This System Design Deep Dive series is your structured roadmap to mastering modern system architecture. Whether you're preparing for interviews or architecting production-grade systems, this guide walks you from foundational principles to advanced scalability techniques.
You'll explore essential topics like SOLID principles, ACID compliance, design patterns, and the CAP theorem, then move into distributed system strategies such as caching, sharding, Kubernetes auto-scaling, and the SAGA pattern. Each topic is explained with practical examples and visual aids to help you connect theory to implementation.
Part 1: Foundational Principles for Scalable Systems
- 🔗 SOLID Principles – Object-oriented foundation for modular, testable design
- 🔗 ACID Principles – Database transaction reliability and integrity
- 🔗 CAP Theorem – Trade-offs between consistency, availability, and partition tolerance
- 🔗 Consistent Hashing – A key distributed systems technique for partitioning and scalability
- 🔗 Design Patterns – Proven reusable solutions for common design problems
Part 2: Designing and Estimating Real-World Systems
- 🔗 Asking Clarifying Questions – Define the problem before you design
- 🔗 Back-of-the-Envelope Estimation – Quickly sizing systems for expected load
- 🔗 High-Level Design (HLD) – Macro architecture: components, flow, and integration
- 🔗 Low-Level Design (LLD) – Detailed class/interface design, database schema, etc.
Part 3: Scaling Infrastructure and Distributed Systems (in-progress)
- 🔗 Distributed Caching – Reduce latency and backend load
- 🔗 Bloom Filters – Memory-efficient existence checks for caching
- 🔗 Database Sharding – Horizontally scaling your data layer
- 🔗 Service Discovery (Eureka) – Dynamic detection of services in a distributed setup
- 🔗 Auto-Scaling with Kubernetes – Elastic infrastructure based on load
- 🔗 Content Delivery Networks (CDNs) – Speed up static asset delivery across geographies
- 🔗 SAGA Pattern – Managing distributed transactions and eventual consistency
Final Thoughts
System design isn’t about memorizing patterns—it’s about understanding trade-offs and making decisions that align with your application’s needs.
Should you prioritize consistency or availability? Optimize for latency or cost? Move fast or build safely? These are the questions real-world architects must answer every day.
This series is designed to build your intuition around those trade-offs. Each post is self-contained but contributes to a complete understanding of how to build resilient, scalable, and efficient systems.
Enjoyed the post? 📌 Bookmark it for later and drop a comment — your feedback keeps the content sharp!