# Distributed Systems Architecture
## Purpose
Define comprehensive distributed systems architecture requirements for enterprise-grade, high-load systems. This section addresses the unique challenges and patterns required for distributed system design.
## Prerequisites
- Functional requirements and scale expectations defined
- Performance and availability requirements established
- Enterprise constraints and governance requirements understood
- Team distributed systems expertise assessed
## Section Structure & Requirements
### 1. Distributed Architecture Strategy
**Objective**: Define overall approach to distributed system design
**Required Elements:**
- **Distribution Strategy**: Why and how the system will be distributed
- **Service Decomposition**: How functionality will be divided into services
- **Deployment Topology**: How services will be deployed and distributed
- **Communication Patterns**: How services will communicate with each other
- **Data Distribution**: How data will be distributed across services
**Quality Criteria:**
- Distribution strategy aligns with business and technical requirements
- Service boundaries are well-defined and logical
- Communication patterns are efficient and reliable
- Data distribution supports consistency and performance needs
**Template:**
## Distributed Architecture Strategy
### Distribution Rationale
[Why distributed architecture is needed and benefits expected]
### Service Decomposition Strategy
- **Decomposition Approach**: [Domain-driven, capability-based, etc.]
- **Service Boundaries**: [How service boundaries are defined]
- **Service Sizing**: [Guidelines for service size and complexity]
- **Service Dependencies**: [How dependencies between services are managed]
### Deployment Topology
- **Service Distribution**: [How services are distributed across infrastructure]
- **Geographic Distribution**: [Multi-region deployment strategy]
- **Environment Strategy**: [Development, staging, production environments]
### Communication Patterns
- **Synchronous Communication**: [REST APIs, GraphQL, gRPC]
- **Asynchronous Communication**: [Message queues, event streaming]
- **Service Discovery**: [How services find and communicate with each other]
### Data Distribution Strategy
[How data is distributed, partitioned, and synchronized across services]
### 2. Microservices Architecture
**Objective**: Define microservices patterns and implementation approach
**Required Elements:**
- **Service Design Principles**: Guidelines for designing individual services
- **API Design Standards**: Standards for service APIs and contracts
- **Service Mesh Architecture**: Service-to-service communication infrastructure
- **Container Strategy**: Containerization and orchestration approach
- **Service Lifecycle Management**: How services are developed, deployed, and maintained
**Template:**
## Microservices Architecture
### Service Design Principles
- **Single Responsibility**: [Each service has one clear responsibility]
- **Autonomous Teams**: [Services owned by autonomous teams]
- **Decentralized Governance**: [Service teams make their own technology choices]
- **Failure Isolation**: [Service failures don't cascade to other services]
### API Design Standards
- **API Protocols**: [REST, GraphQL, gRPC standards]
- **API Versioning**: [How API versions are managed]
- **API Documentation**: [OpenAPI, schema documentation requirements]
- **API Security**: [Authentication, authorization, rate limiting]
### Service Mesh Architecture
- **Service Mesh Technology**: [Istio, Linkerd, Consul Connect]
- **Traffic Management**: [Load balancing, routing, traffic splitting]
- **Security Policies**: [mTLS, service-to-service authentication]
- **Observability**: [Distributed tracing, metrics collection]
### Container Strategy
- **Container Technology**: [Docker, containerd]
- **Orchestration Platform**: [Kubernetes, Docker Swarm]
- **Container Registry**: [Image storage and distribution]
- **Container Security**: [Image scanning, runtime security]
### Service Lifecycle Management
[How services are developed, tested, deployed, and maintained]
### 3. Data Consistency & Transaction Management
**Objective**: Define data consistency patterns for distributed systems
**Required Elements:**
- **Consistency Models**: What consistency guarantees are provided
- **Distributed Transaction Patterns**: SAGA, 2PC, eventual consistency approaches
- **Event Sourcing**: Event-driven data management patterns
- **CQRS Implementation**: Command Query Responsibility Segregation patterns
- **Conflict Resolution**: How data conflicts are detected and resolved
**Template:**
## Data Consistency & Transaction Management
### Consistency Models
- **Strong Consistency**: [Where strong consistency is required]
- **Eventual Consistency**: [Where eventual consistency is acceptable]
- **Causal Consistency**: [Where causal ordering is important]
### Distributed Transaction Patterns
- **SAGA Pattern**: [Long-running transaction management]
- **Two-Phase Commit**: [Where ACID transactions are required]
- **Compensating Actions**: [How to handle transaction failures]
### Event Sourcing
- **Event Store**: [How events are stored and retrieved]
- **Event Replay**: [How system state is reconstructed from events]
- **Event Versioning**: [How event schema evolution is managed]
### CQRS Implementation
- **Command Side**: [How commands are processed and validated]
- **Query Side**: [How read models are built and maintained]
- **Synchronization**: [How command and query sides stay synchronized]
### Conflict Resolution
[How data conflicts are detected, resolved, and prevented]
