[Spark Core]: Support for un-pivoting data ('melt')

[Daniel Davies <da...@gmail.com>]

Level: Intermediate (I think?)
Scenario: Feature Request

Hello dev@,

(First time posting on this mailing list; apologies in advance if this
should have been routed elsewhere or is missing any information).

Un-pivoting data is supported on numerous SQL engines & in Pandas (with the
'melt' function), but it isn't directly available in spark. It's easy
enough to derive this functionality using the 'stack' function or a
combination of struct, array, and explode (e.g. such as the reproduction of
the melt function in pandas-on-pyspark here
<https://github.com/apache/spark/blob/c92bd5cafe62ca5226176446735171cc877e805a/python/pyspark/pandas/frame.py#L9651>),
but I was wondering whether a more native solution had been considered? It
would make end-user code more lightweight at the very least; and I wonder
whether it could be made more efficient than using the stack
function/struct-array-explode method.

I'm happy to try and make a PR if this is something that might be useful
within spark. No worries if this is not something that you think should be
supported; the methods above work and are well documented on StackOverflow.
I was personally just caught out by this, and thought it would be useful to
raise.

I did see a thread in the Pony archive about this issue, but it looks like
it didn't go anywhere. Does anyone else have context on this
<https://lists.apache.org/list?dev@spark.apache.org:lte=60M:unpivot>?

Kind Regards,

-- 
*Daniel Davies*

Re: [Spark Core]: Support for un-pivoting data ('melt')

[Enrico Minack <in...@enrico.minack.dev>]

The melt function has recently been implemented in the PySpark Pandas 
API (because melt is part of the Pandas API). I think, Scala/Java 
Dataset and Python DataFrame APIs deserve this method equally well, 
ideally all based on one implementation.

I'd like to fuel the conversation with some code: 
https://github.com/apache/spark/pull/36150

Cheers,
Enrico


Am 02.01.22 um 20:59 schrieb Daniel Davies:
> Level: Intermediate (I think?)
> Scenario: Feature Request
>
> Hello dev@,
>
> (First time posting on this mailing list; apologies in advance if this 
> should have been routed elsewhere or is missing any information).
>
> Un-pivoting data is supported on numerous SQL engines & in Pandas 
> (with the 'melt' function), but it isn't directly available in spark. 
> It's easy enough to derive this functionality using the 'stack' 
> function or a combination of struct, array, and explode (e.g. such as 
> the reproduction of the melt function in pandas-on-pyspark here 
> <https://github.com/apache/spark/blob/c92bd5cafe62ca5226176446735171cc877e805a/python/pyspark/pandas/frame.py#L9651>), 
> but I was wondering whether a more native solution had been 
> considered? It would make end-user code more lightweight at the very 
> least; and I wonder whether it could be made more efficient than using 
> the stack function/struct-array-explode method.
>
> I'm happy to try and make a PR if this is something that might be 
> useful within spark. No worries if this is not something that you 
> think should be supported; the methods above work and are well 
> documented on StackOverflow. I was personally just caught out by this, 
> and thought it would be useful to raise.
>
> I did see a thread in the Pony archive about this issue, but it looks 
> like it didn't go anywhere. Does anyone else have context on this 
> <https://lists.apache.org/list?dev@spark.apache.org:lte=60M:unpivot>?
>
> Kind Regards,
>
> -- 
> /_*Daniel Davies*_/