Food Delivery Backend

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design a food delivery platform with restaurant listings, ordering, real-time driver tracking, and ETA estimates.
• •Design for a peak load target around 100 RPS (including burst headroom).
• •Daily orders: ~100,000
• •Active drivers (peak): ~5,000
• •Restaurants: ~5,000
• •Order placement latency: < 2 seconds
• •Driver match time: < 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.
• •WebSockets: Use persistent connection gateways and decouple fanout via pub/sub or queues.
• •Message Queues: Move non-blocking and retry-heavy work to async consumers with explicit retry and DLQ policies.
• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •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.
• •Track connection churn, backpressure, and session resumption behavior.
• •Guarantee idempotent consumers and trace every message with correlation IDs.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •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.
• •WebSockets: WebSockets reduce interaction latency but complicate scaling and state management.
• •Message Queues: Async pipelines absorb spikes well, but increase eventual-consistency complexity.
• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •API Design: Rich APIs improve developer speed but can create long-term compatibility burden.

Practical Notes

• •Separate the customer-facing API, restaurant-facing API, and driver-facing API into distinct services - they have different traffic patterns and SLAs.
• •Use a geospatial index (PostGIS or Redis GEO) for 'nearest driver' queries.
• •Order state machine: placed → accepted (restaurant) → preparing → ready → picked up → en route → delivered. Use events to drive transitions.

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 -> Redis Cache -> Message Queue -> Background Workers

Design strengths

• •Cache sits on the read path to absorb repeated queries and keep DB pressure stable.
• •Async queue/event bus isolates bursty workloads and supports retries without blocking synchronous requests.

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.