Document databases handle event sourcing by storing each event as a separate document, enabling append-only operations and efficient querying. Event sourcing relies on capturing all changes to an application’s state as a sequence of immutable events. In a document database like MongoDB or Couchbase, each event is stored as a self-contained document within a collection. These documents typically include fields like an event ID, aggregate identifier (e.g., an order or user ID), event type (e.g., “OrderCreated”), timestamp, and the event’s data payload. For example, an e-commerce system might store an “OrderPlaced” event with details about the customer and items purchased. This approach leverages the schema flexibility of document databases, allowing events with varying structures to coexist in the same collection.
To reconstruct an aggregate’s current state, the system retrieves all events associated with the aggregate ID and applies them in sequence. Document databases support this by enabling queries filtered by the aggregate ID and sorted by timestamp or version. For instance, fetching all events where aggregateId = "order-123" would return the history of changes to that order. However, replaying a large number of events for complex aggregates can become slow. To optimize this, document databases can store periodic snapshots of the aggregate’s state as separate documents. When rebuilding the state, the system loads the latest snapshot and replays only the events that occurred after it, reducing computational overhead. Snapshots might be stored in a separate collection with fields like aggregateId, version, and state.
Document databases also handle concurrency and scalability challenges inherent in event sourcing. Optimistic concurrency checks, such as ensuring an event’s version matches the expected sequence, can be implemented using conditional writes or version fields. For example, MongoDB’s findAndModify operation can atomically check a version number before appending an event. Scalability is achieved through horizontal scaling (sharding), where events are distributed across servers based on the aggregate ID. This ensures events for the same aggregate reside on the same shard, maintaining query efficiency. However, developers must manage schema evolution (e.g., adding new fields to events) and ensure backward compatibility when event formats change. Overall, document databases provide a flexible and scalable foundation for event sourcing, though careful design is required to optimize performance and maintainability.