Personal Blog Platform

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design a personal blogging platform where authors write posts and readers leave comments. ~5 k daily visitors.
• •Design for a peak load target around 100 RPS (including burst headroom).
• •Daily visitors (per blog): ~5,000
• •Posts per blog: ~200
• •Page load time: < 400 ms
• •Image storage per blog: ~2 GB
• •Comments per post: ~50 avg

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.

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.

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.

Practical Notes

• •A single relational database (PostgreSQL) is more than sufficient at this scale.
• •Static site generation (SSG) or server-side rendering (SSR) with caching can eliminate most database reads.
• •Store images in an object store (S3) with a CDN in front for fast delivery.

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 SQL DB -> Redis Cache

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.