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 >>> >>>
