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Design Ticketmaster (Ticket Booking) at Amazon — System Design Interview Guide

The problem at Amazon

Functional: browse events, view a seat map, hold seats, book and pay, get a confirmation/ticket.
Non-functional: never oversell a seat, holds expire if not paid, on-sales survive huge spikes.
Scale: ~500 events; a popular on-sale sees 100K+ concurrent users for a 10K-seat venue.
Consistency: seat inventory must be strongly consistent — overselling a seat is unacceptable.

Out of scope (covered in detail below)

Refunds, transfers, dynamic pricing, resales, and seat upgrades — deferred and discussed in the dedicated section.

At Amazon, this problem is framed around customer-obsessed, data-driven, bar-raising. Expect Amazon-specific scale numbers and a focus on production readiness and the company's engineering bar.

Capacity estimation

Resource	Estimate	Notes
Concurrent users (hot on-sale)	100K+	For a 10K-seat venue
Seats	~10K per event	The scarce resource
Hold duration	~5 min	TTL while the user pays
Peak QPS	tens of thousands	Concentrated at on-sale start

Architecture overview

Inventory + hold + booking

+--------+  hold  +-----------+  Lua script  +-----------------+
| Client |------->| Booking   |------------->| Inventory (Redis)|
+--------+        | Service   |              | seat -> state    |
                  +-----------+              +-----------------+
                       |   ^                         |
          hold TTL     |   |  expire/release          v
                       v   |                  +----------------+
                  +-----------+               | Payment Svc    |
                  | Hold Store|               +----------------+
                  +-----------+                        |
                                                       v
                                               +----------------+
                                               | Ticket / DB    |
                                               +----------------+

A Redis-backed inventory with Lua scripts gives atomic hold/expire at high throughput.

Step-by-step request flow

The user selects seats; the booking service atomically moves them from available to held (with a TTL) via a Redis Lua script.
The user pays; on success, the booking service converts the hold into a sold seat and issues a ticket in the DB.
If the user abandons or the hold TTL expires, a background job (or Redis keyspace notification) releases the seats back to available.
A booking idempotency key prevents double-charging on retry.

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