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SVE: Distributed Video Processing at Facebook Scale (paper)
Facebook's video processing engine: videos become graphs of small tasks that run in parallel, the pattern used in many transcoding pipelines.
By Huang et al. · Facebook Scale · Added 2026-10-12
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What you are about to study
Facebook Scale wrote this paper. Read the abstract and headings first. It assumes you are comfortable with distributed systems.
Ideas you will meet
- Distributed Systems
- Consensus, time, failure and the fundamentals of scale-out.
What we already know about this
Our own lessons on these ideas. Read them here before you start, so it makes sense.
Low-Level Design Practice · 10 minVideo catalogue (Netflix-style)Titles, profiles, playback and recommendations: modelling content, per-profile state and a swappable recommendation strategy.Read here
The problem
Subscribers have accounts with several profiles. They browse a catalogue of movies and series, play titles, resume where they stopped, and see recommendations. This lesson models the domain. The streaming infrastructure (transcoding, CDN) is in the video-streaming lesson of the classic designs course.
Entities
100%Figure SDA-LLD-16-1 · Video catalogue classes · © forcsguy.com
A playable unit is a movie or an episode; the player only needs an asset id and a duration.
Playback and resume
100%Figure SDA-LLD-16-2 · Playback state · © forcsguy.com
class Player { state: "STOPPED" | "PLAYING" | "PAUSED" | "FINISHED" = "STOPPED"; constructor(private profile: Profile, private progress: ProgressStore) {} play(unit: Playable) { if (!canPlay(this.profile, unit)) throw new Error("not allowed"); // rating and plan rules, one place this.state = "PLAYING"; this.seek(this.progress.get(this.profile.id, unit.id) ?? 0); } tick(unit: Playable, pos: number) { this.progress.save(this.profile.id, unit.id, pos); // every few seconds, not every frame if (pos >= unit.duration * 0.95) this.state = "FINISHED"; } }Recommendations as a strategy
interface Recommender { forProfile(p: Profile, n: number): Title[] } const popularInGenre: Recommender = { forProfile: (p, n) => catalog.byGenres(topGenresOf(p)).filter(t => !watched(p, t) && allowed(p, t)).sort(byPopularity).slice(0, n), };Start with a simple strategy (popular in the genres you watch, not yet seen, allowed for the profile). Replace it later with collaborative or learned models behind the same interface.
Design notes
- One rule for access:
canPlayandallowedcombine plan limits, region licensing, maturity rating and device limits, and are used everywhere. - Concurrent streams: the plan limits simultaneous streams; track active sessions and refuse extra ones with a clear message.
- Progress writes are frequent and approximate; buffer and save periodically, and let the latest write win.
- Catalogue changes (titles added or removed by region and date) are data, not code.
- Extensions: downloads, parental controls, ratings, search, and "because you watched" rows.
Real-world reading
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- One rule for access:
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