Oracle system design interview questions

Which levels get a design round

Design questions candidates report

• Movie ticket booking

  Design a ticket booking app where thousands of people fight over the same few hundred seats. Requirements and scale, the booking API and seat data model, then the hard parts: atomic multi-seat holds with expiry, optimistic versus pessimistic locking, tying payment to a hold without double booking, a virtual waiting room for blockbuster releases, live seat maps, and the trade-offs.

  Full walkthrough: Movie booking

• URL shortener

  A full walkthrough of the classic warm-up with the LADDER method: requirements, scale math for 100 million new links a month at a 100 to 1 read ratio, the API and data model, the high-level design, then the parts interviewers probe: generating unique 7-character codes with base62 and ID ranges, 301 vs 302, caching the redirect path, custom aliases, expiry and abuse.

  Full walkthrough: URL shortener Practise on the design board

• Fix bottlenecks in an add-to-cart flow

  Design an online store where millions browse and thousands check out every minute. Requirements and scale, the shopping API and data model, then the hard parts: a read-optimized catalog with search and caching, a cart in a key-value store, inventory reservations that never oversell, a checkout saga with idempotent payments and an outbox, the order state machine, and where you can be eventually consistent.

  Full walkthrough: E-commerce (Amazon)

• Job scheduler

  How to design a shared scheduler that runs millions of one-off and cron jobs on time: a jobs table indexed by next run time, partitioned schedulers that claim due work with leases, a unique run key so each tick fires once, a queue and worker pool with heartbeats, retries, timeouts and fencing tokens, and the midnight thundering herd, priorities and per-team quotas.

  Full walkthrough: Job scheduler

• Search typeahead

  Design the typeahead box that suggests the top five queries as you type. Requirements and scale (over a hundred thousand requests a second at peak), the suggest API, a trie with top-K cached at every node, an offline pipeline that rebuilds it from query logs, a small real-time layer for trending terms, replication versus sharding, client-side tricks and the trade-offs.

  Full walkthrough: Search autocomplete

Also reported

• Distributed key-value store

How to answer any design question: the LADDER method · How the design round is scored by level · Low-level design and machine coding

Questions people ask