01
Understand, don't memorize
Architectures are outputs. What transfers is the reasoning that produced them — the forces, the trade-offs, the why.
02
Play, don't just read
The hard-to-feel ideas — load breaking a server, the latency ladder, tail-at-scale — are live instruments here. Operate them.
03
Direct the build
Every chapter ends by turning understanding into leverage: how to brief an AI to build the thing, and how to tell if it got it wrong.
What's inside
From the one server you already run to the systems that carry millions of requests a day — the building blocks, the hard parts, the algorithms of scale, and six full case studies.
Part I · Foundations
- 01What System Design Actually Is
- 02The One-Server App (and Why It Breaks)
- 03Thinking in Numbers
- 04Latency, Throughput & Speed
Part II · The Building Blocks
- 05Load Balancers
- 06Databases
- 07Caching
- 08CDNs
- 09Queues & Async
- 10Storage & Search
Services & Architecture
- M1Monolith, Microservices & Where to Draw the Line
- M2APIs & Protocols
- M3Discovery, Gateways & the Mesh
Part III · The Hard Parts
- 11Replication: Copies Everywhere
- 12Partitioning & Sharding
- 13Consistency & CAP
- 14Transactions, Sagas & Idempotency
- 15Failure Is Normal: Resilience
- 16Observability
Algorithms of Scale
- G1Consistent Hashing
- G2Quorums, Consensus & Raft
- G3Probabilistic Structures
- G4Rate Limiting
- G5Gossip, Merkle Trees & CRDTs
Part IV · Putting It Together
- 17A Framework for Any Design
- 18Case Study: URL Shortener
- 19Case Study: Chat System
- 20Case Study: News Feed
- 21Case Study: Video Streaming
- 22Case Study: Ride-Hailing
- 23Case Study: Search Autocomplete
- 24The Architect's Mindset
Reference & Threads
- R1Security at Scale
- R2Deployment & DevOps
- R3Cost & Capacity Planning
- R4Glossary & Numbers
Start with Chapter 1
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