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I pushed a bunch of changes to address feedback here and also my own
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- cache the TimeZone instances in generated code
- rename a
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[SPARK-20791][PYSPARK] Use Arrow to create Spark DataFrame from Pandas
## What changes were proposed in this pull request?
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Several things to discuss:
1. Since Spark can't disable speculation during runtime, currently there is
not much benefit to provide an interface for data source to disable
speculation, bec
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@susanxhuynh @skonto The secret-containing protos will be valid in Mesos
1.3 onwards, thus why the scheduler has that requirement. DC/OS with file-based
secrets has Mesos 1.4 thus why we test it the
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Github user smurching commented on the issue:
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Thanks! I'll remove the WIP. To clear things up for the future, I'd thought
[WIP] was the appropriate tag for a PR that's ready for review but not ready to
be merged (based on https://spark.apache
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[SPARK-7][CORE] DiskBlockManager.getAllBlocks now tolerates temp files
## What changes were proposed in this pull request?
Prior to this commit getAllBlocks implicitly assumed that t
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Github user BryanCutler commented on the issue:
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@HyukjinKwon and @ueshin so with Arrow, the Pandas DataFrame from
`toPandas()` timestamp columns will not have a timezone - are we going to do
the same thing for `pandas_udf` Series? I was plan
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LGTM
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@srowen Thanks!
@liufengdb Could you submit a separate PR to fix the issues and also please
include the test cases?
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Hi, @vanzin and @jerryshao .
Could you review this `ConsoleProgressBar` issue when you have some time?
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Gentle ping~, @gatorsmile . :)
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@skonto One more question: in your screen shot of the History Server, I
noticed the "Completed" time is 1969-12-31 for all the drivers (the original
one, retry-1, and retry-2). Is that to be exp
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@HyukjinKwon - Thank you for your comments and analysis of this PR. I will
also try to improve the `flatMap(identity)` as mentioned by @srowen. Also, will
add a python implementation.
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Github user bdrillard commented on the issue:
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I'm blocking out time to prepare the part 2 PR for this issue starting
today over this week, regarding compaction of excess primitive state.
cc: @kiszk
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Sha
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Github user WeichenXu123 commented on the issue:
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@gglanzani And you the `ml.linalg.DenseMatrix` looks have the same bug. Can
you also update it ?
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ping @gglanzani This bug need fixed ASAP. Can you update code when you're
free ? Thanks.
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ping for review @hvanhovell @tejasapatil
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Github user zivanfi commented on a diff in the pull request:
https://github.com/apache/spark/pull/19250#discussion_r143462649
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala ---
@@ -266,6 +267,10 @@ final class DataFrameWriter[T] private[sql](ds:
Datas
Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/19454
BTW, for the answer to
https://github.com/apache/spark/pull/19454#issuecomment-335138642, I think you
should take a look at, for example, `flatMap` as a reference in `rdd.py` and
related tests,
Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/19454
I think @srowen requested to fix it in a more performant way as well, for
example, referring https://github.com/apache/spark/pull/16276, if I understood
correctly and otherwise closing it.
Github user DaimonPl commented on the issue:
https://github.com/apache/spark/pull/16578
@mallman @viirya from my understanding current workaround is for case when
reading columns which are not in file schema
> Parquet-mr will throw an exception if we try to read a superset of
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/19454
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Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/19454
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Github user SparkQA commented on the issue:
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Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/19454
Let's fix up the PR title from `[SPARK-18855 ][SQL]` to
`[SPARK-18855][SQL]` BTW.
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Github user sohum2002 commented on the issue:
https://github.com/apache/spark/pull/19454
Would appreciate some help in the Python implementation of the `flatten`
function as I have never used pyspark. Could someone help me out?
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