The CQRS pattern is often used in conjunction with the Event Sourcing pattern. CQRS-based systems use separate read and write data models, each tailored to relevant tasks and often located in physically separate stores. When used with Event Sourcing, the store of events is the write model, and this is the authoritative source of information. The read model of a CQRS-based system provides materialized views of the data, typically as highly denormalized views. These views are tailored to the interfaces and display requirements of the application, which helps to maximize both display and query performance.

Using the stream of events as the write store, rather than the actual data at a point in time, avoids update conflicts on a single aggregate and maximizes performance and scalability. The events can be used to asynchronously generate materialized views of the data that are used to populate the read store.

Because the event store is the authoritative source of information, it is possible to delete the materialized views and replay all past events to create a new representation of the current state when the system evolves, or when the read model must change. The materialized views are effectively a durable read-only cache of the data.

As with any system where the write and read stores are separate, systems based on this pattern are only eventually consistent. There will be some delay between the event being generated and the data store that holds the results of operations initiated by these events being updated.

The pattern introduces additional complexity because code must be created to initiate and handle events, and assemble or update the appropriate views or objects required by queries or a read model. The inherent complexity of the CQRS pattern when used in conjunction with Event Sourcing can make a successful implementation more difficult, and requires relearning of some concepts and a different approach to designing systems. However, Event Sourcing can make it easier to model the domain, and makes it easier to rebuild views or create new ones because the intent of the changes in the data is preserved.

Generating materialized views for use in the read model or projections of the data by replaying and handling the events for specific entities or collections of entities may require considerable processing time and resource usage, especially if it requires summation or analysis of values over long time periods, because all the associated events may need to be examined. This may be partially resolved by implementing snapshots of the data at scheduled intervals, such as a total count of the number of a specific action that have occurred, or the current state of an entity.