Chirs, an SPIP sounds good to me. I agree with Li that it wouldn't be too difficult to extend the currently functionality to transfer multiple DataFrames. For the SPIP, I would keep it more high-level and I don't think it's necessary to include details of the Python worker, we can hash that out after the SPIP is approved.
Bryan On Mon, Apr 8, 2019 at 10:43 AM Li Jin <ice.xell...@gmail.com> wrote: > Thanks Chris, look forward to it. > > I think sending multiple dataframes to the python worker requires some > changes but shouldn't be too difficult. We can probably sth like: > > > [numberOfDataFrames][FirstDataFrameInArrowFormat][SecondDataFrameInArrowFormat] > > In: > https://github.com/apache/spark/blob/86d469aeaa492c0642db09b27bb0879ead5d7166/sql/core/src/main/scala/org/apache/spark/sql/execution/python/ArrowPythonRunner.scala#L70 > > And have ArrowPythonRunner take multiple input iterator/schema. > > Li > > > On Mon, Apr 8, 2019 at 5:55 AM <ch...@cmartinit.co.uk> wrote: > >> 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 >>>>>>> >>>>>>>
