Re: Runtime Windows/Aggregation Computations in Beam

[Robert Bradshaw]

This may be possible with a custom WindowFn. Where is the configuration of
what aggregations to do coming from?

On Wed, Sep 21, 2016 at 11:27 PM, Chawla,Sumit 
wrote:

> Attaching the Image.
>
>
> ​
>
> Regards
> Sumit Chawla
>
>
> On Wed, Sep 21, 2016 at 11:24 PM, Chawla,Sumit 
> wrote:
>
>> Hi All
>>
>> I am trying to code a solution for following scenarios.
>>
>> 1.  I have a stream of Tuples with multiple numeric fields (e.g. A, B, C,
>> D, E ... etc )
>> 2.  I want the ability to do different Windowing and Aggregation on each
>> field or a group of fields in the Tuple.  e.g. Sum A over a Period of 2
>> minutes, Avg B over a period of 3 minutes,  Sum of C grouped by D over a
>> period of 15 minutes
>> 3.  *These window requirements can be added by user at runtime*.  My
>> pipeline should be able to compute a new aggregation at runtime.
>> 4.  Plan to support only simple aggregation windows like SUM, AVG, MAX,
>> MIN, COUNT etc.
>>
>>
>> As i understand in BEAM pipelines ( with Flink Runner), the DAG of
>> computations cannot be altered once the pipeline is deployed.  I am trying
>> to see how can i support above use case.  I would love to hear your
>> feedback on this, and suggestions on doing it in a completely different
>> way.
>>
>> *My Design:*
>>
>> 1.   Create 1 minute buckets per Field or Group of Fields and compute
>> basic aggregations for bucket.  e.g.  Buckets are highlighted in Yellow
>> here.  For each field i calculate [SUM, COUNT, MAX, MIN] in the bucket.  (
>> Bucket size of 1 minute is defined for simplicity, and would limit the
>> minimum window size to 1 minute)
>>
>> 2.  Downstream process these buckets, and compute the user defined
>> aggregations.  Following diagram depicts Tumbling Window computations.  The
>> Aggregation functions in GREEN are just NATIVE functions consuming
>> different buckets, and doing aggregations on top of these buckets.
>>
>>
>>
>>
>> ​
>> ​
>>
>> *P.S.*
>>
>> * Some of the design choices that i have decided not to go for are:*
>>
>> 1.  Multiple Pipelines for doing computation.  One master pipeline does
>> grouping, and sends to a different topic based on user configured window
>> size. (e.g. topic_window_by_5_min, topic_window_by_15_min), and have a
>> different pipeline consume each topic.
>>
>> 2.  Single pipeline doing all the business with predefined Windows
>> defined for Downstream processing. e.g. 5, 15, 30, 60 minute windows will
>> be defined which will consume from different Side Inputs.  User is only
>> allowed only to select these Window sizes.  Upstream Group By operator will
>> route to the data to different Window Function based on user configuration.
>>
>>
>>
>> Regards
>> Sumit Chawla
>>
>>
>

Runtime Windows/Aggregation Computations in Beam

[Chawla,Sumit]

Hi All

I am trying to code a solution for following scenarios.

1.  I have a stream of Tuples with multiple numeric fields (e.g. A, B, C,
D, E ... etc )
2.  I want the ability to do different Windowing and Aggregation on each
field or a group of fields in the Tuple.  e.g. Sum A over a Period of 2
minutes, Avg B over a period of 3 minutes,  Sum of C grouped by D over a
period of 15 minutes
3.  *These window requirements can be added by user at runtime*.  My
pipeline should be able to compute a new aggregation at runtime.
4.  Plan to support only simple aggregation windows like SUM, AVG, MAX,
MIN, COUNT etc.


As i understand in BEAM pipelines ( with Flink Runner), the DAG of
computations cannot be altered once the pipeline is deployed.  I am trying
to see how can i support above use case.  I would love to hear your
feedback on this, and suggestions on doing it in a completely different
way.

*My Design:*

1.   Create 1 minute buckets per Field or Group of Fields and compute basic
aggregations for bucket.  e.g.  Buckets are highlighted in Yellow here.
For each field i calculate [SUM, COUNT, MAX, MIN] in the bucket.  ( Bucket
size of 1 minute is defined for simplicity, and would limit the minimum
window size to 1 minute)

2.  Downstream process these buckets, and compute the user defined
aggregations.  Following diagram depicts Tumbling Window computations.  The
Aggregation functions in GREEN are just NATIVE functions consuming
different buckets, and doing aggregations on top of these buckets.




​
​

*P.S.*

* Some of the design choices that i have decided not to go for are:*

1.  Multiple Pipelines for doing computation.  One master pipeline does
grouping, and sends to a different topic based on user configured window
size. (e.g. topic_window_by_5_min, topic_window_by_15_min), and have a
different pipeline consume each topic.

2.  Single pipeline doing all the business with predefined Windows defined
for Downstream processing. e.g. 5, 15, 30, 60 minute windows will be
defined which will consume from different Side Inputs.  User is only
allowed only to select these Window sizes.  Upstream Group By operator will
route to the data to different Window Function based on user configuration.



Regards
Sumit Chawla