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name: architecture-paradigm-cqrs-es description: |

Triggers: event-sourcing, architecture, scalability, distributed-systems, audit-trail CQRS and Event Sourcing for auditability, read/write separation, and temporal queries.

Triggers: CQRS, event sourcing, audit trail, temporal queries Use when: read/write scaling differs or audit trail required DO NOT use when: simple CRUD - use architecture-paradigms first. version: 1.0.0 category: architectural-pattern tags: [architecture, CQRS, Event-Sourcing, distributed-systems, audit-trail, scalability] dependencies: [] tools: [event-store, message-broker, projection-builder] usage_patterns:

• paradigm-implementation
• distributed-system-design
• auditability
• scalability-optimization complexity: high estimated_tokens: 800

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The CQRS and Event Sourcing Paradigm

When to Employ This Paradigm

• When read and write workloads have vastly different performance characteristics or scaling requirements.
• When all business events must be captured in a durable, immutable history or audit trail.
• When a business needs to rebuild projections of data or support temporal queries (e.g., "What did the state of this entity look like yesterday?").

Adoption Steps

1. Identify Aggregates: Following Domain-Driven Design principles, specify the bounded contexts and the business invariants that each command must enforce on an aggregate.
2. Model Commands and Events: Define the schemas and validation rules for all commands and the events they produce. Document a clear strategy for versioning and schema evolution.
3. Implement the Write Side (Command Side): Command handlers are responsible for loading an aggregate's event stream, executing business logic, and atomically appending new events to the stream.
4. Build Projections to the Read Side: Create separate read models (projections) that are fed by subscriptions to the event stream. Implement back-pressure and retry policies for these subscriptions.
5. validate Full Observability: Implement detailed logging that includes event IDs, sequence numbers, and metrics for tracking the lag time of each projection.

Key Deliverables

• An Architecture Decision Record (ADR) detailing the aggregates, the chosen event store technology, the projection strategy, and the expected data consistency model (e.g., eventual consistency SLAs).
• A suite of tests for command handlers that use in-memory event streams, complemented by integration tests for the projections.
• Operational tooling for replaying events, taking state snapshots for performance, and managing schema migrations.

Risks & Mitigations

• High Operational Overhead:
  • Mitigation: Bugs related to event ordering and replays can be difficult to diagnose. Invest heavily in automation, Dead-Letter Queues (DLQs) for failed events, and regular "chaos engineering" drills to test resilience.
• Challenges of Eventual Consistency:
  • Mitigation: Users may be confused by delays between performing an action and seeing the result. Clearly document the SLAs for read model updates and manage user-facing expectations accordingly, for example, by providing immediate feedback on the command side.
• Schema Drift:
  • Mitigation: An unplanned change to an event schema can break consumers. Enforce the use of a formal schema registry and implement version gates in the CI/CD pipeline to prevent the emission of unvalidated event versions.

Troubleshooting

Common Issues

Command not found Ensure all dependencies are installed and in PATH

Permission errors Check file permissions and run with appropriate privileges

Unexpected behavior Enable verbose logging with --verbose flag