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Architecture Patterns & Decisions

The GISE Architecture Patterns library provides proven architectural solutions with clear cost-benefit analysis and implementation guidance. Each pattern includes decision rationale, implementation details, and trade-off analysis.

Architecture Pattern Categories​

Quick Pattern Lookup​

By Complexity Level​

PatternComplexityUse CaseTime to Implement
Monolith First🟢 BeginnerSingle team, MVP, rapid development1-2 weeks
Modular Monolith🟡 IntermediateGrowing team, clear boundaries2-4 weeks
API Gateway🟡 IntermediateMultiple services, centralized auth1-3 weeks
Microservices🔴 AdvancedLarge teams, independent deployment6-12 weeks
Event-Driven🔴 AdvancedAsync processing, loose coupling4-8 weeks

By Team Size​

Team SizeRecommended PatternsRationale
1-3 developersMonolith First, Simple CRUDMinimize complexity overhead
4-8 developersModular Monolith, API GatewayClear boundaries, manageable complexity
8-15 developersService-Oriented, Event-DrivenIndependent team ownership
15+ developersMicroservices, Domain-Driven DesignOrganization scalability

By Business Context​

Business NeedPrimary PatternsSecondary Patterns
Rapid MVPMonolith FirstSimple CRUD, Static Assets
Scale ExistingAPI GatewayLoad Balancing, Caching
Complex DomainDomain-Driven DesignCQRS, Event Sourcing
High AvailabilityMulti-RegionCircuit Breaker, Bulkhead

Foundational Architecture Patterns​

1. Monolith First Pattern​

When to Use:

  • New projects with uncertain requirements
  • Small teams (1-5 developers)
  • Rapid prototyping and MVP development
  • Clear performance requirements

Implementation Checklist:

  • Single deployable unit
  • Shared database with clear schema
  • Modular code organization by domain
  • Comprehensive test suite
  • Monitoring and logging configured

Cost-Benefit Analysis:

AspectBenefitCost
Development Speed⭐⭐⭐⭐⭐ Fastest initial development⭐⭐ Slower feature development as codebase grows
Operational Complexity⭐⭐⭐⭐⭐ Single deployment unit⭐⭐ Single point of failure
Team Coordination⭐⭐⭐⭐ Easier coordination⭐⭐⭐ Merge conflicts increase
Technology Flexibility⭐⭐ Single technology stack⭐⭐⭐⭐ Limited technology choices

2. Modular Monolith Pattern​

When to Use:

  • Growing teams (4-8 developers)
  • Clear domain boundaries identified
  • Need for independent module development
  • Future microservices migration planned

Migration Path from Monolith:

  1. Identify domain boundaries
  2. Extract modules with clear APIs
  3. Implement inter-module communication
  4. Add module-specific testing
  5. Plan database separation strategy

3. API Gateway Pattern​

Key Features:

  • Authentication & Authorization: Centralized security
  • Request Routing: Route requests to appropriate services
  • Rate Limiting: Prevent abuse and ensure fair usage
  • Response Transformation: Adapt backend responses for clients
  • Monitoring & Analytics: Centralized observability

Implementation Considerations:

FeatureImplementation OptionsGISE Recommendation
TechnologyKong, AWS API Gateway, Zuul, TraefikTraefik (open source, container-native)
AuthenticationJWT, OAuth2, API KeysJWT with refresh tokens
Rate LimitingToken bucket, sliding windowToken bucket for simplicity
CachingRedis, in-memory, CDNRedis for shared state

Data Architecture Patterns​

1. Single Database Pattern​

Best Practices:

  • Use connection pooling
  • Implement read replicas for scaling
  • Cache frequently accessed data
  • Regular backup and recovery testing

2. Database per Service Pattern​

Challenges & Solutions:

ChallengeProblemGISE Solution
Distributed TransactionsACID across servicesEvent-driven eventual consistency
Data ConsistencyCross-service data integritySaga pattern for complex workflows
Query ComplexityCross-service queriesAPI composition or CQRS
Development ComplexityMultiple database managementStandardized database patterns

