Hi Maxim,
If reducing latency is the goal, then option #1 seems better.
Though you’d need additional logic inside of your AsyncFunction to run all 20 queries in parallel.
I’d also consider a third option...
Use a FlatMapFunction to create 20 copies of the event (assuming it’s not large), with an additional field indicating which query should be made.
Follow that with a rebalance(), and a single AsyncFunction that makes the appropriate query for the event, based on this new field.
Then make sure you’ve got sufficient parallelism for your AsyncFunction to handle this fan-out.
This should let you run the queries for a single event in parallel.
— Ken
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> On Apr 3, 2018, at 9:59 AM, Maxim Parkachov <[hidden email]> wrote:
>
> Hi everyone,
>
> I'm writing streaming job which needs to query Cassandra for each event multiple times, around 20. I would like to use Async IO for that but not sure which option to choose:
>
> 1. Implement One AsyncFunction with 20 queries inside
> 2. Implement 20 AsyncFunctions, each with 1 query inside
>
> Taking into account that each event needs all queries. Reduce amount of queries for each record is not an option.
>
> In this case I would like to minimise processing time of event, even if throughput will suffer. Any advice or consideration is greatly appreciated.
>
> Thanks,
> Maxim.
>
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