Status Page Service

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design a status page service (like Statuspage.io) showing uptime, incident updates, and health checks.
• •Design for a peak load target around 500 RPS (including burst headroom).
• •Customer status pages: ~2,000
• •Health checks/minute: ~20,000
• •Status page load time: < 500 ms
• •Incident notification delay: < 2 minutes
• •Uptime data retention: 1 year

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.
• •Monitoring: Instrument golden signals (latency, traffic, errors, saturation) per tier and per tenant/domain.

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.
• •Alert on user-impact SLOs, not only infrastructure metrics.

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.
• •Monitoring: Deep observability speeds incident response but raises ingestion and tooling costs.

Practical Notes

• •The status page itself must be ultra-reliable - consider serving it from a CDN as static HTML updated every minute.
• •Health check workers should run from multiple geographic locations to avoid false positives from network issues.
• •Store uptime data as 1-minute resolution buckets - aggregate into hourly/daily summaries for the history view.

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 -> Monitoring -> Log Aggregator

Design strengths

• •Monitoring and logs are wired in from day one for rapid incident triage.

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.