performance-profiler

name	performance-profiler
description	Systematic performance profiling for Node.js, Python, and Go applications. Covers CPU flamegraphs, memory leak detection, bundle analysis, database query optimization, N+1 detection, load testing with k6, and before/after measurement methodology. Use when diagnosing slow endpoints, memory growth, large bundles, or preparing for traffic spikes.
license	MIT + Commons Clause
metadata	{"version":"1.0.0","author":"borghei","category":"engineering","domain":"performance-engineering","tier":"POWERFUL","updated":"2026-03-09T00:00:00.000Z","frameworks":"clinic, py-spy, pprof, k6, webpack-bundle-analyzer"}

Performance Profiler

Tier: POWERFUL Category: Engineering / Performance Maintainer: Claude Skills Team

Overview

Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU bottlenecks with flamegraphs, detects memory leaks with heap snapshots, analyzes bundle sizes, optimizes database queries, detects N+1 patterns, and runs load tests with k6 and Artillery. Enforces a measure-first methodology: establish baseline, identify bottleneck, fix, and verify improvement.

Keywords

performance profiling, flamegraph, memory leak, bundle analysis, N+1 queries, load testing, k6, latency, P99, CPU profiling, heap snapshot, database optimization

Golden Rule: Measure First

WRONG: "I think the N+1 query is slow, let me fix it"
RIGHT: Profile → Confirm bottleneck → Fix → Measure again → Verify improvement

Every optimization must have:
1. Baseline metrics (before)
2. Profiler evidence (what's actually slow)
3. The fix
4. Post-fix metrics (after)
5. Delta calculation (improvement %)

Core Capabilities

1. CPU Profiling

• Node.js: Clinic.js flamegraphs, V8 CPU profiles
• Python: py-spy flamegraphs, cProfile, scalene
• Go: pprof CPU profiles, trace visualization
• Browser: Chrome DevTools Performance panel

2. Memory Profiling

• Heap snapshots and comparison (before/after)
• Garbage collection pressure analysis
• Memory leak detection patterns
• Retained object graph analysis

3. Database Optimization

• EXPLAIN ANALYZE for query plan analysis
• N+1 query detection and batching
• Slow query log analysis
• Missing index identification
• Connection pool sizing

4. Bundle Analysis

• webpack-bundle-analyzer visualization
• Next.js bundle analyzer
• Tree-shaking effectiveness
• Dynamic import opportunities
• Heavy dependency identification

5. Load Testing

• k6 scripts with ramp-up patterns
• SLA threshold enforcement in CI
• Latency percentile tracking (P50, P95, P99)
• Concurrent user simulation

When to Use

• App is slow and you do not 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
• After a dependency upgrade to verify no regressions

Node.js CPU Profiling

Method 1: Clinic.js Flamegraph

# Install
npm install -g clinic

# Generate flamegraph (starts server, applies load, generates HTML report)
clinic flame -- node server.js

# With specific load profile
clinic flame --autocannon [ /api/endpoint -c 10 -d 30 ] -- node server.js

# Analyze specific scenario
clinic flame --on-port 'autocannon -c 50 -d 60 http://localhost:$PORT/api/heavy-endpoint' -- node server.js

Method 2: V8 CPU Profile

# Start Node with inspector
node --inspect server.js

# Or profile on demand
node --cpu-prof --cpu-prof-dir=./profiles server.js
# Load the .cpuprofile file in Chrome DevTools > Performance

# Programmatic profiling of a specific function
const { Session } = require('inspector');
const session = new Session();
session.connect();

session.post('Profiler.enable', () => {
  session.post('Profiler.start', () => {
    // Run the code you want to profile
    runHeavyOperation();

    session.post('Profiler.stop', (err, { profile }) => {
      require('fs').writeFileSync('profile.cpuprofile', JSON.stringify(profile));
    });
  });
});

Memory Leak Detection

Node.js Heap Snapshots

// Take heap snapshots programmatically
const v8 = require('v8');
const fs = require('fs');

function takeHeapSnapshot(label) {
  const snapshotPath = `heap-${label}-${Date.now()}.heapsnapshot`;
  const stream = v8.writeHeapSnapshot(snapshotPath);
  console.log(`Heap snapshot written to: ${snapshotPath}`);
  return snapshotPath;
}

// Leak detection pattern: compare two snapshots
// 1. Take snapshot at startup
takeHeapSnapshot('baseline');

// 2. Run operations that you suspect leak
// ... process 1000 requests ...

