Distributed Systems Architecture Patterns
Distributed systems architecture patterns provide the foundation for building scalable, resilient, and maintainable software systems that can handle the demands of modern applications. Understanding these patterns is crucial for developers working on large-scale applications, microservices, and cloud-native solutions.
Core Distributed Systems Principles
Scalability Patterns
Distributed systems must handle growth in users, data, and computational demands. Key scalability approaches include:
Horizontal Scaling (Scale Out):
Load Balancer
├── Server 1
├── Server 2
├── Server 3
└── Server N
Vertical Scaling (Scale Up):
Single Powerful Server
├── CPU Cores: 64+
├── RAM: 1TB+
└── Storage: 100TB+
Database Sharding:
User Database
├── Shard 1: Users A-M
├── Shard 2: Users N-Z
└── Shard 3: Users 0-9
Reliability and Fault Tolerance
Building systems that survive failures requires multiple strategies:
Replication Strategies:
- Master-Slave: One master handles writes, multiple slaves handle reads
- Multi-Master: Multiple nodes can handle both reads and writes
- Quorum-Based: Requires majority agreement for operations
Circuit Breaker Pattern:
Service A ──► Circuit Breaker ──► Service B
▲ │
└───────────────┴─── Open when failures exceed threshold
Consensus Algorithms
Paxos Algorithm
Paxos ensures consensus in distributed systems through a three-phase protocol:
Phase 1: Prepare
Proposer → Acceptors: Prepare(N)
Acceptors → Proposer: Promise(N, accepted_value)
Phase 2: Accept
Proposer → Acceptors: Accept(N, value)
Acceptors → Proposer: Accepted(N, value)
Phase 3: Learn
Acceptors → Learners: Accepted(N, value)
Raft Consensus Algorithm
Raft provides a more understandable consensus approach with three main roles:
Leader Election:
Followers → Candidates: RequestVote RPC
Candidates → Followers: Vote responses
Majority → Leader: Elected
Log Replication:
Leader → Followers: AppendEntries RPC
Followers → Leader: Success responses
Leader: Commit when majority acknowledge
Communication Patterns
Synchronous Communication
Request-Response Pattern:
Client ──► Service ──► Database
◄─────────────── Response
Pros:
- Simple to implement and debug
- Immediate response handling
- ACID transaction support
Cons:
- Tight coupling between services
- Cascading failures possible
- Reduced availability during outages
Asynchronous Communication
Message Queue Pattern:
Producer ──► Queue ──► Consumer
│
└─► Consumer 2
Event-Driven Architecture:
Service A ──► Event Bus ──► Service B
▲ │
└───────────────────────┼─► Service C
│
└─► Service D
Publish-Subscribe Pattern:
Publisher ──► Topic ──► Subscriber 1
│
├─► Subscriber 2
│
└─► Subscriber N
Data Consistency Models
CAP Theorem
The CAP theorem states that distributed systems can only guarantee two of three properties:
Consistency: All nodes see the same data simultaneously Availability: System remains operational despite failures Partition Tolerance: System continues despite network partitions
CAP Triangle:
C
/ \
/ \
A ─── P
Eventual Consistency
Systems that prioritize availability over immediate consistency:
Conflict Resolution Strategies:
- Last Write Wins (LWW): Most recent update prevails
- Version Vectors: Track causality and resolve conflicts
- CRDTs (Conflict-Free Replicated Data Types): Mathematically guaranteed convergence
Distributed Caching Strategies
Cache-Aside Pattern
Application ──► Cache ──► Database
▲ │ │
└──────────────┼──────────┘
▼
Cache Miss
Write-Through Cache
Application ──► Cache ──► Database
│
└─► Write to both simultaneously
Write-Behind Cache
Application ──► Cache ──► Async Write ──► Database
│
└─► Immediate response
Service Discovery Patterns
Client-Side Discovery
Client ──► Service Registry ──► Service Instance
▲ │
└──────────────┘
Health checks
Server-Side Discovery
Client ──► Load Balancer ──► Service Registry ──► Service Instance
Monitoring and Observability
Distributed Tracing
Request Flow: API Gateway → Service A → Service B → Database
Trace ID: 12345-67890-abcde
Spans: [gateway, auth, business_logic, db_query]
Health Checks and Circuit Breakers
Health Check Endpoints:
{
"status": "healthy",
"checks": {
"database": "up",
"cache": "up",
"dependencies": "healthy"
},
"timestamp": "2024-01-15T10:30:00Z"
}
Anti-Patterns to Avoid
Distributed Monoliths
❌ Single large service handling everything
✅ Microservices with clear boundaries
Chatty Communications
❌ Service A → Service B (100 calls/second)
✅ Service A → Service B (batch requests)
Tight Coupling
❌ Direct service-to-service calls
✅ Event-driven loose coupling
Implementation Best Practices
Error Handling
- Implement exponential backoff for retries
- Use circuit breakers to prevent cascade failures
- Provide meaningful error messages and codes
Security Considerations
- Implement mutual TLS for service communication
- Use API gateways for authentication and authorization
- Encrypt data in transit and at rest
Performance Optimization
- Implement connection pooling
- Use compression for network traffic
- Cache frequently accessed data
- Optimize database queries and indexes
GitScrum Integration
Distributed Team Coordination
- Use GitScrum boards to track distributed tasks
- Implement cross-team dependencies tracking
- Set up automated notifications for service failures
Monitoring Dashboards
- Create custom dashboards for system health
- Track service-level objectives (SLOs)
- Monitor distributed tracing data
Incident Response
- Use GitScrum for incident tracking and resolution
- Coordinate between distributed teams during outages
- Maintain post-mortem documentation