Looks like the bug https://issues.apache.org/
jira/browse/FLINK-5479is entirely preventing this feature to be used if there are any idle partitions. It would be nice to mention in documentation that currently this requires all subscribed partitions to have a constant stream of data with growing timestamps. When watermark gets stalled on an idle partition it blocks everything.Link to current documentation:https://ci.apache.org/ projects/flink/flink-docs- master/dev/connectors/kafka. html#kafka-consumers-and- timestamp-extractionwatermark- emissionOn Mon, Dec 4, 2017 at 4:29 PM, Fabian Hueske <email@example.com> wrote:You are right, offsets cannot be used for tracking processing progress. I think setting Kafka offsets with respect to some progress notion other than "has been consumed" would be highly application specific and hard to generalize.Have you considered tracking progress using watermarks?
As you said, there might be a window (such as a session window) that is open much longer than all other windows and which would hold back the offset. Other applications might not use the built-in windows at all but custom ProcessFunctions.2017-12-04 14:42 GMT+01:00 Juho Autio <firstname.lastname@example.org>:Thank you Fabian. Really clear explanation. That matches with my observation indeed (data is not dropped from either small or big topic, but the offsets are advancing in kafka side already before those offsets have been triggered from a window operator).This means that it's a bit harder to meaningfully monitor the job's progress solely based on kafka consumer offsets. Is there a reason why Flink couldn't instead commit the offsets after they have been triggered from downstream windows? I could imagine that this might pose a problem if there are any windows that remain open for a very long time, but in general it would be useful IMHO. Or Flink could even commit both (read vs. triggered) offsets to kafka for monitoring purposes.On Mon, Dec 4, 2017 at 3:30 PM, Fabian Hueske <email@example.com> wrote:Hi Juho,the partitions of both topics are independently consumed, i.e., at their own speed without coordination. With the configuration that Gordon linked, watermarks are generated per partition.Each source task maintains the latest (and highest) watermark per partition and propagates the smallest watermark. The same mechanism is applied for watermarks across tasks (this is what Kien referred to).In the case that you are describing, the partitions of the smaller topic are faster consumed (hence the offsets are faster aligned) but watermarks are emitted "at the speed" of the bigger topic.Therefore, the timestamps of records from the smaller topic can be much ahead of the watermark.In principle, that does not pose a problem. Stateful operators (such as windows) remember the "early" records and process them when they receive a watermark passes the timestamps of the early records.Regarding your question "Are they committed to Kafka before their watermark has passed on Flink's side?":The offsets of the smaller topic might be checkpointed when all partitions have been read to the "end" and the bigger topic is still catching up.The watermarks are moving at the speed of the bigger topic, but all "early" events of the smaller topic are stored in stateful operators and are checkpointed as well.So, you do not lose neither early nor late data.Best, Fabian2017-12-01 13:43 GMT+01:00 Juho Autio <firstname.lastname@example.org>:Thanks for the answers, I still don't understand why I can see the offsets being quickly committed to Kafka for the "small topic"? Are they committed to Kafka before their watermark has passed on Flink's side? That would be quite confusing.. Indeed when Flink handles the state/offsets internally, the consumer offsets are committed to Kafka just for reference.Otherwise, what you're saying sounds very good to me. The documentation just doesn't explicitly say anything about how it works across topics.On Kien's answer: "When you join multiple stream with different watermarks", note that I'm not joining any topics myself, I get them as a single stream from the Flink kafka consumer based on the list of topics that I asked for.Thanks,JuhoOn Wed, Nov 22, 2017 at 2:57 PM, Tzu-Li (Gordon) Tai <email@example.com> wrote:Hi!
The FlinkKafkaConsumer can handle watermark advancement with
per-Kafka-partition awareness (across partitions of different topics).
You can see an example of how to do that here .
Basically what this does is that it generates watermarks within the Kafka
consumer individually for each Kafka partition, and the per-partition
watermarks are aggregated and emitted from the consumer in the same way that
watermarks are aggregated on a stream shuffle; only when the low watermark
advances across all partitions, should a watermark be emitted from the
Therefore, this helps avoid the problem that you described, in which a
"big_topic" has subscribed partitions that lags behind others. In this case
and when the above feature is used, the event time would advance along with
the lagging "big_topic" partitions and would not result in messages being
recognized as late and discarded.
/flink/flink-docs-release-1.3/ dev/event_timestamps_watermark s.html#timestamps-per-kafka-pa rtition
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