Document databases manage conflicts in distributed systems by employing various strategies to ensure data consistency and integrity despite potential changes from multiple sources. When multiple clients try to update the same document simultaneously, a conflict can arise. Document databases can handle these conflicts using versioning, consensus algorithms, or operational transformations, depending on the underlying architecture and requirements of the application.
One common approach is versioning, where each document keeps track of its revision history. For example, in a document database like CouchDB, when a document is updated, a new revision ID is generated. If another update occurs on the same document before the first update is completed, CouchDB will reject the second update and require the client to pull the latest revision. This approach ensures that developers are aware of conflicts and can handle them appropriately—either by retrying the update or merging changes manually.
Another strategy involves using consensus algorithms, like those in MongoDB. These databases often employ Replica Sets, which allow one primary and multiple secondary nodes to handle reads and writes. In the event of a conflict, the primary node makes write decisions based on the most recent update, while other nodes remain synchronized. Any discrepancies can then be resolved through automatic retries or logging for later manual review. By implementing these methods, document databases can maintain a level of consistency and minimize issues related to data conflicts in distributed environments.