How do distributed databases deal with network partitioning and data consistency?

Distributed databases handle network partitioning and data consistency by making trade-offs between availability and consistency, guided by the CAP theorem. The CAP theorem states that during a network partition (a split in the network that isolates nodes), a system can prioritize either consistency (all nodes see the same data) or availability (nodes remain responsive), but not both. To manage this, databases implement strategies like replication protocols, quorum-based writes, and conflict resolution mechanisms. For example, a system might use a majority quorum, where writes require acknowledgment from over half the nodes to ensure consistency during partitions, even if some nodes are unreachable. This approach reduces the risk of data divergence but may temporarily limit availability if the quorum isn’t reachable.

To maintain data consistency, distributed databases use consistency models like strong consistency, eventual consistency, or tunable consistency. Strong consistency guarantees that all reads return the latest write, often achieved through synchronous replication and protocols like Raft or Paxos for consensus. However, this can increase latency during partitions. Eventual consistency allows temporary inconsistencies but ensures all nodes converge to the same state once the partition resolves. Tunable consistency lets developers choose the level per operation. For instance, Apache Cassandra allows configuring read/write consistency levels (e.g., requiring acknowledgments from a specific number of nodes). Conflict resolution tools, such as version vectors or application-defined logic, help reconcile conflicting updates after partitions. Amazon DynamoDB uses “last-write-wins” by default but supports conditional writes for custom conflict handling.

Real-world systems balance these trade-offs based on use cases. For example, financial systems often prioritize consistency, using synchronous replication and sacrificing availability during partitions. Social media platforms might favor availability, accepting eventual consistency to keep services running. Tools like Riak employ vector clocks to track data version history, enabling automated or manual conflict resolution. Additionally, modern databases leverage hybrid approaches: Google Spanner combines strong consistency with global atomic clocks to minimize partitions’ impact, while CockroachDB uses hybrid logical clocks and distributed transactions. Developers must understand these mechanisms to configure systems effectively, ensuring the right balance for their application’s reliability and performance needs.