Each concept below follows the same structure: What it is → Why it exists → When to use → When NOT to use → Trade-offs → Real examples → Diagram → Beginner mistakes.

The 12 Concept are :

1. Requirements Clarification

2. Back-of-Envelope Estimation

3. Databases — SQL vs NoSQL

4. Caching

5. Load Balancing

6. API Design

7. Message Queues + Event-Driven Systems

8. Consistency vs Availability (CAP Theorem)

9. Scaling Strategies

10. Fault Tolerance + Reliability

11. .CDN (Content Delivery Network)

12. Data Partitioning (Sharding)

But in this article we cover only two concept

1. Requirements Clarification

What it is: The process of translating an ambiguous problem into a precise, bounded spec.

WHY it exists: Ambiguity is the enemy of design. 'Design Twitter' could mean 1,000 different systems depending on scope.

When to use: Always. First. Before any component.

When NOT to skip: Never skip this. Even 5 minutes of clarification saves hours of rework.

Trade-offs: More time clarifying = less time building. But building the wrong thing is worse.

Real example: Google Maps v1 wasn't Google Maps today. It was turn-by-turn directions for a browser. Scope was tight.

Beginner mistake: Assuming you know what the problem means. Ask. Always ask.

2. Back-of-Envelope Estimation

What it is: Quick math to determine system scale before designing anything.

WHY it exists: Scale determines architecture. 1,000 users needs a single server. 100M users needs sharding, caching, CDNs.

Key numbers to memorize:

The estimation flowchart:

DAU → actions/day → QPS
QPS × record_size → bandwidth
QPS × record_size × 86400 → storage/day

Thresholds that change your design:

• QPS > 1,000 → consider load balancer

• QPS > 10,000 → definitely need cache

• Storage > 1TB → think about sharding

• Storage > 10TB → definitely need distributed storage

Beginner mistake: Skipping this and going straight to components. Your cache size, shard count, and server count all come from estimation.