Awesome Scalability
Awesome Scalability is a curated list of patterns, principles, articles, books, and talks focused on building scalable, reliable, and performant large-scale systems. The repository is organized into sections covering scalability, availability, stability, performance, and other cross-cutting concerns. It aggregates high-quality resources from various sources, including engineering blogs, academic papers, conference talks, and open-source documentation. The list includes real-world case studies from companies like Google, Amazon, Netflix, and Facebook, offering insights into proven architectural patterns. The project is maintained by the open-source community and serves as a go-to reference for developers and architects preparing for system design interviews or designing distributed systems. It is not a software library but a knowledge base.
- Category
- AI/ML
- Pricing
- Open Source
Problem solved
Helps engineers navigate the overwhelming amount of system design information by providing a curated, categorized list of high-quality resources on scalability, reliability, and performance, saving time and offering a structured learning path.
Use cases
- Preparing for system design interviews
- Researching scalability patterns for distributed systems
- Finding articles and books on high-availability architecture
- Studying real-world case studies from large tech companies
- Building a personal reference for large-scale system design
Audience
- Developers
- ML Engineers
- Data Scientists
- Enterprise
- Startups
Pros
- Comprehensive and well-organized collection of resources
- Free and open-source with community contributions
- Covers a wide range of scalability topics
- Includes links to authoritative sources and case studies
- Useful for both beginners and experienced engineers
Cons
- Only aggregates links; no original content or in-depth explanations
- May contain outdated or broken links over time
- Requires self-directed learning; no interactive exercises
- Not a substitute for hands-on system design experience
Official website: https://github.com/binhnguyennus/awesome-scalability
Canonical LambdaBase page: https://www.lambdabase.com/tools/awesomescalability