![]() ![]() For example, a system can maintain a materialized view of all customer orders that's used to populate parts of the UI. Typical uses of the events published by the event store are to maintain materialized views of entities as actions in the application change them, and for integration with external systems. Notice that the application code that generates the events is decoupled from the systems that subscribe to the events. Consumers could, for example, initiate tasks that apply the operations in the events to other systems, or perform any other associated action that's required to complete the operation. The event store typically publishes these events so that consumers can be notified and can handle them if needed. The events are persisted in an event store that acts as the system of record (the authoritative data source) about the current state of the data. Each event represents a set of changes to the data (such as AddedItemToOrder). Application code sends a series of events that imperatively describe each action that has occurred on the data to the event store, where they're persisted. The Event Sourcing pattern defines an approach to handling operations on data that's driven by a sequence of events, each of which is recorded in an append-only store. Unless there's another auditing mechanism that records the details of each operation in a separate log, history is lost. In a collaborative domain with many concurrent users, data update conflicts are more likely because the update operations take place on a single item of data. These operations can slow down performance and responsiveness and can limit scalability, due to the processing overhead it requires. For example, in the traditional create, read, update, and delete (CRUD) model a typical data process is to read data from the store, make some modifications to it, and update the current state of the data with the new values-often by using transactions that lock the data.ĬRUD systems perform update operations directly against a data store. Most applications work with data, and the typical approach is for the application to maintain the current state of the data by updating it as users work with it. It can also provide consistency for transactional data, and maintain full audit trails and history that can enable compensating actions. This can simplify tasks in complex domains, by avoiding the need to synchronize the data model and the business domain, while improving performance, scalability, and responsiveness. The store acts as the system of record and can be used to materialize the domain objects. Instead of storing just the current state of the data in a domain, use an append-only store to record the full series of actions taken on that data. ![]()
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