Distributed databases handle concurrent reads and writes through various mechanisms that ensure data consistency and availability across different nodes. These mechanisms often rely on locking, versioning, and consensus algorithms. When multiple clients attempt to read or write data simultaneously, the database system needs to manage these operations carefully to prevent issues like race conditions or data corruption.
One common approach is using locking strategies. In this scenario, when a write operation is initiated, the system may place a lock on the affected data record or table. This prevents other write processes from modifying the same data until the lock is released. For instance, in a distributed database that uses a simple row-level locking mechanism, if User A locks a row for updating, User B will be unable to write to that row until User A completes their transaction. However, this can lead to performance bottlenecks, especially in high-traffic applications, which is why databases often implement more advanced locking protocols.
Alternative methods include optimistic concurrency control, where the system allows multiple transactions to proceed without locking resources but checks for conflicts before finalizing the update. If a conflict is detected, one of the transactions is rolled back. For example, in systems that use Multi-Version Concurrency Control (MVCC), like PostgreSQL, each transaction sees a snapshot of the database at a certain time. This allows reads to occur without blocking writes, improving performance and reducing contention. Alongside these techniques, distributed databases often employ consensus algorithms, like Paxos or Raft, to ensure agreement among nodes about the current state of the data, which is crucial for maintaining consistency across distributed environments.