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As a team working on optimising the performance I want to profile the ML API notifications handler so that identify what performance problems in the code.
Note:
In the transfers performance characterisation work, an issue was identified in the ML-API-Adapter Notification Handler that deserves closer scrutiny. I.e. its one of the components showing the usage of resources (i.e CPU).
Running this in a profiler in an isolated environment will help show up the problem. This story is to do that.
Acceptance Criteria:
Verify that the ML-API-Adapter Notification Handler implementation matches the design [@sri-miriyala]
Document any gaps or issues, and create follow-up stories as needed
Verify that the ML-API-Adapter Notification Handler is tested in isolation
Verify that the ML-API-Adapter Notification Handler is profiled
Verify that noteworthy findings/observations are documented
Verify that the problems identified are either fixed if small, or that stories to perform the fix are created.
Verify if http keep alive is configured and tested
Complexity: <High|Medium|Low> > A short comment to remind the reason for the rating
Uncertainty: <High|Medium|Low> > A short comment to remind the reason for the rating
Tasks:
Compare ML API Notification Handler design against the code (Sri)
Review ML documentation on notification handler
kafka cat example notification messages for load
Profile notification handler with mock cl admin and dumped kafkacat messages
Create stories for identified issues
Apply fixes to central-services-shared
Run isolated performance tests and capture graphana dashbaords
Document findings in performance repo
Done
Acceptance Criteria pass
Designs are up-to date
Unit Tests pass
Integration Tests pass
Code Style & Coverage meets standards
Changes made to config (default.json) are broadcast to team and follow-up tasks added to update helm charts and other deployment config.
TBD
Pull Requests:
TBD
Follow-up:
N/A
Dependencies:
N/A
Accountability:
Owner: TBC
QA/Review: TBC
The text was updated successfully, but these errors were encountered:
Goal:
As a
team working on optimising the performanceI want to
profile the ML API notifications handlerso that
identify what performance problems in the code.Note:
In the transfers performance characterisation work, an issue was identified in the ML-API-Adapter Notification Handler that deserves closer scrutiny. I.e. its one of the components showing the usage of resources (i.e CPU).
Running this in a profiler in an isolated environment will help show up the problem. This story is to do that.
Acceptance Criteria:
Complexity: <High|Medium|Low> > A short comment to remind the reason for the rating
Uncertainty: <High|Medium|Low> > A short comment to remind the reason for the rating
Tasks:
Done
Pull Requests:
Follow-up:
Dependencies:
Accountability:
The text was updated successfully, but these errors were encountered: