feat: track read options via seen logline

[joshuarli]

edit: this has now been changed to emit logs instead - no databases! and we want 100% sampling so the way to not slam logs is to just keep a seen cache on the options manager to emit at most 1 logline for each newly seen option

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as part of the sentry options migration i'd like to clean up unused options which seem to represent a not-insignificant portion of options registered today

according to options.html in getsentry/sentry-options#123 there's about 1,100 total registered options in getsentry, of which 100 or so unreferenced options that should be safe to remove, and ~300 more dynamically generated options that couldn't be determined via static analysis alone, rather i identified them by warming up getsentry then reading all registered options

i'm not 100% sure how to reliably determine if those dynamically generated options are referenced so i want to x-reference them against the actual options db

the current sentry_options/sentry_controloptions tables do not have something like access time

so: this introduces a new sentry_option_seen table so I can determine the set of options that are not being read at all so that they can be deleted

since options.get is pretty hot my design is essentially a tripwire based on row existence, and on startup we read all rows so we can fastpath out (1 string set membership) without touching the db if an option's already read

i think even just a few days data would be sufficient to make the call?

also added a docs/MIGRATIONS.md to help agents write migrations!

[wedamija]

I'm a little wary about this because it's potentially a lot of querires on postgres.

One option is if instead of writing this to a table, we just write logs? There will be a lot, but if it's only a few days it won't be too expensive, and then we can just query them from GCP. We could also sample at 1% to reduce the cost here.

The other is putting this functionality behind an option if that's possible, so we can quickly revert it if it costs a lot of load during deploys.

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