3. CQRS (Command Query Responsibility Segregation)​

When to Use CQRS:

  • Complex read/write requirements
  • Different scaling needs for reads vs writes
  • Event sourcing implementation
  • Performance optimization needs

Security Architecture Patterns​

1. Zero Trust Security Model​

Key Principles:

  • Never Trust, Always Verify: Every request authenticated and authorized
  • Principle of Least Privilege: Minimum necessary access granted
  • Continuous Monitoring: All access monitored and logged
  • Data Protection: Encryption at rest and in transit

2. OAuth2 + JWT Pattern​

JWT Implementation Best Practices:

  • Short expiration times (15-30 minutes)
  • Refresh token rotation
  • Secure token storage (httpOnly cookies)
  • Algorithm whitelisting (RS256 recommended)

Deployment Architecture Patterns​

1. Blue-Green Deployment​

Deployment Process:

  1. Deploy new version to Green environment
  2. Run automated tests on Green
  3. Switch load balancer to Green
  4. Monitor metrics and rollback if needed
  5. Keep Blue as backup until next deployment

2. Container Orchestration Pattern​

Container Best Practices:

  • Multi-stage builds for optimization
  • Non-root user containers
  • Health checks and readiness probes
  • Resource limits and requests
  • Security scanning in CI/CD

Pattern Decision Framework​

Architecture Decision Record (ADR) Template​

# ADR-001: Choose Database Technology

## Status
Accepted

## Context
We need to choose a primary database technology for our e-commerce application with the following requirements:
- ACID compliance for financial transactions
- Support for complex queries
- Horizontal scaling capability
- Strong ecosystem and community

## Decision
We will use PostgreSQL as our primary database technology.

## Consequences

### Positive
- Strong ACID compliance and reliability
- Excellent performance for complex queries
- Rich feature set including JSON support
- Large community and ecosystem
- Proven scalability options

### Negative
- Learning curve for team members familiar with NoSQL
- Vertical scaling limitations require careful planning
- More complex backup/recovery procedures

### Mitigation
- Team training on PostgreSQL best practices
- Implement read replicas for scaling
- Automated backup and monitoring setup

Decision Matrix Template​

Use this template to compare architectural options:

CriteriaWeightOption AOption BOption C
Development Speed25%8/106/107/10
Scalability20%6/109/108/10
Operational Complexity20%9/105/107/10
Cost15%8/106/107/10
Team Expertise10%9/104/106/10
Future Flexibility10%6/108/108/10
Weighted Score7.46.77.2

Implementation Roadmaps​

From Monolith to Microservices​

Cloud Migration Pattern​

Anti-Patterns to Avoid​

1. Distributed Monolith​

Problem: Services that are too tightly coupled, requiring coordinated deployments.

Signs:

  • Services frequently deployed together
  • Shared database across services
  • Synchronous communication chains
  • No independent business value

Solution: Properly define service boundaries and implement async communication.

2. Chatty Interface​

Problem: Too many fine-grained API calls causing performance issues.

Signs:

  • N+1 query problems
  • Multiple round trips for single operations
  • High network latency impact
  • Poor mobile performance

Solution: Implement proper API design with bulk operations and GraphQL where appropriate.

3. Shared Database​

Problem: Multiple services accessing the same database, creating coupling.

Signs:

  • Direct database access from multiple services
  • Schema changes affecting multiple services
  • Difficult independent deployment
  • Data consistency challenges

Solution: Database per service pattern with API-based communication.

Pattern Combinations​

E-commerce Platform Architecture​

Pattern Combination Benefits:

  • API Gateway for centralized concerns
  • Database per service for independence
  • Caching for performance
  • Message queue for async processing
  • Centralized monitoring for observability

Next Steps​

  1. Assess Current Architecture: Use the decision framework to evaluate your current setup
  2. Identify Pain Points: Document what's not working well
  3. Plan Migration: Create a roadmap using the migration patterns
  4. Start Small: Begin with the highest-impact, lowest-risk changes
  5. Measure Progress: Use metrics to validate architectural decisions

Related Resources: