What is Command Query Responsibility Segregation (CQRS)?

The problem of the traditional approach (CRUD)

In traditional architectures, often based on the CRUD (Create, Read, Update, Delete) pattern, the same domain model and the same data store (e.g., a relational database) are used to handle all operations. This can lead to design compromises, where the model is too complex to effectively handle both records (requiring validation and business logic) and the various, often denormalized queries needed to display data to users.

How does CQRS work?

In the CQRS architecture:

• Commands (Commands): They represent the intention to change the state of the system (e.g., PlaceOrder, UpdateCustomerAddress). They are processed by a dedicated write model (write model), which contains business logic, validation and is responsible for consolidating the changes into a write-optimized data store (write store). Commands usually do not return data, only a success or error message.
• Queries (Queries): They are used to read data from the system for presentation to the user. They are processed by a dedicated read model that retrieves data from a read-optimized data store (read store). The read model is often simplified and denormalized, prepared specifically for specific views or queries. Queries never modify the state of the system.
• Data synchronization (optional): In some CQRS implementations, the data stores for writing and reading are separate. In this case, a mechanism for synchronizing data between them is required, often implemented asynchronously (e.g., by publishing events by the write model, to which the read model responds by updating its state). This leads to an eventual consistency model.

Benefits of using CQRS

Separation of writing and reading responsibilities brings a number of benefits:

• Model optimization: the ability to design separate data and logic models, optimized specifically for write (complex logic, validation) and read (simple structures, denormalization for performance) operations.
• Scalability: The ability to scale the write portion of the system and the read portion independently, depending on the load. Often the read traffic is much higher than the write traffic.
• Performance: Optimizing data stores (e.g., using different database technologies for writing and reading) can lead to improved performance for both write and read operations.
• Flexibility: Easier to make changes to the read model without affecting the write model, and vice versa.
• Better fit for complex domains: CQRS works well in systems with complex business logic and diverse data presentation requirements.

Challenges and complexities of CQRS

The CQRS pattern also introduces additional complexity:

• Larger number of components: The system consists of more moving parts (separate models, potentially separate data stores).
• Data synchronization: If separate data stores are used, it is necessary to implement and manage a synchronization mechanism, which can be complicated and introduces definitional consistency.
• Eventual Consistency: Users may see stale data for a while if the read model has not yet been updated after a write operation. This requires proper user interface design and expectation management.
• Increased complexity for simple systems: For simple CRUD-type applications, the overhead associated with implementing CQRS may not be justified.

CQRS a Event Sourcing

CQRS is often used in conjunction with another architectural pattern – Event Sourcing, where the state of the system is not stored directly, but reconstructed based on a sequence of events (events) describing all the changes that have occurred in the system. Event Sourcing naturally supports the write model in CQRS and facilitates synchronization with the read model.

Summary

CQRS is a powerful architectural pattern that, by separating write (command) and read (query) operations, allows for more scalable, efficient and flexible systems, especially for complex business domains. However, it introduces additional complexity and requires conscious data integrity management, so it should be used where the benefits outweigh the implementation costs.