Performance Testing

<default_to_action> When testing performance or planning load tests:

1. DEFINE SLOs: p95 response time, throughput, error rate targets
2. IDENTIFY critical paths: revenue flows, high-traffic pages, key APIs
3. CREATE realistic scenarios: user journeys, think time, varied data
4. EXECUTE with monitoring: CPU, memory, DB queries, network
5. ANALYZE bottlenecks and fix before production

Quick Test Type Selection:

• Expected load validation → Load testing
• Find breaking point → Stress testing
• Sudden traffic spike → Spike testing
• Memory leaks, resource exhaustion → Endurance/soak testing
• Horizontal/vertical scaling → Scalability testing

Critical Success Factors:

• Performance is a feature, not an afterthought
• Test early and often, not just before release
• Focus on user-impacting bottlenecks </default_to_action>

Quick Reference Card

When to Use

• Before major releases
• After infrastructure changes
• Before scaling events (Black Friday)
• When setting SLAs/SLOs

Test Types

Key Metrics

Tools

• k6: Modern, JS-based, CI/CD friendly
• JMeter: Enterprise, feature-rich
• Artillery: Simple YAML configs
• Gatling: Scala, great reporting

Agent Coordination

• qe-performance-tester: Load test orchestration
• qe-quality-analyzer: Results analysis
• qe-production-intelligence: Production comparison

Defining SLOs

Bad: "The system should be fast" Good: "p95 response time < 200ms under 1,000 concurrent users"

export const options = {
  thresholds: {
    http_req_duration: ['p(95)<200'],  // 95% < 200ms
    http_req_failed: ['rate<0.01'],     // < 1% failures
  },
};

Realistic Scenarios

Bad: Every user hits homepage repeatedly Good: Model actual user behavior

// Realistic distribution
// 40% browse, 30% search, 20% details, 10% checkout
export default function () {
  const action = Math.random();
  if (action < 0.4) browse();
  else if (action < 0.7) search();
  else if (action < 0.9) viewProduct();
  else checkout();

  sleep(randomInt(1, 5)); // Think time
}

Common Bottlenecks

Database

Symptoms: Slow queries under load, connection pool exhaustion Fixes: Add indexes, optimize N+1 queries, increase pool size, read replicas

N+1 Queries

// BAD: 100 orders = 101 queries
const orders = await Order.findAll();
for (const order of orders) {
  const customer = await Customer.findById(order.customerId);
}

// GOOD: 1 query
const orders = await Order.findAll({ include: [Customer] });

Synchronous Processing

Problem: Blocking operations in request path (sending email during checkout) Fix: Use message queues, process async, return immediately

Memory Leaks

Detection: Endurance testing, memory profiling Common causes: Event listeners not cleaned, caches without eviction

External Dependencies

Solutions: Aggressive timeouts, circuit breakers, caching, graceful degradation

k6 CI/CD Example

// performance-test.js
import http from 'k6/http';
import { check, sleep } from 'k6';

export const options = {
  stages: [
    { duration: '1m', target: 50 },   // Ramp up
    { duration: '3m', target: 50 },   // Steady
    { duration: '1m', target: 0 },    // Ramp down
  ],
  thresholds: {
    http_req_duration: ['p(95)<200'],
    http_req_failed: ['rate<0.01'],
  },
};

export default function () {
  const res = http.get('https://api.example.com/products');
  check(res, {
    'status is 200': (r) => r.status === 200,
    'response time < 200ms': (r) => r.timings.duration < 200,
  });
  sleep(1);
}

# GitHub Actions
- name: Run k6 test
  uses: grafana/k6-action@v0.3.0
  with:
    filename: performance-test.js

Analyzing Results

Good Results

Load: 1,000 users | p95: 180ms | Throughput: 5,000 req/s
Error rate: 0.05% | CPU: 65% | Memory: 70%

Problems

Load: 1,000 users | p95: 3,500ms ❌ | Throughput: 500 req/s ❌
Error rate: 5% ❌ | CPU: 95% ❌ | Memory: 90% ❌

Root Cause Analysis

1. Correlate metrics: When response time spikes, what changes?
2. Check logs: Errors, warnings, slow queries
3. Profile code: Where is time spent?
4. Monitor resources: CPU, memory, disk
5. Trace requests: End-to-end flow

Anti-Patterns

Agent-Assisted Performance Testing

// Comprehensive load test
await Task("Load Test", {
  target: 'https://api.example.com',
  scenarios: {
    checkout: { vus: 100, duration: '5m' },
    search: { vus: 200, duration: '5m' },
    browse: { vus: 500, duration: '5m' }
  },
  thresholds: {
    'http_req_duration': ['p(95)<200'],
    'http_req_failed': ['rate<0.01']
  }
}, "qe-performance-tester");

// Bottleneck analysis
await Task("Analyze Bottlenecks", {
  testResults: perfTest,
  metrics: ['cpu', 'memory', 'db_queries', 'network']
}, "qe-performance-tester");

// CI integration
await Task("CI Performance Gate", {
  mode: 'smoke',
  duration: '1m',
  vus: 10,
  failOn: { 'p95_response_time': 300, 'error_rate': 0.01 }
}, "qe-performance-tester");

Agent Coordination Hints

Memory Namespace

aqe/performance/
├── results/*       - Test execution results
├── baselines/*     - Performance baselines
├── bottlenecks/*   - Identified bottlenecks
└── trends/*        - Historical trends

Fleet Coordination

const perfFleet = await FleetManager.coordinate({
  strategy: 'performance-testing',
  agents: [
    'qe-performance-tester',
    'qe-quality-analyzer',
    'qe-production-intelligence',
    'qe-deployment-readiness'
  ],
  topology: 'sequential'
});

Pre-Production Checklist

• Load test passed (expected traffic)
• Stress test passed (2-3x expected)
• Spike test passed (sudden surge)
• Endurance test passed (24+ hours)
• Database indexes in place
• Caching configured
• Monitoring and alerting set up
• Performance baseline established

Related Skills

• agentic-quality-engineering - Agent coordination
• api-testing-patterns - API performance
• chaos-engineering-resilience - Resilience testing

Remember

Performance is a feature: Test it like functionality Test continuously: Not just before launch Monitor production: Synthetic + real user monitoring Fix what matters: Focus on user-impacting bottlenecks Trend over time: Catch degradation early

With Agents: Agents automate load testing, analyze bottlenecks, and compare with production. Use agents to maintain performance at scale.

Run History

After each performance test run, append results to run-history.json in this skill directory:

node -e "
const fs = require('fs');
const h = JSON.parse(fs.readFileSync('.claude/skills/performance-testing/run-history.json'));
h.runs.push({date: new Date().toISOString().split('T')[0], scenario: 'load', p95_ms: P95, throughput_rps: RPS, error_rate_pct: ERR});
fs.writeFileSync('.claude/skills/performance-testing/run-history.json', JSON.stringify(h, null, 2));
"

Read run-history.json before each run — compare with baselines. Alert if p95 increases >20% from baseline.

Gotchas

• k6 scripts generated by agent often hardcode base URLs — use environment variables for portability
• Load tests in containers hit resource limits before app limits — ensure container has 2x the resources of target
• Agent forgets to include think time between requests — without it, load is unrealistically bursty
• P95 vs P99 matters — agent defaults to averages which hide tail latency problems
• Baseline comparison requires consistent environment — CI runner variance can cause 20%+ noise