# binhnguyennus/awesome-scalability

The Patterns of Scalable, Reliable, and Performant Large-Scale Systems

Repository: https://github.com/binhnguyennus/awesome-scalability
Canonical: https://ross.abutalabs.com/products/awesome-scalability
License: MIT
License Family: permissive
Topics: system-design, backend, scalability, interview, architecture, devops, design-patterns, interview-questions, awesome-list, big-data, awesome, resources, lists, web-development, programming, system, interview-practice, computer-science, distributed-systems, machine-learning
Last push: 2026-01-04T03:51:47+00:00

## Health v2 (maintenance only)
Score: 59/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 60, release rhythm 35, longevity 100
- inputs: {"age_days": 3171, "days_push": 241, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 73543, forks 7106 (observed 2026-08-28T04:12:21.310154+00:00)

## What it is
A curated, organized reading list of articles, papers, and case studies illustrating patterns of scalable, reliable, and performant large-scale systems. It covers design principles, scalability, availability, performance, real-world architectures, and system design interview preparation from engineers at major tech companies.

## Use cases
- prepare for a system design interview
- learn how large-scale systems achieve scalability and reliability
- understand scalability vs performance problems in distributed systems
- study real-world architectures of systems serving millions of users
- find reading material on availability and stability patterns
- learn how tech companies scale teams and engineering organizations

## When to choose
- you want curated, high-quality articles and case studies on system design and scalability
- you are preparing for backend or system design interviews
- you want battle-tested architectural patterns from companies like Google and Uber

## When to avoid
- you need runnable code, libraries, or tools rather than reading material
- you want a structured course with exercises instead of a link collection
- you need beginner-level programming tutorials

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools, documentation
- domain: microservices, backend, big-data, machine-learning, awesome-lists, tutorials
- platform: -
- tags: system-design, scalability, architecture, interview-preparation, reading-list, distributed-systems, performance, devops, web-server

## Member repositories
- binhnguyennus/awesome-scalability (main) score 59

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:21.310154+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:15:00.856020+00:00, confidence not recorded.
  - readme: https://github.com/binhnguyennus/awesome-scalability (fetched 2026-08-28T04:12:21.310154+00:00, sha 3dddc5f7a672)
- Data as of 2026-08-30T08:39:29.467469+00:00.