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From ch...@cmartinit.co.uk
Subject Re: Thoughts on dataframe cogroup?
Date Mon, 08 Apr 2019 09:55:44 GMT
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.xelloss@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 <chris@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.winandy@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.xelloss@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.xelloss@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.solimando@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.xelloss@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
>>>>>>> 

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