Github user pwendell commented on the pull request:
https://github.com/apache/spark/pull/1486#issuecomment-54776543
Hey @cmccabe - thanks for looking at this. From what I can tell the current
approach has the drawback that if there are both cached-and-non-cached
locations, the non-cached locations will simply be ignored. This could actually
regress performance for workloads where having e.g. 3 machine-local replicas is
better than having 1 cached replica.
To get proper delay scheduling with this, where we triage from cached
copies to non-cached copies - I think it would be best to just use the existing
mechanism we have for preferring replicas that are cached on a specific node in
Sparks in-process. This corresponds to the PROCESS_LOCAL locality level.
To get this I think you can make a relatively surgical change which is that
Hadoop RDD should return a `TaskLocation` with the `executorId` populated if
there is an in-memory replica available on that node. Then we can change the
documentation a bit to explain that this field is a bit overloaded at the
moment to mean two things. You would need to add a lookup to see if we
presently have an executor on that node and find its ID.
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