How do distributed database systems handle network partitions?

Distributed database systems handle network partitions—situations where parts of the system lose connectivity—by making design trade-offs between consistency, availability, and fault tolerance. The CAP theorem states that during a partition, a system can prioritize either consistency (ensuring all nodes have the same data) or availability (allowing nodes to keep serving requests), but not both. For example, systems like Apache Cassandra prioritize availability by allowing writes and reads to continue on reachable nodes, even if this leads to temporary inconsistencies. Others, like Google Spanner, prioritize consistency by limiting operations until partitions resolve, ensuring data remains accurate but potentially making some services unavailable.

To maintain availability during partitions, many distributed databases use techniques like replication and eventual consistency. For instance, DynamoDB uses a quorum-based system where a majority of replicas must acknowledge a write before it’s considered successful. If a partition occurs, nodes in the smaller network segment may temporarily operate in a degraded mode, accepting writes that are later reconciled when connectivity resumes. Conflict resolution methods, such as “last write wins” or application-defined logic, help merge divergent data versions. This approach ensures the system remains responsive but requires developers to handle potential conflicts or stale data in their application logic.

For systems prioritizing consistency, strategies like strict quorums or consensus algorithms (e.g., Raft or Paxos) are common. In MongoDB, a replica set elects a primary node to handle all writes, and during a partition, only the majority-connected segment retains write access. Nodes in the minority segment become read-only until the partition heals. Similarly, CockroachDB uses a combination of Raft consensus and timestamp ordering to ensure linearizable consistency across partitions. These methods minimize data inconsistency but may introduce delays or temporary unavailability. Developers must choose strategies based on their application’s tolerance for latency, data accuracy, and downtime.