Design Google Maps

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design Google Maps - map tile rendering, real-time navigation, traffic prediction, and place search for 1 B+ users.
• •Design for a peak load target around 69,444 RPS (including burst headroom).
• •Monthly active users: 1,000,000,000+
• •Map tiles (total): Billions
• •Road network edges: Hundreds of millions
• •Routing latency: < 1 second
• •Tile load time: < 100 ms

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.
• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •CDN: Serve static and cacheable content from edge and keep origin strictly for misses and dynamic requests.
• •Geo Distribution: Route users to nearest region/edge while keeping write-consistency boundaries explicit.
• •Storage: Use object storage for large blobs and keep metadata/authorization separate in the API tier.
• •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.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Define cache keys and purge workflows before launch to avoid stale/global outages.
• •Design region failover and data residency controls as first-class requirements.
• •Enforce lifecycle policies, retention tiers, and checksum validation.

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.
• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •CDN: Long TTL improves latency/cost; short TTL improves freshness.
• •Geo Distribution: Global latency improves, but cross-region consistency and operations become harder.
• •Storage: Object storage is cheap and durable, but random low-latency reads are weaker than databases/caches.

Practical Notes

• •Pre-render the top 5 zoom levels and the most-viewed tiles (major cities). Use on-demand rendering with caching for long-tail tiles.
• •Routing: use Contraction Hierarchies or A* with landmarks to speed up shortest-path queries on massive graphs. Pre-process the graph offline.
• •Live traffic: partition roads by S2 cell. Aggregate speed data from GPS traces per road segment. Store in a time-series structure.

Why This Solution Works

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

Reference flow: Web Clients -> DNS -> CDN Edge -> Load Balancer -> Core Service -> Primary SQL DB -> Read Model DB -> Redis Cache

Design strengths

• •Cache sits on the read path to absorb repeated queries and keep DB pressure stable.
• •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.