Event time issues when Kinesis consumer receives batched messages

[Randal Pitt]

Hi there,

We're using Flink to read from a Kinesis stream. The stream contains
messages that themselves contain lists of events and we want our Flink jobs
(using the event time characteristic) to process those events individually.
We have this working using flatMap in the DataStream but we're having
trouble correctly assigning timestamps to the events.

We have been using FlinkKinesisConsumer.setPeriodicWatermarkAssigner() as
that should mean the watermarks are generated correctly, but it results in
all events in one message sharing a timestamp, resulting in some events
being assigned to the wrong window.

Using DataStream.assignTimestampsAndWatermarks() after the flatMap means we
can assign the correct timestamps, but the watermarks may not necessarily be
correct with respect to the Kinesis shards.

Is there are strategy we can use that gets us both watermarks from the
Kinesis consumer and correct timestamps for individual events?

Best regards,

Randal.



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[r_khachatryan]

Hi Randal,

Can you share the code for the 1st approach (FlinkKinesisConsumer.setPeriodicWatermarkAssigner))?

I think the 2nd approach (flatMap) can be improved by partitioning the stream the same way kinesis does (i.e. same partition key).

On Mon, Dec 7, 2020 at 2:44 PM Randal Pitt <[hidden email]> wrote:

Hi there,

We're using Flink to read from a Kinesis stream. The stream contains
messages that themselves contain lists of events and we want our Flink jobs
(using the event time characteristic) to process those events individually.
We have this working using flatMap in the DataStream but we're having
trouble correctly assigning timestamps to the events.

We have been using FlinkKinesisConsumer.setPeriodicWatermarkAssigner() as
that should mean the watermarks are generated correctly, but it results in
all events in one message sharing a timestamp, resulting in some events
being assigned to the wrong window.

Using DataStream.assignTimestampsAndWatermarks() after the flatMap means we
can assign the correct timestamps, but the watermarks may not necessarily be
correct with respect to the Kinesis shards.

Is there are strategy we can use that gets us both watermarks from the
Kinesis consumer and correct timestamps for individual events?

Best regards,

Randal.



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[Randal Pitt]

Hi Roman,

We're using a custom watermarker that uses a histogram to calculate a "best
fit" event time as the data we receive can be very unordered.

As you can see we're using the timestamp from the first event in the batch,
so we're essentially sampling the timestamps rather than using them all.

FlinkKinesisConsumer<Batch&lt;EventType>> consumer = new
FlinkKinesisConsumer<>(...);

consumer.setPeriodicWatermarkAssigner(
    new HistogramWatermarker<>(Time.minutes(30), 100) {
        @Override
        public long extractTimestamp(final Batch<EventType> element) {
            return element.getBatch().get(0).getDate().getTime();
        }
    }
);

Cheers,
Randal.



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[r_khachatryan]

Thanks, Randal,

Yes, I think the only way is to partition the stream the same way as kinesis does (as I wrote before).

On Tue, Dec 8, 2020 at 1:38 PM Randal Pitt <[hidden email]> wrote:

Hi Roman,

We're using a custom watermarker that uses a histogram to calculate a "best
fit" event time as the data we receive can be very unordered.

As you can see we're using the timestamp from the first event in the batch,
so we're essentially sampling the timestamps rather than using them all.

FlinkKinesisConsumer<Batch&lt;EventType>> consumer = new
FlinkKinesisConsumer<>(...);

consumer.setPeriodicWatermarkAssigner(
    new HistogramWatermarker<>(Time.minutes(30), 100) {
        @Override
        public long extractTimestamp(final Batch<EventType> element) {
            return element.getBatch().get(0).getDate().getTime();
        }
    }
);

Cheers,
Randal.



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[Randal Pitt]

Thanks Roman, I'll look into how I go about doing that.



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