Event Sourcing Pattern


Context and Problem



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 the data. For example, in the traditional create, read, update, and delete (CRUD) model a typical data process will be 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


The CRUD approach has some limitations:


  • The fact that CRUD systems perform update operations directly against a data store may hinder performance and responsiveness, and limit scalability, due to the processing overhead it requires.
  • In a collaborative domain with many concurrent users, data update conflicts are more likely to occur because the update operations take place on a single item of data.
  • Unless there is an additional auditing mechanism, which records the details of each operation in a separate log, history is lost.



The Event Sourcing pattern defines an approach to handling operations on data that is driven by a sequence of events, each of which is recorded in an append-only store. Application code sends a series of events that imperatively describe each action that has occurred on the data to the event store, where they are persisted. Each event represents a set of changes to the data (such as AddedItemToOrder).


The events are persisted in an event store that acts as the source of truth or system of record (the authoritative data source for a given data element or piece of information) about the current state of the data. The event store typically publishes these events so that consumers can be notified and can handle them if needed. Consumers could, for example, initiate tasks that apply the operations in the events to other systems, or perform any other associated action that is required to complete the operation. Notice that the application code that generates the events is decoupled from the systems that subscribe to the events.


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. For example, a system may maintain a materialized view of all customer orders that is used to populate parts of the UI. As the application adds new orders, adds or removes items on the order, and adds shipping information, the events that describe these changes can be handled and used to update the materialized view.


In addition, at any point in time it is possible for applications to read the history of events, and use it to materialize the current state of an entity by effectively “playing back” and consuming all the events related to that entity. This may occur on demand in order to materialize a domain object when handling a request, or through a scheduled task so that the state of the entity can be stored as a materialized view to support the presentation layer.


event store



shows a logical overview of the pattern, including some of the options for using the event stream such as creating a



The Event Sourcing pattern provides many advantages, including the following:


  • Events are immutable and so can be stored using an append-only operation. The user interface, workflow, or process that initiated the action that produced the events can continue, and the tasks that handle the events can run in the background. This, combined with the fact that there is no contention during the execution of transactions, can vastly improve performance and scalability for applications, especially for the presentation level or user interface.
  • Events are simple objects that describe some action that occurred, together with any associated data required to describe the action represented by the event. Events do not directly update a data store; they are simply recorded for handling at the appropriate time. These factors can simplify implementation and management.
  • Events typically have meaning for a domain expert, whereas the complexity of the object-relational impedance mismatch might mean that a database table may not be clearly understood by the domain expert. Tables are artificial constructs that represent the current state of the system, not the events that occurred.
  • Event sourcing can help to prevent concurrent updates from causing conflicts because it avoids the requirement to directly update objects in the data store. However, the domain model must still be designed to protect itself from requests that might result in an inconsistent state.
  • The append-only storage of events provides an audit trail that can be used to monitor actions taken against a data store, regenerate the current state as materialized views or projections by replaying the events at any time, and assist in testing and debugging the system. In addition, the requirement to use compensating events to cancel changes provides a history of changes that were reversed, which would not be the case if the model simply stored the current state. The list of events can also be used to analyze application performance and detect user behavior trends, or to obtain other useful business information.
  • The decoupling of the events from any tasks that perform operations in response to each event raised by the event store provides flexibility and extensibility. For example, the tasks that handle events raised by the event store are aware only of the nature of the event and the data it contains. The way that the task is executed is decoupled from the operation that triggered the event. In addition, multiple tasks can handle each event. This may enable easy integration with other services and systems that need only listen for new events raised by the event store. However, the event sourcing events tend to be very low level, and it may be necessary to generate specific integration events instead.

When to Use this Pattern

This pattern is ideally suited to the following scenarios:


  • When you want to capture “intent,” “purpose,” or “reason” in the data. For example, changes to a customer entity may be captured as a series of specific event types such as Moved home, Closed account, or Deceased.
  • When it is vital to minimize or completely avoid the occurrence of conflicting updates to data.
  • When you want to record events that occur, and be able to replay them to restore the state of a system; use them to roll back changes to a system; or simply as a history and audit log. For example, when a task involves multiple steps you may need to execute actions to revert updates and then replay some steps to bring the data back into a consistent state.
  • When using events is a natural feature of the operation of the application, and requires little additional development or implementation effort.
  • When you need to decouple the process of inputting or updating data from the tasks required to apply these actions. This may be to improve UI performance, or to distribute events to other listeners such as other applications or services that must take some action when the events occur. An example would be integrating a payroll system with an expenses submission website so that events raised by the event store in response to data updates made in the expenses submission website are consumed by both the website and the payroll system.
  • When you want flexibility to be able to change the format of materialized models and entity data if requirements change, or—when used in conjunction with CQRS—you need to adapt a read model or the views that expose the data.
  • When used in conjunction with CQRS, and eventual consistency is acceptable while a read model is updated or, alternatively, the performance impact incurred in rehydrating entities and data from an event stream is acceptable.

This pattern might not be suitable in the following situations:


  • Small or simple domains, systems that have little or no business logic, or non-domain systems that naturally work well with traditional CRUD data management mechanisms.
  • Systems where consistency and real-time updates to the views of the data are required.
  • Systems where audit trails, history, and capabilities to roll back and replay actions are not required.
  • Systems where there is only a very low occurrence of conflicting updates to the underlying data. For example, systems that predominantly add data rather than updating it.