// 3. Force GC and take another snapshot
if (global.gc) global.gc(); // requires --expose-gc flag
takeHeapSnapshot('after-load');

// Load both .heapsnapshot files in Chrome DevTools > Memory
// Use "Comparison" view to find objects that grew

Python Memory Profiling

# Install tracemalloc-based profiler
pip install memray

# Profile a script
memray run my_script.py
memray flamegraph memray-output.bin -o flamegraph.html

# Profile a specific function
python -c "
import tracemalloc
tracemalloc.start()

# Run your code
from my_module import heavy_function
heavy_function()

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')
print('Top 10 memory allocations:')
for stat in top_stats[:10]:
    print(stat)
"

Database Query Optimization

EXPLAIN ANALYZE Workflow

-- Step 1: Get the actual execution plan (not just estimated)
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT t.*, p.name as project_name
FROM tasks t
JOIN projects p ON p.id = t.project_id
WHERE p.workspace_id = 'ws_abc123'
  AND t.status = 'in_progress'
  AND t.deleted_at IS NULL
ORDER BY t.updated_at DESC
LIMIT 20;

-- What to look for in the output:
-- Seq Scan on tasks  → MISSING INDEX (should be Index Scan)
-- Rows Removed by Filter: 99000  → INDEX NOT SELECTIVE ENOUGH
-- Sort Method: external merge  → NOT ENOUGH work_mem
-- Nested Loop with inner Seq Scan  → MISSING INDEX ON JOIN COLUMN
-- Actual rows=1000 vs estimated rows=1  → STALE STATISTICS (run ANALYZE)

N+1 Query Detection

// PROBLEM: N+1 query pattern
async function getProjectsWithTasks(workspaceId: string) {
  const projects = await db.query.projects.findMany({
    where: eq(projects.workspaceId, workspaceId),
  });

  // This executes N additional queries (one per project)
  for (const project of projects) {
    project.tasks = await db.query.tasks.findMany({
      where: eq(tasks.projectId, project.id),
    });
  }
  return projects;
}
// Total queries: 1 + N (where N = number of projects)

// FIX: Single query with JOIN or relation loading
async function getProjectsWithTasks(workspaceId: string) {
  return db.query.projects.findMany({
    where: eq(projects.workspaceId, workspaceId),
    with: {
      tasks: true,  // Drizzle generates a single JOIN or subquery
    },
  });
}
// Total queries: 1-2 (depending on ORM strategy)

N+1 Detection Script

# Log query count per request (add to middleware)
# Node.js with Drizzle:
let queryCount = 0;
const originalQuery = db.execute;
db.execute = (...args) => { queryCount++; return originalQuery.apply(db, args); };

// After request completes:
if (queryCount > 10) {
  console.warn(`N+1 ALERT: ${req.method} ${req.path} executed ${queryCount} queries`);
}

Bundle 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(nextConfig);

# Run analysis
ANALYZE=true pnpm build
# Opens browser with interactive treemap

Quick Bundle Size Check

# Check what you're shipping
npx source-map-explorer .next/static/chunks/*.js

# Size of individual imports
npx import-cost  # VS Code extension for inline size

# Find heavy dependencies
npx depcheck --json | jq '.dependencies'
npx bundlephobia-cli <package-name>

Common Bundle Wins

Before	After	Savings
import _ from 'lodash'	import groupBy from 'lodash/groupBy'	~70KB
import moment from 'moment'	import { format } from 'date-fns'	~60KB
import { icons } from 'lucide-react'	import { Search } from 'lucide-react'	~50KB
Static import of heavy component	dynamic(() => import('./HeavyChart'))	Deferred
All routes in one chunk	Code splitting per route (automatic in Next.js)	Per-route

