What is the impact of a network partition on a distributed database’s consistency?

A network partition in a distributed database occurs when nodes or clusters lose communication, splitting the system into isolated groups. This directly impacts consistency because nodes in separate partitions can’t synchronize data updates. The CAP theorem states that during a partition, a system must choose between consistency (all nodes see the same data) and availability (serving requests despite the partition). Databases prioritizing consistency (CP systems) will block writes or reads in the affected partitions to prevent inconsistencies, while AP systems allow operations but risk temporary data mismatches. For example, a CP database like MongoDB in a sharded setup might reject writes in a minority partition until connectivity is restored, ensuring data remains consistent but sacrificing availability.

The consistency model determines how partitions affect the system. In strongly consistent databases (e.g., Google Spanner), a partition may cause timeouts or errors for clients trying to write data, as the system enforces strict agreement across nodes. In eventually consistent systems (e.g., Apache Cassandra), writes can continue in isolated partitions, but conflicting updates must be resolved later. For instance, if a partition splits a database into two groups, and both accept updates to the same record, the system might use timestamps or application logic to merge changes after the partition heals. This can lead to data loss or unexpected results if conflicts aren’t handled carefully, especially in scenarios like inventory management where overselling could occur if counts diverge.

To mitigate these issues, distributed databases often use trade-offs and recovery mechanisms. Quorum-based systems (e.g., Amazon DynamoDB) require a majority of nodes to acknowledge writes, reducing the risk of splits but increasing latency. Consensus algorithms like Raft ensure only one partition (the majority) remains active during a network failure, while others pause. However, developers must design applications to handle temporary inconsistencies, such as by retrying failed operations or using idempotent requests. For example, a banking app might temporarily disable transfers during a partition but allow balance checks with a warning that data could be stale. The choice between consistency and availability depends on the use case—financial systems often prioritize consistency, while social media apps might favor availability and resolve conflicts later.