Apache's JIRA Issue Tracker | Erik Selin | 1 year ago
  1. 0

    When running a large spark sql query including multiple joins I see tasks failing with the following trace: {code} java.lang.NegativeArraySizeException at org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder.grow( at org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter.write( at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source) at org.apache.spark.sql.execution.joins.OneSideOuterIterator.getRow(SortMergeOuterJoin.scala:288) at at at scala.collection.Iterator$$anon$ at org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write( at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) at at org.apache.spark.executor.Executor$ at java.util.concurrent.ThreadPoolExecutor.runWorker( at java.util.concurrent.ThreadPoolExecutor$ at {code} From the spark code it looks like this is due to a integer overflow when growing a buffer length. The offending line {{}} is the following in the version I'm running: {code} final byte[] tmp = new byte[length * 2]; {code} This seems to indicate to me that this buffer will never be able to hold more then 2G worth of data. And likely will hold even less since any length > 1073741824 will cause a integer overflow and turn the new buffer size negative. I hope I'm simply missing some critical config setting but it still seems weird that we have a (rather low) upper limit on these buffers.

    Apache's JIRA Issue Tracker | 1 year ago | Erik Selin
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    NegativeArraySizeException‘΍ô(Javaƒ}ƒXƒ^[) | 3 months ago

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    Root Cause Analysis

    1. java.lang.NegativeArraySizeException

      No message provided

      at org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder.grow()
    2. Spark Project Catalyst
      1. org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder.grow(
      2. org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter.write(
      3. org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
      4. org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
      4 frames
    3. Spark Project SQL
      1. org.apache.spark.sql.execution.joins.OneSideOuterIterator.getRow(SortMergeOuterJoin.scala:288)
      3 frames
    4. Scala
      1. scala.collection.Iterator$$anon$
      1 frame
    5. Spark
      1. org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(
      2. org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
      3. org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
      5. org.apache.spark.executor.Executor$
      5 frames
    6. Java RT
      1. java.util.concurrent.ThreadPoolExecutor.runWorker(
      2. java.util.concurrent.ThreadPoolExecutor$
      3 frames