Docstore is Uber's in-house, distributed database built on top of MySQL®. Storing tens of PBs of data and serving tens of millions of requests/second, it is one of the largest database engines at Uber used by microservices from all business verticals. Since its inception in 2020, Docstore users and use cases are growing, and so are the request volume and data footprint. The growing number of demands from business verticals and offerings introduces complex microservices and dependency call graphs. As a result, applications demand low latency, higher performance, and scalability from the database, while simultaneously generating higher workloads.

The Uber Engineering Blog offers insights, technical deep dives, and updates on the engineering challenges and solutions behind Uber's platform and services. Covering topics such as distributed systems, data infrastructure, and mobile development, the blog provides resources for developers interested in large-scale systems and real-world engineering problems. Developers can learn about Uber's technology stack, engineering culture, and best practices for building scalable and reliable systems.

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Uber has built an integrated caching solution called CacheFront for their in-house distributed database, Docstore. CacheFront helps improve latencies, reduces load on the storage engine, and saves resources. It offers transparent caching, cache invalidation using Flux, and supports over 40 million requests per second.

How Uber Serves Over 40 Million Reads Per Second from Online Storage Using an Integrated Cache