Hi all,
I was wondering if there exists either a test suite of pathological failure cases for postgres, or a dataset of failure scenarios. I'm not exactly sure what such a dataset would look like, possibly a bunch of snapshots of test databases when undergoing a bunch of different failure scenarios?
I'm experimenting with machine learning and I had an idea to build a classifier to determine if a running postgres database is having issues. Right now "issues" is very ambiguously defined, but I'm thinking of problems I've encountered at work, such as resource saturation, long running transactions, lock contention, etc. I know a lot of this is already covered by existing monitoring solutions, but I'm specifically interested to see if a ML model can learn monitoring rules on its own.
If the classifier turns out to be feasible then my hope would to be to expand the ML model to have some diagnostic capabilities -- I've had difficulty in the past figuring out exactly what is going wrong with postgres when my workplace's production environment was having database issues.
Thanks,
Ben Simmons