Distributed databases manage consistency during network failures by implementing various strategies that balance the trade-offs between availability and consistency. One of the most common approaches is to use consensus algorithms, such as Paxos or Raft, which help nodes agree on the current state of data even when some parts of the network are unreachable. These algorithms ensure that all nodes eventually converge on the same data version once connectivity is restored, minimizing inconsistency during the failure. The nodes communicate with each other to exchange updates and perform reads or writes only when a majority is available, thus protecting data integrity.
Another approach is to employ eventual consistency, a model where updates to the database may not be immediately visible to all nodes. In situations where a network partition occurs, distributed databases allow read and write operations to proceed on different nodes. After the partition is resolved, these databases synchronize the divergent data states to reach a consistent state over time. Amazon Dynamo and Apache Cassandra are examples of systems that adopt this model, allowing for high availability even when some nodes are temporarily out of sync. They use mechanisms such as versioning or timestamps to help resolve conflicts once normal communication is reinstated.
Finally, some distributed databases may choose to restrict operations during a network failure to maintain strict consistency, a model known as strong consistency. For example, Google Spanner implements true global transactions with precise time synchronization to avoid inconsistencies across regions. If a network failure occurs, Spanner may temporarily reject write requests until it can ensure that the data is not only available but consistent across the nodes. This trade-off means the system might be less available during failures, but it guarantees that all nodes reflect the same state. Overall, each approach has unique implications for performance, availability, and consistency, and developers must choose the right model based on specific application needs.