Architecture Patterns

Architecture Patterns Expertise

Architecture patterns provide proven solutions for common architectural challenges in distributed and enterprise systems. I apply these patterns to build resilient, scalable, and maintainable solutions.

CQRS (Command Query Responsibility Segregation)

• Separation of Reads and Writes: Separating command (write) and query (read) models to optimize each independently. Using different data models, databases, or stores for reads versus writes.

  Implementing CQRS to handle high read/write ratios, complex query requirements, or performance optimization needs. Using read models optimized for queries and command models focused on business rules.

• Event Sourcing Integration: Combining CQRS with event sourcing for complete audit trails and temporal queries. Storing events as source of truth and projecting read models from events.

  Handling event replay, snapshots, and read model updates. Managing consistency between command and query models through eventual consistency patterns.

Circuit Breaker Pattern

• Failure Protection: Preventing cascading failures by detecting and isolating failing services. Opening circuit when failure threshold is reached to stop requests to failing service.

  Implementing circuit states (Closed, Open, Half-Open) with appropriate timeouts and retry strategies. Using libraries like Polly for .NET to implement circuit breakers with configurable policies.

• Fallback Strategies: Providing fallback responses when circuit is open. Implementing graceful degradation and cached responses to maintain service availability.

  Designing fallback mechanisms that maintain user experience while backend services recover. Monitoring circuit state changes and alerting on persistent failures.

Strangler Fig Pattern

• Legacy System Modernization: Gradually replacing legacy systems by incrementally building new functionality around existing system. Using facade to route requests to old or new implementation.

  Implementing strangler fig for risk-free modernization of monolithic applications. Migrating features one by one while maintaining system functionality throughout migration process.

• Migration Strategy: Planning migration path that minimizes disruption. Using feature flags, routing logic, and gradual traffic shifting to new implementation.

  Handling data migration, API compatibility, and coexistence of old and new systems. Eventually decommissioning legacy system once all functionality is migrated.

Publish/Subscribe Pattern

• Event-Driven Communication: Implementing loose coupling between publishers and subscribers through message brokers. Enabling multiple subscribers to receive same event independently.

  Using message brokers (RabbitMQ, Azure Service Bus, AWS SNS/SQS) for reliable event distribution. Implementing topic-based or content-based routing for event filtering.

• Event Delivery Guarantees: Ensuring at-least-once, at-most-once, or exactly-once delivery semantics based on requirements. Implementing idempotent handlers and deduplication strategies.

  Handling message ordering, partitioning, and consumer scaling. Implementing dead letter queues for failed message processing and retry mechanisms.

Retry Pattern

• Transient Fault Handling: Retrying operations that fail due to transient issues (network timeouts, temporary unavailability). Implementing exponential backoff to avoid overwhelming recovering services.

  Using retry libraries (Polly, Resilience.Net) with configurable retry policies, jitter, and maximum attempts. Distinguishing between transient and permanent failures to avoid infinite retries.

• Retry Strategies: Implementing immediate retry, fixed interval, exponential backoff, and custom retry strategies. Combining retry with circuit breaker and timeout patterns.

  Handling idempotency requirements for retried operations. Logging retry attempts and monitoring retry success rates for system health assessment.