Performance Profiler

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

# 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

# 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

• 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

Optimization Playbook — Template, Checklist, Pitfalls, Best Practices

The advisory layer behind the performance-profiler workflow: use this when writing up an optimization, scanning for quick wins, or reviewing a performance PR.

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

performance-profiler reference

Node.js Profiling

CPU Flamegraph

# Method 1: clinic.js (best for development)
npm install -g clinic

# CPU flamegraph
clinic flame -- node dist/server.js

# Heap profiler
clinic heapprofiler -- node dist/server.js

# Bubble chart (event loop blocking)
clinic bubbles -- node dist/server.js

# Load with autocannon while profiling
autocannon -c 50 -d 30 http://localhost:3000/api/tasks &
clinic flame -- node dist/server.js

# Method 2: Node.js built-in profiler
node --prof dist/server.js
# After running some load:
node --prof-process isolate-*.log | head -100

# Method 3: V8 CPU profiler via inspector
node --inspect dist/server.js
# Open Chrome DevTools → Performance → Record

Heap Snapshot / Memory Leak Detection

// Add to your server for on-demand heap snapshots
import v8 from 'v8'
import fs from 'fs'

// Endpoint: POST /debug/heap-snapshot (protect with auth!)
app.post('/debug/heap-snapshot', (req, res) => {
  const filename = `heap-${Date.now()}.heapsnapshot`
  const snapshot = v8.writeHeapSnapshot(filename)
  res.json({ snapshot })
})

# Take snapshots over time and compare in Chrome DevTools
curl -X POST http://localhost:3000/debug/heap-snapshot
# Wait 5 minutes of load
curl -X POST http://localhost:3000/debug/heap-snapshot
# Open both snapshots in Chrome → Memory → Compare

Detect Event Loop Blocking

// Add blocked-at to detect synchronous blocking
import blocked from 'blocked-at'

blocked((time, stack) => {
  console.warn(`Event loop blocked for ${time}ms`)
  console.warn(stack.join('\n'))
}, { threshold: 100 }) // Alert if blocked > 100ms

Node.js Memory Profiling Script

// scripts/memory-profile.mjs
// Run: node --experimental-vm-modules scripts/memory-profile.mjs

import { createRequire } from 'module'
const require = createRequire(import.meta.url)

function formatBytes(bytes) {
  return (bytes / 1024 / 1024).toFixed(2) + ' MB'
}

function measureMemory(label) {
  const mem = process.memoryUsage()
  console.log(`\n[${label}]`)
  console.log(`  RSS:       ${formatBytes(mem.rss)}`)
  console.log(`  Heap Used: ${formatBytes(mem.heapUsed)}`)
  console.log(`  Heap Total:${formatBytes(mem.heapTotal)}`)
  console.log(`  External:  ${formatBytes(mem.external)}`)
  return mem
}

const baseline = measureMemory('Baseline')

// Simulate your operation
for (let i = 0; i < 1000; i++) {
  // Replace with your actual operation
  const result = await someOperation()
}

const after = measureMemory('After 1000 operations')

console.log(`\n[Delta]`)
console.log(`  Heap Used: +${formatBytes(after.heapUsed - baseline.heapUsed)}`)

// If heap keeps growing across GC cycles, you have a leak
global.gc?.() // Run with --expose-gc flag
const afterGC = measureMemory('After GC')
if (afterGC.heapUsed > baseline.heapUsed * 1.1) {
  console.warn('⚠️  Possible memory leak detected (>10% growth after GC)')
}

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Python Profiling

CPU Profiling with py-spy

# Install
pip install py-spy

# Profile a running process (no code changes needed)
py-spy top --pid $(pgrep -f "uvicorn")

# Generate flamegraph SVG
py-spy record -o flamegraph.svg --pid $(pgrep -f "uvicorn") --duration 30

# Profile from the start
py-spy record -o flamegraph.svg -- python -m uvicorn app.main:app

# Open flamegraph.svg in browser — look for wide bars = hot code paths

cProfile for function-level profiling

# scripts/profile_endpoint.py
import cProfile
import pstats
import io
from app.services.task_service import TaskService

def run():
    service = TaskService()
    for _ in range(100):
        service.list_tasks(user_id="user_1", page=1, limit=20)

profiler = cProfile.Profile()
profiler.enable()
run()
profiler.disable()

