ENGINEERING
Slow-Query Regression Hunter from pg_stat_statements
Hourly, snapshots pg_stat_statements, compares mean execution time against the prior baseline, and opens a GitHub issue for any query whose latency regressed past a threshold.
How it runs
The automated pipeline, trigger to output.
- TriggerHourly schedule
- ActionRead pg_stat_statementsPostgres
- ActionLoad prior baseline snapshotPostgres
- LogicFlag queries past regression threshold
- OutputOpen GitHub issue per regressionGitHub
- ActionPersist new baseline snapshotPostgres
What it does
It watches your production Postgres for queries that got slower. Every hour it reads `pg_stat_statements`, computes per-query mean execution time, and diffs it against the previous snapshot stored in a control table. Any normalized query that crosses your regression threshold (e.g. 2x slower or +200ms) gets a GitHub issue with the fingerprint, before/after timings, and call counts.
When to use it
Run this when a deploy or data-growth event silently degrades query latency and you only find out from user complaints. It turns invisible drift into a tracked, assignable ticket the moment it happens.
How it works
- 1A schedule fires hourly.
- 2Query `pg_stat_statements` for queryid, normalized text, mean_exec_time, and calls.
- 3Read the prior snapshot from a `query_baselines` table and join on queryid.
- 4A logic step flags rows where mean_exec_time grew past the configured multiplier and absolute floor.
- 5For each flagged query, open a GitHub issue labeled `perf-regression` with the diff.
- 6Write the new snapshot back as the next baseline.
Set it up
What you configure once, before turning it on.
- 1Connect PostgresAny Postgres URL — query, write, migrate.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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