Query Plan Baseline Tracking & Performance Regression Automation

A production-focused resource for automating query plan tracking, detecting performance regressions, and enforcing CI/CD gates — built for Database SREs, query optimization engineers, Python DevOps builders, platform teams.

Explore the content Automated EXPLAIN Capture & Storage Workflows Core Architecture & Baselining Fundamentals Regression Detection & Rule Engines: Automated Query Plan Baseline Governance

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.

Start here

New to baseline tracking? These are the load-bearing guides — one from each stage of the pipeline. Read them in order or jump to the problem in front of you.

What you’ll find

The content is grouped into three pillars. Each pillar links to focused guides and hands-on runbooks that drill into the specifics.