Fan-out: 4 real scaling case studies
4 of the 73 engineering write-ups in our database are tagged with the technique "fan-out". They come from 4 companies (Discord, Meta, Slack and Zalando) and were published between 2017 and 2026. Stack that shows up most often in these stories: Java (2). Often combined with consistent hashing (2) and load shedding (2). 2 of the 4 state concrete scale numbers, for example "1 million+ requests/sec moved off shared ingress" (Zalando). Each card below links to our short summary and the original post.
Shipping Media Over QUIC at Meta Scale for Instagram Live Ingest
Instagram Live ingest moved from Meta's in-house RUSH protocol to the open IETF Media over QUIC standard on iOS and Android. Edge endpoints translate MoQ frames into the RUSH format, so the backend did not change. Server-side routing let the experiment resize in minutes, the transport falls back to TCP when UDP is blocked, and the launch matched RUSH on quality and reliability.
Client-Side Load Balancing at a Million Requests Per Second
Zalando moved the internal fan-out of its Product Read API off the shared Skipper ingress into an in-process JVM load balancer that rebuilds the same xxHash64 consistent-hash ring. They added N-ring fade-in so new pods warm only the products they will keep, bounded load based on occupancy (Little's Law) weighted by latency, and a capped ring walk. Pods ran more evenly, the autoscaler target rose from 50% to 65% CPU, and the fleet shrank by over 25%.
Real-time Messaging
An overview of Slack's channel and gateway servers that fan messages out to millions of connected clients in real time.
How Discord Scaled Elixir to 5,000,000 Concurrent Users
Discord tuned its Elixir guild processes and fan-out paths, building custom libraries to push events to millions of concurrent users.