Social and Content Platforms
social-content-platforms
System Design
Design a URL Shortener (TinyURL)
Design a URL shortening service like TinyURL or bit.ly that maps a long URL to a 7 character code, redirects clicks in under 50 ms, and survives a 100:1 read-to-write ratio. This lesson walks through capacity estimation, the choice between counter based and hash based key generation, the database split between a key store and an analytics store, and the caching strategy that lets a single mid-tier service handle 10K redirects per second on commodity hardware.
Design Pastebin
Design a service like Pastebin or GitHub Gist where users dump up to 10 MB of text and share a link. The interview twist over a URL shortener: pastes are big, so you store them in object storage (S3) and only keep metadata in your database. This lesson covers the metadata vs blob split, expiration via S3 lifecycle policies, presigned URLs for direct uploads, syntax highlighting strategy, and how to handle the read pattern when most pastes are read once and never again.
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 YouTube (Video Platform)
Design a video platform like YouTube with 2 billion users, 500 hours of video uploaded every minute, and 1 billion hours watched per day. The interview centerpiece is the video pipeline: chunked uploads, parallel transcoding to 8 resolutions and 3 codecs, HLS/DASH adaptive streaming over a global CDN, and the metadata service that ties it all together. We also cover recommendations (the secondary feed problem), comment scaling, view-counter accuracy, and how YouTube serves 200 Tbps of egress without melting the internet.
Design TikTok (Short-Form Video)
Design TikTok with 1.5B monthly active users, 100M short videos uploaded daily, and the For You Page that decides which video plays next for every viewer in under 100 ms. Unlike Instagram and Twitter, TikTok has no follower-driven feed - the For You Page is pure ML recommendation from a global pool. The interview centerpiece is the recommendation system architecture: candidate retrieval, two-tower models, online ranking with engagement signals, and how to keep video pre-loaded so the next swipe is instant. We also cover content moderation at scale, edge caching for the long-form-of-short-form access pattern, and why TikTok's product choice eliminated the celebrity fan-out problem entirely.
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.