System Architecture

Expertise in designing scalable, maintainable systems and understanding distributed architecture patterns

Microservices Architecture

Microservices Concepts

Service Decomposition: Identifying service boundaries
Service Communication: Synchronous (REST) vs. asynchronous (events)
Data Management: Per-service databases, eventual consistency
Resilience: Fault isolation, graceful degradation
Scalability: Independent scaling of services

Service Design

Single Responsibility Principle
Clear API contracts
Service versioning
Backward compatibility
Service discovery

Inter-Service Communication

REST APIs
Synchronous RPC calls
Event-driven communication
Message queues
Service mesh concepts

Distributed System Challenges

Network partitions
Consistency vs. availability (CAP theorem)
Eventual consistency patterns
Distributed transactions
Saga pattern for distributed workflows
Idempotency for reliability

Monolithic vs. Distributed

When to Use Each

Monolithic advantages: Simpler deployment, easier debugging, transactional consistency
Microservices advantages: Independent scaling, fault isolation, technology flexibility
Hybrid approaches: Modular monoliths, strategic services

Migration Patterns

Strangler pattern
Service extraction
Incremental migration
Rollback capabilities

System Design Patterns

Architectural Patterns

Layered Architecture: UI, business, persistence, database layers
Event-Driven Architecture: Publish-subscribe, event sourcing
CQRS (Command Query Responsibility Segregation): Separating read and write models
API Gateway Pattern: Single entry point for clients
Service Mesh: Infrastructure layer for service communication

Design Patterns

Singleton: Global state management (use carefully)
Factory: Object creation abstraction
Observer: Event-driven patterns
Strategy: Pluggable algorithms
Adapter: Interface translation
Facade: Simplified interfaces

Data Patterns

Repository Pattern: Data access abstraction
DAO (Data Access Object): Database abstraction
Cache-Aside: Lazy loading pattern
Write-Through: Immediate consistency
Event Sourcing: Event-based state management

Concurrency Patterns

Lock-Based Synchronization: Mutexes, semaphores
Lock-Free Algorithms: Atomic operations
Actor Model: Message-based concurrency
Thread Pools: Executor services

API Design

REST API Design

Resource-oriented design
Proper HTTP methods
Meaningful status codes
Error response format
Hypermedia (HATEOAS)
Pagination and filtering
Versioning strategies

API Versioning

URL-based versioning (/v1/, /v2/)
Header-based versioning
Accept header versioning
Backward compatibility
Deprecation policies

GraphQL (Basic Understanding)

Query language for APIs
Type system
Resolver functions
Advantages and trade-offs

Scalability

Horizontal Scaling

Stateless services
Load balancing
Distributed caching
Database replication
Database sharding

Vertical Scaling

Resource optimization
Caching strategies
Code efficiency
Database optimization
Limits and considerations

Performance Optimization

Profiling and bottleneck identification
Query optimization
Caching layers
Asynchronous processing
Resource pooling

Security Architecture

Authentication & Authorization

Session-based authentication
Token-based authentication (JWT)
OAuth2 / OpenID Connect
Role-based access control (RBAC)
Attribute-based access control (ABAC)

Network Security

TLS/SSL encryption
Firewalls and network segmentation
VPN and secure tunneling
DDoS mitigation
Intrusion detection

Data Security

Encryption at rest
Encryption in transit
Key management
Secrets management
PII protection
Data classification

Application Security

Input validation
SQL injection prevention
Cross-site scripting (XSS) prevention
Cross-site request forgery (CSRF) prevention
Secure coding practices

Reliability & Resilience

High Availability

Redundancy
Failover mechanisms
Load distribution
Health monitoring
Self-healing systems

Fault Tolerance

Circuit breaker pattern
Retry logic with exponential backoff
Bulkhead isolation
Graceful degradation
Timeout management

Observability

Logging: Structured logs, log aggregation
Metrics: System and application metrics
Tracing: Distributed request tracing
Alerting: Threshold-based notifications
Dashboards: Real-time visualization

Cost Optimization

Resource Efficiency

Right-sizing resources
Utilization monitoring
Cost allocation
Reserved capacity
Auto-scaling optimization

Infrastructure Costs

Cloud provider selection
Spot instances or cheaper alternatives
Data transfer optimization
Storage tiering
Reserved capacity planning

Testing Strategy

Test Pyramid

Unit tests (70%)
Integration tests (20%)
End-to-end tests (10%)
Test coverage metrics

Testing Types

Unit Testing: Individual component testing
Integration Testing: Component interaction testing
System Testing: Full system testing
Performance Testing: Load and stress testing
Chaos Engineering: Resilience testing

Test Environment

Production parity
Test data management
Mocking external services
Continuous integration

Deployment Strategy

Deployment Approaches

Blue-Green Deployment: Zero-downtime updates
Canary Deployment: Gradual rollout
Rolling Deployment: Sequential updates
Feature Flags: Decoupled deployment from release

CI/CD Pipeline

Automated testing
Build optimization
Deployment automation
Rollback capabilities
Artifact management

Database Architecture

ACID vs. BASE

ACID: Atomicity, Consistency, Isolation, Durability
BASE: Basically Available, Soft state, Eventually consistent
Trade-offs and use cases

Database Selection

Relational vs. NoSQL
Document stores
Key-value stores
Time-series databases
Graph databases

Data Consistency

Strong consistency
Eventual consistency
Consistency boundaries
Distributed transactions

Monolith Modularization

Modular Monoliths

Separate bounded contexts
Clear module boundaries
Module APIs
Dependency management
Evolution toward microservices

Technology Decisions

Evaluation Criteria

Team expertise
Performance requirements
Operational overhead
Community and ecosystem
Cost considerations
Learning curve
Long-term support

Trade-offs

Performance vs. maintainability
Consistency vs. availability
Simplicity vs. features
Cost vs. capability

Documentation

Architecture Documentation

Architecture Decision Records (ADRs)
System diagrams
Component descriptions
Data flow diagrams
Interface specifications

Living Documentation

Up-to-date specifications
Change tracking
Version control
Review and validation

Principles

SOLID Principles: Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, Dependency Inversion
DRY (Don’t Repeat Yourself): Code reuse and single source of truth
KISS (Keep It Simple, Stupid): Simplicity in design
YAGNI (You Aren’t Gonna Need It): Avoid premature optimization
Conway’s Law: System structure reflects communication structure

Measurement & Monitoring

Key Metrics

Latency: Response time
Throughput: Requests per second
Availability: Uptime percentage
Error rate: Failed requests percentage
Resource utilization: CPU, memory, disk

SLOs and SLIs

Service Level Objectives (SLOs): Targets
Service Level Indicators (SLIs): Measurements
Error budgets: Acceptable failures
Alerting thresholds

Best Practices

Keep systems as simple as possible
Measure before optimizing
Plan for failure
Think about operations early
Document critical decisions
Regular architecture reviews
Technology diversity within bounds
Team skill consideration