Notification System - Multi-Channel Delivery

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Build a platform-wide notification service delivering 100 M messages/day across push, email, SMS, and in-app.
• •Design for a peak load target around 5,787 RPS (including burst headroom).
• •Daily notifications: ~100,000,000
• •Channels: 4 (push, email, SMS, in-app)
• •Critical delivery latency: < 5 seconds
• •Normal delivery latency: < 5 minutes
• •Digest window: 15 minutes

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

• •Message Queues: Move non-blocking and retry-heavy work to async consumers with explicit retry and DLQ policies.
• •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.
• •Notifications: Model notifications as event-driven fanout with per-channel workers (email/push/webhook).
• •Monitoring: Instrument golden signals (latency, traffic, errors, saturation) per tier and per tenant/domain.

4) Reliability and Failure Strategy

• •Guarantee idempotent consumers and trace every message with correlation IDs.
• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Apply strict input validation and backward-compatible versioning.
• •Track delivery state machine and dead-letter undeliverable events.
• •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

• •Message Queues: Async pipelines absorb spikes well, but increase eventual-consistency complexity.
• •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.
• •Notifications: Multi-channel coverage increases reach but adds per-channel failure modes and policy complexity.
• •Monitoring: Deep observability speeds incident response but raises ingestion and tooling costs.

Practical Notes

• •Use a priority queue with at least two tiers - critical notifications skip batching and go to the fast lane.
• •A fan-out service reads user preferences and routes each notification to the appropriate channel worker.
• •Deduplication via a short-lived cache of (userId + notificationType + timeWindow) keys prevents spam.

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 -> Load Balancer -> API Gateway -> API Service -> Primary NoSQL DB -> Message Queue -> Background Workers -> Notification Fanout

Design strengths

• •Async queue/event bus isolates bursty workloads and supports retries without blocking synchronous requests.
• •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.