Distributed databases handle concurrency control by coordinating operations across multiple nodes to ensure data consistency while allowing simultaneous access. Since data is spread across different servers or locations, traditional single-node locking mechanisms aren’t sufficient. Instead, distributed systems use techniques like distributed locking, multi-version concurrency control (MVCC), timestamp ordering, or consensus protocols to manage conflicting reads and writes. The goal is to balance performance and correctness, preventing issues like dirty reads, lost updates, or inconsistent states.
One common approach is optimistic concurrency control (OCC) and MVCC. OCC assumes conflicts are rare and allows transactions to proceed without locks, checking for conflicts only at commit time. For example, Apache Cassandra uses lightweight transactions with a “compare-and-set” mechanism to validate updates. MVCC, used in systems like CockroachDB, maintains multiple versions of data. When a transaction reads data, it sees a snapshot consistent with its start time, avoiding locks on writes. Writes create new versions, and conflicts are resolved by checking version compatibility during commits. This reduces contention and improves read performance in distributed environments.
Another method involves distributed locking and consensus algorithms. For instance, Google Spanner uses globally synchronized clocks (via TrueTime) and two-phase commit (2PC) with Paxos for cross-node coordination. Locks are acquired in a distributed manner, and transactions are ordered using timestamps derived from synchronized clocks. Alternatively, systems like etcd use the Raft consensus algorithm to serialize operations: all writes go through a leader node, ensuring a single order of operations. While locking can introduce latency, these methods guarantee strict consistency. Choosing the right approach depends on the system’s requirements—latency tolerance, consistency guarantees, and the frequency of conflicting operations.