awesome-scalability

Awesome Scalability is a reading list on the patterns of scalable, reliable, performant large-scale systems. Case studies come from systems that serve millions to billions of users.

The repository describes itself as an updated and organized reading list for illustrating the patterns of scalable, reliable, and performant large-scale systems. Concepts are explained through articles by prominent engineers and credible references, and case studies come from battle-tested systems that serve millions to billions of users. The MIT license covers the repository.

The README is organized around problems. When a system goes slow, it says to first tell the two apart: a scalability problem means fast for a single user but slow under heavy load, while a performance problem means slow even for a single user. Design principles and how tech companies solve both problems follow. A separate section is set aside for people working with data and machine learning at scale.

For outages, the README quotes Thuan Pham, former CTO of Uber: even if you lose all one day, you can build all over again if you retain your calm. The advice is to keep calm and mind the availability and stability matters. Reliability is treated less as a checklist and more as a state of mind.

System design interviews get their own path, pointing to interview notes and real-world architectures with completed diagrams, plus talks from engineers at tech giants. For team building, the README argues the goal is not growing team size but increasing team output and value, with sections on hiring, management, organization, culture, and communication.

Contributions are welcome, with guidelines to read first. If a linked article is no longer maintained or no longer fits, the README asks for a pull request. It also asks readers who find the project helpful to share it on social platforms, ending with the line that knowledge shared is power multiplied.

• Source 1 · Project repository
• Source 2 · README