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In the case of using reactive messaging application to implement streaming application à la kafkastreams, we are missing some metrics that cannot (or hardly) be deduced by the raw consumer/producer metrics already present.
When a topic/partition start to lag we need to identify if it's because
more messages are coming in (causes an input lag)
or the reactive messaging application latency start to increase (because performance issues).
The idea would be to replicate the metrics already present in kafkastreams for the streams thread such as :
process-rate: The average number of processed operations per second across all tasks.
process-latency-avg: The average execution time in ms, for the respective across all running tasks of
Or at node/task level (the equivalent of @incoming+@ougtoing method) level :
record-e2e-latency-avg: The average end-to-end latency of a record, measured by comparing the record timestamp with the system time when it has been fully processed by the node.
Moreover, I think these metrics can conceptually have meaning for other queuing system and could be implemented for all connectors type.
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Hello
In the case of using reactive messaging application to implement streaming application à la kafkastreams, we are missing some metrics that cannot (or hardly) be deduced by the raw consumer/producer metrics already present.
When a topic/partition start to lag we need to identify if it's because
The idea would be to replicate the metrics already present in kafkastreams for the streams thread such as :
Or at node/task level (the equivalent of @incoming+@ougtoing method) level :
Moreover, I think these metrics can conceptually have meaning for other queuing system and could be implemented for all connectors type.
Waiting for your inputs
Regards
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