# Print top 20 functions by cumulative time
stream = io.StringIO()
stats = pstats.Stats(profiler, stream=stream)
stats.sort_stats('cumulative')
stats.print_stats(20)
print(stream.getvalue())

Memory profiling with memory_profiler

# pip install memory-profiler
from memory_profiler import profile

@profile
def my_function():
    # Function to profile
    data = load_large_dataset()
    result = process(data)
    return result

# Run with line-by-line memory tracking
python -m memory_profiler scripts/profile_function.py

# Output:
# Line #    Mem usage    Increment   Line Contents
# ================================================
#     10   45.3 MiB   45.3 MiB   def my_function():
#     11   78.1 MiB   32.8 MiB       data = load_large_dataset()
#     12  156.2 MiB   78.1 MiB       result = process(data)

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Go Profiling with pprof

// main.go — add pprof endpoints
import _ "net/http/pprof"
import "net/http"

func main() {
    // pprof endpoints at /debug/pprof/
    go func() {
        log.Println(http.ListenAndServe(":6060", nil))
    }()
    // ... rest of your app
}

# CPU profile (30s)
go tool pprof -http=:8080 http://localhost:6060/debug/pprof/profile?seconds=30

# Memory profile
go tool pprof -http=:8080 http://localhost:6060/debug/pprof/heap

# Goroutine leak detection
curl http://localhost:6060/debug/pprof/goroutine?debug=1

# In pprof UI: "Flame Graph" view → find the tallest bars

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Bundle Size Analysis

Next.js Bundle Analyzer

# Install
pnpm add -D @next/bundle-analyzer

# next.config.js
const withBundleAnalyzer = require('@next/bundle-analyzer')({
  enabled: process.env.ANALYZE === 'true',
})
module.exports = withBundleAnalyzer({})

# Run analyzer
ANALYZE=true pnpm build
# Opens browser with treemap of bundle

What to look for

# Find the largest chunks
pnpm build 2>&1 | grep -E "^\s+(λ|○|●)" | sort -k4 -rh | head -20

# Check if a specific package is too large
# Visit: https://bundlephobia.com/package/moment@2.29.4
# moment: 67.9kB gzipped → replace with date-fns (13.8kB) or dayjs (6.9kB)

# Find duplicate packages
pnpm dedupe --check

# Visualize what's in a chunk
npx source-map-explorer .next/static/chunks/*.js

Common bundle wins

// Before: import entire lodash
import _ from 'lodash'  // 71kB

// After: import only what you need
import debounce from 'lodash/debounce'  // 2kB

// Before: moment.js
import moment from 'moment'  // 67kB

// After: dayjs
import dayjs from 'dayjs'  // 7kB

// Before: static import (always in bundle)
import HeavyChart from '@/components/HeavyChart'

// After: dynamic import (loaded on demand)
const HeavyChart = dynamic(() => import('@/components/HeavyChart'), {
  loading: () => <Skeleton />,
})

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Database Query Optimization

Find slow queries

-- PostgreSQL: enable pg_stat_statements
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;

-- Top 20 slowest queries
SELECT
  round(mean_exec_time::numeric, 2) AS mean_ms,
  calls,
  round(total_exec_time::numeric, 2) AS total_ms,
  round(stddev_exec_time::numeric, 2) AS stddev_ms,
  left(query, 80) AS query
FROM pg_stat_statements
WHERE calls > 10
ORDER BY mean_exec_time DESC
LIMIT 20;

-- Reset stats
SELECT pg_stat_statements_reset();

# MySQL slow query log
mysql -e "SET GLOBAL slow_query_log = 'ON'; SET GLOBAL long_query_time = 0.1;"
tail -f /var/log/mysql/slow-query.log

EXPLAIN ANALYZE

-- Always use EXPLAIN (ANALYZE, BUFFERS) for real timing
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT t.*, u.name as assignee_name
FROM tasks t
LEFT JOIN users u ON u.id = t.assignee_id
WHERE t.project_id = 'proj_123'
  AND t.deleted_at IS NULL
ORDER BY t.created_at DESC
LIMIT 20;

