从餐厅到零售：将 Uber Eats 扩展至万物配送

With Uber Eats, you can get almost, almost anything. We support every type of store you can find in your local community, from restaurants to small convenience stores, giant supermarkets, pet stores, pharmacies, and so on. This blog introduces the backbone for managing the retailer product data and inventory at Uber: INCA (INventory and CAtalog).

通过 Uber Eats，你可以买到几乎、几乎任何东西。我们支持本地社区里的各种商店，从餐厅到小型便利店、大型超市、宠物店、药店等等。这篇博客介绍了 Uber 管理零售商商品数据与库存的骨干系统：INCA（INventory and CAtalog）。

Uber Eats was born in restaurants—optimized for menus with a few hundred dishes and a relatively simple set of use cases. But as our ambitions grew beyond food to include grocery, pharmacy, retail, and more, we faced a transformational challenge: could we scale our systems from serving meals to cataloging the modern retail universe?

Uber Eats 诞生于餐厅——最初为仅有几百道菜、使用场景相对简单的菜单而优化。但随着我们的雄心从餐饮扩展到杂货、药房、零售等更多领域，我们面临一个转型挑战：我们能否将系统从“送餐”扩展到“编录现代零售宇宙”？

Retailers like Walmart® and Carrefour™ operate with product catalogs that are orders of magnitude larger—hundreds of thousands of SKUs, rich metadata, and constantly shifting inventories. Supporting this complexity required a fundamental rethinking of our data infrastructure.

像 Walmart® 和 Carrefour™ 这样的零售商，其商品目录规模要大几个数量级——数十万 SKU、丰富的元数据以及不断变化的库存。要支持这种复杂性，就需要对我们数据基础设施进行根本性的重新思考。

Our original restaurant-centric architecture assumed low data volume, minimal variation, and a direct pass-through model: what merchants sent, consumers saw. That simplicity was a strength in the early days—but it quickly became a bottleneck. Retail catalogs demand precision, extensibility, and context. A head of lettuce might need nutritional info, freshness validation, and local compliance tagging—all before it reaches the consumer.

我们最初以餐厅为中心的架构假设数据量低、变化少，并采用直接透传模式：商家发送什么，消费者就看到什么。这种简单性在早期是优势——但很快就变成了瓶颈。零售目录需要精确性、可扩展性和上下文。一颗生菜在到达消费者之前，可能需要营养信息、新鲜度验证和本地合规标签——所有这些都要预先完成。

To meet this challenge, we built INCA (INventory and CAtalog): a next-generation catalog system engineered to ingest, enrich, and publish massive, diverse inventories in ...