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Performance Profiler

Domain: Engineering - POWERFUL | Skill: performance-profiler | Source: engineering/performance-profiler/SKILL.md

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

Golden Rule: Measure First

# 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

Node.js Profiling

→ See references/profiling-recipes.md for details

Before/After Measurement Template

## Performance Optimization: [What You Fixed]

**Date:** 2026-03-01  
**Engineer:** @username  
**Ticket:** PROJ-123  

### Problem
[1-2 sentences: what was slow, how was it observed]

### Root Cause
[What the profiler revealed]

### Baseline (Before)
| Metric | Value |
|--------|-------|
| P50 latency | 480ms |
| P95 latency | 1,240ms |
| P99 latency | 3,100ms |
| RPS @ 50 VUs | 42 |
| Error rate | 0.8% |
| DB queries/req | 23 (N+1) |

Profiler evidence: [link to flamegraph or screenshot]

### Fix Applied
[What changed — code diff or description]

### After
| Metric | Before | After | Delta |
|--------|--------|-------|-------|
| P50 latency | 480ms | 48ms | -90% |
| P95 latency | 1,240ms | 120ms | -90% |
| P99 latency | 3,100ms | 280ms | -91% |
| RPS @ 50 VUs | 42 | 380 | +804% |
| Error rate | 0.8% | 0% | -100% |
| DB queries/req | 23 | 1 | -96% |

### Verification
Load test run: [link to k6 output]

Optimization Checklist

Quick wins (check these first)

Database
□ Missing indexes on WHERE/ORDER BY columns
□ N+1 queries (check query count per request)
□ Loading all columns when only 2-3 needed (SELECT *)
□ No LIMIT on unbounded queries
□ Missing connection pool (creating new connection per request)

Node.js
□ Sync I/O (fs.readFileSync) in hot path
□ JSON.parse/stringify of large objects in hot loop
□ Missing caching for expensive computations
□ No compression (gzip/brotli) on responses
□ Dependencies loaded in request handler (move to module level)

Bundle
□ Moment.js → dayjs/date-fns
□ Lodash (full) → lodash/function imports
□ Static imports of heavy components → dynamic imports
□ Images not optimized / not using next/image
□ No code splitting on routes

API
□ No pagination on list endpoints
□ No response caching (Cache-Control headers)
□ Serial awaits that could be parallel (Promise.all)
□ Fetching related data in a loop instead of JOIN

Common Pitfalls

  • Optimizing without measuring — you'll optimize the wrong thing
  • Testing in development — profile against production-like data volumes
  • Ignoring P99 — P50 can look fine while P99 is catastrophic
  • Premature optimization — fix correctness first, then performance
  • Not re-measuring — always verify the fix actually improved things
  • Load testing production — use staging with production-size data

Best Practices

  1. Baseline first, always — record metrics before touching anything
  2. One change at a time — isolate the variable to confirm causation
  3. Profile with realistic data — 10 rows in dev, millions in prod — different bottlenecks
  4. Set performance budgets — p(95) < 200ms in CI thresholds with k6
  5. Monitor continuously — add Datadog/Prometheus metrics for key paths
  6. Cache invalidation strategy — cache aggressively, invalidate precisely
  7. Document the win — before/after in the PR description motivates the team