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Posted to issues@spark.apache.org by "liupengcheng (JIRA)" <ji...@apache.org> on 2019/03/20 11:36:00 UTC
[jira] [Updated] (SPARK-27214) Upgrading locality level when lots
of pending tasks have been waiting more than locality.wait
[ https://issues.apache.org/jira/browse/SPARK-27214?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
liupengcheng updated SPARK-27214:
---------------------------------
Description:
Currently, Spark locality wait mechanism is not friendly for large job, when number of tasks is large（e.g. 10000+）and with a large number of executors(e.g. 2000), executors may be launched on some nodes where the locality is not the best(not the same nodes hold HDFS blocks). There are cases when `TaskSetManager.lastLaunchTime` is refreshed due to finished tasks within `spark.locality.wait` but coming at low rate(e.g. every `spark.locality.wait` seconds a task is finished), so locality level would not be upgraded and lots of pending tasks will wait a long time.
In this case, when `spark.dynamicAllocation.enabled=true`, then lots of executors may be removed by Driver due to become idle and finally slow down the job.
Actually, we can optimize this by following formula:
Suppose numPendingTasks=10000, localityExecutionGainFactor=0.1, probabilityOfNextLocalitySchedule=0.5
```
maxStarvingTasks = numPendingTasks * medianOfTaskExecutionTime * localityExecutionGainFactor * probabilityOfNextLocalitySchedule / `spark.locality.wait`
if (numStavingTasks > maxStarvingTasks)
{ upgrading locality level... }
....
```
was:
Currently, Spark locality wait mechanism is not friendly for large job, when tasks is large（e.g. 10000+）, there are cases when `TaskSetManager.lastLaunchTime` is refreshed due to finished tasks within `spark.locality.wait` but coming at low rate(e.g. every `spark.locality.wait` seconds a task is finished), so locality level would not be upgraded and lots of pending tasks will wait a long time.
In this case, when `spark.dynamicAllocation.enabled=true`, then lots of executors may be removed by Driver due to become idle and finally slow down the job.
Actually, we can optimize this by following formula:
Suppose numPendingTasks=10000, localityExecutionGainFactor=0.1, probabilityOfNextLocalitySchedule=0.5
```
maxStarvingTasks = numPendingTasks * medianOfTaskExecutionTime * localityExecutionGainFactor * probabilityOfNextLocalitySchedule / `spark.locality.wait`
if (numStavingTasks > maxStarvingTasks) {
upgrading locality level...
}
....
```
> Upgrading locality level when lots of pending tasks have been waiting more than locality.wait
> ---------------------------------------------------------------------------------------------
>
> Key: SPARK-27214
> URL: https://issues.apache.org/jira/browse/SPARK-27214
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.1.0, 2.4.0
> Reporter: liupengcheng
> Priority: Major
>
> Currently, Spark locality wait mechanism is not friendly for large job, when number of tasks is large（e.g. 10000+）and with a large number of executors(e.g. 2000), executors may be launched on some nodes where the locality is not the best(not the same nodes hold HDFS blocks). There are cases when `TaskSetManager.lastLaunchTime` is refreshed due to finished tasks within `spark.locality.wait` but coming at low rate(e.g. every `spark.locality.wait` seconds a task is finished), so locality level would not be upgraded and lots of pending tasks will wait a long time.
> In this case, when `spark.dynamicAllocation.enabled=true`, then lots of executors may be removed by Driver due to become idle and finally slow down the job.
> Actually, we can optimize this by following formula:
> Suppose numPendingTasks=10000, localityExecutionGainFactor=0.1, probabilityOfNextLocalitySchedule=0.5
> ```
> maxStarvingTasks = numPendingTasks * medianOfTaskExecutionTime * localityExecutionGainFactor * probabilityOfNextLocalitySchedule / `spark.locality.wait`
> if (numStavingTasks > maxStarvingTasks)
> { upgrading locality level... }
> ....
> ```
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