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cqrs-event-sourcing-architect

CQRS pattern, event stores, projections, eventual consistency. Activate on: CQRS, event sourcing, event store, read model, projection, aggregate, domain event, command handler. NOT for: message broker setup (use event-driven-architecture-expert), database optimization (use data-warehouse-optimizer).

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Works withPostgreSQL

Run score Not tested

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CQRS & Event Sourcing Architect

Design systems that separate read/write models and derive state from immutable event logs using EventStoreDB, Marten, or custom stores.

Activation Triggers

Activate on: "CQRS", "event sourcing", "event store", "read model", "projection", "aggregate", "domain event", "command handler", "EventStoreDB", "Marten"

NOT for: Message broker infrastructure → event-driven-architecture-expert | Database query optimization → data-warehouse-optimizer | API design → api-architect

Quick Start

  1. Model aggregates — identify consistency boundaries in your domain
  2. Define domain events — past-tense facts: OrderPlaced, ItemShipped
  3. Implement command handlers — validate, then emit events
  4. Build event store — append-only log (EventStoreDB, PostgreSQL, DynamoDB)
  5. Create projections — materialize read models from event streams

Core Capabilities

DomainTechnologies
Event StoresEventStoreDB 24.x, Marten 7.x, custom PostgreSQL
FrameworksNestJS CQRS, Axon (JVM), Eventuous, Commanded (Elixir)
ProjectionsIn-memory, PostgreSQL views, Elasticsearch, Redis
SerializationJSON with versioned schemas, Protobuf
TestingGiven-When-Then event test pattern

Architecture Patterns

CQRS Separation

Command Side                    Query Side
─────────────                   ──────────
POST /orders                    GET /orders/:id
     ↓                               ↑
Command Handler                 Read Model (Postgres/ES)
     ↓                               ↑
Aggregate.apply(event)          Projection Handler
     ↓                               ↑
Event Store (append)  ──────→  Event Subscription

Aggregate with Event Replay

class OrderAggregate {
  private status: 'draft' | 'placed' | 'shipped' = 'draft';
  private items: OrderItem[] = [];
  private uncommitted: DomainEvent[] = [];

  place(command: PlaceOrderCommand): void {
    if (this.status !== 'draft') throw new Error('Order already placed');
    this.apply(new OrderPlaced({ orderId: command.orderId, items: command.items }));
  }

  private apply(event: DomainEvent): void {
    this.when(event);           // mutate state
    this.uncommitted.push(event); // track for persistence
  }

  private when(event: DomainEvent): void {
    if (event instanceof OrderPlaced) {
      this.status = 'placed';
      this.items = event.items;
    }
  }

  static rehydrate(events: DomainEvent[]): OrderAggregate {
    const agg = new OrderAggregate();
    events.forEach(e => agg.when(e));
    return agg;
  }
}

Projection Rebuild Strategy

1. Stop projection subscription
2. Drop/truncate read model table
3. Replay all events from position 0
4. Resume subscription at current position

Key: projections are disposable — the event log is the source of truth

Anti-Patterns

  1. CRUD in disguise — if you just save/load entities without events, you have CRUD with extra steps
  2. Anemic events — events like OrderUpdated are useless; capture what changed specifically
  3. Querying the event store — never query raw events for read use cases; build a projection
  4. Deleting events — event stores are append-only; use compensating events for corrections
  5. Ignoring eventual consistency — read models lag behind writes; design UIs to handle this gracefully

Quality Checklist

  • Aggregates enforce invariants before emitting events
  • Events are immutable, past-tense, and contain all needed data
  • Event store is append-only with optimistic concurrency
  • Projections can be rebuilt from scratch without data loss
  • Event versioning strategy defined (upcasting or schema registry)
  • Given-When-Then tests cover all aggregate state transitions
  • Snapshot strategy defined for aggregates with long event streams (>1000 events)
  • Eventual consistency communicated clearly in API contracts
  • Idempotent projection handlers (replaying events produces same read model)

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