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From Yury Ruchin <yuri.ruc...@gmail.com>
Subject Re: Blocking RichFunction.open() and backpressure
Date Mon, 19 Dec 2016 09:27:51 GMT
Thanks Fabian, that quite explains what's going on.

2016-12-19 12:19 GMT+03:00 Fabian Hueske <fhueske@gmail.com>:

> Hi Yury,
>
> Flink's operators start processing as soon as they receive data. If an
> operator produces more data than its successor task can process, the data
> is buffered in Flink's network stack, i.e., its network buffers.
> The backpressure mechanism kicks in when all network buffers are in use
> and no more data can be buffered. In this case, a producing task will block
> until a network buffer becomes available.
>
> If the window operator in your job aggregates the data, only the
> aggregates will be buffered.
> This might explain why the first operators of job are able to start
> processing while the FlatMap operator is still setting up itself.
>
> Best,
> Fabian
>
> 2016-12-17 13:42 GMT+01:00 Yury Ruchin <yuri.ruchin@gmail.com>:
>
>> Hi all,
>>
>> I have a streaming job that essentially looks like this: KafkaSource ->
>> Map -> EventTimeWindow -> RichFlatMap -> CustomSink. The RichFlatMap part
>> does some heavy lifting in open(), so that the open() call blocks for
>> several minutes. I assumed that until open() returns the backpressure
>> mechanism would slow down the entire upstream up to the KafkaSource, so
>> that no new records would be emitted to the pipeline until the RichFlatMap
>> is ready. What I actually observe is that the KafkaSource, Map and
>> EventTimeWindow continue processing - the in/out records, in/out MB
>> counters keep increasing. The RichFlatMap and its downstream CustomSink
>> have 0 as expected, until the RichFlatMap is actually done with open(). The
>> backpressure monitor in Flink UI shows "OK" for all operators.
>>
>> Why doesn't backpressure mechanism work in this case?
>>
>> Thanks,
>> Yury
>>
>
>

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