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From Yassine Marzougui <yassmar...@gmail.com>
Subject Re: No output when using event time with multiple Kafka partitions
Date Mon, 15 Aug 2016 09:13:14 GMT
Hi Aljoscha,

Sorry for the late response, I was busy and couldn't make time to work on
this again again until now.
Indeed, it turns out only one of the partitions is not receiving elements.
The reason is that the producer will stick to a partition for
topic.metadata.refresh.interval.ms (defaults to 10 mins) time before
picking another partition at random. So I reduced the
topic.metadata.refresh.interval.ms, and I was able to get an output as soon
as the messages are produced.
I still have some questions about an unclear behavior regarding the
parallelism and watermarks assignment when one partition is empty, which I
will ask in a new mailing thread.
Thanks a lot for your help!

Best,
Yassine

On Fri, Jul 29, 2016 at 12:43 PM, Aljoscha Krettek <aljoscha@apache.org>
wrote:

> Hi,
> when running in local mode the default parallelism is always the number of
> (possibly virtual) CPU cores. The parallelism of the sink is set before it
> gets a chance to find out how many Kafka partitions there are. I think the
> reason for the behavior you're observing is that only one of your two
> partitions ever receives elements and that thus the watermark does not
> advance for that partition. Could that be the case?
>
> Cheers,
> Aljoscha
>
> On Wed, 27 Jul 2016 at 14:58 Yassin Marzouki <yassmarzou@gmail.com> wrote:
>
>> I just tried playing with the source paralleism setting, and I got a very
>> strange result:
>>
>> If specify the source parallism using env.addSource(kafka).setParallelism(N),
>> results are printed correctly for any number N except for N=4. I guess
>> that's related to the number of task slots since I have a 4 CPU cores, but
>> what is the explanation of that?
>> So I suppose that if I don't specify the source parallelism, it is set
>> automatically to 4. Isn't it supposed to be set to the number of topic
>> patitions (= 2) by default?
>>
>>
>> On Wed, Jul 27, 2016 at 2:33 PM, Yassin Marzouki <yassmarzou@gmail.com>
>> wrote:
>>
>>> Hi Kostas,
>>>
>>> When I remove the window and the apply() and put print() after
>>> assignTimestampsAndWatermarks, the messages are printed correctly:
>>>
>>> 2> Request{ts=2015-01-01, 06:15:34:000}
>>> 2> Request{ts=2015-01-02, 16:38:10:000}
>>> 2> Request{ts=2015-01-02, 18:58:41:000}
>>> 2> Request{ts=2015-01-02, 19:10:00:000}
>>> 2> Request{ts=2015-01-02, 23:36:51:000}
>>> 2> Request{ts=2015-01-03, 17:38:47:000}
>>> ...
>>>
>>> But strangely using only one task. If I set the source parallelism to 1
>>> using env.addSource(kafka).setParallelism(1) (the window and the
>>> apply() still removed), results are printed using all available slots
>>> (number of CPU cores):
>>>
>>> 4> Request{ts=2015-01-01, 06:15:34:000}
>>> 4> Request{ts=2015-01-02, 16:38:10:000}
>>> 2> Request{ts=2015-01-02, 19:10:00:000}
>>> 4> Request{ts=2015-01-02, 23:36:51:000}
>>> 1> Request{ts=2015-01-02, 18:58:41:000}
>>> 2> Request{ts=2015-01-03, 17:38:47:000}
>>> 3> Request{ts=2015-01-03, 17:56:42:000}
>>> ...
>>>
>>> Now if I keep the window and apply() with without specifying source
>>> parallelism, no messages are printed (only regular kafka consumer and flink
>>> logs), and if the source parallelism is set to 1, messages are printed
>>> correctly:
>>>
>>> 1> Window: TimeWindow{start=1420070400000, end=1420156800000}
>>> 2> Request{ts=2015-01-01, 06:15:34:000}
>>> 1> Request{ts=2015-01-02, 16:38:10:000}
>>> 4> Request{ts=2015-01-02, 19:10:00:000}
>>> 3> Window: TimeWindow{start=1420156800000, end=1420243200000}
>>> 3> Request{ts=2015-01-02, 18:58:41:000}
>>> 2> Request{ts=2015-01-02, 23:36:51:000}
>>> 3> Window: TimeWindow{start=1420416000000, end=1420502400000}
>>> 2> Request{ts=2015-01-03, 17:38:47:000}
>>> 4> Window: TimeWindow{start=1420243200000, end=1420329600000}
>>> 1> Request{ts=2015-01-03, 17:56:42:000}
>>> 1> Request{ts=2015-01-05, 17:13:45:000}
>>> 4> Request{ts=2015-01-05, 01:25:55:000}
>>> 2> Request{ts=2015-01-05, 14:27:45:000}
>>> ...
>>>
>>> On Wed, Jul 27, 2016 at 1:41 PM, Kostas Kloudas <
>>> k.kloudas@data-artisans.com> wrote:
>>>
>>>> Hi Yassine,
>>>>
>>>> Could you just remove the window and the apply, and  just put a print()
>>>> after the:
>>>>
>>>> .assignTimestampsAndWatermarks(new AscendingTimestampExtractor<Request>()
>>>> {
>>>>     @Override
>>>>     public long extractAscendingTimestamp(Request req) {
>>>>         return req.ts;
>>>>     }
>>>> })
>>>>
>>>>
>>>> This at least will tell us if reading from Kafka works as expected.
>>>>
>>>> Kostas
>>>>
>>>> On Jul 25, 2016, at 3:39 PM, Yassin Marzouki <yassmarzou@gmail.com>
>>>> wrote:
>>>>
>>>> Hi everyone,
>>>>
>>>> I am reading messages from a Kafka topic with 2 partitions and using
>>>> event time. This is my code:
>>>>
>>>> .assignTimestampsAndWatermarks(new AscendingTimestampExtractor<Request>()
>>>> {
>>>>     @Override
>>>>     public long extractAscendingTimestamp(Request req) {
>>>>         return req.ts;
>>>>     }
>>>> })
>>>> .windowAll(TumblingEventTimeWindows.of(Time.days(1)))
>>>> .apply((TimeWindow window, Iterable<Request> iterable,
>>>> Collector<String> collector) -> {
>>>>     collector.collect("Window: " + window.toString());
>>>>     for (Request req : iterable) {
>>>>         collector.collect(req.toString());
>>>>     }
>>>> })
>>>> .print()
>>>>
>>>> I could get an output only when setting the kafka source parallelism to
>>>> 1. I guess that is because messages from multiple partitions arrive
>>>> out-of-order to the timestamp exctractor according to this thread
>>>> <http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Kafka-partition-alignment-for-event-time-td4782.html#a4804>,
>>>> correct?
>>>> So I replaced the AscendingTimestampExtractor with a
>>>> BoundedOutOfOrdernessGenerator as in the documentation example
>>>> <https://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/event_timestamps_watermarks.html#tab_java_3>
(with
>>>> a higher delay) in order to handle out-of-order events, but I still can't
>>>> get any output. Why is that?
>>>>
>>>> Best,
>>>> Yassine
>>>>
>>>>
>>>>
>>>
>>

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