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From Koert Kuipers <ko...@tresata.com>
Subject Re: Confusion SparkSQL DataFrame OrderBy followed by GroupBY
Date Fri, 04 Nov 2016 02:25:36 GMT
thats an interesting thought about orderBy and mapPartitions. i guess i
could emulate a groupBy with secondary sort using those two. however isn't
using an orderBy expensive since it is a total sort? i mean a groupBy with
secondary sort is also a total sort under the hood, but its on
(hashCode(key), secondarySortColumn) which is easier to distribute and
therefore can be implemented more efficiently.





On Thu, Nov 3, 2016 at 8:59 PM, Michael Armbrust <michael@databricks.com>
wrote:

> It is still unclear to me why we should remember all these tricks (or add
>> lots of extra little functions) when this elegantly can be expressed in a
>> reduce operation with a simple one line lamba function.
>>
> I think you can do that too.  KeyValueGroupedDataset has a reduceGroups
> function.  This probably won't be as fast though because you end up
> creating objects where as the version I gave will get codgened to operate
> on binary data the whole way though.
>
>> The same applies to these Window functions. I had to read it 3 times to
>> understand what it all means. Maybe it makes sense for someone who has been
>> forced to use such limited tools in sql for many years but that's not
>> necessary what we should aim for. Why can I not just have the sortBy and
>> then an Iterator[X] => Iterator[Y] to express what I want to do?
>>
> We also have orderBy and mapPartitions.
>
>> All these functions (rank etc.) can be trivially expressed in this, plus
>> I can add other operations if needed, instead of being locked in like this
>> Window framework.
>>
>  I agree that window functions would probably not be my first choice for
> many problems, but for people coming from SQL it was a very popular
> feature.  My real goal is to give as many paradigms as possible in a single
> unified framework.  Let people pick the right mode of expression for any
> given job :)
>

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