Load Testing with k6

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

const apiLatency = new Trend('api_latency')
const errorRate = new Rate('errors')

export const options = {
  stages: [
    { duration: '1m', target: 20 },    // ramp up
    { duration: '3m', target: 100 },   // sustain
    { duration: '1m', target: 0 },     // ramp down
  ],
  thresholds: {
    http_req_duration: ['p(95)<200', 'p(99)<500'],
    errors: ['rate<0.01'],
    api_latency: ['p(95)<150'],
  },
}

export default function () {
  const res = http.get(`${__ENV.BASE_URL}/api/v1/projects?limit=20`, {
    : { :  },
  })

  apiLatency.(res..)
  (res, {
    :  r. === ,
    :  .(r.). !== ,
  }) || errorRate.()

  ()
}

# Run locally
k6 run load-test.k6.js -e BASE_URL=http://localhost:3000 -e TOKEN=$TOKEN

# Run with cloud reporting
k6 cloud load-test.k6.js

Before/After Measurement Template

## Performance Optimization: [What You Fixed]

**Date:** YYYY-MM-DD
**Ticket:** PROJ-123

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

### Root Cause
[What the profiler revealed — include flamegraph link or screenshot]

### Baseline (Before)
| Metric | Value |
|--------|-------|
| P50 latency | XXms |
| P95 latency | XXms |
| P99 latency | XXms |
| Throughput (RPS) | XX |
| DB queries/request | XX |
| Bundle size | XXkB |

### Fix Applied
[Brief description + link to PR]

### After
| Metric | Before | After | Delta |
|--------|--------|-------|-------|
| P50 | XXms | XXms | -XX% |
| P95 | XXms | XXms | -XX% |
| P99 | XXms | XXms | -XX% |
| RPS | XX | XX | +XX% |
| DB queries/req | XX | XX | -XX% |

### Verification
[Link to k6 output, CI run, or monitoring dashboard]

Quick-Win Optimization Checklist

DATABASE
[ ] Missing indexes on WHERE/ORDER BY columns
[ ] N+1 queries (check query count per request)
[ ] SELECT * when only 2-3 columns needed
[ ] No LIMIT on unbounded queries
[ ] Missing connection pool (new connection per request)
[ ] Stale statistics (run ANALYZE on busy tables)

NODE.JS
[ ] Sync I/O (fs.readFileSync) in request handlers
[ ] JSON.parse/stringify of large objects in hot loops
[ ] Missing response compression (gzip/brotli)
[ ] Dependencies loaded inside request handlers (move to module level)
[ ] Sequential awaits that could be Promise.all

BUNDLE
[ ] Full lodash/moment import instead of specific functions
[ ] Static imports of heavy components (use dynamic import)
[ ] Images not optimized / not using next/image
[ ] No code splitting on routes

API
[ ] No pagination on list endpoints
[ ] No Cache-Control headers on stable responses
[ ] Serial fetches that could run in parallel
[ ] Fetching related data in loops instead of JOINs

Common Pitfalls

• Optimizing without measuring — you will optimize the wrong thing
• Testing with development data — 10 rows in dev vs millions in prod reveals different bottlenecks
• Ignoring P99 — P50 can look fine while P99 is catastrophic for some users
• Premature optimization — fix correctness first, then measure and optimize
• Not re-measuring after the fix — always verify the fix actually improved the metrics
• Load testing production — use staging with production-sized data volumes instead

Best Practices

1. Baseline first, always — record P50/P95/P99, RPS, and error rate before touching anything
2. One change at a time — isolate the variable to confirm causation, not correlation
3. Profile with realistic data volumes — performance characteristics change dramatically with scale
4. Set performance budgets — p(95) < 200ms as a CI gate with k6
5. Monitor continuously — add Datadog/Prometheus/Grafana metrics for key code paths
6. Cache aggressively, invalidate precisely — cache is the fastest optimization but hardest to debug
7. Document the win — before/after in the PR description motivates the team and creates institutional knowledge

