flink-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Aljoscha Krettek <aljos...@apache.org>
Subject Re: No output when using event time with multiple Kafka partitions
Date Fri, 29 Jul 2016 10:43:48 GMT
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
>>>
>>>
>>>
>>
>

Mime
View raw message