System Design Framework

Overview

The System Design Framework is a proven methodology for architecting distributed systems that can scale to millions of users while remaining maintainable and cost-effective.

Core Thesis

Modern systems require:

• Clarity over cleverness: Simple designs scale better
• Separation of concerns: Each layer has one job
• Incremental complexity: Add sophistication only when needed
• Operational awareness: Design for deployment, not just development

Framework Phases

1. Requirements Gathering

Functional Requirements:

• What does the system do?
• Who are the users?
• What are the core use cases?

Non-Functional Requirements:

• Scale: How many users? Requests per second?
• Performance: Latency requirements?
• Availability: Uptime targets?
• Consistency: Strong vs eventual?

2. Capacity Estimation

Calculate:

• Storage needs (data volume × retention period)
• Bandwidth requirements (requests/sec × payload size)
• Memory for caching (hot data percentage)
• Compute capacity (CPU/memory per request)

3. System Interface Definition

Design APIs first:

• RESTful endpoints or GraphQL schema
• Request/response formats
• Authentication/authorization
• Rate limiting strategy

4. Data Modeling

Choose storage strategy:

• SQL: Relational data, ACID transactions
• NoSQL: High write throughput, flexible schema
• Cache: Hot data, read-heavy workloads
• Object Storage: Large files, media

5. High-Level Design

Components:

• Load balancers
• Application servers
• Caching layer
• Database (primary/replica)
• Message queues
• CDN for static assets

6. Detailed Component Design

For each component:

• Scalability strategy (horizontal/vertical)
• Failure modes and recovery
• Monitoring and alerting
• Operational runbooks

7. Trade-offs and Bottlenecks

Evaluate:

• CAP theorem implications
• Consistency vs availability
• Latency vs cost
• Complexity vs maintainability

Key Patterns

1. Service & Repository Pattern: Separate business logic from data access
2. CQRS: Split read and write models for scalability
3. Event Sourcing: Capture all changes as events
4. Saga Pattern: Manage distributed transactions

Real-World Applications

Amazon: Service-oriented architecture with caching at every layer

Netflix: Microservices with circuit breakers and fallbacks

Uber: Geo-distributed architecture with strong consistency for critical paths

When to Use This Framework

Use for:

• Greenfield large-scale systems
• Modernizing monolithic applications
• Interview preparation (system design rounds)
• Architecture reviews

Skip for:

• MVPs or prototypes
• Internal tools with < 100 users
• Single-server applications

Implementation Checklist

• Define functional and non-functional requirements
• Estimate capacity needs (storage, bandwidth, compute)
• Design API contracts
• Choose data storage strategy
• Create high-level architecture diagram
• Identify single points of failure
• Plan monitoring and alerting
• Document operational procedures
• Design for failure (circuit breakers, retries, timeouts)
• Plan for deployment and rollback

This framework is not about perfect designs—it's about systematic thinking that leads to robust, scalable systems.