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 >>>>>>>> >>>>>>>>
