Uber system design interview questions

Sample questions with short answers

Tap a question to reveal the short answer and key points.

1. 1Design the ride-matching system that pairs riders with drivers in real time.HardUber-specificShow short answer

   Short answer

   Ingest driver GPS updates into an in-memory geo index partitioned by city/H3 cell. On a ride request, query nearby cells, score candidates by ETA and other factors, and dispatch with a short acceptance timeout and fallback to the next driver. Discuss consistency (one driver, one trip), surge pricing inputs, and partitioning hot cities.

   Interviewers listen for

   • geo-partitioned in-memory index
   • score candidates by ETA
   • dispatch with acceptance timeout and fallback
2. 2How would you design surge pricing?MediumUber-specificShow short answer

   Short answer

   Compute supply and demand per cell and time window from streaming events, derive a multiplier with smoothing and caps, and publish it to the pricing service with a short TTL. Show the multiplier up front to riders, log it for audit, and guard against oscillation and abuse. Explain the trade-off between responsiveness and stability.

   Interviewers listen for

   • supply and demand per cell from streams
   • smoothed, capped multiplier
   • show the multiplier up front and guard against oscillation
3. 3Design a URL shortener. Walk through the key decisions.MediumAsked everywhereShow short answer

   Short answer

   Clarify scale (e.g. 100M new URLs/month, 10:1 read:write) and features (custom aliases, expiry, analytics). Generate short keys with base-62 encoding of a counter or a hash with collision checks. Store the mapping in a key-value store, put a cache in front for hot links, and make redirects 301/302 depending on whether you need click analytics. Discuss sharding by key and rate-limiting abuse.

   Interviewers listen for

   • clarify scale and read/write ratio
   • base-62 key generation
   • cache for hot redirects
   • sharding the key-value store
4. 4What is the CAP theorem and how does it shape real system choices?MediumAsked everywhereShow short answer

   Short answer

   During a network partition a distributed system must choose between consistency (every read sees the latest write) and availability (every request gets a response). Banking ledgers lean CP; social feeds and shopping carts lean AP with eventual consistency. In interviews, name the partition scenario explicitly and justify which side the product can tolerate.

   Interviewers listen for

   • partition forces consistency vs availability
   • banking leans CP, feeds lean AP
   • justify choice from product tolerance
5. 5How would you design a rate limiter for an API gateway?HardAsked everywhereShow short answer

   Short answer

   Choose an algorithm — token bucket is the usual default (allows bursts, simple to reason about); sliding window log is exact but memory-heavy. Keep counters in Redis keyed by client and window, using atomic INCR/EXPIRE or a Lua script to avoid races. For multiple gateway nodes, centralise counters or accept slight over-admission with local buckets. Return 429 with Retry-After headers.

   Interviewers listen for

   • token bucket or sliding window
   • Redis counters with atomic operations
   • distributed nodes and 429 responses