awesome-scalability README

The project's README from github.com/binhnguyennus/awesome-scalability. As one markdown file: README.md.

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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.

If your system goes slow

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.

If your system goes down

"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!

If you are having a system design interview

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!

If you are building your dream team

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.

Community power

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 If you see a link here that is no longer maintained or is not a good fit, please submit a pull request!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!

Content

Principle

Scalability

awesome-scalability (binhnguyennus/awesome-scalability)

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Awesome-scalability
这是什么？这是一份经过整理、持续更新的阅读清单，收录了 Netflix、Uber、Facebook、Amazon 等公司工程师撰写的文章、案例研究和演讲，展示了构建可扩展、高可靠、高性能大规模系统的具体模式。
这是教程还是只是一堆链接？它纯粹是一份外部文章和演讲的精选索引，按主题分组（Principle、Scalability、Availability、Stability、Performance、Intelligence、Architecture、Interview、Organization、Talk、Book），没有原创教程内容，只是指向真实工程技术文章的链接。
应该从哪里开始读？没有必须遵循的顺序，直接跳到与你当前问题相符的章节即可：核心概念看 'Principle'，具体问题看 'Scalability'、'Performance' 或 'Availability'，准备系统设计面试则看 'Interview'。
收录的文章会及时更新吗？条目由社区维护。如果某个链接失效或不再合适，任何人都可以提交 pull request 进行修正或删除，因此内容的新鲜度取决于社区的持续贡献，而不是固定的更新计划。
这份清单适合哪些人？适合正在排查扩展性或可靠性问题的后端和基础设施工程师、正在准备系统设计面试的工程师，以及关注公司如何扩展系统和团队的技术负责人。
Intelligence 章节涵盖哪些内容？它面向大规模处理数据和机器学习的工程师，涵盖大数据和深度学习基础设施的模式，而不是泛泛的系统设计。
它对系统设计面试准备有专门帮助吗？有。Interview 章节收录了面试笔记以及大型科技公司的真实架构图，适合在白板上做系统设计之前先行研读。
What are similar projects?awesome-design-patterns, awesome-system-design-resources, architect-awesome, system-design-academy, system-design-interview, system-design-primer, from_coder_to_expert, professional-programming
Which topics is it in?system-design-interview, distributed-tracing, scalable, interview-practice, lists, zookeeper, interview-prep, system, scalability, design-patterns, big-data, system-design
Creator
binhnguyennus
Size
1.6 MB
Stars
74.8K
Updated
CommitJan 04
Age
8y
Commits · 90 Days