Document databases handle concurrency by prioritizing flexibility and scalability over strict transactional control, using techniques like versioning, optimistic concurrency, and multi-version concurrency control (MVCC). Unlike traditional relational databases that rely on locks to prevent conflicting writes, document databases often allow multiple operations to proceed concurrently and resolve conflicts after they occur. This approach reduces contention and supports high throughput, which is critical for distributed systems. For example, when two users update the same document simultaneously, the database might track versions of the document and let the application decide how to merge changes or flag conflicts.
Specific implementations vary. MongoDB, for instance, uses atomic operations at the document level (like findOneAndUpdate) to ensure isolated updates without locking the entire database. It also supports optimistic concurrency by checking a document’s version identifier before applying changes. CouchDB takes a different approach with MVCC: every document update creates a new revision, and conflicts are detected during replication. If two clients edit the same document offline, the database stores both revisions and marks them as conflicting, requiring application logic to resolve them. Firebase Firestore uses timestamps and version vectors to track changes, automatically rejecting stale updates based on server-side timestamps if a newer version exists.
Developers must consider trade-offs. While document databases avoid locking and scale well, handling conflicts often shifts responsibility to the application layer. For example, in CouchDB, unresolved conflicts remain until explicitly addressed, which could complicate data consistency. Systems like Firestore offer eventual or strong consistency modes, letting developers choose based on use case. However, eventual consistency can mean temporary mismatches between replicas. To mitigate this, many document databases provide tools like conflict-free replicated data types (CRDTs) for automatic merging of certain data (e.g., counters or sets). Properly managing concurrency in document databases requires understanding these mechanisms and designing workflows that align with the database’s conflict-resolution strategy.