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https://issues.apache.org/jira/browse/FLINK-2549?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14904869#comment-14904869
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ASF GitHub Bot commented on FLINK-2549:
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Github user StephanEwen commented on the pull request:
https://github.com/apache/flink/pull/1161#issuecomment-142672766
This looks super impressive and very well tested.
The way that the operator is integrated into the system needs some
improvement, though. The problem is mainly how the managed memory is obtained.
The MemoryManager's memory is shared among all concurrently running tasks.
This implementation takes up to half the total memory, which will cause
programs to crash that have other memory consumers in the same pipeline. The
tests here run, because the operator is executed in isolation, with no other
memory consuming operators in the test program.
Memory consumers need to be known to the Optimizer (in the program
generation) to compute what maximal fraction of memory a certain consumer may
request. That value is part of the Task's configuration and used by the memory
consumer to obtain the right maximum amount.
Integrating operators into the optimizer's planning is a bit tedious and
not as easy as it could be (we did not get around to refactoring this so far,
unfortunately). Maybe we can add some tooling that would mark a UDF as
MemoryConsuming and would in that case expose a Memory Allocator that returns
the right amount of memory.
What we could do is the following: I will try to get to refactoring some of
the Managed Memory Allocation abstractions (we need this anyways for more
components) and then expose a MemoryAllocator in the runtime context, which is
accessible if a user-defined function has been annotated as a memory consumer.
This may take me two weeks (I am currently in the mids of working on the
streaming windows), but if you don't mind letting this rest for some days, I
think that is the cleanest approach.
The other parts of the code look good, so after I finish my part, it should
be a simple rebase of the TopKMapPartition function and the TopKReducer, and
then this is good to merge.
What do you think?
> Add topK operator for DataSet
> -----------------------------
>
> Key: FLINK-2549
> URL: https://issues.apache.org/jira/browse/FLINK-2549
> Project: Flink
> Issue Type: New Feature
> Components: Core, Java API, Scala API
> Reporter: Chengxiang Li
> Assignee: Chengxiang Li
> Priority: Minor
>
> topK is a common operation for user, it would be great to have it in Flink.
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