[jira] [Updated] (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:all-tabpanel
]
Yuanjian Li updated SPARK-28699:
Description:
It's another case for the indeterminate stage/RDD rerun while stage rerun
happened.
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, 1 * 1, 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:
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, 1 * 1, 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: 2.3.3, 3.0.0, 2.4.3
>Reporter: Yuanjian Li
>Priority: Blocker
> Labels: correctness
>
> It's another case for the indeterminate stage/RDD rerun while stage rerun
> happened.
> 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, 1 * 1, 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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[jira] [Updated] (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:all-tabpanel
]
Dongjoon Hyun updated SPARK-28699:
--
Affects Version/s: 2.3.3
2.4.3
> 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: 2.3.3, 3.0.0, 2.4.3
>Reporter: Yuanjian Li
>Priority: Blocker
> 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, 1 * 1, 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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[jira] [Updated] (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:all-tabpanel
]
Yuanjian Li updated SPARK-28699:
Affects Version/s: 2.3.3
2.4.3
> 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: 2.3.3, 3.0.0, 2.4.3
>Reporter: Yuanjian Li
>Priority: Blocker
> 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, 1 * 1, 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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[jira] [Updated] (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:all-tabpanel
]
Dongjoon Hyun updated SPARK-28699:
--
Target Version/s: 2.3.4, 2.4.4
Affects Version/s: (was: 2.4.3)
(was: 2.3.3)
> 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: Blocker
> 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, 1 * 1, 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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[jira] [Updated] (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:all-tabpanel
]
Dongjoon Hyun updated SPARK-28699:
--
Priority: Blocker (was: Major)
> 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: Blocker
> 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, 1 * 1, 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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[jira] [Updated] (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: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, 1 * 1, 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, 1 * 1, 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, 1 * 1, 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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[jira] [Updated] (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:all-tabpanel
]
Dongjoon Hyun updated SPARK-28699:
--
Description:
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, 1 * 1, 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, 1 * 1, 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
>
> 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, 1 * 1, 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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[jira] [Updated] (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:all-tabpanel
]
Yuanjian Li updated SPARK-28699:
Description:
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, 1 * 1, 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 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, 1 * 1, 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
>
> 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, 1 * 1, 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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[jira] [Updated] (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:all-tabpanel
]
Josh Rosen updated SPARK-28699:
---
Labels: correctness (was: )
> 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
>
> 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, 1 * 1, 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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