Stress testing to determine website load limits

Stress Testing: Determining Load Limits

Stress test intentionally overloads the system beyond normal operation to find the breaking point. Answers questions: at what RPS do errors start rising? How does the system recover after overload? What's the bottleneck—DB, CPU, memory, network?

Methodology

Step 1: Define baseline. Run normal load (50–70% of expected peak) and record metrics: p95 latency, error rate, CPU/memory.

Step 2: Stepwise increase. Raise load in steps of 10–20% every 2–5 minutes. Record point where errors or latency start rising.

Step 3: Find breaking point. Continue until degradation (error rate > 5% or latency > 5x baseline).

Step 4: Recovery. Remove load and observe how quickly the system returns to normal.

k6 Stress Test Scenario

// tests/stress/breaking-point.js
import http from 'k6/http'
import { check, sleep } from 'k6'
import { Rate, Trend, Counter } from 'k6/metrics'

const errorRate = new Rate('errors')
const requestsPerSecond = new Counter('requests_per_second')

export const options = {
  stages: [
    // Warmup to normal traffic
    { duration: '2m',  target: 50 },
    { duration: '3m',  target: 50 },   // baseline level

    // Stepwise increase
    { duration: '2m',  target: 100 },
    { duration: '3m',  target: 100 },

    { duration: '2m',  target: 200 },
    { duration: '3m',  target: 200 },

    { duration: '2m',  target: 400 },
    { duration: '3m',  target: 400 },

    { duration: '2m',  target: 800 },
    { duration: '3m',  target: 800 },

    { duration: '2m',  target: 1600 },
    { duration: '3m',  target: 1600 },

    // Cooldown and observe recovery
    { duration: '5m',  target: 50 },
    { duration: '3m',  target: 0 },
  ],

  // Don't abort on threshold breach—need to see full picture
  thresholds: {
    http_req_duration: [
      { threshold: 'p(95)<2000', abortOnFail: false },
    ],
    errors: [
      { threshold: 'rate<0.1', abortOnFail: false }
    ]
  }
}

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

export default function() {
  const responses = http.batch([
    ['GET', `${BASE_URL}/api/products?limit=20`],
    ['GET', `${BASE_URL}/api/categories`],
  ])

  responses.forEach(r => {
    check(r, { 'status 2xx': (r) => r.status >= 200 && r.status < 300 })
    errorRate.add(r.status >= 400)
  })

  requestsPerSecond.add(2)
  sleep(0.1)
}

export function handleSummary(data) {
  // Find degradation point from collected data
  const stages = analyzeStages(data)
  return {
    'stress-results.json': JSON.stringify(data, null, 2),
    stdout: generateReport(stages)
  }
}

function generateReport(stages) {
  return `
=== STRESS TEST REPORT ===
Breaking Point Analysis:
${stages.map(s => `  VUs: ${s.vus} | p95: ${s.p95}ms | Errors: ${(s.errorRate*100).toFixed(1)}%`).join('\n')}
`
}

Monitoring During Test

Run system metrics collection in parallel:

#!/bin/bash
# scripts/monitor-stress-test.sh

TARGET_HOST="app-server-ip"
INTERVAL=10  # seconds

while true; do
  TIMESTAMP=$(date -u +%Y-%m-%dT%H:%M:%SZ)

  # CPU, Memory, Load Average
  ssh $TARGET_HOST "
    echo -n '$TIMESTAMP '
    echo -n 'cpu:'; top -bn1 | grep 'Cpu(s)' | awk '{print \$2}'; echo -n ' '
    echo -n 'mem:'; free | grep Mem | awk '{print \$3/\$2 * 100}'; echo -n ' '
    echo -n 'load:'; cat /proc/loadavg | awk '{print \$1}'
    echo -n 'conns:'; ss -s | grep -o 'estab [0-9]*' | awk '{print \$2}'
  "

  # PostgreSQL: active queries and locks
  ssh $TARGET_HOST "
    PGPASSWORD=pass psql -U app -d appdb -t -c \"
      SELECT 'active_queries:', count(*) FROM pg_stat_activity
        WHERE state = 'active' AND query NOT LIKE '%pg_stat%';
      SELECT 'long_queries:', count(*) FROM pg_stat_activity
        WHERE state = 'active' AND query_start < NOW() - interval '5 seconds';
      SELECT 'locks:', count(*) FROM pg_locks WHERE NOT granted;
    \"
  "

  sleep $INTERVAL
done | tee stress-monitor.log

Analyzing Results with Prometheus + Grafana

# k6 with Prometheus Remote Write
k6 run \
  -o experimental-prometheus-rw \
  --env K6_PROMETHEUS_RW_SERVER_URL=http://prometheus:9090/api/v1/write \
  --env K6_PROMETHEUS_RW_TREND_AS_NATIVE_HISTOGRAM=true \
  tests/stress/breaking-point.js

# Grafana queries for stress test analysis

# RPS in real time
rate(k6_http_reqs_total[30s])

# Error rate over time (find degradation moment)
rate(k6_http_req_failed_total[30s]) / rate(k6_http_reqs_total[30s])

# p95 latency in real time
histogram_quantile(0.95, rate(k6_http_req_duration_seconds_bucket[30s]))

# Correlation: load vs latency vs errors

Identifying Bottleneck

# analyze_stress_results.py
import json
import pandas as pd

def analyze_breaking_point(results_file):
    with open(results_file) as f:
        data = json.load(f)

    # Extract time series
    metrics = data['metrics']

    analysis = {
        'max_rps_before_errors': find_max_sustainable_rps(metrics),
        'error_threshold_rps': find_error_threshold(metrics),
        'latency_degradation_point': find_latency_degradation(metrics),
        'recovery_time_seconds': find_recovery_time(metrics),
    }

    print("=== Breaking Point Analysis ===")
    print(f"Max sustainable RPS (< 1% errors): {analysis['max_rps_before_errors']}")
    print(f"Error threshold RPS: {analysis['error_threshold_rps']}")
    print(f"p95 > 1s at RPS: {analysis['latency_degradation_point']}")
    print(f"Recovery time after load removal: {analysis['recovery_time_seconds']}s")

    # Recommendations
    if analysis['max_rps_before_errors'] < 100:
        print("\n[!] LOW capacity. Consider: DB connection pooling, caching, horizontal scaling")
    elif analysis['recovery_time_seconds'] > 120:
        print("\n[!] SLOW recovery. Consider: circuit breakers, graceful degradation")

    return analysis

Typical Bottlenecks and Diagnostics

Timeline

Stress test with stepwise load profile, monitoring, and breaking point analysis—2–3 business days.