Thanks Fabian. I am seeing thia consistently and can definitely use some help. I have plenty of graphana views I can share if that helps :)
Sent from Yahoo Mail on Android On Tue, Nov 7, 2017 at 3:54 AM, Fabian Hueske<fhue...@gmail.com> wrote: Hi Ashish, Gordon (in CC) might be able to help you. Cheers, Fabian 2017-11-05 16:24 GMT+01:00 Ashish Pokharel <ashish...@yahoo.com>: All, I am starting to notice a strange behavior in a particular streaming app. I initially thought it was a Producer issue as I was seeing timeout exceptions (records expiring in queue. I did try to modify request.timeout.ms, linger.ms etc to help with the issue if it were caused by a sudden burst of data or something along those lines. However, what it caused the app to increase back pressure and made the slower and slower until that timeout is reached. With lower timeouts, app would actually raise exception and recover faster. I can tell it is not related to connectivity as other apps are running just fine around the same time frame connected to same brokers (we have at least 10 streaming apps connected to same list of brokers) from the same data nodes. We have enabled Graphite Reporter in all of our applications. After deep diving into some of consumer and producer stats, I noticed that consumer fetch-rate drops tremendously while fetch-size grows exponentially BEFORE the producer actually start to show higher response-time and lower rates. Eventually, I noticed connection resets start to occur and connection counts go up momentarily. After which, things get back to normal. Data producer rates remain constant around that timeframe - we have Logstash producer sending data over. We checked both Logstash and Kafka metrics and they seem to be showing same pattern (sort of sin wave) throughout. It seems to point to Kafka issue (perhaps some tuning between Flink App and Kafka) but wanted to check with the experts before I start knocking down Kafka Admin’s doors. Are there anything else I can look into. There are quite a few default stats in Graphite but those were the ones that made most sense. Thanks, Ashish