Troubleshooting

Problem	Cause	Solution
Flamegraph shows only (idle) frames	Profiling during low-load period; no meaningful CPU work captured	Apply realistic load with autocannon or k6 during profiling, target the specific endpoint under investigation
Heap snapshot comparison shows no growth but memory still climbs	Native memory leak outside V8 heap (e.g., native addon, file descriptor leak)	Use process.memoryUsage().rss tracking alongside heap snapshots; profile with Valgrind or memray for native allocations
EXPLAIN ANALYZE shows Index Scan but query is still slow	Index exists but is not selective enough, or query returns too many rows for index to help	Check index selectivity with SELECT count(DISTINCT col)/count(*) FROM table; consider composite index or partial index
k6 load test passes locally but fails in CI	CI runner has limited CPU/memory; network latency differs from local	Run k6 against a dedicated staging environment, not localhost in CI; adjust thresholds for CI-specific baselines
Bundle analyzer shows expected size but app still loads slowly	Large bundle is code-split but critical path has render-blocking resources	Audit the critical rendering path separately with Lighthouse; check for synchronous scripts and unoptimized images
py-spy cannot attach to running process	Insufficient permissions or SIP (System Integrity Protection) on macOS	Run with sudo py-spy record --pid <PID>; on macOS, disable SIP or use --subprocesses flag with a fresh process
N+1 detection middleware reports false positives	Legitimate batch operations trigger high query counts per request	Add endpoint-level allowlists to the detection middleware; distinguish between N+1 patterns and intentional batch queries by checking for repeated identical query templates

Success Criteria

• Baseline coverage: Every optimization PR includes documented before/after metrics with P50, P95, and P99 latency values
• Latency targets met: P95 API response time stays below 200ms and P99 below 500ms as validated by k6 threshold checks in CI
• Memory stability: No heap growth exceeding 10% over a 24-hour soak test under sustained load
• Bundle budget enforced: JavaScript bundle size for initial page load remains under 200kB gzipped, verified by CI gate
• N+1 elimination: Query count per API request stays below 10 for all critical endpoints, validated by request-level query logging
• Load test confidence: Staging load tests demonstrate the system handles 2x expected peak traffic with error rate below 1%
• Regression detection: Performance regressions are caught within one CI cycle, not discovered in production monitoring

Scope & Limitations

This skill covers:

• CPU and memory profiling for Node.js, Python, and Go applications using flamegraphs and heap snapshots
• Database query optimization including EXPLAIN ANALYZE interpretation, N+1 detection, and index recommendations
• Frontend bundle analysis and size reduction strategies for webpack and Next.js projects
• Load testing methodology with k6 including ramp-up patterns, threshold enforcement, and CI integration

This skill does NOT cover:

• Application Performance Monitoring (APM) platform setup and configuration (Datadog, New Relic, Grafana) — see engineering/observability-designer
• Infrastructure-level performance tuning (kernel parameters, network stack, container resource limits) — see engineering/senior-devops
• Security-focused performance concerns such as DDoS mitigation or rate limiting — see engineering/senior-security
• Mobile application profiling (iOS Instruments, Android Profiler) — see engineering/senior-mobile

Integration Points

Skill	Integration	Data Flow
engineering/observability-designer	Performance profiling findings feed into observability dashboard design; alerting thresholds derived from profiling baselines	Profiler baselines and SLA thresholds → Prometheus/Grafana alert rules and dashboard panels
engineering/ci-cd-pipeline-builder	k6 load tests and bundle size checks integrate as CI pipeline gates	k6 threshold configs and bundle budget scripts → CI pipeline stage definitions
engineering/database-designer	Query optimization recommendations inform schema design decisions; index suggestions feed back to schema migrations	EXPLAIN ANALYZE findings and index recommendations → schema migration files and index definitions
engineering/senior-backend	Backend architecture decisions incorporate profiling data; connection pool sizing and caching strategies validated by load tests	Profiling reports and load test results → architecture decision records and implementation guidance
engineering/tech-debt-tracker	Performance regressions and unresolved bottlenecks are tracked as technical debt items with measured impact	Before/after measurement reports and unresolved findings → tech debt backlog with quantified cost
engineering/senior-frontend	Bundle analysis results drive frontend optimization work; code-splitting and lazy-loading decisions backed by profiler data	Bundle analyzer output and Lighthouse scores → frontend optimization tasks and component refactoring plans