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Posted to issues@spark.apache.org by "Adam Binford (Jira)" <ji...@apache.org> on 2023/01/13 12:39:00 UTC
[jira] [Commented] (SPARK-39979) IndexOutOfBoundsException on groupby + apply pandas grouped map udf function
[ https://issues.apache.org/jira/browse/SPARK-39979?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17676610#comment-17676610 ]
Adam Binford commented on SPARK-39979:
--------------------------------------
We have encountered this as well and I'm am working on a fix. This is a limitation of using the BaseVariableWidthVector based classes for string and binary and not the BaseLargeVariableWidthVector based types. A single string or binary vector cannot hold more than 2 GiB of data, because it stores offsets to each value and the offsets are stored as 4 byte integers. The large types use 8 bytes for the offset instead, thus removing the limitation.
Options are:
* Add a config you can set to use the large types instead of the normal types
* Just use the large types for everything, with the expense of an additional 4 bytes per record in the vector for storing offsets (not sure if there are additional overheads)
Let me know if there's any thoughts or opinions on which route to go.
> IndexOutOfBoundsException on groupby + apply pandas grouped map udf function
> ----------------------------------------------------------------------------
>
> Key: SPARK-39979
> URL: https://issues.apache.org/jira/browse/SPARK-39979
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 3.2.1
> Reporter: yaniv oren
> Priority: Major
>
> I'm grouping on relatively small subset of groups with big size groups.
> Working with pyarrow version 2.0.0, machines memory is {color:#444444}64 GiB.{color}
> I'm getting the following error:
> {code:java}
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 387 in stage 162.0 failed 4 times, most recent failure: Lost task 387.3 in stage 162.0 (TID 29957) (ip-172-21-129-187.eu-west-1.compute.internal executor 71): java.lang.IndexOutOfBoundsException: index: 2147483628, length: 36 (expected: range(0, 2147483648))
> at org.apache.arrow.memory.ArrowBuf.checkIndex(ArrowBuf.java:699)
> at org.apache.arrow.memory.ArrowBuf.setBytes(ArrowBuf.java:890)
> at org.apache.arrow.vector.BaseVariableWidthVector.setSafe(BaseVariableWidthVector.java:1087)
> at org.apache.spark.sql.execution.arrow.StringWriter.setValue(ArrowWriter.scala:251)
> at org.apache.spark.sql.execution.arrow.ArrowFieldWriter.write(ArrowWriter.scala:130)
> at org.apache.spark.sql.execution.arrow.ArrowWriter.write(ArrowWriter.scala:95)
> at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.$anonfun$writeIteratorToStream$1(ArrowPythonRunner.scala:92)
> at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1474)
> at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.writeIteratorToStream(ArrowPythonRunner.scala:103)
> at org.apache.spark.api.python.BasePythonRunner$WriterThread.$anonfun$run$1(PythonRunner.scala:435)
> at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:2031)
> at org.apache.spark.api.python.BasePythonRunner$WriterThread.run(PythonRunner.scala:270) {code}
> Why do I hit this 2 GB limit? according SPARK-34588 this is supported, perhaps related to SPARK-34020.
> Please assist.
> Note:
> Is it related to the usage of BaseVariableWidthVector and not BaseLargeVariableWidthVector?
>
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