Hi, Just to say, I really do think this is useful and am currently working on a SPIP to formally propose this. One concern I do have, however, is that the current arrow serialization code is tied to passing through a single dataframe as the udf parameter and so any modification to allow multiple dataframes may not be straightforward. If anyone has any ideas as to how this might be achieved in an elegant manner I’d be happy to hear them!
Thanks, Chris > On 26 Feb 2019, at 14:55, Li Jin <ice.xell...@gmail.com> wrote: > > Thank you both for the reply. Chris and I have very similar use cases for > cogroup. > > One of the goals for groupby apply + pandas UDF was to avoid things like > collect list and reshaping data between Spark and Pandas. Cogroup feels very > similar and can be an extension to the groupby apply + pandas UDF > functionality. > > I wonder if any PMC/committers have any thoughts/opinions on this? > >> On Tue, Feb 26, 2019 at 2:17 AM <ch...@cmartinit.co.uk> wrote: >> Just to add to this I’ve also implemented my own cogroup previously and >> would welcome a cogroup for datafame. >> >> My specific use case was that I had a large amount of time series data. >> Spark has very limited support for time series (specifically as-of joins), >> but pandas has good support. >> >> My solution was to take my two dataframes and perform a group by and collect >> list on each. The resulting arrays could be passed into a udf where they >> could be marshaled into a couple of pandas dataframes and processed using >> pandas excellent time series functionality. >> >> If cogroup was available natively on dataframes this would have been a bit >> nicer. The ideal would have been some pandas udf version of cogroup that >> gave me a pandas dataframe for each spark dataframe in the cogroup! >> >> Chris >> >>> On 26 Feb 2019, at 00:38, Jonathan Winandy <jonathan.wina...@gmail.com> >>> wrote: >>> >>> For info, in our team have defined our own cogroup on dataframe in the past >>> on different projects using different methods (rdd[row] based or union all >>> collect list based). >>> >>> I might be biased, but find the approach very useful in project to simplify >>> and speed up transformations, and remove a lot of intermediate stages >>> (distinct + join => just cogroup). >>> >>> Plus spark 2.4 introduced a lot of new operator for nested data. That's a >>> win! >>> >>> >>>> On Thu, 21 Feb 2019, 17:38 Li Jin, <ice.xell...@gmail.com> wrote: >>>> I am wondering do other people have opinion/use case on cogroup? >>>> >>>>> On Wed, Feb 20, 2019 at 5:03 PM Li Jin <ice.xell...@gmail.com> wrote: >>>>> Alessandro, >>>>> >>>>> Thanks for the reply. I assume by "equi-join", you mean "equality full >>>>> outer join" . >>>>> >>>>> Two issues I see with equity outer join is: >>>>> (1) equity outer join will give n * m rows for each key (n and m being >>>>> the corresponding number of rows in df1 and df2 for each key) >>>>> (2) User needs to do some extra processing to transform n * m back to the >>>>> desired shape (two sub dataframes with n and m rows) >>>>> >>>>> I think full outer join is an inefficient way to implement cogroup. If >>>>> the end goal is to have two separate dataframes for each key, why joining >>>>> them first and then unjoin them? >>>>> >>>>> >>>>> >>>>>> On Wed, Feb 20, 2019 at 5:52 AM Alessandro Solimando >>>>>> <alessandro.solima...@gmail.com> wrote: >>>>>> Hello, >>>>>> I fail to see how an equi-join on the key columns is different than the >>>>>> cogroup you propose. >>>>>> >>>>>> I think the accepted answer can shed some light: >>>>>> https://stackoverflow.com/questions/43960583/whats-the-difference-between-join-and-cogroup-in-apache-spark >>>>>> >>>>>> Now you apply an udf on each iterable, one per key value (obtained with >>>>>> cogroup). >>>>>> >>>>>> You can achieve the same by: >>>>>> 1) join df1 and df2 on the key you want, >>>>>> 2) apply "groupby" on such key >>>>>> 3) finally apply a udaf (you can have a look here if you are not >>>>>> familiar with them >>>>>> https://docs.databricks.com/spark/latest/spark-sql/udaf-scala.html), >>>>>> that will process each group "in isolation". >>>>>> >>>>>> HTH, >>>>>> Alessandro >>>>>> >>>>>>> On Tue, 19 Feb 2019 at 23:30, Li Jin <ice.xell...@gmail.com> wrote: >>>>>>> Hi, >>>>>>> >>>>>>> We have been using Pyspark's groupby().apply() quite a bit and it has >>>>>>> been very helpful in integrating Spark with our existing pandas-heavy >>>>>>> libraries. >>>>>>> >>>>>>> Recently, we have found more and more cases where groupby().apply() is >>>>>>> not sufficient - In some cases, we want to group two dataframes by the >>>>>>> same key, and apply a function which takes two pd.DataFrame (also >>>>>>> returns a pd.DataFrame) for each key. This feels very much like the >>>>>>> "cogroup" operation in the RDD API. >>>>>>> >>>>>>> It would be great to be able to do sth like this: (not actual API, just >>>>>>> to explain the use case): >>>>>>> >>>>>>> @pandas_udf(return_schema, ...) >>>>>>> def my_udf(pdf1, pdf2) >>>>>>> # pdf1 and pdf2 are the subset of the original dataframes that is >>>>>>> associated with a particular key >>>>>>> result = ... # some code that uses pdf1 and pdf2 >>>>>>> return result >>>>>>> >>>>>>> df3 = cogroup(df1, df2, key='some_key').apply(my_udf) >>>>>>> >>>>>>> I have searched around the problem and some people have suggested to >>>>>>> join the tables first. However, it's often not the same pattern and >>>>>>> hard to get it to work by using joins. >>>>>>> >>>>>>> I wonder what are people's thought on this? >>>>>>> >>>>>>> Li >>>>>>>
