Resources
System Design is about understanding how large-scale applications work behind the scenes — how data flows, how systems handle millions of users, and how everything stays fast, secure, and reliable.
This section includes the best learning resources and core techniques to build that foundation.
🎥 Recommended Videos
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FAANG System Design Playlist (Tech Dummies) — Watch here Great beginner-friendly breakdowns of popular design questions.
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Gaurav Sen — System Design Concepts — Watch here Deep conceptual explanations with real-world analogies.
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System Design for Interviews (ByteByteGo) — Watch here Clean visual diagrams and clear explanation of trade-offs.
📘 Cheatsheets & Reading
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Design Gurus Cheat Sheet — designgurus.io/blog/system-design-cheat-sheet Covers scalability, databases, load balancing, caching, etc.
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Medium Guide (Java Revisited) — System Design Cheatsheet Good for quick revision and interview prep.
🧠 Core Techniques & Concepts
Below are some of the most common ideas that appear across all system design problems:
| Concept | Description |
|---|---|
| Load Balancing | Distribute incoming traffic across multiple servers to avoid overload. |
| Caching | Store frequently accessed data in memory (e.g., Redis, Memcached) to reduce latency. |
| Database Sharding | Split large databases into smaller pieces (shards) to improve performance. |
| Replication | Keep copies of data across servers to ensure reliability and faster reads. |
| Message Queues | Use systems like Kafka or RabbitMQ to handle asynchronous communication. |
| CDN (Content Delivery Network) | Serve static files (images, CSS, JS) from the nearest geographical servers. |
| Rate Limiting | Control how many requests a user/system can make to prevent abuse or overload. |
| Microservices | Split large applications into smaller independent services that communicate via APIs. |
| API Gateway | Central entry point that manages requests, authentication, and routing. |
| CAP Theorem | In distributed systems, only two of Consistency, Availability, and Partition Tolerance can coexist. |
| Consistency Models | Understand Strong vs Eventual Consistency trade-offs in distributed databases. |
| Data Partitioning | Separate data logically (by user ID, region, etc.) to scale horizontally. |
| Monitoring & Logging | Track performance and detect failures (Prometheus, Grafana, ELK). |
| Fault Tolerance | Design systems that keep running even when parts fail. |