Software Architecture & System Design
Software Architecture & System Design separates junior coders from high-earning engineering leaders. Master Low-Level Design (LLD - OOP principles, SOLID, GoF Design Patterns, UML), High-Level Design (HLD - CAP theorem, database sharding, consistent hashing, CQRS, Event Sourcing), and architecting systems at scale (designing Uber, Netflix, WhatsApp).
🇮🇳 Indian Market Benchmark
Core Track Highlights
Scalable System Design & Distributed Architecture Blueprint
DNS routing, Global CDN, API Gateway, Sharded databases, Consistent Hashing, and Event Bus.
SOLID & Clean Code (LLD)
Single Responsibility, Open-Closed, Liskov, Interface Segregation, Dependency Inversion.
Scalability & Sharding (HLD)
Consistent hashing, database horizontal sharding, and master-slave replication.
Event-Driven Patterns
CQRS (Command Query Responsibility Segregation), Sagas, and Event Sourcing.
High Availability & Resiliency
Active-Active multi-region deployments, circuit breakers, and fault-tolerant fallbacks.
Structured Phase-by-Phase Syllabus
Focus on build-by-doing milestones rather than passive video consumption.
Phase 1: Low-Level Design (LLD) & Object-Oriented Principles
- SOLID design principles with real-world refactoring examples in clean code
- GoF Design Patterns: Factory, Singleton, Strategy, Observer, Decorator, Adapter, and Builder
- Designing LLD systems: Parking Lot, BookMyShow booking engine, Splitwise expense sharing, Elevator system
Phase 2: High-Level Design (HLD) Building Blocks
- CAP Theorem, PACELC theorem, and Base vs ACID consistency models
- Database partitioning: Horizontal vs Vertical Sharding, Consistent Hashing algorithms
- Load balancing algorithms, distributed locking, and message delivery guarantees (At-least-once, Exactly-once)
Phase 3: Large-Scale System Design Case Studies
- Designing a Global Video Streaming Platform (Netflix / YouTube architecture: Transcoding, CDN, metadata)
- Designing a Real-Time Messaging App (WhatsApp: WebSockets, XMPP, E2E encryption, message queue states)
- Designing a Distributed Rate Limiter and Distributed Cache at billion-request scale
Technical Interview Questions & Answers
Q1: How does Consistent Hashing minimize data movement when scaling out a distributed cache cluster?
In traditional modular hashing (hash(key) % N), adding or removing a server changes the divisor N, causing almost 100% of keys to rehash to different servers and destroying the cache. Consistent Hashing maps both keys and servers to positions on an abstract 360-degree hash ring. When a server is added or removed, only the keys residing between that server and its immediate neighbor need to be migrated (an average of K/N keys), minimizing data re-balancing overhead. Virtual nodes are added to prevent hot spots.
Frequently Asked Questions
How is a System Design interview structured at top tech product companies?
Typically 45–60 minutes: (1) Scope requirements (functional/non-functional) & back-of-the-envelope estimation (5-10m), (2) High-level component diagram & API contracts (15m), (3) Database schema & deep dive into bottleneck components (15m), (4) Scalability, failure recovery, and edge-case tradeoffs (10m).
Target Job Roles
Staff Software Engineer / Lead Architect
Demand: Very HighPrincipal System Architect / Director of Engineering
Demand: HighRelated Career Tracks
Need a Personalized Career Plan?
Take our 20+ Signal Career Compass to assess aptitude and discover suitable roadmaps.
Start Career Compass