Github user jinxing64 commented on the issue:
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@cloud-fan @JoshRosen @mridulm @squito @viirya
Thanks a lot for taking so much time reviewing this patch !
Sorry for the stupid mistakes I made. I will be more careful next time :)
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good job! merging to master/2.2!
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hey don't forget this comment :)
https://github.com/apache/spark/pull/16989/files#r118183414
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last few minor comments, I think we are ready to go :)
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In current change:
1) remove the partial written file when failing
2) remove all shuffle files when `cleanup()`(this is registered as a task
completion callback)
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yea let's remove 1)
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@cloud-fan
In current change, the shuffle files are deleted twice:
1). After the `ManagedBuffer.release`
2). In the `cleanup()`, the `cleanup()` is already registered as a task
Github user cloud-fan commented on the issue:
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LGTM, only one comment:
https://github.com/apache/spark/pull/16989#discussion_r118151720
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@cloud-fan
Yes, thanks a lot for merging #18031
I will update soon !
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#18031 has been merged, can you update? thanks!
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@JoshRosen
Thanks a lot for taking time looking into this pr. I'm reading your
comments carefully.
Yes, I think it's good to integrate with memory manager later.
I will break this
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Github user cloud-fan commented on the issue:
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+1 on @JoshRosen 's suggestion, we can integrate it with memory manager
later.
cc @JoshRosen shall we put this patch to branch 2.2?
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Also, I noticed that the PR description doesn't quite align with
implementation AFAIK:
> Track average size and also the outliers(which are larger than 2*avgSize)
in MapStatus;
Github user JoshRosen commented on the issue:
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Update: I realize that I overlooked the change to set a default for
`spark.memory.offHeap.size`. Thus I'll retract my original objections regarding
`MemoryMode.OFF_HEAP` but I'd still like to
Github user JoshRosen commented on the issue:
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A few more high-level thoughts about this PR:
- It seems like the benefits here come from three interrelated changes:
- Improving the accuracy of map output size reporting for large
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I think that the current use of `MemoryMode.OFF_HEAP` allocation will cause
problems in out-of-the-box deployments using the default configurations. In
Spark's current memory manager
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Checking the code:
https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/internal/config/ConfigProvider.scala#L59
`SparkConfigProvider` just check if the key is
Github user cloud-fan commented on the issue:
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that seems impossible, can you give an example? BTW if this blocks you,
just revert the off-heap config changes.
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It seems like `SparkConfigProvider` is not checking alternatives in
`SparkConf`. That's why spark.memory.offHeap.enabled is not set(still the
default value), though we've already set
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retest this please
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Jenkins, retest this please.
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@cloud-fan Thanks, I will refine the documents.
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@jinxing64 Apologies for the delays in my response ...
Can you take over this PR review @cloud-fan ? You have been doing the
reviews way more than me on this anyway :-)
Unfortunately I
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@cloud-fan Thanks a lot. I will refine :)
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LGTM except for one comment
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In current code, `spark.memory.offHeap.enabled` is used when decide
`tungstenMemoryMode`.
`spark.memory.offHeap.enabled` doesn't decide remote blocks are shuffled to
whether onHeap or
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Very gentle ping to @cloud-fan and @mridulm
How do you think about the current change :) ?
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@cloud-fan @mridulm
I think it's good idea to make 2000 configurable. But checking the code,
I'm a little bit hesitant to do that in this pr. I think it's bigger change and
some related code
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/16989
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Github user jinxing64 commented on the issue:
https://github.com/apache/spark/pull/16989
@cloud-fan
Yes, I think it's a good idea to make `2000` configurable. I will refine.
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