Recipe Sharing App

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design a recipe sharing platform with ingredient search, meal planning, and grocery lists.
• •Design for a peak load target around 100 RPS (including burst headroom).
• •Registered users: ~50,000
• •Daily active users: ~10,000
• •Total recipes: ~20,000
• •Search latency: < 300 ms
• •Images per recipe: 5-10

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.
• •Search: Use primary store for writes and async index updates for search relevance + scale.
• •Storage: Use object storage for large blobs and keep metadata/authorization separate in the API tier.

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.
• •Track indexing lag and support reindex from source of truth.
• •Enforce lifecycle policies, retention tiers, and checksum validation.

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.
• •Search: Search index gives rich querying but introduces eventual consistency and index ops overhead.
• •Storage: Object storage is cheap and durable, but random low-latency reads are weaker than databases/caches.

Practical Notes

• •Ingredient-based search requires an inverted index of ingredient → recipe_ids. PostgreSQL full-text search or a simple tag-based filter works at this scale.
• •Grocery list generation: aggregate all ingredients from selected recipes, merge duplicates by ingredient name, and sum quantities.
• •Store recipe images in S3 with a CDN; generate thumbnails on upload for listing views.

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 -> Object Storage -> Search Index

Design strengths

• •The architecture keeps synchronous paths short and isolates stateful dependencies behind clear boundaries.

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.