2 min read
System Design Index
Start Here
Tier 1 -- Building Blocks
Scale Reads
Scale Writes
Database Selection
Traffic Control
Consistency & Coordination
Estimation
Tier 2 -- Core Systems
Tier 3 -- Location & Real-Time
Tier 4 -- Infrastructure & Data
Tier 5 -- Finance & Commerce
Tier 6 -- Advanced & Collaborative
System Design Index
Start Here
Tier 1 -- Building Blocks
Scale Reads
Scale Writes
Database Selection
Traffic Control
Consistency & Coordination
Estimation
Tier 2 -- Core Systems
Tier 3 -- Location & Real-Time
Tier 4 -- Infrastructure & Data
Tier 5 -- Finance & Commerce
Tier 6 -- Advanced & Collaborative
Getting Started with System Design
This guide helps you use the Design Lab efficiently and build real interview readiness in a structured way.
What you have in Design Lab
- Tiered system design docs from foundations to advanced systems.
- Interactive glossary highlights for difficult terms.
- "Quiz This Topic" quick path on normal docs to jump into focused quiz practice.
- A progressive roadmap so you can move from concepts to complete system case studies.
A junior engineer is studying system design and wants to build knowledge progressively — starting from core concepts before tackling complex, real-world architectures. Which learning structure best supports this goal?
Recommended study flow
1) Start with Tier 1 Building Blocks
Learn core primitives first:
- Caching and read scaling
- Write scaling and
- Database selection trade-offs
- Traffic control and resilience patterns
- Consistency and coordination models
Goal: explain each building block in simple language and know when to apply it.
2) Practice right after each concept
After reading a concept doc, do focused quiz practice while the idea is fresh. Use mistakes as signal:
- If you miss trade-off questions, review "why" and failure modes.
- If you miss scenario questions, practice mapping requirements to components.
Goal: convert passive reading into active recall and decision-making.
3) Move tier by tier
Follow the catalog order:
- Tier 2 Core Systems
- Tier 3 Location and Real-Time
- Tier 4 Infrastructure and Data
- Tier 5 Finance and Commerce
- Tier 6 Advanced and Collaborative
Do not rush. Stay in a tier until you can explain common architecture patterns without notes.
A junior engineer is studying system design and has just finished reading about caching and read scaling. According to a structured study approach, what should they do immediately after finishing that concept?
Weekly rhythm (simple and sustainable)
- 3 reading sessions per week (30-45 minutes each)
- 2 focused quiz sessions per week (20-30 minutes each)
- 1 synthesis session per week:
- pick one problem
- draw architecture from memory
- review bottlenecks, consistency choices, and failure handling
A developer wants to build a sustainable weekly study routine for system design. They plan three reading sessions and two quiz sessions each week. For their sixth session type, they decide to pick one system design problem, draw its architecture from memory, and then review bottlenecks, consistency trade-offs, and failure scenarios. What is the primary benefit of this sixth session compared to simply doing more reading or quizzing?
How to know you are improving
- You can articulate trade-offs, not just name components.
- You can estimate scale quickly before drawing architecture.
- You can defend design choices under changing constraints.
A senior engineer asks you to justify why you chose a relational database over a NoSQL store for a new service. Partway through your explanation, they change the requirement: 'Now assume the write volume doubles every month for the next year.' Which response best demonstrates genuine system design proficiency?
Common mistakes to avoid
- Jumping to complex systems before mastering foundations.
- Memorizing diagrams without understanding bottlenecks.
- Skipping feedback loops from quiz errors.
Keep your pace consistent. Strong foundations make advanced systems much easier.
A junior engineer is studying distributed systems. They spend most of their time memorizing architecture diagrams of systems like Kafka and Cassandra, but when asked why a particular design uses consistent hashing or how it handles a sudden spike in write throughput, they struggle to answer. What is the most likely root cause of this gap?
Glossary History
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Glossary History
Click dotted jargon to save explanations here.