Query Plan Baseline Tracking & Performance Regression Automation

Automate the boundary between “fast” and “regressed”

Query performance regressions are among the most insidious failure modes in data-intensive platforms. They rarely trip infrastructure alerts, yet they quietly degrade p95/p99 latency, inflate compute spend, and cascade into application-level circuit breakers. This site treats query plan tracking as a first-class control plane: capture execution plans automatically, anchor them to deterministic baselines, and gate risky changes before they reach production.

Everything here is organized around a strict, automation-first pipeline — Capture → Regression → CI Gate → Index Sync → Debugging — with explicit input/output contracts at every stage. You’ll find deterministic plan hashing, cross-engine cost normalization, statistically grounded thresholds, and ready-to-adapt Python and CI patterns for PostgreSQL, MySQL, and beyond.

Whether you operate read replicas at scale, tune the optimizer for a living, or wire performance guardrails into GitHub Actions and GitLab CI, the guides and runbooks below give you reproducible, production-grade building blocks.