Here is related code:
final int length = totalSize() + neededSize;
if (buffer.length < length) {
// This will not happen frequently, because the buffer is re-used.
final byte[] tmp = new byte[length * 2];
Looks like length was positive (since it was bigger than buffer.length) but
length * 2 became negative.
We just need to allocate length bytes instead of length * 2 bytes.
On Sun, Mar 13, 2016 at 10:39 PM, Ravindra Rawat
wrote:
> Greetings,
>
> I am getting following exception on joining a few parquet files. SPARK-12089
> description has details of the overflow condition which is marked as fixed in
> 1.6.1. I recall seeing another issue related to csv files creating same
> exception.
>
> Any pointers on how to debug this or possible workarounds? Google searches
> and JIRA comments point to either a > 2GB record size (less likely) or RDD
> sizes being too large.
>
> I had upgraded to Spark 1.6.1 due to Serialization errors from Catalyst while
> reading Parquet files.
>
> Related JIRA Issue => https://issues.apache.org/jira/browse/SPARK-12089
>
> Related PR => https://github.com/apache/spark/pull/10142
>
>
> java.lang.NegativeArraySizeException
> at
> org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder.grow(BufferHolder.java:45)
> at
> org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter.write(UnsafeRowWriter.java:196)
> 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 scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
> at
> org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:164)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
>
>
> Thanks.
>
> --
> Regards
> Ravindra
>
