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Data Pruning for Anomaly Detection #1193
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Before we turn this on in production with ENABLE_CELERY_WORKERs, lets get #1192 in and add move this beat task to a conditional for anomaly detection. |
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We also need some metrics published from the Celerey task so that we can keep track of:
These are in addition to the regular queue related metrics like queue size, time spent waiting etc. We should check with Jenn if we need to do anything specific for those to work. |
I did include Sentry tracing for the cleanup task so I think everything aside from the third point (less than 7 days of data) should be viewable. I'm unsure about the other queue related metrics though since Sentry doesn't support direct metrics anymore (https://docs.sentry.io/product/explore/metrics/). |
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src/seer/db.py
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@@ -58,6 +59,8 @@ class Base(DeclarativeBase): | |||
migrate = Migrate(directory="src/migrations") | |||
Session = sessionmaker(autoflush=False, expire_on_commit=False) | |||
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TaskStatus = Literal["not_queued", "processing", "queued"] |
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We can convert this to string enum and use it in both the business logic as well as in the data layer.
Wondering if we should add the alert id as a tag so that we can look at metrics at an alert level. Let us ask Zach and Jenn about the celery metrics in the next standup. Rest is good. |
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DbDynamicAlert
to includedata_purge_flag
andqueued_at
columns to reflect Celery task status