 11:45:11 ERROR util.Utils: uncaught error in thread SparkListenerBus, stopping SparkContext
java.lang.OutOfMemoryError: GC overhead limit exceeded
    at com.fasterxml.jackson.databind.ser.DefaultSerializerProvider$Impl.createInstance(DefaultSerializerProvider.java:526)
    at com.fasterxml.jackson.databind.ser.DefaultSerializerProvider$Impl.createInstance(DefaultSerializerProvider.java:505)
    at com.fasterxml.jackson.databind.ObjectMapper._serializerProvider(ObjectMapper.java:2846)
    at com.fasterxml.jackson.databind.ObjectMapper.writeValue(ObjectMapper.java:1902)
    at com.fasterxml.jackson.core.base.GeneratorBase.writeObject(GeneratorBase.java:280)
    at com.fasterxml.jackson.core.JsonGenerator.writeObjectField(JsonGenerator.java:1255)
    at org.json4s.jackson.JValueSerializer.serialize(JValueSerializer.scala:22)
    at org.json4s.jackson.JValueSerializer.serialize(JValueSerializer.scala:7)
    at com.fasterxml.jackson.databind.ser.DefaultSerializerProvider.serializeValue(DefaultSerializerProvider.java:128)
    at com.fasterxml.jackson.databind.ObjectMapper.writeValue(ObjectMapper.java:1902)
    at com.fasterxml.jackson.core.base.GeneratorBase.writeObject(GeneratorBase.java:280)
    at org.json4s.jackson.JValueSerializer.serialize(JValueSerializer.scala:17)
    at org.json4s.jackson.JValueSerializer.serialize(JValueSerializer.scala:7)
    at com.fasterxml.jackson.databind.ser.DefaultSerializerProvider.serializeValue(DefaultSerializerProvider.java:128)
    at com.fasterxml.jackson.databind.ObjectMapper.writeValue(ObjectMapper.java:1902)
    at com.fasterxml.jackson.core.base.GeneratorBase.writeObject(GeneratorBase.java:280)
    at com.fasterxml.jackson.core.JsonGenerator.writeObjectField(JsonGenerator.java:1255)
    at org.json4s.jackson.JValueSerializer.serialize(JValueSerializer.scala:22)
    at org.json4s.jackson.JValueSerializer.serialize(JValueSerializer.scala:7)
    at com.fasterxml.jackson.databind.ser.DefaultSerializerProvider.serializeValue(DefaultSerializerProvider.java:128)
    at com.fasterxml.jackson.databind.ObjectMapper.writeValue(ObjectMapper.java:1902)
    at com.fasterxml.jackson.core.base.GeneratorBase.writeObject(GeneratorBase.java:280)
    at org.json4s.jackson.JValueSerializer.serialize(JValueSerializer.scala:17)
    at org.json4s.jackson.JValueSerializer.serialize(JValueSerializer.scala:7)
    at com.fasterxml.jackson.databind.ser.DefaultSerializerProvider.serializeValue(DefaultSerializerProvider.java:128)
    at com.fasterxml.jackson.databind.ObjectMapper.writeValue(ObjectMapper.java:1902)
    at com.fasterxml.jackson.core.base.GeneratorBase.writeObject(GeneratorBase.java:280)
    at com.fasterxml.jackson.core.JsonGenerator.writeObjectField(JsonGenerator.java:1255)
    at org.json4s.jackson.JValueSerializer.serialize(JValueSerializer.scala:22)
    at org.json4s.jackson.JValueSerializer.serialize(JValueSerializer.scala:7)
    at com.fasterxml.jackson.databind.ser.DefaultSerializerProvider.serializeValue(DefaultSerializerProvider.java:128)
    at com.fasterxml.jackson.databind.ObjectMapper._configAndWriteValue(ObjectMapper.java:2881)

驱动程序或执行程序端是否存在这些错误？

我对Spark使用的内存变量有点困惑。我目前的设置是

spark-env.sh 

export SPARK_WORKER_MEMORY=6G
export SPARK_DRIVER_MEMORY=6G
export SPARK_EXECUTOR_MEMORY=4G

火花defaults.conf 

# spark.driver.memory              6G
# spark.executor.memory            4G
# spark.executor.extraJavaOptions  ' -Xms5G -Xmx5G '
# spark.driver.extraJavaOptions   ' -Xms5G -Xmx5G '

我是否需要取消注释spark-defaults.conf中包含的任何变量，或者它们是多余的？

例如设置SPARK_WORKER_MEMORY相当于设置spark.executor.memory？

我的scala代码的一部分，它在几次迭代后停止：

   val filteredNodesGroups = connCompGraph.vertices.map{ case(_, array) => array(pagerankIndex) }.distinct.collect
    for (id <- filteredNodesGroups){
        val clusterGraph = connCompGraph.subgraph(vpred = (_, attr) => attr(pagerankIndex) == id)
        val pagerankGraph = clusterGraph.pageRank(0.15)
        val completeClusterPagerankGraph = clusterGraph.outerJoinVertices(pagerankGraph.vertices) {
            case (uid, attrList, Some(pr)) => 
                attrList :+ ("inClusterPagerank:" + pr)
            case (uid, attrList, None) => 
                attrList :+ ""
        }
        val sortedClusterNodes = completeClusterPagerankGraph.vertices.toArray.sortBy(_._2(pagerankIndex + 1))
       println(sortedClusterNodes(0)._2(1) + " with rank: " + sortedClusterNodes(0)._2(pagerankIndex + 1))

     }

许多问题伪装成一个。提前谢谢！

1 个答案:

答案 0 :(得分：0)

我不是Spark的专家，但有一条线似乎对我很怀疑：

val filteredNodesGroups = connCompGraph.vertices.map{ case(_, array) => array(pagerankIndex) }.distinct.collect

基本上，通过使用collect方法，您将从执行程序（甚至在处理它之前）的所有数据返回到驱动程序。您对这些数据的大小有任何了解吗？

为了解决这个问题，您应该以更实用的方式继续。要提取不同的值，您可以使用groupBy和map：

val pairs = connCompGraph.vertices.map{ case(_, array) => array(pagerankIndex) }
pairs.groupBy(_./* the property to group on */)
     .map { case (_, arrays) => /* map function */ }

关于collect，应该有一种方法对每个分区进行排序，然后将（已处理的）结果返回给驱动程序。我想帮助你更多，但我需要更多关于你要做什么的信息。

<强>更新

在挖掘一下之后，您可以按照here

所述的改组方式对数据进行排序

<强>更新

到目前为止，我已经尝试避免收集，并尽可能地将数据发回给驱动程序，但我不知道如何解决这个问题：

val filteredNodesGroups = connCompGraph.vertices.map{ case(_, array) => array(pagerankIndex) }.distinct()
val clusterGraphs = filteredNodesGroups.map { id => connCompGraph.subgraph(vpred = (_, attr) => attr(pagerankIndex) == id) }
val pageRankGraphs = clusterGraphs.map(_.pageRank(0.15))

基本上，你需要连接两个RDD [Graph [Array [String]，String]]，但是我不知道要使用什么密钥，其次这必然会返回RDD的RDD（我不＆＃ 39;不知道你是否能做到这一点）。我今天晚些时候会尝试找些东西。

相关问题
最新问题