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An updated and organized reading list for illustrating the patterns of scalable, reliable, and performant large-scale systems. Concepts are explained in the articles of prominent engineers and credible references. Case studies are taken from battle-tested systems that serve millions to billions of users.
Understand your problems: scalability problem (fast for a single user but slow under heavy load) or performance problem (slow for a single user) by reviewing some design principles and checking how scalability and performance problems are solved at tech companies. The section of intelligence are created for those who work with data and machine learning at big (data) and deep (learning) scale.
"Even if you lose all one day, you can build all over again if you retain your calm!" - Thuan Pham, former CTO of Uber. So, keep calm and mind the availability and stability matters!
Look at some interview notes and real-world architectures with completed diagrams to get a comprehensive view before designing your system on whiteboard. You can check some talks of engineers from tech giants to know how they build, scale, and optimize their systems. Good luck!
The goal of scaling team is not growing team size but increasing team output and value. You can find out how tech companies reach that goal in various aspects: hiring, management, organization, culture, and communication in the organization section.
Contributions are greatly welcome! You may want to take a look at the contribution guidelines. If you see a link here that is no longer maintained or is not a good fit, please submit a pull request!
Many long hours of hard work have gone into this project. If you find it helpful, please share on Facebook, on Twitter, on Weibo, or on your chat groups! Knowledge is power, knowledge shared is power multiplied. Thank you!
Roses are red. Violets are blue. Binh likes sweet. Treat Binh a tiramisu? 
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