模型一次，处处表示：Netflix 的 UDA（统一数据架构）
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By Alex Hutter, Alexandre Bertails, Claire Wang, Haoyuan He, Kishore Banala, Peter Royal, Shervin Afshar
由 Alex Hutter, Alexandre Bertails, Claire Wang, Haoyuan He, Kishore Banala, Peter Royal, Shervin Afshar
As Netflix’s offerings grow — across films, series, games, live events, and ads — so does the complexity of the systems that support it. Core business concepts like ‘actor’ or ‘movie’ are modeled in many places: in our Enterprise GraphQL Gateway powering internal apps, in our asset management platform storing media assets, in our media computing platform that powers encoding pipelines, to name a few. Each system models these concepts differently and in isolation, with little coordination or shared understanding. While they often operate on the same concepts, these systems remain largely unaware of that fact, and of each other.
随着 Netflix 的产品不断增长 — 涉及电影、系列、游戏、现场活动和广告 — 支持这些产品的系统的复杂性也在增加。核心业务概念如“演员”或“电影”在许多地方建模：在我们的企业 GraphQL 网关中为内部应用提供支持，在我们的资产管理平台中存储媒体资产，在我们的媒体计算平台中为编码管道提供支持，仅举几例。每个系统以不同的方式和孤立的方式建模这些概念，几乎没有协调或共享理解。尽管它们通常在相同的概念上运作，但这些系统在很大程度上对这一事实以及彼此保持无知。
As a result, several challenges emerge:
因此，出现了几个挑战：
- Duplicated and Inconsistent Models — Teams re-model the same business entities in different systems, leading to conflicting definitions that are hard to reconcile.
- 重复和不一致的模型 — 团队在不同系统中重新建模相同的业务实体，导致难以调和的冲突定义。
- Inconsistent Terminology — Even within a single system, teams may use different terms for the same concept, or the same term for different concepts, making collaboration harder.
- 术语不一致 — 即使在单一系统内，团队也可能对同一概念使用不同的术语，或对不同概念使用相同的术语，增加了协作的难度。
- Data Quality Issues — Discrepancies and broken references are hard to detect across our many microservices. While identifiers and foreign keys exist, they are inconsistently modeled and poorly documented, requiring manual work fr...