### 4. Service Communication & Integration
**Objective**: Define service communication patterns and integration strategies
**Required Elements:**
- **API Gateway Pattern**: Centralized API management and routing
- **Message Queue Architecture**: Asynchronous messaging patterns
- **Event Streaming**: Real-time event processing and streaming
- **Service Discovery**: How services find and connect to each other
- **Circuit Breaker Patterns**: Fault tolerance and resilience patterns
**Template:**
## Service Communication & Integration
### API Gateway Architecture
- **Gateway Technology**: [Kong, Ambassador, AWS API Gateway]
- **Routing Strategy**: [How requests are routed to services]
- **Rate Limiting**: [How API usage is controlled and limited]
- **Authentication**: [How API access is authenticated and authorized]
### Message Queue Architecture
- **Message Broker**: [RabbitMQ, Apache Kafka, AWS SQS]
- **Queue Patterns**: [Point-to-point, publish-subscribe, request-reply]
- **Message Durability**: [How message persistence is guaranteed]
- **Dead Letter Queues**: [How failed messages are handled]
### Event Streaming
- **Streaming Platform**: [Apache Kafka, AWS Kinesis, Azure Event Hubs]
- **Event Schema**: [How event schemas are defined and evolved]
- **Stream Processing**: [How events are processed in real-time]
- **Event Ordering**: [How event ordering is maintained]
### Service Discovery
- **Discovery Mechanism**: [DNS, service registry, service mesh]
- **Health Checking**: [How service health is monitored]
- **Load Balancing**: [How traffic is distributed across service instances]
### Circuit Breaker Patterns
- **Failure Detection**: [How service failures are detected]
- **Circuit States**: [Open, closed, half-open circuit behavior]
- **Fallback Strategies**: [What happens when circuits are open]
- **Recovery Procedures**: [How circuits are reset and recovered]
### 5. Fault Tolerance & Resilience
**Objective**: Define fault tolerance and resilience patterns
**Required Elements:**
- **Failure Modes**: Types of failures the system must handle
- **Resilience Patterns**: Bulkhead, timeout, retry, fallback patterns
- **Chaos Engineering**: How system resilience is tested
- **Disaster Recovery**: How the system recovers from major failures
- **Business Continuity**: How business operations continue during failures
### 6. Distributed System Monitoring
**Objective**: Define monitoring and observability for distributed systems
**Required Elements:**
- **Distributed Tracing**: How requests are traced across services
- **Metrics Collection**: What metrics are collected and how
- **Log Aggregation**: How logs from multiple services are collected
- **Alerting Strategy**: How alerts are generated and escalated
- **Performance Monitoring**: How system performance is monitored
## Information Gathering Requirements
### Distributed Systems Context Needed:
- Scale and performance requirements
- Team distributed systems expertise
- Existing infrastructure and constraints
- Compliance and security requirements
- Operational capabilities and preferences
### Validation Requirements:
- Distributed systems architecture review
- Performance and scalability validation
- Security and compliance review
- Operational readiness assessment
## Cross-Reference Requirements
### Must Reference:
- Performance and scalability requirements
- Security and compliance requirements
- Operational and monitoring requirements
- Team capabilities and constraints
### Must Support:
- Implementation planning and estimation
- Testing and validation strategies
- Deployment and operations planning
- Monitoring and maintenance procedures
## Common Pitfalls to Avoid
### Architecture Pitfalls:
- **Distributed monolith**: Creating distributed system without benefits
- **Premature distribution**: Distributing before understanding requirements
- **Chatty interfaces**: Creating too many service-to-service calls
- **Data inconsistency**: Not properly handling distributed data consistency
### Communication Pitfalls:
- **Synchronous coupling**: Over-relying on synchronous communication
- **Message ordering**: Not considering message ordering requirements
- **Error propagation**: Not properly handling communication failures
- **Protocol mismatch**: Using inappropriate communication protocols
## Edge Case Considerations
### When Team Lacks Distributed Systems Experience:
- Start with simpler distributed patterns
- Plan for extensive training and mentoring
- Consider external consulting for architecture review
- Build monitoring and observability from day one
### When Performance Requirements are Extreme:
- Focus on critical path optimization
- Plan for extensive performance testing
- Consider specialized technologies and patterns
- Build performance monitoring and alerting
## Validation Checkpoints
### Before Finalizing Section:
- [ ] Distribution strategy is well-justified and appropriate
- [ ] Service boundaries are logical and well-defined
- [ ] Communication patterns are efficient and reliable
- [ ] Data consistency approach is appropriate for requirements
- [ ] Fault tolerance patterns are comprehensive
### Cross-Section Validation:
- [ ] Architecture supports functional requirements
- [ ] Performance requirements can be met with proposed architecture
- [ ] Security requirements are properly integrated
- [ ] Operational requirements are supported
## Output Quality Standards
- Architecture is appropriate for scale and complexity requirements
- Service design follows distributed systems best practices
- Communication patterns are efficient and reliable
- Fault tolerance and resilience are properly addressed
- Monitoring and observability are comprehensive