What are some techniques for data consistency in distributed databases?

Data consistency in distributed databases is typically achieved through techniques like ACID transactions, consensus protocols, and conflict resolution strategies. These methods ensure that data remains accurate and synchronized across nodes, even during concurrent operations or network partitions. Below are three key approaches developers can use.

ACID Transactions and Two-Phase Commit (2PC): ACID (Atomicity, Consistency, Isolation, Durability) transactions enforce strict consistency by ensuring operations either fully succeed or fail. In distributed systems, the Two-Phase Commit protocol is often used. In 2PC, a coordinator node first asks all participating nodes if they can commit a transaction (“prepare” phase). If all agree, the coordinator instructs them to finalize the commit (“commit” phase). For example, Google Spanner uses a globally synchronized clock and 2PC-like mechanisms to maintain ACID compliance across regions. However, 2PC can introduce latency and blocking if the coordinator fails, making it less suitable for highly available systems. Developers often balance this trade-off based on their application’s need for strict consistency versus performance.

Consensus Protocols (Raft, Paxos): Consensus protocols like Raft and Paxos ensure agreement among nodes on data state changes. Raft simplifies consensus by electing a leader node responsible for replicating logs to followers. Once a majority confirms the log entry, the change is committed. For instance, etcd (used in Kubernetes) relies on Raft for consistent key-value storage. Paxos, though more complex, underpins systems like Amazon’s DynamoDB for configuration management. These protocols are ideal for scenarios requiring strong consistency, such as financial systems, but they may add overhead due to the need for majority agreement. Developers often optimize by limiting consensus to critical operations.

Version Vectors and Conflict-Free Replicated Data Types (CRDTs): For systems prioritizing availability over strict consistency, version vectors and CRDTs help manage conflicts. Version vectors track update timestamps across nodes, allowing databases like Apache Cassandra to detect and resolve conflicting writes. CRDTs, used in databases like Riak, ensure data merges automatically without conflicts by designing data structures (e.g., counters, sets) that converge deterministically. For example, a shopping cart CRDT might merge item additions and removals across users. These methods suit applications like collaborative editing or IoT systems, where occasional temporary inconsistencies are acceptable. Developers implement them by choosing conflict resolution rules that align with business logic.

By combining these techniques, developers can tailor consistency models to their system’s requirements, balancing accuracy, performance, and fault tolerance.