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https://issues.apache.org/jira/browse/SPARK-22765?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16297529#comment-16297529
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Xuefu Zhang commented on SPARK-22765:
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{quote}
SPARK-21656 and the dynamic allocation should handle that, the target number in
the dynamic allocation manager is supposed to be based on all running stages
for pending and running tasks. Are you saying that is not true?
{quote}
I verified and it does seem that SPARK-21656 covers concurrent stages. My job
ran too fast, so I had to limit maxExecutors to a smaller number to observe
more closely. It's no issue, after all. This is great!
> Create a new executor allocation scheme based on that of MR
> -----------------------------------------------------------
>
> Key: SPARK-22765
> URL: https://issues.apache.org/jira/browse/SPARK-22765
> Project: Spark
> Issue Type: Improvement
> Components: Scheduler
> Affects Versions: 1.6.0
> Reporter: Xuefu Zhang
>
> Many users migrating their workload from MR to Spark find a significant
> resource consumption hike (i.e, SPARK-22683). While this might not be a
> concern for users that are more performance centric, for others conscious
> about cost, such hike creates a migration obstacle. This situation can get
> worse as more users are moving to cloud.
> Dynamic allocation make it possible for Spark to be deployed in multi-tenant
> environment. With its performance-centric design, its inefficiency has also
> unfortunately shown up, especially when compared with MR. Thus, it's believed
> that MR-styled scheduler still has its merit. Based on our research, the
> inefficiency associated with dynamic allocation comes in many aspects such as
> executor idling out, bigger executors, many stages (rather than 2 stages only
> in MR) in a spark job, etc.
> Rather than fine tuning dynamic allocation for efficiency, the proposal here
> is to add a new, efficiency-centric scheduling scheme based on that of MR.
> Such a MR-based scheme can be further enhanced and be more adapted to Spark
> execution model. This alternative is expected to offer good performance
> improvement (compared to MR) still with similar to or even better efficiency
> than MR.
> Inputs are greatly welcome!
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