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Github user jiangxb1987 commented on the issue:
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It may happen that a busy executor is marked as lost and later it
re-register to the driver, in that case currently we call `makeOffers()` and
that will add the executor into
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What do you mean by "better performance" ? If that means we can spend less
time on `TaskSchedulerImpl.resourceOffers()` then I agree it's true, but AFAIK
it's never reported this can be a
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Thank you, @heary-cao . Merged to master.
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Since this is JSON benchmark, could you merge this, @HyukjinKwon ? :)
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GitHub user gengliangwang opened a pull request:
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[SPARK-25886][SQL] Improve error message of `FailureSafeParser` and
`from_avro` in FAILFAST mode
## What changes were proposed in this pull request?
Currently in `FailureSafeParser`
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merged to master
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@gatorsmile Sure Sean.
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@dilipbiswal Could you take this over?
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any more comments @ifilonenko @skonto ?
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jenkins, retest this please
known flaky: https://issues.apache.org/jira/browse/SPARK-24285
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LGTM
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Lgtm
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added sections to the resource manager sections.
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thanks @cloud-fan
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Thanks a lot @srowen
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I can add a note here for deployments here and then we can do version
specific ones after
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I think these are good changes. In a separate PR for the versions-specific
docs, we could add a similar note to
https://spark.apache.org/docs/latest/spark-standalone.html as much of the
security
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Updated to have a section on security in the quickstart and overview, let
me know what you think and if wording needs updated. If this ok I can followup
with something on the website
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@cloud-fan I would answer yes. At least, I haven't found a real case when
this can cause a bug visible to end users, but I am not 100% that there is no
weird case where someone could hit it.
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LGTM
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Merged to master
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LGTM. To confirm, this is a potential bug, currently end-users can't hit
it, right?
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Install Spark I mentioned above.
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Thank you for your contribution.
Could you please write appropriate title and descriptions based on
http://spark.apache.org/contributing.html ?
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Github user mgaido91 commented on the issue:
https://github.com/apache/spark/pull/22894
Practically, though, it generates a whole copy of the map at every update,
so for 10 items, the implementation in the PR generates 9 copies of 1, 2, 3,
... elements, while the current one
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