[9.1] [ML] Fix ML calendar event update scalability issues (#136886) #138007
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Fixes issue where calendar events failed to update some jobs when associated with large numbers of jobs (>1000) due to queue capacity limits and sequential processing. Problem: UpdateJobProcessNotifier has a 1000-item queue and processes updates sequentially. It uses offer() on the queue, which silently drops updates when the queue is full. However, calendar/filter updates don't need ordering guarantees. Hence, JobManager.submitJobEventUpdate() can bypass the queue and avoid the bottleneck of the queue size. Another problem is the "fire-and-forget" pattern: submitJobEventUpdate() returns immediately without waiting for the update to complete. I introduce RefCountingListener to track the calendar updates. We start a background thread that updates the jobs and tracks succeeded, failed, and skipped jobs, while the request is returned immediately to prevent a timeout. Finally, if the problem with failed job updates persists, I enhanced the logging throughout the system to create a trace for future diagnostics. Refactor JobManager.submitJobEventUpdate() to bypass UpdateJobProcessNotifier queue Use RefCountingListener for parallel calendar/filter updates Add comprehensive logging throughout the system Create CalendarScalabilityIT integration tests Add helper methods to base test class
valeriy42
added
:ml
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Backports the following commits to 9.1: