/architecture-paradigm-cqrs-es

name: architecture-paradigm-cqrs-es
description: Applies CQRS and Event Sourcing for read/write separation and audit trails. Use when designing systems with complex domain logic or full state-change history.
alwaysApply: false
category: architectural-pattern
tags:
- architecture
- CQRS
- Event-Sourcing
- distributed-systems
- audit-trail
- scalability
dependencies: []
tools: []
usage_patterns:
- paradigm-implementation
- distributed-system-design
- auditability
- scalability-optimization
complexity: high
estimated_tokens: 800

The CQRS and Event Sourcing Paradigm

When To Use

• Designing event-sourced systems with complex domain logic
• Systems requiring full audit trails of state changes

When NOT To Use

• Simple CRUD applications without complex domain logic
• Small projects where event sourcing adds unnecessary complexity

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.

Concrete Components

Vocabulary for the tools and abstractions an implementation of this paradigm tends to carry. Not dependencies, and not ``tools:`` frontmatter.

• ``event-store``: append-only log of domain events; the system of record from which projections are built
• ``message-broker``: carries commands and integration events between bounded contexts
• ``projection-builder``: rebuilds read-side views by replaying the event store

Exit Criteria

• [ ] An ADR is produced naming the aggregates, the chosen event store technology, the projection

strategy, and the eventual-consistency SLA for each read model.

• [ ] Command handler tests use in-memory event streams and pass before any persistence adapter

is wired.

• [ ] A schema registry or versioning policy for event schemas is documented, and a CI gate

prevents emission of event versions not registered in that policy.

• [ ] Operational tooling for event replay and state snapshots is identified (not just mentioned)

before the write-side is declared production-ready.