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@jiangxb1987 This should be addressed by #21927 . Could you close this PR?
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LGTM pending Jenkins.
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test this please
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*this ID will
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LGTM. Merged into master. Thanks!
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*this ID will
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I don't think so. The offsets for the file source need to be consumer
owned, because they need to work with files that were generated outside of
Spark.
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I'd like to fix the AnalysisError message and I noted one small nit in the
tests.
+1 when the AnalysisError message is fixed.
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> I still count 10 undefined names in this repo.
Do you mean you found more instance to fix, right?
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package
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Sure @lrytz can have a second look on this, also it needs to be battle
tested.
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@cloud-fan, I'll fix the conflicts and re-run tests. Yesterday's tests
passed after I updated for your feedback. I'd like to try to get this in soon
because it is taking so much time to resolve
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@tgravescs I updated it. Thanks.
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Yes, I was hoping to improve that eg using filename as offset or other non
consumer-owned approach, but that would be rather long term. Do you think it is
solvable?
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@tgravescs I updated. Thanks.
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These test failures are in Spark streaming. Is this just an intermittent
test failure or actually caused by this PR?
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Was this ever implemented?
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Implementing spilling over seems a lot of work because this is a queue. If
data is spilled over to disk and you need to pop from the queue, it is not
clear to me what is the best way to do that.
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For file streams, the offsets are just indices into a log the source keeps
of which files it's seen. So a file sink doesn't have any access to those
offsets.
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[SPARK-24795][CORE][FOLLOWUP] Combine BarrierTaskContext with
BarrierTaskContextImpl
## What changes were proposed in this pull request?
According to
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Hello, I updated the description and title
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@jose-torres why it wouldnt make sense? According to the documentation all
SS sources have offsets, but not all sinks can also be SS sources e.g. ForEach
doesnt have offsets in general. So usually
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This LGTM as a fix. However, ideally we should also support reusing an
exchange used in different joins. There is no need to shuffle write the same
table twice, we just need read it differently.
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