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Posted to issues@spark.apache.org by "Yuanjian Li (JIRA)" <ji...@apache.org> on 2019/08/12 14:25:00 UTC
[jira] [Commented] (SPARK-28699) Cache an indeterminate RDD could
lead to incorrect result while stage rerun
[ https://issues.apache.org/jira/browse/SPARK-28699?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16905250#comment-16905250 ]
Yuanjian Li commented on SPARK-28699:
-------------------------------------
The current [approach|https://github.com/apache/spark/pull/25420] just a bandage fix for returning the wrong answer.
After we finish the work of indeterminate stage rerunning(SPARK-25341), we can fix this by unpersisting the original RDD and rerunning the cached indeterminate stage. Gives a preview codebase [here|https://github.com/xuanyuanking/spark/tree/SPARK-28699-RERUN].
> Cache an indeterminate RDD could lead to incorrect result while stage rerun
> ---------------------------------------------------------------------------
>
> Key: SPARK-28699
> URL: https://issues.apache.org/jira/browse/SPARK-28699
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 3.0.0
> Reporter: Yuanjian Li
> Priority: Major
>
> Related with SPARK-23207 SPARK-23243
> It's another case for the indeterminate stage/RDD rerun while stage rerun happened. In the CachedRDDBuilder, we miss tracking the `isOrderSensitive` characteristic to the newly created MapPartitionsRDD.
> We can reproduce this by the following code, thanks to Tyson for reporting this!
>
> {code:scala}
> import scala.sys.process._
> import org.apache.spark.TaskContext
> val res = spark.range(0, 10000 * 10000, 1).map\{ x => (x % 1000, x)}
> // kill an executor in the stage that performs repartition(239)
> val df = res.repartition(113).cache.repartition(239).map { x =>
> if (TaskContext.get.attemptNumber == 0 && TaskContext.get.partitionId < 1 && TaskContext.get.stageAttemptNumber == 0) {
> throw new Exception("pkill -f -n java".!!)
> }
> x
> }
> val r2 = df.distinct.count()
> {code}
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