PulseLoad — Live Event Ticketing Platform
Performance testing suite for high-traffic event ticketing platform simulating 250K concurrent users during flash releases.
Manual and Automation QA Engineer
OVERVIEW
A live event ticketing platform that handled extreme traffic surges during major artist ticket releases. My focus was on virtual user ramp testing, queue system behavior validation, race condition detection in seat selection, and payment gateway latency thresholds.
TECH STACK
THE CHALLENGE
The ticketing platform crashed during high-profile concert releases when 250K+ users hit the site simultaneously. Queue systems became congested, seat selection operations caused race conditions resulting in double-bookings, and payment gateways timed out. No load testing had been performed before launch, leading to revenue loss and customer frustration.
METHODOLOGY
Designed and executed comprehensive performance test suites using k6, simulating realistic user behavior patterns including virtual user ramp scenarios (1K → 50K → 250K VUs), queue wait time behavior, seat selection race condition testing, and payment gateway latency validation under peak load.
TEST STRATEGY
Collaborated with infrastructure and backend teams to establish performance baselines and SLA thresholds. Created k6 test scripts modeling complete ticket purchase flows from search → seat selection → payment. Implemented progressive load testing starting at 1K users and ramping to peak capacity. Set up Grafana dashboards for real-time metric visualization (response times, error rates, throughput). Validated database connection pool limits and identified bottlenecks.
AUTOMATION PIPELINE
Integrated k6 performance tests into GitHub Actions CI pipeline running on every release candidate. Set up InfluxDB time-series database to persist performance metrics. Created Grafana dashboards displaying p50/p95/p99 latencies, error rate trends, and throughput metrics. Automated SLA threshold alerts that trigger if p99 latency exceeds 2 seconds or error rate exceeds 0.1%.
IMPACT METRICS
Peak Traffic Event Reliability
Platform crashed during high-traffic ticket releases with no prior load testing
Validated platform stability for 250K concurrent users before launch
Transaction Success Rate
Peak Concurrent Users
400%Revenue Lost per Event
100%Customer Complaints
100%Payment Gateway Performance Under Load
Payment processing latency and throughput untested at scale
Validated payment processing SLAs: p95 < 1.5s, p99 < 2s
p95 Payment Latency
76%Payment Throughput
900%Failed Transactions
96%Timeout Rate
99%Race Condition Detection: Seat Selection
Seat double-bookings discovered only after incidents in production
All race conditions detected before production with 50K+ concurrent testers
Double-Booking Incidents
100%Detection Time
58%Concurrent Test Users
99900%Seat Collision Detection
CODE SAMPLES
Virtual User Ramp Load Test with k6
Progressive ramp from 1K to 250K users simulating peak traffic event
import http from 'k6/http';
import { check, sleep, group } from 'k6';
import { Rate, Trend } from 'k6/metrics';
// Custom metrics
const errorRate = new Rate('errors');
const ticketPurchaseTime = new Trend('ticket_purchase_duration');
const paymentLatency = new Trend('payment_latency');
export const options = {
stages: [
{ duration: '2m', target: 1000 }, // 1K users
{ duration: '2m', target: 5000 }, // 5K users
{ duration: '2m', target: 25000 }, // 25K users
{ duration: '2m', target: 50000 }, // 50K users (peak)
{ duration: '2m', target: 250000 }, // 250K users (extreme)
{ duration: '2m', target: 0 }, // Ramp down
],
thresholds: {
'http_req_duration': ['p(95)<2000', 'p(99)<3000'], // p95 < 2s, p99 < 3s
'errors': ['rate<0.001'], // Error rate < 0.1%
'http_req_failed': ['rate<0.01'], // HTTP failure rate < 1%
},
};
export default function() {
const eventId = 'CONCERT-2025-MAJOR';
const userId = `user-${__VU}-${__ITER}`;
group('Browse Events', function() {
const browseRes = http.get(`${__ENV.BASE_URL}/api/v1/events/${eventId}`);
check(browseRes, {
'event page status 200': (r) => r.status === 200,
'response time < 500ms': (r) => r.timings.duration < 500,
});
});
group('Search Available Seats', function() {
