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More details regarding back pressure metrics: I'm specifically looking for x number of DB updates made until time t and y was captured by capture started task at the same point in time (t) and z was published by apply engine at same point in time (t) so that we can compute below at t:
Capture backlog (related to source DB)= x-y
Apply backlog (related to source DB)=x-z
Backlog lag between Capture & Apply=y-z
We also like to get the back pressure metrics from DB & capture part (ie., # of records to be processed for publishing related to DB updates)
While doing the Performance and Scalability test for IMS to Kafka Subscriptions and are trying to identify bottlenecks at the Source end (IMS) .
As per the documentation we see bottlenecks can be identified as per below -
Bottlenecks
Displays a bar chart featuring the five components of the pipeline: Log Reader, Source Engine, Communication, Target Engine, and Target Database. The results of the bar chart will help you isolate which components are causing bottlenecks.
https://www.ibm.com/docs/en/idr/11.4.0?topic=tuning-using-management-console-identify-performance-bottlenecks
We are unable to get this information for Subscriptions with IMS as a Source. We are currently not seeing the source engine (i.e., IMS) details in Management Console. It's not possible to view IMS source engine information in the Performance tab.
This is a huge gap in the observability and causes operational risk for us. Hope IBM can fix flaws in the IIDR CDC product design through proper enhancements.
Another narrow down requirement: We like to have Apply for Kafka implement monitoring interceptors provided by Confluent for End-to-End monitoring.
Ref Link: https://docs.confluent.io/platform/7.5/control-center/installation/clients.html
Thanks for filing this requirement.
Addressing requirements for pushing artifacts to observabiity tools following open standards is engaging our attention (initially research planning). We will take this requirement into account.