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# How do you performance-test a high-demand ticket onsale?

> Load tests, failure scenarios, metrics and launch readiness for high-demand ticket sales.

**Performance-testing a high-demand ticket onsale means rehearsing the full purchase workflow at realistic burst traffic and failure levels, not simply measuring how many homepage requests a website serves.** Teams should simulate queue admission, event discovery, live inventory reads, basket reservations, checkout, payments and ticket confirmation while checking that no capacity is oversold.

## What should a realistic test cover?

Model the expected arrival curve, including presale-code traffic, announcement peaks, customer refreshes and retries. Represent different customer journeys: browsing without buying, seat selection, multi-ticket baskets and checkout abandonment. Test with appropriate isolated payment environments and synthetic tickets; never run uncontrolled production purchases to reach a target.

| Layer                        | What to observe                                                |
| ---------------------------- | -------------------------------------------------------------- |
| **Edge and queue**           | Arrival rate, admitted users, queue delay                      |
| **Event and inventory APIs** | Latency by percentile, stale reads, throttling                 |
| **Reservations**             | Lock contention, hold expiry, duplicate reservation prevention |
| **Checkout**                 | Payment handoff, failed retries, completed orders              |
| **Fulfilment**               | Ticket issuance, notifications and delayed jobs                |
| **Recovery**                 | Rollback, replay, reconciliation and manual intervention       |

## Which numbers matter?

Track successful orders per minute, p95/p99 latency, error rates, queue-to-checkout completion, reservation collision rates and the time required for dependent systems to recover. Average response time hides the slowest customers. Test both the target peak and a higher stress level to learn the failure mode.

## What should happen when dependencies fail?

Rehearse a payment provider timeout, delayed message delivery, cache failures, scanner sync latency and an operator pausing admission. Retries need idempotency so that a customer clicking “pay” twice does not create duplicate financial transactions or duplicate ticket entitlements. Verify alert thresholds and named incident roles during a tabletop exercise.

**Example:** If the queue admits 1,000 buyers per minute but checkout can process only 500 successfully, the queue is not protecting the application; it is shifting congestion into basket and payment workflows.

## When is a system ready?

Readiness requires passing business invariants (never oversell, never issue a ticket without a valid order), acceptable customer experience and an agreed recovery plan. Document assumptions: forecast demand is not observed demand, and synthetic testing cannot perfectly reproduce customers, payment banks or bot traffic.

## Related explanations

#### [How do you design ticketing integrations for high-demand onsales?](/scale-reliability/how-do-you-design-ticketing-integrations-for-high-demand-onsales)

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#### [How can ticketing checkout remain responsive under heavy demand?](/scale-reliability/how-can-ticketing-checkout-remain-responsive-under-heavy-demand)

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#### [How do you evaluate ticketing platform reliability and uptime?](/scale-reliability/how-do-you-evaluate-ticketing-platform-reliability-and-uptime)

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#### [What is an onsale in ticketing?](/scale-reliability/what-is-an-onsale-in-ticketing)

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