Github user kiszk commented on the issue:
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For 1, I agree with you that it is not good to introduce many APIs at
first. On the other hand, it would be good to prepare only a few APIs that are
frequently used, not to prepare many APIs. It make
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@kiszk I've added a benchmark according to your recommendation, please
review.
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Github user dbtsai commented on the issue:
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I decided to remove the hack I put to get the Spark UI consistent since
this hack will bring into more problems.
@som-snytt Is it possible to move the `printWelcome` and `splash.start()`
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@icexelloss @HyukjinKwon It is always simple to deprecate the confs in the
release of Spark 3.0. Let us make it configurable in the next release which is
Spark 2.4.
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Github user jerryshao commented on the issue:
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I think here the problem is that `filters` are only added for those handler
who added before server starts.
This may not be the only problem for `ThriftserverTab`, I guess `SQLTable`
or
Github user HyukjinKwon commented on the issue:
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How about we just start to have configurations classified for each version
if it sounds better to have configurations for each behaviour change? For
example, we could have postfix like spark_23
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}
/**
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retest this please
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retest this please.
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[SPARK-24513][ML] Attribute support in UnaryTransformer
## What changes were proposed in this pull request?
This PR adds Metadata support in `UnaryTransformer`, as a preliminary work
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Finally, I made it work by adding `StopWordsRemover.getDefaultLocale`! How
about this approach? @mengxr @viirya
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[SPARK-24515][CORE] No need to warning when output commit coordination
enabled
## What changes were proposed in this pull request?
No need to warning user when output commit coordination
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HasInputCol, HasOutputCol, JavaMLReadabl
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@kiszk Seems good to me. I will go to create and use those APIs in pieces
of PRs.
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you're right @jerryshao, thanks. The reason why I didn't do a general fix
on first instance was that this would apply also to the metrics and I was not
it was right. But thinking more carefully
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@squito For PeakMemoryMetrics in api.scala, changing to the array gives
REST API output of:
"peakMemoryMetrics" : {
"metrics" : [ 755008624, 100519936, 0, 0, 47962185, 0,
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Is the CI broken? oO
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retest this please.
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@felixcheung rebased to master and fixed failing unit tests
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If needed, I can propose a draft version of
[SPARK-13998](https://issues.apache.org/jira/browse/SPARK-13998) implemented on
top of this work.
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Github user jaceklaskowski commented on the issue:
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@kiszk @jerryshao @srowen Added `s` (and even more scaladoc). Thanks for
reviewing (and hopefully merging right after :))!
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Github user HeartSaVioR commented on the issue:
https://github.com/apache/spark/pull/21500
After enabling option, I've observed small expected latency whenever
starting batch per each partition per each batch. Median/average was 4~50 ms
for my case, but max latency was a bit higher
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/21260
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