Distributed systems architecture enables applications to scale horizontally across multiple servers, providing fault tolerance and high availability. Understanding consensus algorithms, data consistency, and network partitioning is essential for building resilient systems.

Core Concepts

• CAP Theorem: Trade-offs between consistency, availability, and partition tolerance in distributed databases.

• Consensus Algorithms: Raft and Paxos enable agreement among distributed nodes.

• Event Sourcing: Store state changes as events for auditability and reconstruction.

• CQRS: Separate read and write models for optimized performance.

• Service Mesh: Manage service-to-service communication with observability.

Patterns and Practices

Implement circuit breakers for fault isolation, use message queues for asynchronous communication, deploy load balancers for traffic distribution, apply database sharding for horizontal scaling, and implement distributed caching for performance.

Challenges

Network latency affects performance, partial failures require graceful degradation, data consistency across nodes needs careful design, debugging distributed systems is complex, and operational complexity increases with scale.

Tools and Platforms

Kubernetes orchestrates containerized applications, Apache Kafka handles event streaming, Consul provides service discovery, Prometheus monitors distributed systems, and Istio manages service mesh infrastructure.