Design a scalable food news feed

Q: Design a scalable food news feed

This question evaluates expertise in scalable system architecture, data modeling for feeds (posts, comments, reactions), API and caching design, consistency and availability trade-offs, and operational concerns such as observability and rate limiting.

Q: How do I approach System Design interview questions?

System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty System Design question, commonly asked during Onsite rounds at DoorDash.

Q: What role is this question designed for?

This question is commonly asked for Software Engineer candidates at DoorDash during technical interviews.

Question

System Design: News Feed for a Food Discovery App

Context

You are designing a news-feed feature for a mobile/web food discovery app where users explore dishes, restaurants, and creator posts. The feed is a scrollable list of items and a tap opens a detailed view. Assume a mix of global and lightly personalized feeds (by cuisine, location, or follows) and mobile clients with intermittent connectivity.

Requirements

Feed and Detail

Feed items show: title, author, summary, engagement counts (likes, comments, saves), thumbnail.
Detail view shows: full description, images, comments (threaded or flat), related items.
Infinite scroll on feed and comments.
Rank by a blend of recency and engagement.
Basic search and filter (e.g., keywords, cuisine tags, location radius, author).

Data and Operations

CRUD for posts and comments, reactions (like/save) on posts and comments.
Data model and storage for posts, comments, reactions, counters, and related items.

Interfaces

Read/Write APIs with pagination (cursor-based preferred), idempotency for writes.

Architecture & Platform

End-to-end request path: edge/CDN, API gateway, stateless app tier, caches, DB, background jobs, queues/streams, data processing/indexing.
Caching, indexing, and denormalization for fast reads.
Scalability: partitioning/sharding, rate limiting, backpressure, horizontal scaling.
Consistency choices (strong vs eventual) and idempotency.
Error handling and retries (client/server), DLQs.
Observability: logging, metrics, tracing, SLOs.
Performance optimizations for p95 latency and cost.