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2017-12-27 Thread icexelloss
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@ueshin I pushed some more change to address your comments. There is one 
regression in existing test `SQLTests.test_udf_with_aggregate_function`. I will 
try to fix it tomorrow.


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2017-12-19 Thread ueshin
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@ramacode2014 Hi, I'm not sure why you received notifications from this PR, 
but I guess you can unsubscribe by the "Unsubscribe" button in the right column 
of this page. Sorry for the inconvenience. Thanks!


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2017-12-19 Thread ramacode2014
Github user ramacode2014 commented on the issue:

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Please unsubscribe me from this spam

On Wed, Dec 20, 2017 at 10:46 AM, Takuya UESHIN 
wrote:

> *@ueshin* commented on this pull request.
> --
>
> In sql/core/src/main/scala/org/apache/spark/sql/execution/
> python/AggregateInPandasExec.scala
> :
>
> > +  // Add rows to queue to join later with the result.
> +  val projectedRowIter = grouped.map { case (groupingKey, rows) =>
> +queue.add(groupingKey.asInstanceOf[UnsafeRow])
> +rows
> +  }
> +
> +  val columnarBatchIter = new ArrowPythonRunner(
> +pyFuncs, bufferSize, reuseWorker,
> +PythonEvalType.SQL_PANDAS_GROUP_AGG_UDF, argOffsets, schema,
> +sessionLocalTimeZone, pandasRespectSessionTimeZone)
> +.compute(projectedRowIter, context.partitionId(), context)
> +
> +  val joined = new JoinedRow
> +  val resultProj = UnsafeProjection.create(output, output)
> +
> +  columnarBatchIter.map(_.rowIterator.next()).map{ outputRow =>
>
> Sorry, I meant columnarBatchIter.flatMap(_.rowIterator.asScala). I'd
> prefer this one.
>
> —
> You are receiving this because you are subscribed to this thread.
> Reply to this email directly, view it on GitHub
> , or 
mute
> the thread
> 

> .
>



-- 




Best Regard,


Rama



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2017-12-07 Thread ueshin
Github user ueshin commented on the issue:

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@icexelloss I'm sorry for the late response.
Actually I tried to implement prototypes of Pandas UDAF with partial 
aggregation and combining existing aggregate functions, but they are still much 
complicated (https://github.com/ueshin/apache-spark/pull/2, 
https://github.com/ueshin/apache-spark/pull/3, 
https://github.com/ueshin/apache-spark/pull/4). I was thinking about easier way 
to achieve that but not yet.
I've not looked into this pr yet but I guess we can start this pr and pick 
some functionalities from my prototypes if needed.


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[GitHub] spark issue #19872: WIP: [SPARK-22274][PySpark] User-defined aggregation fun...

2017-12-07 Thread icexelloss
Github user icexelloss commented on the issue:

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And to @holdenk 's question. Pandas group_agg udf fundamentally uses 
different physical plan than the existing java/scala udf and therefore it's 
hard to combine them together. I don't know a good way to do this, the closest 
is maybe to compute java/scala and python aggregation separately and join them 
together.


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2017-12-07 Thread icexelloss
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I end up removing `UDFColumn` class and using the existing `Aggregate` 
logical plan for pandas group_agg UDF. This reuses a lot of code being to 
existing `Aggregate` and minimize the code changes needed for pandas group_agg 
UDF.

The code works and three tests (test_basic, test_alias, test_multiple) 
passes now but the code is kind of messy. I am going on vacation next week but 
I will clean up the code and move this PR forward when I get back (Dec 16).

Thanks all.


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2017-12-04 Thread icexelloss
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> I thought @ueshin is working on this BTW.

Oh, I certainly don't want to duplicate @ueshin 's work. I am under the 
impression that @ueshin is working on two-stage PySpark UDAF with pandas_udf, 
but I cannot really find the Jira for it...

@ueshin can you point me to what you are working on so I don't overstep?



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2017-12-03 Thread icexelloss
Github user icexelloss commented on the issue:

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cc @HyukjinKwon @holdenk @ueshin 

Passing some basic tests. I will work on this more next week to clean up 
and add more testing.


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