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From Govindarajan Srinivasaraghavan <govindragh...@gmail.com>
Subject Re: Eventime window
Date Wed, 02 Aug 2017 15:13:40 GMT
Thanks Timo. Basically my requirement is based on event time the window has to be created but
the trigger has to happen either when it has received the next element >10s or 10s has
passed. Exactly the same way as you described. Let me try the AssignerWithPeriodicWatermarks


> On Aug 2, 2017, at 7:46 AM, Timo Walther <twalthr@apache.org> wrote:
> I forgot about the AssignerWithPeriodicWatermarks [1]. I think it could solve your problem
> Timo
> [1] https://ci.apache.org/projects/flink/flink-docs-release-1.3/dev/event_timestamps_watermarks.html#with-periodic-watermarks
>> Am 02.08.17 um 16:30 schrieb Timo Walther:
>> The question is what defines your `10 seconds`. In event-time the incoming events
determine when 10 seconds have passed. Your description sounds like you want to have results
after 10 seconds wall-clock/processing-time. So either you use a processing-time window or
you implement a custom trigger that triggers both on event-time or on a timer that you have
set after 10 s processing-time.
>> Timo
>>> Am 02.08.17 um 16:20 schrieb Govindarajan Srinivasaraghavan:
>>> Thanks Timo. The next message will arrive only after a minute or so. Is there
a way to evict whatever is there in window buffer just after 10 seconds irrespective of whether
a new message arrives or not. 
>>> Thanks,
>>> Govind
>>> On Aug 2, 2017, at 6:56 AM, Timo Walther <twalthr@apache.org> wrote:
>>>> Hi Govind,
>>>> if the window is not triggered, this usually indicates that your timestamp
and watermark assignment is not correct. According to your description, I don't think that
you need a custom trigger/evictor. How often do events arrive from one device? There must
be another event from the same device that has a timestamp >10s in order to trigger the
window evaluation.
>>>> Instead of using the Kafka timestamp, maybe you could also convert your timestamps
to UTC in the TimestampExtractor.
>>>> There are no official limitation. However, each window comes with some overhead.
So you should choose your memory/state backends and parallelism accordingly.
>>>> Hope that helps.
>>>> Timo
>>>>> Am 02.08.17 um 06:54 schrieb Govindarajan Srinivasaraghavan:
>>>>> Hi,
>>>>> I have few questions regarding event time windowing. My scenario is devices
from various timezones will send messages with timestamp and I need to create a window per
device for 10 seconds. The messages will mostly arrive in order.
>>>>> Here is my sample code to perform windowing and aggregating the messages
after the window to further process it.
>>>>> streamEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
>>>>> FlinkKafkaConsumer010 consumer = new FlinkKafkaConsumer010("STREAM1",
>>>>>                     new DeserializationSchema(),
>>>>>                     kafkaConsumerProperties);
>>>>> DataStream<Message> msgStream = streamEnv
>>>>> 					.addSource(consumer)
>>>>> 					.assignTimestampsAndWatermarks(new TimestampExtractor(Time.of(100,
TimeUnit.MILLISECONDS))); // TimestampExtractor implements BoundedOutOfOrdernessTimestampExtractor
>>>>> KeyedStream<Message, String> keyByStream = msgStream.keyBy(new
>>>>> WindowedStream<Message, String, TimeWindow> windowedStream =
>>>>>         keyByStream.window(TumblingEventTimeWindows.of(org.apache.flink.streaming.api.windowing.time.Time.seconds(10)));
>>>>> SingleOutputStreamOperator<Message> aggregatedStream = windowedStream.apply(new
>>>>> My questions are
>>>>> - In the above code, data gets passed till the window function but even
after window time the data is not received in the apply function. Do I have to supply a custom
evictor or trigger?
>>>>> - Since the data is being received from multiple timezones and each device
will have some time difference, would it be ok to assign the timestamp as that of received
timestamp in the message at source (kafka). Will there be any issues with this?
>>>>> - Are there any limitations on the number of time windows that can be
created at any given time? In my scenario if there are 1 million devices there will be 1 million
tumbling windows.
>>>>> Thanks,
>>>>> Govind

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