performance-profiler

Performance Profiler

Tier: POWERFUL
Category: Engineering
Domain: Performance Engineering

Overview

Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks; generates flamegraphs; analyzes bundle sizes; optimizes database queries; detects memory leaks; and runs load tests with k6 and Artillery. Always measures before and after.

Core Capabilities

• CPU profiling — flamegraphs for Node.js, py-spy for Python, pprof for Go
• Memory profiling — heap snapshots, leak detection, GC pressure
• Bundle analysis — webpack-bundle-analyzer, Next.js bundle analyzer
• Database optimization — EXPLAIN ANALYZE, slow query log, N+1 detection
• Load testing — k6 scripts, Artillery scenarios, ramp-up patterns
• Before/after measurement — establish baseline, profile, optimize, verify

When to Use

• App is slow and you don't know where the bottleneck is
• P99 latency exceeds SLA before a release
• Memory usage grows over time (suspected leak)
• Bundle size increased after adding dependencies
• Preparing for a traffic spike (load test before launch)
• Database queries taking >100ms

Quick Start

# Analyze a project for performance risk indicators
python3 scripts/performance_profiler.py /path/to/project
 
# JSON output for CI integration
python3 scripts/performance_profiler.py /path/to/project --json
 
# Custom large-file threshold

Golden Rule: Measure First

Node.js Profiling

→ See references/profiling-recipes.md for details

References

• references/profiling-recipes.md — Node.js/Python/Go profiling commands, flamegraph generation, heap snapshots
• references/optimization-playbook.md — before/after measurement template, quick-win optimization checklist (DB/Node/bundle/API), common pitfalls, best practices