Hi, As promised I’ve raised SPARK-27463 for this.
All feedback welcome! Chris > On 9 Apr 2019, at 13:22, Chris Martin <ch...@cmartinit.co.uk> wrote: > > Thanks Bryan and Li, that is much appreciated. Hopefully should have the > SPIP ready in the next couple of days. > > thanks, > > Chris > > > > >> On Mon, Apr 8, 2019 at 7:18 PM Bryan Cutler <cutl...@gmail.com> wrote: >> 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
