Distributed databases handle data consistency in multi-master systems primarily through techniques such as conflict resolution, consensus algorithms, and eventual consistency models. In a multi-master setup, multiple nodes can accept write operations, leading to potential conflicts when different nodes receive updates to the same data concurrently. To manage these conflicts, databases often implement strategies like versioning. Each update includes a timestamp or a version number, allowing the system to determine which changes are more recent and to decide which version to keep or how to merge them.
Another important approach is the use of consensus algorithms like Paxos or Raft. These algorithms help ensure that all writes are agreed upon by a majority of nodes before they are committed. By requiring a quorum of nodes to confirm a transaction, these algorithms help maintain a consistent view of the data across all nodes. For instance, if one node receives a write request, it can propose the change to the other nodes, and only after a majority agrees on that change can it be applied across the system. This minimizes data discrepancies but can introduce latency, as nodes must communicate and agree on every write.
Finally, some distributed databases adopt an eventual consistency model, where updates may not be immediately consistent across all nodes but will converge over time. This allows for higher availability and partition tolerance since nodes can serve reads even if not all recent writes have been reflected everywhere. Systems like Amazon Dynamo and Cassandra utilize this approach, providing mechanisms for clients to check for the most recent updates, ensuring that, while data may not be consistent instantly, it will eventually become consistent. Overall, handling data consistency in multi-master systems requires a careful balance of availability, performance, and accuracy.