interviewready/system-design-resources

★ 18,149Audience · developerComplexity · 1/5QuietLicenseSetup · easy

Mindmap

mindmap
  root((repo))
    What it covers
      Databases and storage
      Messaging and queues
      Distributed systems
      Infrastructure tools
    Learning resources
      Engineering blogs
      Research papers
      Video tutorials
    Key topics
      Scaling and performance
      Reliability and consensus
      Caching and CDNs
      Monitoring and logging
    Use cases
      Interview preparation
      System architecture study
      Production design patterns

mindmap root((repo)) What it covers Databases and storage Messaging and queues Distributed systems Infrastructure tools Learning resources Engineering blogs Research papers Video tutorials Key topics Scaling and performance Reliability and consensus Caching and CDNs Monitoring and logging Use cases Interview preparation System architecture study Production design patterns

Things people build with this

USE CASE 1

Prepare for system design interviews by studying real-world architectural patterns and trade-offs.

USE CASE 2

Learn how companies like Google and Amazon design databases, caching layers, and load balancers at scale.

USE CASE 3

Build a mental model of distributed systems concepts like consensus, replication, and rate limiting before designing your own.

USE CASE 4

Reference best practices for messaging systems, service meshes, and monitoring when architecting a new backend.

Getting it running

Difficulty · easy Time to first run · 5min

Use it freely, but any project you distribute that includes this code must also be GPL-licensed and open source.

In plain English

This repository is a curated reading list of resources for learning about system design, the discipline of planning how large-scale software systems are built to be reliable, fast, and able to handle many users at once. It is organized as a table of contents with links to articles, blog posts, research papers, and videos grouped by topic. Topics covered include video processing at scale, cluster and workflow management, messaging systems (and common pitfalls when using databases as message queues), service meshes (a way of managing communication between parts of a system), distributed file storage, time-series databases, rate limiting, in-memory databases, network protocols, collaborative document editing, API design, database replication, containers, load balancing, logging, metrics, caching, distributed consensus (how multiple computers agree on a value), authorization, content delivery networks, and testing distributed systems, among others. The linked resources draw from engineering blogs of large technology companies alongside academic papers and community explanations. It is intended as a study guide rather than original content, a starting point for people preparing for technical interviews or broadening their understanding of how production systems work at scale.

Copy-paste prompts

Prompt 1

I'm preparing for a system design interview. Walk me through the resources in this repo on database replication and distributed consensus so I understand the trade-offs.

Prompt 2

Show me how to use the articles on caching and CDNs from this repo to design a content delivery system for a global audience.

Prompt 3

Based on the messaging systems resources in this repo, explain why using a database as a message queue is a bad idea and what to use instead.

Prompt 4

Help me understand the service mesh topic from this repo's resources, what problem does it solve and when would I use one?

Prompt 5

Create a study plan using this repo's resources to learn about scaling databases, load balancing, and handling millions of concurrent users.

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