Design Google Docs

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design Google Docs - real-time collaborative editing, conflict resolution, version history, and offline support for 1 B+ users.
• •Design for a peak load target around 80,000 RPS (including burst headroom).
• •Total users: 1,000,000,000+
• •Active documents (concurrent): ~50,000,000
• •Max concurrent editors/doc: 100
• •Edit propagation latency: < 100 ms
• •Autosave interval: ~3 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

• •WebSockets: Use persistent connection gateways and decouple fanout via pub/sub or queues.
• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.
• •Consistency: Classify operations by consistency requirement: strong for money/inventory, eventual for feeds/analytics.
• •Storage: Use object storage for large blobs and keep metadata/authorization separate in the API tier.
• •Auth: Centralize identity verification and keep authorization checks close to domain resources.
• •API Design: Standardize API boundaries, idempotency keys, pagination, and error contracts first.

4) Reliability and Failure Strategy

• •Track connection churn, backpressure, and session resumption behavior.
• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Use idempotency keys and conflict-resolution rules on retried/distributed writes.
• •Enforce lifecycle policies, retention tiers, and checksum validation.
• •Use short-lived tokens and secure key rotation workflows.

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

• •WebSockets: WebSockets reduce interaction latency but complicate scaling and state management.
• •Databases: SQL gives stronger transactional guarantees; NoSQL often gives better write scaling and flexibility.
• •Consistency: Stronger consistency improves correctness, but often increases latency and coordination costs.
• •Storage: Object storage is cheap and durable, but random low-latency reads are weaker than databases/caches.
• •Auth: Central auth simplifies policy, but makes auth service availability/security critical.

Practical Notes

• •OT (Operational Transformation): a central server transforms concurrent operations to maintain consistency. Google Docs uses OT - simpler centralized model but requires a single source of truth per document.
• •Each document is managed by a single 'document session server' - all edits for that doc route to the same server for OT processing.
• •Store the document as a log of operations (event sourcing). Periodically snapshot the current state to avoid replaying the entire history.

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 -> Load Balancer -> API Gateway -> Core Service -> Auth Service -> Primary NoSQL DB -> Replica SQL DB -> Realtime Bus

Design strengths

• •Security controls are enforced at ingress to protect downstream capacity.

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.