performance-profiler

CategoryCoding
AuthorAlireza Rezvani
LicenseMIT
Rating4.80/5
Uses11.3K

Performance Profiler

Tier: POWERFUL
Category: Engineering
Domain: Performance Engineering

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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

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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

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Quick Start

bash
# 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

python3 scripts/performance_profiler.py /path/to/project --large-file-threshold-kb 256

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Golden Rule: Measure First

bash
# Establish baseline BEFORE any optimization

Record: P50, P95, P99 latency | RPS | error rate | memory usage


Wrong: "I think the N+1 query is slow, let me fix it"


Right: Profile → confirm bottleneck → fix → measure again → verify improvement

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Node.js Profiling

→ See references/profiling-recipes.md for details

References

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