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https://issues.apache.org/jira/browse/SPARK-28699?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Dongjoon Hyun updated SPARK-28699:
----------------------------------
Description:
It's another case for the indeterminate stage/RDD rerun while stage rerun
happened. In the CachedRDDBuilder.
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}
was:
Related with SPARK-23207 SPARK-23243
It's another case for the indeterminate stage/RDD rerun while stage rerun
happened. In the CachedRDDBuilder.
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}
> 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
> Labels: correctness
>
> It's another case for the indeterminate stage/RDD rerun while stage rerun
> happened. In the CachedRDDBuilder.
> 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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