Cool, learn something new every day. Thanks again. On Tue, Aug 9, 2016 at 4:08 PM ayan guha <guha.a...@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.barksd...@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.akhil...@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.barksd...@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.a...@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.barksd...@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-unsubscr...@spark.apache.org >>> >>> >>> >>> >>> >>> -- >>> >>> Best Regards, >>> Ayan Guha >>> >>>
