Design Uber

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design Uber's ride-hailing platform - real-time matching, surge pricing, ETA prediction, and global dispatch for 100 M+ users.
• •Design for a peak load target around 9,028 RPS (including burst headroom).
• •Monthly active users: 130,000,000
• •Daily trips: ~25,000,000
• •Active drivers (peak): ~5,000,000
• •Location updates/second: ~1,250,000
• •Match time: < 15 seconds

2) Capacity Planning Method

• •Convert traffic and growth constraints into request rate, storage growth, and concurrency budgets.
• •Keep at least 2-3x safety margin per tier (ingress, compute, storage, async workers).
• •Reserve explicit latency budgets per hop so p95 can be defended in review.

3) Architecture Decisions

• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.
• •WebSockets: Use persistent connection gateways and decouple fanout via pub/sub or queues.
• •Message Queues: Move non-blocking and retry-heavy work to async consumers with explicit retry and DLQ policies.
• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •Geo Distribution: Route users to nearest region/edge while keeping write-consistency boundaries explicit.
• •Analytics: Maintain separate OLTP and analytics paths; stream events into a warehouse/time-series layer.

4) Reliability and Failure Strategy

• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Track connection churn, backpressure, and session resumption behavior.
• •Guarantee idempotent consumers and trace every message with correlation IDs.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Design region failover and data residency controls as first-class requirements.

5) Validation Plan

• •Run one peak-load test, one dependency-degradation test, and one failover test.
• •Verify idempotency for all retried writes and async consumers.
• •Track user-facing SLOs first: p95 latency, error rate, and successful throughput.

6) Trade-offs to Call Out in Interviews

• •Databases: SQL gives stronger transactional guarantees; NoSQL often gives better write scaling and flexibility.
• •WebSockets: WebSockets reduce interaction latency but complicate scaling and state management.
• •Message Queues: Async pipelines absorb spikes well, but increase eventual-consistency complexity.
• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •Geo Distribution: Global latency improves, but cross-region consistency and operations become harder.

Practical Notes

• •Use a geospatial index (S2 geometry cells or geohash) to partition the world into cells. Each cell tracks active drivers. 'Find nearby drivers' = query the cell + neighboring cells.
• •Driver locations live in-memory (Redis with geospatial commands or a custom spatial index) - not in a traditional database.
• •Surge pricing: a stream processor aggregates supply/demand per S2 cell every 30 seconds. Price = f(demand/supply ratio).

Why This Solution Works

Request path: The solution keeps ingress, service logic, and stateful dependencies separated so each layer can scale independently.

Reference flow: Mobile Clients -> DNS -> Load Balancer -> API Gateway -> Core Service -> Primary NoSQL DB -> Replica SQL DB -> Redis Cache

Design strengths

• •Cache sits on the read path to absorb repeated queries and keep DB pressure stable.
• •Async queue/event bus isolates bursty workloads and supports retries without blocking synchronous requests.
• •Analytics pipeline is separated from OLTP path to avoid reporting workloads impacting transactions.

Interview defense

• •This design makes bottlenecks explicit (ingress, core compute, persistence, async workers).
• •It supports progressive scaling without re-architecting the core request path.
• •It keeps correctness-sensitive state changes in durable systems while offloading background work asynchronously.