Essential Patterns in Distributed Systems

Distributed systems present unique challenges. Let’s explore essential patterns that help build reliable distributed applications.

The CAP Theorem

Understanding CAP is crucial:

• Consistency: All nodes see the same data at the same time
• Availability: Every request receives a response
• Partition Tolerance: System continues to operate despite network failures

type ConsistencyLevel int

const (
    Strong ConsistencyLevel = iota
    Eventual
    Causal
)

type DistributedStore struct {
    consistency ConsistencyLevel
}

Pattern 1: Circuit Breaker

Prevent cascading failures:

type CircuitBreaker struct {
    failures     int
    threshold    int
    resetTimeout time.Duration
    lastFailure  time.Time
}

func (cb *CircuitBreaker) Execute(fn func() error) error {
    if cb.isOpen() {
        return errors.New("circuit breaker is open")
    }
    
    if err := fn(); err != nil {
        cb.recordFailure()
        return err
    }
    
    return nil
}

Pattern 2: CQRS

Command Query Responsibility Segregation separates read and write operations:

type CommandHandler interface {
    Handle(cmd Command) error
}

type QueryHandler interface {
    Handle(query Query) (interface{}, error)
}

Best Practices

1. Idempotency

   • Design operations to be safely retryable
   • Use unique identifiers for requests

2. Backpressure

   • Implement rate limiting
   • Use bounded queues

3. Monitoring

   • Track key metrics
   • Implement proper logging

Remember: Distributed systems require careful consideration of failure modes and eventual consistency.