Route Profiler — Automated Performance Profiling

Project Context

As web apps grow, dozens or hundreds of endpoints appear. Many have performance issues invisible in dev but critical in prod.

Typical problems:

• Routes shipped without perf review
• N+1 queries in the ORM
• Slow serialization of large payloads
• Memory leaks in long‑running handlers
• Poor database indexing
• Excessive external API calls

Manual profiling doesn’t scale: you can’t check every route, there’s no baseline, regressions slip, and some routes have no test data at all.

Goal

Build an automated profiler that:

• Auto‑discovers routes (Django/Flask/FastAPI)
• Validates fixture/mock data for each route
• Fails when route lacks test data
• Profiles time, memory, DB queries
• Generates reports/visualizations and alerts
• Integrates into CI/CD for continuous perf checks
• Tracks regressions against a baseline

Solution

Architecture

Route discovery → Fixture validation → Execution & profiling → Report & baseline → CI status & alerts

Outputs per route: latency percentiles, memory delta, DB queries count/summary, payload sizes, and flame/diff views. Visualizations via Plotly/Pandas; configuration per route and global thresholds.

Results

Lessons

• “If it’s not measured, it’s broken.” Perf must be enforced by automation.
• Enforce fixtures early to prevent untested routes.
• Make reports consumable; otherwise teams ignore the signal.