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Posted to user@flink.apache.org by Tay Zhen Shen <tz...@student.usm.my> on 2018/02/24 06:58:23 UTC
Implementing CountWindow in Window Join and continuous joining for 2
datastreams
Hi , I'm currently working on Flink with a simple stock market analysis.Basically i need to have the sum of 100 element (Count Window)(sliding size : 10) and also sum of 20 element(Count Window) (sliding size: 10) respectively. I realised that i have to calculate the both sum on 2 different stream respectively and so i did. Now i have 2 streams 1 containing the sum of 100 element and the other one containing the sum of 20 element. Now i wanted to join the both datastream into 1 datastream. I'm using join and i realised that it can only supports Time Windows and Tumbling Window? Is there any other functions that i can use to solve my problem?
Example:
records:
date,price1,price2
date,price1,price2
sum to become:
date,sum_of_price1_for_100_element,sum_of_price2_for_20_element)
Re: Implementing CountWindow in Window Join and continuous joining
for 2 datastreams
Posted by Fabian Hueske <fh...@gmail.com>.
Hi,
If I understood your problem correctly, you want to join two records, one
from each windowed stream.
You can do this by keying and connecting the two streams and apply a
stateful CoFlatMapFunction or CoProcessFunction to join them.
DataStream<X> windowed1 = ...
DataStream<Y> windowed2 = ...
windowed1.keyBy(x).connect(windowed2.keyBy(y)).flatMap(new
YourJoiningCoFlatMapFunction());
Best, Fabian
2018-02-24 7:58 GMT+01:00 Tay Zhen Shen <tz...@student.usm.my>:
> Hi , I'm currently working on Flink with a simple stock market
> analysis.Basically i need to have the sum of 100 element (Count
> Window)(sliding size : 10) and also sum of 20 element(Count Window)
> (sliding size: 10) respectively. I realised that i have to calculate the
> both sum on 2 different stream respectively and so i did. Now i have 2
> streams 1 containing the sum of 100 element and the other one containing
> the sum of 20 element. Now i wanted to join the both datastream into 1
> datastream. I'm using join and i realised that it can only supports Time
> Windows and Tumbling Window? Is there any other functions that i can use to
> solve my problem?
>
>
> Example:
>
> records:
>
> date,price1,price2
>
> date,price1,price2
>
>
> sum to become:
>
> date,sum_of_price1_for_100_element,sum_of_price2_for_20_element)
>
>