java.lang.OutOfMemoryError: GC overhead limit exceeded

Stack Overflow | Ajeet | 3 months ago
  1. 0

    Spark flat map function is throwing "OutOfMemory"

    Stack Overflow | 3 months ago | Ajeet
    java.lang.OutOfMemoryError: GC overhead limit exceeded
  2. 0

    Getting java heap space issues in Tomcat 7

    Stack Overflow | 4 years ago | Ved
    java.lang.OutOfMemoryError: GC overhead limit exceeded
  3. 0

    Memory Issues

    GitHub | 2 years ago | frankvolkel
    java.lang.OutOfMemoryError: GC overhead limit exceeded
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  5. 0

    GitHub comment 36#56517666

    GitHub | 2 years ago | malaverdiere
    java.lang.OutOfMemoryError: GC overhead limit exceeded
  6. 0

    Xtext: What does this exception mean?

    Stack Overflow | 1 year ago | Raven
    org.eclipse.emf.common.util.WrappedException: java.lang.reflect.InvocationTargetException

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    Root Cause Analysis

    1. java.lang.OutOfMemoryError

      GC overhead limit exceeded

      at java.util.HashMap.createEntry()
    2. Java RT
      HashSet.add
      1. java.util.HashMap.createEntry(HashMap.java:897)
      2. java.util.HashMap.addEntry(HashMap.java:884)
      3. java.util.HashMap.put(HashMap.java:505)
      4. java.util.HashSet.add(HashSet.java:217)
      4 frames
    3. com.pb.hadoop
      HexGenMapFunction.call
      1. com.pb.hadoop.spark.hexgen.function.HexGenMapFunction.call(HexGenMapFunction.java:56)
      2. com.pb.hadoop.spark.hexgen.function.HexGenMapFunction.call(HexGenMapFunction.java:21)
      2 frames
    4. Spark
      JavaRDDLike$$anonfun$fn$1$1.apply
      1. org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$1$1.apply(JavaRDDLike.scala:129)
      2. org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$1$1.apply(JavaRDDLike.scala:129)
      2 frames
    5. Scala
      Iterator$$anon$11.hasNext
      1. scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
      2. scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
      3. scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
      4. scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
      4 frames
    6. Spark
      Executor$TaskRunner.run
      1. org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1$$anonfun$13$$anonfun$apply$6.apply$mcV$sp(PairRDDFunctions.scala:1197)
      2. org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1$$anonfun$13$$anonfun$apply$6.apply(PairRDDFunctions.scala:1197)
      3. org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1$$anonfun$13$$anonfun$apply$6.apply(PairRDDFunctions.scala:1197)
      4. org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1251)
      5. org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1$$anonfun$13.apply(PairRDDFunctions.scala:1205)
      6. org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1$$anonfun$13.apply(PairRDDFunctions.scala:1185)
      7. org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
      8. org.apache.spark.scheduler.Task.run(Task.scala:89)
      9. org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
      9 frames
    7. Java RT
      Thread.run
      1. java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
      2. java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
      3. java.lang.Thread.run(Thread.java:745)
      3 frames