awesome-scalability

This repository is an updated and organized reading list for learning about patterns of scalable, reliable, and performant large-scale systems. It collects articles, talks, and case studies from engineers at major technology companies such as Google, Netflix, Uber, Facebook, Amazon, and others. The content is grouped into several main sections: Principle covers fundamental design ideas like CAP theorem, ACID/BASE, consistent hashing, caching, and latency; Scalability examines microservices, distributed caching, locking, tracking, scheduling, and monitoring in production; Availability and Stability discuss keeping systems up and handling failures; Performance focuses on latency and throughput; Intelligence addresses data and machine learning at scale; Architecture contains real-world architecture descriptions with diagrams; Interview provides system design interview notes; Organization covers how tech companies scale engineering teams; Talk lists conference talks; Book lists recommended reading. The project welcomes contributions and provides contribution guidelines.