How Netflix, Uber, and YouTube Handle Scale

Four companies. Billions of users. Same underlying problem, completely different answers. And one shared set of building blocks underneath it all.Netflix. 325M subscribers. 1,000+ microservices on AWS. In 2008, a monolithic database corruption took down DVD shipping for 3 days. That triggered a 7-year cloud migration. In 2011 they built Chaos Monkey — software that randomly kills production servers, on purpose, every business day. Open Connect, their CDN, serves 73 terabits per second at a 98% cache hit rate. And they open-sourced almost all of it — Zuul, Eureka, Hystrix, Chaos Monkey, EVCache. Compete on content, not on plumbing.Uber. 36-42M trips per day. 202M MAU. The problem isn't bits, it's physical space. They built H3 — a hexagonal hierarchical geospatial index. Hexagons because every neighbor is equidistant (squares have diagonals 41% farther than edges). Finding nearby drivers is effectively O(1). Matching uses a bipartite-graph Hungarian-style algorithm that minimizes total wait across all riders. The closest driver isn't always your driver — because that might leave someone else with a 20-minute wait. Plus hot path / cold path / surge as real-time supply-and-demand code.YouTube. 500 hours of new video every minute. 2.7B MAU. ~1B viewing hours per day. ~15% of global internet traffic. Transcoding into H.264 / VP9 / AV1 — AV1 is 10-100X slower to encode but 30% smaller files. Google built a custom ASIC, the VCU, because general CPUs couldn't keep up. Maybe 5 companies on Earth can justify designing their own silicon.Spotify. 751M MAU, 290M premium. The real product isn't music — Apple, Amazon, YouTube Music have the same songs. It's recommendations. Hybrid engine: collaborative filtering + content-based audio analysis + NLP on lyrics. Discover Weekly is part exploration, part exploitation.Then the four universal building blocks — consistent hashing, cache-aside with TTL, Kafka vs RabbitMQ vs SQS, and Raft consensus (the algorithm designed specifically to be understandable enough to implement correctly, now under every Kubernetes cluster via etcd).Closes with three predictions for 2026-2028.RELATED EPISODESSystem Design Interview: What Interviewers Actually Look For — the building-block vocabulary appliedHow LLM Inference Actually Works — the infrastructure / hardware layer underneath all of thisPlatform Engineering Goes AI-Native — what comes next after the microservices eraRAG in Production — the recommendation-engine analog for AI applicationsCHAPTERS00:00 Cold open — 4 companies, billions of users, same problems solved differently01:18 Netflix — 325M subs, 1,000+ microservices, the 2008 outage that triggered the cloud migration07:25 Chaos Monkey — breaking production on purpose, every business day09:21 Open Connect CDN — 73 Tbps at 98% cache hit, 400 Gbps per box on FreeBSD12:43 Uber — 36-42M trips/day, the physical-space problem13:44 H3 hexagonal geospatial index — why hexagons beat squares, O(1) proximity15:55 Bipartite-graph Hungarian matching — minimize total wait, not just yours18:35 Surge pricing as real-time supply-and-demand across 10,000+ cities20:13 YouTube — 500 hours/min, 2.7B MAU, ingest + transcoding pipeline21:37 Codec wars — H.264 vs VP9 vs AV1 (10-100X slower encoding for 30% smaller files)24:20 The VCU custom ASIC — when designing silicon saves money28:15 Spotify — 751M MAU, hybrid recommendation engine, explore-exploit35:53 Four building blocks — consistent hashing, cache-aside, queues, Raft42:40 Three predictions for 2026-2028SOURCESNetflix Open Connect engineering blog + Q4 2025 earningsChaos Monkey / Simian Army — Netflix Tech Blog (2011)Uber H3 — open-source Engineering blog + GitHubYouTube VCU — Google Research ASIC paperSpotify Discover Weekly — Spotify EngineeringRaft consensus — Ongaro + Ousterhout, USENIX 2014Karger consistent hashing — MIT, 1997