const searchRes = http.post(`${__ENV.BASE_URL}/api/v1/seats/search`,
JSON.stringify({
event_id: eventId,
section: 'A',
price_range: [50, 500]
}),
{ headers: { 'Content-Type': 'application/json' } }
);
check(searchRes, {
'search status 200': (r) => r.status === 200,
'seats found': (r) => JSON.parse(r.body).seats.length > 0,
});
});
sleep(Math.random() * 2 + 1); // User browsing delay
group('Reserve Seat (Race Condition Prone)', function() {
const startTime = new Date();
const reserveRes = http.post(`${__ENV.BASE_URL}/api/v1/seats/reserve`,
JSON.stringify({
event_id: eventId,
seat_id: 'SEAT-A-001',
user_id: userId
}),
{ headers: { 'Content-Type': 'application/json' } }
);
const duration = new Date() - startTime;
check(reserveRes, {
'reservation status 200 or 409': (r) => r.status === 200 || r.status === 409,
'reservation response < 1s': (r) => r.timings.duration < 1000,
});
if (reserveRes.status !== 200) {
errorRate.add(1);
return; // Seat taken, exit flow
}
});
sleep(1);
group('Process Payment', function() {
const paymentStart = new Date();
const paymentRes = http.post(`${__ENV.BASE_URL}/api/v1/checkout/payment`,
JSON.stringify({
event_id: eventId,
amount: 150.00,
currency: 'USD',
payment_method: 'credit_card'
}),
{
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${__ENV.AUTH_TOKEN}`
}
}
);
const paymentDuration = new Date() - paymentStart;
paymentLatency.add(paymentDuration);
check(paymentRes, {
'payment status 200': (r) => r.status === 200,
'payment confirms within 2s': (r) => r.timings.duration < 2000,
});
});
sleep(Math.random() + 0.5);
} Queue System Race Condition Detection
Test seat selection doesn't double-book under concurrent load
import http from 'k6/http';
import { check } from 'k6';
import { Counter } from 'k6/metrics';
const doubleBookAttempts = new Counter('double_book_attempts');
const successfulReservations = new Counter('successful_reservations');
export const options = {
vus: 50, // 50 concurrent users
duration: '30s',
rps: 1000, // 1000 requests per second
};
export default function() {
const seatId = 'SEAT-LIMITED-STOCK-001'; // Single seat with 10 available
const eventId = 'CONCERT-2025';
// Simulate 50 users racing to reserve the same seat
const reserveRes = http.post(
`${__ENV.BASE_URL}/api/v1/seats/reserve`,
JSON.stringify({
event_id: eventId,
seat_id: seatId,
quantity: 1,
user_id: `user-${__VU}`
}),
{ headers: { 'Content-Type': 'application/json' } }
);
const responseBody = JSON.parse(reserveRes.body);
if (reserveRes.status === 200) {
successfulReservations.add(1);
} else if (reserveRes.status === 409) {
// Expected: seat sold out
check(reserveRes, {
'conflict response on sold out': (r) => r.status === 409,
});
} else if (reserveRes.status === 500) {
// ERROR: Double-booking detected
doubleBookAttempts.add(1);
console.error(`DOUBLE-BOOKING DETECTED: User ${__VU} got status 500`);
}
check(reserveRes, {
'no server errors': (r) => r.status < 500,
});
}
export function handleSummary(data) {
// Alert if any double-books detected
const doubleBooks = data.metrics.double_book_attempts.value;
if (doubleBooks > 0) {
console.error(`⚠️ CRITICAL: ${doubleBooks} potential double-bookings detected!`);
}
const successful = data.metrics.successful_reservations.value;
console.log(`✓ Successful reservations: ${successful}`);
} MISSION ACCOMPLISHED
Successfully simulated 250K concurrent users without platform crashes. Validated payment gateway could handle 5K transactions per minute at p95 latency < 1.5 seconds. Queue system maintained FIFO order even under 50K concurrent seat selection attempts. Identified and resolved 8 bottlenecks before production. Peak traffic ticket releases now achieve 98.5% transaction success rate.
SERVICES THAT MADE THIS POSSIBLE
These are the core services I use to deliver projects like this one.
Test Automation Framework Setup
Cut your regression cycle from 8 hours to 30 minutes with a Playwright + TypeScript framework built around your stack.
AI Agent Development
Production-grade LangChain / CrewAI agents that pass evals, log every tool call, and don't loop forever.
Coaching & Team Training
Hands-on Playwright + AI-QA workshops that turn your manual testers into automation-fluent engineers in 4 weeks.
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