System Design Interview Prep: From Monolith to Microservices

System design interviews assess your ability to architect large-scale systems. This guide breaks down the core concepts and approach patterns.

The Interview Structure

A typical system design interview spans 45-60 minutes and evaluates:

1. Requirements gathering - Ask clarifying questions
2. High-level design - Sketch the system architecture
3. Deep dives - Explore specific components in detail
4. Scaling - Discuss how to handle growth
5. Trade-offs - Explain design choices

Understanding Scale

Small scale:   1,000 users, 1GB data
Medium scale:  1M users, 1TB data
Large scale:   100M users, 100TB data+

Monolithic Architecture

A single codebase handling all functionality:

┌─────────────────────────────┐
│     Single Application      │
│  - User Service             │
│  - Order Service            │
│  - Payment Service          │
│  - Notification Service     │
└──────────┬──────────────────┘
           │
        Database

Pros: Simple to build and test initially

Cons:

• Hard to scale individual components
• Technology lock-in
• Complex deployments
• Can't scale teams independently

Microservices Architecture

Decompose into independent services:

┌──────────────┐  ┌──────────────┐  ┌──────────────┐
│ User Service │  │ Order Service│  │Payment Service
└──────────────┘  └──────────────┘  └──────────────┘
      │                  │                  │
   User DB          Order DB          Payment DB

Pros:

• Scale each service independently
• Different tech stacks per service
• Faster deployments
• Team ownership per service

Cons:

• Distributed system complexity
• Network latency
• Data consistency challenges
• Operational overhead

Key Design Patterns

Load Balancing

           ┌─ Server 1
Client ─ Load Balancer ─┼─ Server 2
           └─ Server 3

Distributes requests across multiple servers. Algorithms: Round Robin, Least Connections, IP Hash.

Caching

Request → Cache (hit?)
             ├─ Yes → Return immediately
             └─ No → Database → Update cache

Reduces database load dramatically. Tools: Redis, Memcached.

Message Queues

Service A → Message Queue → Service B
(Producer)                 (Consumer)

Decouples services, enables async processing. Tools: RabbitMQ, Kafka, AWS SQS.

Database Replication

Master Database
       │
   Slave 1 (Read-only)
   Slave 2 (Read-only)

Improves read performance and resilience.

Sharding Strategy

Split data across multiple databases:

Users 1-1M  → Database 1
Users 1M-2M → Database 2
Users 2M+   → Database 3

Based on user ID, geography, or other keys.

Example: Design Twitter

Requirements

• 100M active users
• Read-heavy: 200:1 read-to-write ratio
• Real-time feed updates
• Complex search

High-Level Design

┌─────────┐  ┌────────┐  ┌─────────┐
│  CDN    │  │  API   │  │ Tweet   │
│ (Static)│──│ Gateway├──│ Service │
└─────────┘  └────────┘  └─────────┘
                │              │
             Cache         Database

Components

1. Tweet Service: CRUD operations, validation
2. Feed Service: Real-time feed generation
3. Search Service: Elasticsearch for searching
4. Cache Layer: Redis for hot tweets
5. CDN: Static content distribution

Scaling Decisions

• Read replicas for search queries
• Message queue for async operations
• Cache for feed queries (expensive)
• Sharding by user ID
• CDN for media distribution

Trade-offs to Discuss

Interview Tips

1. Ask questions - Clarify ambiguous requirements
2. Think out loud - Explain your reasoning
3. Trade-offs - Acknowledge pros and cons
4. Start simple - Handle scale incrementally
5. Draw diagrams - Visualize architecture
6. Discuss metrics - Latency, throughput, availability

Common Mistakes

❌ Jumping to solutions without understanding requirements ❌ Overengineering for current scale ❌ Not discussing monitoring and logging ❌ Ignoring security considerations ❌ No fallback/disaster recovery plans

Practice Problems

1. Design a URL shortener (bit.ly)
2. Design an e-commerce system (Amazon)
3. Design a real-time chat application
4. Design a video streaming platform (YouTube)
5. Design a search engine (Google)

Ready to practice? Sharpen your skills with Kodion's AI mock interviews, system design rounds included.