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From Jon Barksdale <jon.barksd...@gmail.com>
Subject Re: Cumulative Sum function using Dataset API
Date Tue, 09 Aug 2016 23:16:55 GMT
Cool, learn something new every day.  Thanks again.

On Tue, Aug 9, 2016 at 4:08 PM ayan guha <guha.ayan@gmail.com> wrote:

> Thanks for reporting back. Glad it worked for you. Actually sum with
> partitioning behaviour is same in oracle too.
> On 10 Aug 2016 03:01, "Jon Barksdale" <jon.barksdale@gmail.com> wrote:
>
>> Hi Santoshakhilesh,
>>
>> I'd seen that already, but I was trying to avoid using rdds to perform
>> this calculation.
>>
>> @Ayan, it seems I was mistaken, and doing a sum(b) over(order by b)
>> totally works.  I guess I expected the windowing with sum to work more like
>> oracle.  Thanks for the suggestion :)
>>
>> Thank you both for your help,
>>
>> Jon
>>
>> On Tue, Aug 9, 2016 at 3:01 AM Santoshakhilesh <
>> santosh.akhilesh@huawei.com> wrote:
>>
>>> You could check following link.
>>>
>>>
>>> http://stackoverflow.com/questions/35154267/how-to-compute-cumulative-sum-using-spark
>>>
>>>
>>>
>>> *From:* Jon Barksdale [mailto:jon.barksdale@gmail.com]
>>> *Sent:* 09 August 2016 08:21
>>> *To:* ayan guha
>>> *Cc:* user
>>> *Subject:* Re: Cumulative Sum function using Dataset API
>>>
>>>
>>>
>>> I don't think that would work properly, and would probably just give me
>>> the sum for each partition. I'll give it a try when I get home just to be
>>> certain.
>>>
>>> To maybe explain the intent better, if I have a column (pre sorted) of
>>> (1,2,3,4), then the cumulative sum would return (1,3,6,10).
>>>
>>> Does that make sense? Naturally, if ordering a sum turns it into a
>>> cumulative sum, I'll gladly use that :)
>>>
>>> Jon
>>>
>>> On Mon, Aug 8, 2016 at 4:55 PM ayan guha <guha.ayan@gmail.com> wrote:
>>>
>>> You mean you are not able to use sum(col) over (partition by key order
>>> by some_col) ?
>>>
>>>
>>>
>>> On Tue, Aug 9, 2016 at 9:53 AM, jon <jon.barksdale@gmail.com> wrote:
>>>
>>> Hi all,
>>>
>>> I'm trying to write a function that calculates a cumulative sum as a
>>> column
>>> using the Dataset API, and I'm a little stuck on the implementation.
>>> From
>>> what I can tell, UserDefinedAggregateFunctions don't seem to support
>>> windowing clauses, which I think I need for this use case.  If I write a
>>> function that extends from AggregateWindowFunction, I end up needing
>>> classes
>>> that are package private to the sql package, so I need to make my
>>> function
>>> under the org.apache.spark.sql package, which just feels wrong.
>>>
>>> I've also considered writing a custom transformer, but haven't spend as
>>> much
>>> time reading through the code, so I don't know how easy or hard that
>>> would
>>> be.
>>>
>>> TLDR; What's the best way to write a function that returns a value for
>>> every
>>> row, but has mutable state, and gets row in a specific order?
>>>
>>> Does anyone have any ideas, or examples?
>>>
>>> Thanks,
>>>
>>> Jon
>>>
>>>
>>>
>>>
>>> --
>>> View this message in context:
>>> http://apache-spark-user-list.1001560.n3.nabble.com/Cumulative-Sum-function-using-Dataset-API-tp27496.html
>>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>>
>>> ---------------------------------------------------------------------
>>> To unsubscribe e-mail: user-unsubscribe@spark.apache.org
>>>
>>>
>>>
>>>
>>>
>>> --
>>>
>>> Best Regards,
>>> Ayan Guha
>>>
>>>

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