从事件驱动的混乱到闪电般快速的服务 API

Real-time data access is critical in e-commerce, ensuring accurate pricing and availability. At Zalando, our event-driven architecture for Price and Stock updates became a bottleneck, introducing delays and scaling challenges.

实时数据访问在电子商务中至关重要，确保准确的定价和可用性。在Zalando，我们的事件驱动架构用于价格和库存更新，成为瓶颈，导致延迟和扩展挑战。

This post covers how we redesigned our approach and built a blazingly fast API capable of serving millions of requests per second with single-digit-millisecond latency. You'll learn about the caching strategies, low-latency optimizations, and architectural decisions that enabled us to deliver this performance.

这篇文章介绍了我们如何重新设计方法，并构建了一个能够每秒处理数百万请求、延迟在个位数毫秒的超快API。您将了解使我们能够提供这种性能的缓存策略、低延迟优化和架构决策。

The Product Platform with No Read API

没有读取 API 的产品平台

In 2016, Zalando built a microservices architecture where independent CRUD APIs onboarded different parts of product data. Once complete, each product was materialised as an event, requiring teams to consume the event stream to serve product data via their own APIs.

在 2016 年，Zalando 建立了一个微服务架构，其中独立的 CRUD API 负责不同部分的产品数据。一旦完成，每个产品都被物化为一个事件，要求团队消费事件流以通过自己的 API 提供产品数据。

In practice, this approach distributed the challenges of API serving across the company. A simple request—"I’m building a new feature and need access to product data. Where do I get it?"—had an unreasonable answer: "Subscribe to our event stream, replay events from the dawn of time, and build your own local store."

在实践中，这种方法将API服务的挑战分散到整个公司。一个简单的请求——“我正在构建一个新功能，需要访问产品数据。我该从哪里获取？”——得到了一个不合理的回答：“订阅我们的事件流，从时间的起点重放事件，并构建自己的本地存储。”

Teams with engineering capacity consumed events, modified data to fit their needs, and exposed their own APIs or event streams. Those without the capacity relied on an existing unified data source, such as our Presentation API, inheriting its version of product data. This led to competing sources of truth.

具备工程能力的团队消费事件，修改数据以满足他们的需求，并公开他们自己的API或事件流。那些没有能力的团队依赖于现有的统一数据源，例如我们的Presentation API，继承其版本的产品数据。这导致了竞争的真相来源。

A good analogy is the children's whispering game—product data was altered at each ste...