System Design Space
A knowledge space for system design
System Design Space has grown from system design interview materials into a full learning platform: a chapter catalog, a relationship graph, personalized tracks, progress, bookmarks and RU/EN material versions. Inside: architecture, distributed systems, databases, Cloud Native, SRE, Security, Frontend, ML and AI Engineering.
Why this site
- 01Turn scattered architecture knowledge into one connected map
- 02Prepare for system design, troubleshooting and platform interviews without memorizing templates
- 03Find the right chapters quickly through the catalog, search and filters by material type and difficulty
- 04Connect fundamentals to production practice: AI/ML, SRE, Security, Frontend and Cloud Native
Core learning mechanics
Materials catalog
All themes and chapters are gathered in a searchable catalog with filters by material type and difficulty, progress state and active-track mode.
Knowledge graph
More than two thousand conceptual links between chapters: focus on a theme, open adjacent materials and see the route from foundations to advanced concepts.
Personalized tracks
Choose a preparation horizon, level and background — the site builds a week, month or year route and splits it into foundation, core and stretch phases.
Progress and bookmarks
Enable tracking, mark chapters as completed, save important materials and return to them from Settings.
Material formats
Chapters are grouped by format — mix them to fit your learning goal.
Book notes
Key ideas from books on architecture, distributed systems, ML/AI, SRE and engineering practice with takeaways and source links.
System case studies
Step-by-step system design: from URL shorteners and CDNs to ML pipelines, payments, search, realtime and platform cases.
Documentaries
The history of technologies, languages, platforms, cloud native and AI through documentaries, interviews, timelines and sources.
Original chapters
Original chapters on design approaches, patterns, operational reliability, security and modern AI Engineering.
Topic map
16 thematic blocks — from hiring and interviews to dedicated ML Engineering and AI Engineering sections.
Who it's for
- Engineers preparing for system design, troubleshooting or platform interviews
- Architects and tech leads building a personal architecture knowledge map
- Backend, frontend, platform and SRE engineers who need a shared systems view
- ML/AI engineers connecting models, data, serving, evaluation and production architecture
Open and start
Graph, catalog, tracks, 307 chapters — pick a route and move at your own pace.
Go to system-design.space