Distributed Systems

Distributed systems involve multiple computers working together to appear as a single coherent system.

Key Concepts

CAP Theorem

You can only guarantee two of three:

• Consistency: All nodes see the same data
• Availability: Every request gets a response
• Partition Tolerance: System works despite network failures

Eventual Consistency

Data will become consistent over time, but may be temporarily inconsistent.

Strong Consistency

All reads return the most recent write.

Common Challenges

Network Failures

• Timeouts
• Packet loss
• Network partitions
• Latency

Concurrent Operations

• Race conditions
• Deadlocks
• Distributed transactions

Data Consistency

• Replication lag
• Conflict resolution
• Version vectors

Patterns & Solutions

Service Discovery

Automatically detect network locations of service instances.

Load Balancing

Distribute requests across multiple servers.

Circuit Breaker

Prevent cascading failures by stopping requests to failing services.

Saga Pattern

Manage distributed transactions with compensating actions.

Event Sourcing

Store state as sequence of events.

CQRS (Command Query Responsibility Segregation)

Separate read and write models.

Communication Patterns

Synchronous

• REST APIs
• gRPC
• GraphQL

Asynchronous

• Message queues (RabbitMQ, SQS)
• Pub/Sub (Redis, Kafka)
• Event streaming

Consensus Algorithms

Raft

Leader-based consensus for log replication.

Paxos

Distributed consensus protocol.

Two-Phase Commit

Coordinate distributed transactions.

Monitoring & Observability

• Distributed tracing
• Centralized logging
• Metrics collection
• Health checks

Best Practices

• Design for failure
• Implement retries with exponential backoff
• Use idempotent operations
• Monitor system health
• Plan for scalability
• Document system architecture