# System Design Interview Basics

System design interviews ask you to design a large-scale distributed system (like "Design Twitter" or "Design Uber"). These questions are open-ended, ambiguous, and test your ability to analyze requirements, make trade-offs, and define architecture.

## 1. A 4-Step Framework

### Step 1: Understand the Problem & Establish Design Scope
Don't jump into drawing boxes immediately. Ask questions to clarify requirements.
*   **Functional Requirements:** What does the system actually do? (e.g., Users can post tweets, follow others).
*   **Non-Functional Requirements:** Scalability, Latency, Consistency, Availability. (e.g., System must handle 100M DAU).
*   **Constraints:** Traffic volume, read/write ratio.

### Step 2: Propose High-Level Design
Draw a diagram representing the core components and how data flows between them.
*   Clients (Mobile/Web)
*   Load Balancer
*   Web Servers (API)
*   Database
*   Cache

### Step 3: Design Deep Dive
Identify bottlenecks and scale the system.
*   **Scaling:** Vertical vs Horizontal.
*   **Database:** SQL vs NoSQL? Sharding? Replication?
*   **Caching:** Where to cache? (CDN, Redis).
*   **Async Processing:** Message queues for heavy lifting.

### Step 4: Wrap Up
*   Discuss potential bottlenecks.
*   How would you monitor the system?
*   Summary of trade-offs made.

## 2. Key Concepts to Master

*   **Scalability:** Horizontal vs Vertical scaling.
*   **Reliability & Availability:** Redundancy, failover.
*   **Consistency Patterns:** Strong vs Eventual consistency (CAP Theorem).
*   **Data Partitioning:** Sharding strategies.

## 3. Back-of-the-Envelope Calculations

You often need to estimate storage or bandwidth requirements.
*   **Powers of Two:** $2^{10} \approx 10^3$ (1KB), $2^{20} \approx 10^6$ (1MB), $2^{30} \approx 10^9$ (1GB).
*   **Latency Numbers:**
    *   L1 cache: 0.5 ns
    *   Main memory ref: 100 ns
    *   Disk seek: 10,000,000 ns (10 ms) -> **Disk is slow!**

[[programming/distributed-systems]]
[[programming/load-balancing]]
[[programming/caching-strategies]]
[[programming/database-basics]]