Conflict resolution in distributed databases ensures consistency when multiple nodes update the same data independently. Common methods include version vectors, conflict-free replicated data types (CRDTs), and application-defined logic. Each approach addresses trade-offs between consistency, availability, and latency, depending on the system’s requirements.
Version vectors and timestamps are straightforward methods for resolving conflicts by tracking the order of updates. Each write operation is tagged with a timestamp or version number, and the system prioritizes the most recent update. For example, Apache Cassandra uses a “last-write-wins” strategy, where conflicting writes are resolved by selecting the one with the latest timestamp. However, this relies on synchronized clocks across nodes, which can be challenging in globally distributed systems. If clocks drift, conflicts may not resolve accurately, leading to data loss if older writes inadvertently override newer ones. Despite this limitation, timestamp-based methods are simple to implement and work well for systems where slight timing discrepancies are acceptable.
CRDTs are data structures designed to handle concurrent updates without direct coordination. They ensure that all replicas eventually converge to the same state by using mathematical properties (like commutativity and idempotence) to merge changes. For instance, a counter CRDT might track increments and decrements separately, allowing it to combine results correctly even if operations occur out of order. Redis uses CRDTs in its Active-Active Geo-Distribution feature to synchronize data across regions. While CRDTs are powerful, they are limited to specific data types (e.g., counters, sets) and require careful design to avoid unexpected behavior. For example, merging conflicting text edits in a collaborative document might need more nuanced logic than a basic CRDT provides.
Application-level resolution lets developers implement custom logic to handle conflicts based on business needs. This approach is flexible but requires deeper integration. PostgreSQL’s BDR (Bi-Directional Replication) allows defining conflict handlers via triggers or functions. For example, a banking app might resolve conflicting balance updates by summing deposits but flagging withdrawals that exceed limits. While this method offers precision, it adds complexity, as developers must anticipate and test edge cases. Tools like Amazon DynamoDB also enable conditional writes, where updates succeed only if predefined criteria (e.g., “balance > 0”) are met, reducing the need for post-hoc conflict resolution. Choosing the right method depends on balancing correctness, performance, and development effort.