🏗️ Software System Design Guide

Learn how large-scale systems are built — from scalability and caching to databases and real-world case studies. A clear, free guide for engineering students and interview preparation.

13Lessons
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InterviewReady

📚 Fundamentals

The core building blocks behind every scalable system.

🧭01

How to Approach System Design

A simple, repeatable framework for any system design problem — clarify requirements, estimate scale, sketch the high-level design, deep-dive components and find bottlenecks.

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📈02

Scalability: Vertical vs Horizontal Scaling

Understand scalability in system design — vertical vs horizontal scaling, stateless services, and why horizontal scaling is the backbone of large systems.

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⚖️03

Load Balancing

How load balancers distribute traffic across servers — round robin, least connections, hashing, Layer 4 vs Layer 7, and health checks for high availability.

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04

Caching

How caching speeds up systems — where to cache, write-through vs write-back vs write-around, eviction policies like LRU, and the hard problem of cache invalidation.

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🗄️05

SQL vs NoSQL Databases

When to choose SQL vs NoSQL in system design — ACID vs BASE, relational vs document/key-value/wide-column/graph stores, and how to pick for your workload.

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🧩06

Sharding and Replication

How to scale databases with replication (copies for reads and failover) and sharding (partitioning data across nodes) — strategies, hotspots and trade-offs.

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🔺07

The CAP Theorem

The CAP theorem in plain English — consistency, availability and partition tolerance, why you can only pick two during a partition, and the PACELC extension.

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📨08

Message Queues & Async Processing

How message queues decouple services and smooth spikes — producers, consumers, pub/sub, at-least-once delivery, and when to process work asynchronously.

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🔗09

Consistent Hashing

Why naive hashing breaks when servers change, and how consistent hashing (the hash ring + virtual nodes) minimises data movement in distributed caches and databases.

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🔌10

API Design & Rate Limiting

Principles of good API design — REST, idempotency, pagination and versioning — plus rate limiting algorithms like token bucket and sliding window.

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📐 Case Studies

Apply the building blocks to real design problems asked in interviews.

Preparing for placements?

System design pairs with coding rounds and company prep. Keep going: