Document databases handle conflicts in distributed systems through versioning, conflict resolution strategies, and application-level logic. When multiple nodes in a distributed database update the same document independently, conflicts arise because changes aren’t immediately synchronized. Document databases typically use mechanisms like vector clocks, timestamps, or application-defined rules to detect and resolve these conflicts. For example, a database might track version numbers for each document and flag conflicting versions when synchronization occurs. The resolution process can be automatic (e.g., “last write wins”) or delegated to the application for custom handling.
One common approach is version-based conflict detection. Systems like Apache CouchDB use a revision ID (a combination of a sequence number and a hash) to track document changes. When a document is updated, the revision ID changes. If two nodes modify the same document independently, the database identifies conflicting revisions during replication. The conflicting versions are retained until resolved, either by the system (e.g., picking the latest timestamp) or by the application. Another example is MongoDB, which uses timestamps and logical clocks in its oplog (operation log) to order changes. However, in multi-document transactions or partitioned scenarios, conflicts may still require manual intervention, such as application-defined merge logic.
For resolution, databases often provide application-level hooks. For instance, CouchDB allows developers to write a conflict resolver function that examines conflicting versions and merges them programmatically. Similarly, Amazon DynamoDB offers conditional writes, where updates succeed only if the document’s current state matches expected criteria, reducing accidental overwrites. In distributed systems, eventual consistency models mean conflicts are inevitable, but strategies like CRDTs (conflict-free replicated data types) can also help. For example, a counter in a document could be designed as a CRDT, allowing increments from multiple nodes to merge without conflicts. Ultimately, the choice depends on the database’s design and the application’s requirements for consistency and availability.