Social Feed 1 - MVP Launch

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Build a simple social feed app with posts, follows, and a chronological timeline.
• •Design for a peak load target around 100 RPS (including burst headroom).
• •Registered users: 100,000
• •Daily active users: ~20,000
• •Avg posts per user/day: 3
• •Avg follows per user: 50
• •Post size: ≤ 300 characters

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.
• •API Design: Standardize API boundaries, idempotency keys, pagination, and error contracts first.
• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •Storage: Use object storage for large blobs and keep metadata/authorization separate in the API tier.

4) Reliability and Failure Strategy

• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Apply strict input validation and backward-compatible versioning.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •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.
• •API Design: Rich APIs improve developer speed but can create long-term compatibility burden.
• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •Storage: Object storage is cheap and durable, but random low-latency reads are weaker than databases/caches.

Practical Notes

• •A fan-out-on-read approach (pull model) works fine at low scale - query follows at read time.
• •Consider indexing the posts table by (author_id, created_at) for efficient profile page queries.
• •A simple cache in front of hot user profiles can reduce database load significantly.

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 -> API Gateway -> API Service -> Primary NoSQL DB -> Redis Cache -> Object Storage

Design strengths

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

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.