CQRS (Command Query Responsibility Segregation)

CQRS separates the model for reading data from the model for writing data. Instead of using a single data model for both operations, you maintain separate models optimized for their specific purposes.

In collaborative systems, this pattern is particularly powerful because read and write operations often have very different requirements. Write operations need to enforce business rules, validate transitions, and maintain consistency. Read operations need to be fast, support complex queries, and present data in formats optimized for different user contexts.

Use CQRS when you have:

• Different read and write scales: Your system reads far more than it writes, or vice versa
• Complex queries: Read operations need to aggregate, filter, or join data in ways that would complicate the write model
• Different performance requirements: Reads need to be fast and cached, while writes need strong consistency
• Multiple read models: Different users or contexts need the same data presented in different formats
• Event sourcing: You're using events as the source of truth and deriving read models from them
• Team boundaries: Different teams can own read and write models independently

Avoid CQRS for simple CRUD applications where the overhead of maintaining two models isn't justified, or when strong consistency between reads and writes is critical.

Consider a case management system where agents handle customer support cases:

The write model ensures cases can only be assigned to available agents and enforces state transitions. The read model is optimized for fast queries, supports complex filters, and can be cached without affecting write consistency.

CQRS aligns naturally with the State-Based Collaboration Framework. The framework's architecture inherently separates read and write concerns through its component structure.

The write model in the framework consists of:

• States: The current state of collaborative objects
• Transitions: How state changes are defined and validated
• Actions: User operations that trigger state changes
• Events: Records of what happened, forming the source of truth
• Action-Control Policies: Rules about who can do what

The read model in the framework consists of:

• Projections: Read-optimized views derived from states and events
• Filters: Context-specific views that show relevant subsets of data
• Data-Access Policies: Rules about what data each user can see

The framework's Projections component implements the read side of CQRS. Projections are derived, read-optimized views computed from the write model (states and events).

Projections in the framework:

• Are computed from events or current state, not directly written to
• Can be denormalized for fast querying
• Can be cached without affecting write consistency
• Can be optimized for specific use cases (dashboards, reports, timelines)
• Can lag behind the write model (eventual consistency) for better performance

Multiple projections can exist for the same underlying data, each optimized for different purposes, without affecting the write model.

In the framework, the read model (projections) is kept in sync with the write model through events:

1. Write operations: Actions trigger transitions, which generate events. Events are the source of truth.
2. Event processing: Projections subscribe to events and update themselves as events occur.
3. Eventual consistency: Projections can lag slightly behind the write model, trading consistency for performance.
4. Replay capability: Since projections are derived from events, they can be rebuilt by replaying the event stream.

This approach ensures that the write model remains the single source of truth, while read models can be optimized, cached, and scaled independently.