Feed Ranking
feed-ranking
System Design
Design Instagram (Photo Sharing)
Design a photo sharing service like Instagram with 500M daily active users uploading 100M photos a day, served as personalized feeds at sub-200 ms p99. The interview centerpiece is the news feed: fan-out on write versus fan-out on read, the celebrity problem, and the hybrid pull-on-read model that real Instagram uses. We also cover photo upload pipelines (presigned URLs, multi-resolution generation, CDN), the metadata data model, and how to scale follow graphs that go from a few friends to hundreds of millions of followers.
Design Twitter / X (Social Feed)
Design a microblogging service like Twitter or X with 250M daily active users posting 500M tweets a day, served as a personalized timeline at sub-200 ms p99. The interview centerpiece is the home timeline: hybrid fan-out at the celebrity boundary, write amplification math, and how Twitter built Manhattan and the Timeline Service to make 250M people see fresh tweets within seconds. We also cover trending topics, the search index, retweet semantics, and how Twitter handles 50,000 tweets per second when a major event happens.
Design Reddit (Forum / Voting)
Design a community-driven forum like Reddit with 50M daily active users, 500K subreddits, and the famous hot/top/best ranking algorithms that decide which posts you see. The interview centerpiece is the ranking system: how to score posts in real time as votes pour in, how to make the front page personalized without per-user fan-out, and how to render nested comment trees at sub-200 ms when a popular thread has 10,000 nested replies. We also cover voting fraud detection, the difference between hot and Wilson score, and the tiered cache that makes 50K reads per second on the front page survive a viral post.
Design Facebook News Feed
Design Facebook's News Feed for 2 billion daily active users where every feed open reads from a personalized, ML-ranked timeline assembled from thousands of candidate posts in real time. Unlike Instagram's chronological precomputed feed or TikTok's pure recommendation, Facebook blends a friend graph, group memberships, page follows, and ads into one ranked stream via the legendary EdgeRank-and-successor algorithms. The interview centerpiece is the aggregator pattern: parallel candidate retrieval from many sources, real-time feature lookup, ML scoring, and online filtering, all under a 200 ms p99 budget. We also cover real-time updates (push notifications when a friend posts), edge ranking signals, and how Meta keeps the feed fresh with no precomputed timeline.