Managed DNS Service

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design a managed DNS service with zone hosting, health-check routing, geo-based routing, and fast propagation.
• •Design for a peak load target around 57,870 RPS (including burst headroom).
• •DNS queries/day: ~1,000,000,000
• •Hosted zones: ~100,000
• •Global PoPs: 30+
• •Query response time: < 10 ms
• •Record propagation: < 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

• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.
• •Geo Distribution: Route users to nearest region/edge while keeping write-consistency boundaries explicit.
• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •Monitoring: Instrument golden signals (latency, traffic, errors, saturation) per tier and per tenant/domain.
• •API Design: Standardize API boundaries, idempotency keys, pagination, and error contracts first.

4) Reliability and Failure Strategy

• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Design region failover and data residency controls as first-class requirements.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Alert on user-impact SLOs, not only infrastructure metrics.
• •Apply strict input validation and backward-compatible versioning.

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.
• •Geo Distribution: Global latency improves, but cross-region consistency and operations become harder.
• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •Monitoring: Deep observability speeds incident response but raises ingestion and tooling costs.
• •API Design: Rich APIs improve developer speed but can create long-term compatibility burden.

Practical Notes

• •DNS servers are read-heavy - load all zone data into memory for O(1) lookups. At 100 k zones with ~20 records each, the dataset fits in RAM.
• •Propagation: on record change, publish an event to a message bus. All PoPs subscribe and update their in-memory zone data.
• •Anycast routing: announce the same IP prefix from all PoPs. BGP naturally routes queries to the nearest PoP.

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 -> Load Balancer -> API Gateway -> API Service -> Primary SQL DB -> Read Model DB -> Redis Cache

Design strengths

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