Cloud Drive 1 - Personal File Storage

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design a Dropbox-like cloud storage service for 1 M users with sync, sharing, and versioning.
• •Design for a peak load target around 100 RPS (including burst headroom).
• •Registered users: 1,000,000
• •Total storage: ~10 PB
• •Max file size: 5 GB
• •Upload throughput: ~10,000 concurrent uploads
• •Sync latency: < 30 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

• •Storage: Use object storage for large blobs and keep metadata/authorization separate in the API tier.
• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.
• •API Design: Standardize API boundaries, idempotency keys, pagination, and error contracts first.
• •CDN: Serve static and cacheable content from edge and keep origin strictly for misses and dynamic requests.
• •Auth: Centralize identity verification and keep authorization checks close to domain resources.

4) Reliability and Failure Strategy

• •Enforce lifecycle policies, retention tiers, and checksum validation.
• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Apply strict input validation and backward-compatible versioning.
• •Define cache keys and purge workflows before launch to avoid stale/global outages.
• •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

• •Storage: Object storage is cheap and durable, but random low-latency reads are weaker than databases/caches.
• •Databases: SQL gives stronger transactional guarantees; NoSQL often gives better write scaling and flexibility.
• •API Design: Rich APIs improve developer speed but can create long-term compatibility burden.
• •CDN: Long TTL improves latency/cost; short TTL improves freshness.
• •Auth: Central auth simplifies policy, but makes auth service availability/security critical.

Practical Notes

• •Chunked uploads (e.g., 4 MB chunks) enable resumable uploads and deduplication at the chunk level.
• •Content-addressable storage - hash each chunk and deduplicate across users to save space.
• •A metadata service (SQL database) tracks the folder tree, file versions, and permissions; actual bytes go to object storage (S3).

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 -> API Gateway -> API Service -> Auth Service -> Primary SQL DB

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.