-- Look for:
-- Seq Scan on large table → needs index
-- Nested Loop with high rows → N+1, consider JOIN or batch
-- Sort → can index handle the sort?
-- Hash Join → fine for moderate sizes

Detect N+1 Queries

// Add query logging in dev
import { db } from './client'

// Drizzle: enable logging
const db = drizzle(pool, { logger: true })

// Or use a query counter middleware
let queryCount = 0
db.$on('query', () => queryCount++)

// In tests:
queryCount = 0
const tasks = await getTasksWithAssignees(projectId)
expect(queryCount).toBe(1)  // Fail if it's 21 (1 + 20 N+1s)

# Django: detect N+1 with django-silk or nplusone
from nplusone.ext.django.middleware import NPlusOneMiddleware
MIDDLEWARE = ['nplusone.ext.django.middleware.NPlusOneMiddleware']
NPLUSONE_RAISE = True  # Raise exception on N+1 in tests

Fix N+1 — Before/After

// Before: N+1 (1 query for tasks + N queries for assignees)
const tasks = await db.select().from(tasksTable)
for (const task of tasks) {
  task.assignee = await db.select().from(usersTable)
    .where(eq(usersTable.id, task.assigneeId))
    .then(r => r[0])
}

// After: 1 query with JOIN
const tasks = await db
  .select({
    id: tasksTable.id,
    title: tasksTable.title,
    assigneeName: usersTable.name,
    assigneeEmail: usersTable.email,
  })
  .from(tasksTable)
  .leftJoin(usersTable, eq(usersTable.id, tasksTable.assigneeId))
  .where(eq(tasksTable.projectId, projectId))

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Load Testing with k6

// tests/load/api-load-test.js
import http from 'k6/http'
import { check, sleep } from 'k6'
import { Rate, Trend } from 'k6/metrics'

const errorRate = new Rate('errors')
const taskListDuration = new Trend('task_list_duration')

export const options = {
  stages: [
    { duration: '30s', target: 10 },   // Ramp up to 10 VUs
    { duration: '1m',  target: 50 },   // Ramp to 50 VUs
    { duration: '2m',  target: 50 },   // Sustain 50 VUs
    { duration: '30s', target: 100 },  // Spike to 100 VUs
    { duration: '1m',  target: 50 },   // Back to 50
    { duration: '30s', target: 0 },    // Ramp down
  ],
  thresholds: {
    http_req_duration: ['p(95)<500'],   // 95% of requests < 500ms
    http_req_duration: ['p(99)<1000'],  // 99% < 1s
    errors: ['rate<0.01'],              // Error rate < 1%
    task_list_duration: ['p(95)<200'],  // Task list specifically < 200ms
  },
}

const BASE_URL = __ENV.BASE_URL || 'http://localhost:3000'

export function setup() {
  // Get auth token once
  const loginRes = http.post(`${BASE_URL}/api/auth/login`, JSON.stringify({
    email: 'loadtest@example.com',
    password: 'loadtest123',
  }), { headers: { 'Content-Type': 'application/json' } })
  
  return { token: loginRes.json('token') }
}

export default function(data) {
  const headers = {
    'Authorization': `Bearer ${data.token}`,
    'Content-Type': 'application/json',
  }
  
  // Scenario 1: List tasks
  const start = Date.now()
  const listRes = http.get(`${BASE_URL}/api/tasks?limit=20`, { headers })
  taskListDuration.add(Date.now() - start)
  
  check(listRes, {
    'list tasks: status 200': (r) => r.status === 200,
    'list tasks: has items': (r) => r.json('items') !== undefined,
  }) || errorRate.add(1)
  
  sleep(0.5)
  
  // Scenario 2: Create task
  const createRes = http.post(
    `${BASE_URL}/api/tasks`,
    JSON.stringify({ title: `Load test task ${Date.now()}`, priority: 'medium' }),
    { headers }
  )
  
  check(createRes, {
    'create task: status 201': (r) => r.status === 201,
  }) || errorRate.add(1)
  
  sleep(1)
}

export function teardown(data) {
  // Cleanup: delete load test tasks
}

# Run load test
k6 run tests/load/api-load-test.js \
  --env BASE_URL=https://staging.myapp.com

# With Grafana output
k6 run --out influxdb=http://localhost:8086/k6 tests/load/api-load-test.js

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