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*_/
