Software Architecture
Design scalable, maintainable software systems through sound architectural principles.
System Design Process
- •
Requirements Analysis
- •Functional requirements (features)
- •Non-functional requirements (performance, scale, security)
- •Constraints (budget, timeline, team skills)
- •
High-Level Design
- •Component identification
- •Data flow mapping
- •Integration points
- •Technology selection
- •
Detailed Design
- •API contracts
- •Data models
- •Sequence diagrams
- •Error handling
- •
Validation
- •Review against requirements
- •Identify trade-offs
- •Document decisions
Architectural Patterns
Monolith
code
Best for: Early-stage products, small teams Trade-offs: Simple to develop, harder to scale independently
Microservices
code
Best for: Large teams, independent scaling needs Trade-offs: Complex operations, better team autonomy
Event-Driven
code
Best for: Decoupled systems, async workflows Trade-offs: Eventual consistency, good scalability
Layered (N-Tier)
code
Best for: Clear separation of concerns Trade-offs: Simple structure, potential performance overhead
API Design
REST Guidelines
- •Use nouns for resources:
/users,/orders - •HTTP verbs for actions: GET, POST, PUT, DELETE
- •Version APIs:
/v1/users - •Consistent error responses
GraphQL Guidelines
- •Single endpoint
- •Client-specified queries
- •Strong typing with schema
- •Batch requests naturally
Data Modeling
Relational
- •Normalize to 3NF by default
- •Denormalize for read performance
- •Index query patterns
- •Consider partitioning for scale
NoSQL Selection
| Type | Use Case |
|---|---|
| Document (MongoDB) | Flexible schema, nested data |
| Key-Value (Redis) | Caching, sessions |
| Column (Cassandra) | Time-series, high write throughput |
| Graph (Neo4j) | Relationships, social networks |
Scalability Patterns
Horizontal Scaling
- •Stateless services
- •Load balancing
- •Database sharding
- •Caching layers
Vertical Scaling
- •Increase resources
- •Optimize algorithms
- •Database tuning
- •Limited ceiling
Common Bottlenecks
- •Database queries (add indexes, cache, read replicas)
- •Network latency (CDN, regional deployment)
- •CPU-bound operations (async processing, workers)
- •Memory (cache eviction, pagination)
Technology Selection
Decision Criteria
- •Team expertise
- •Community support
- •Maturity and stability
- •Performance characteristics
- •Licensing and cost
Documentation Template
markdown
## Decision: [Title] **Date**: [Date] **Status**: [Proposed/Accepted/Deprecated] ### Context [Why is this decision needed?] ### Options Considered 1. [Option A] - [Pros/Cons] 2. [Option B] - [Pros/Cons] ### Decision [What was decided and why] ### Consequences [What are the implications?]
Security Considerations
Authentication
- •OAuth2/OIDC for third-party auth
- •JWT for stateless tokens
- •Session cookies for web apps
Authorization
- •RBAC for role-based access
- •ABAC for attribute-based rules
- •Principle of least privilege
Data Protection
- •Encrypt at rest and in transit
- •Sanitize inputs
- •Audit sensitive operations
Documentation Outputs
Architecture Decision Records (ADRs)
- •One decision per record
- •Include context and alternatives
- •Track status over time
System Diagrams
- •C4 model (Context, Container, Component, Code)
- •Sequence diagrams for flows
- •Data flow diagrams
API Documentation
- •OpenAPI/Swagger for REST
- •GraphQL schema with descriptions
- •Example requests/responses