Guide · Open source
How to use awesome-scalability: a reading guide to large-scale systems
Updated · written with AI from the project's own documentation and checked against our catalogue data
awesome-scalability is an MIT-licensed, curated list of links to articles, papers, talks and case studies about building large-scale software systems. It is aimed at engineers, students and teams who want to learn how big services handle heavy load, failures and data growth. After this guide you will know how the list is organized and how to pick the right section for your problem.
awesome-scalabilitybinhnguyennus/awesome-scalability · ★ 75.3k · MIT Install options & downloads Quick start
- 1Open the README. Go to the repository on GitHub and read the README, or clone it with the git command shown in this guide.
- 2Identify your problem. Decide whether your issue is a scalability problem, a performance problem, an availability problem or interview preparation.
- 3Pick a section. Use the Content list to jump to Principle, Availability, Performance, Architecture, Interview or another matching section.
- 4Read the linked sources. Open the articles, papers and talks one at a time and take notes on the trade-offs they describe.
- 5Check dates and context. Confirm that each source still applies to your situation, since linked pages are maintained by other authors.
- 6Suggest changes. If a link is dead or a poor fit, follow the contribution guidelines in CONTRIBUTING.md and submit a pull request.
What awesome-scalability is and what it is not
The README describes the project as an updated and organized reading list for illustrating the patterns of scalable, reliable, and performant large-scale systems. Its entries are links to material written by other engineers and credible references, and the case studies come from systems that serve millions to billions of users.
It is not a software tool. There is no installable product or runnable code, and the README offers little commentary of its own, so you read the linked sources to get the explanations.
- Type: a categorized list of links, not a library or application
- Install method: read online; no prerequisites are listed
- Licence: MIT
Get the list: read online or clone the repository
The simplest way to use the list is to open the repository on GitHub and read the README in your browser. Cloning is optional and only gives you a local copy of the same files.
To clone the repository, the README gives this command.
git clone https://github.com/binhnguyennus/awesome-scalabilityFind your topic with the section list
The README has a Content list near the top that links to each section. The sections are Principle, Scalability, Availability, Stability, Performance, Intelligence, Architecture, Interview, Organization, Talk and Book.
Principle contains foundational reading such as CAP theorem trade-offs, sharding, consistent hashing, caching and SQL versus NoSQL. Architecture holds real-world designs with diagrams. Interview holds notes for system design interviews, and Talk links to engineers from large technology companies describing how they build and scale systems.
- Principle: design ideas and trade-offs, such as CAP theorem and ACID versus BASE
- Architecture: real-world architectures with completed diagrams
- Interview: notes for system design interviews
- Talk: conference talks by engineers at large companies
- Organization: hiring, management, culture and communication for scaling teams
Use the problem-based shortcuts
The README groups guidance by situation. If your system is slow, it suggests first working out whether you have a scalability problem (fast for one user but slow under heavy load) or a performance problem (slow for a single user). If your system goes down, it points to the availability and stability sections.
The Intelligence section is for people working with data and machine learning at large scale.
- Slow system: start with Principle, then Scalability and Performance
- System goes down: start with Availability and Stability
- Interview preparation: start with Interview and Architecture
Read critically and contribute back
Because the entries are written by different authors, some linked pages may be old or no longer maintained. The README asks readers to submit a pull request if they find a link that is no longer maintained or is not a good fit. Its contribution guidelines are in CONTRIBUTING.md.
Check the date and context of each article before applying its advice to your own system.
- Contribution guidelines are in CONTRIBUTING.md
- Report dead or poor-fit links by submitting a pull request
What people use it for
- Preparing for a system design interview by reading the Interview and Architecture sections
- Learning why a large service uses sharding, caching or eventually consistent data stores
- Finding background reading on scalability and availability trade-offs for a team design discussion
- Studying distributed systems concepts through the Principle section as a student
Frequently asked questions
Is awesome-scalability free to use?
Yes. The project is licensed under MIT, as stated in the repository information. You can read the list online at no cost, and cloning is optional.
How do I install awesome-scalability?
There is nothing to install. The install method is read-online, and no prerequisites are listed. If you want a local copy, the README gives the command git clone https://github.com/binhnguyennus/awesome-scalability.
Does awesome-scalability include runnable code?
No. It is a categorized list of links to articles, papers, talks and case studies. It is not a software tool, so you will not find code to run in it.
Who is awesome-scalability best for?
It suits engineers preparing for system design interviews, developers who want to learn how large services are built, teams seeking background reading on scalability trade-offs, and students of distributed systems.
What are good alternatives to awesome-scalability?
Comparable projects listed for this item are donnemartin/system-design-primer, ashishps1/awesome-system-design-resources and mtdvio/every-programmer-should-know. Pick one based on whether you want links, a primer or a structured resource list.
Why does a link in the list not work or seem outdated?
The README says some linked material may no longer be maintained or may not be a good fit. Report such a link by submitting a pull request, following the guidelines in CONTRIBUTING.md.
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Our verdict
Use awesome-scalability if you want a well-organized set of high-quality links on how large systems handle load, failures and data, especially for interview prep or background study. Pick a different resource, such as one of the comparable projects, if you need original explanations, structured lessons or hands-on material.
Install options & downloads