A distributed database manages concurrency control by using a combination of protocols and mechanisms to ensure data integrity while allowing multiple users to access and modify data at the same time. One common approach is to implement locking mechanisms, where a user must acquire a lock on a piece of data before making changes. There are two main types of locks: shared locks, which allow multiple transactions to read the data but not modify it, and exclusive locks, which permit only one transaction to change the data. This prevents conflicts and ensures consistency, though it can lead to bottlenecks if many users are waiting for locks.
Another common method for handling concurrency is through the use of multi-version concurrency control (MVCC). MVCC allows multiple transactions to read and write to the database simultaneously by maintaining multiple versions of data items. Rather than locking the data, when a transaction makes an update, it creates a new version of the data, leaving the old version intact for transactions that are still in progress. This allows readers to access the previous version without waiting for the writer to complete, improving performance and reducing deadlock scenarios. Databases like PostgreSQL use this approach, allowing for high levels of concurrency with minimal waiting times.
Additionally, distributed databases implement consensus protocols, such as Paxos or Raft, to manage consistency across nodes during concurrent transactions. These protocols ensure that all nodes in the distributed system agree on the state of the data, even if some nodes experience failures or network issues. For instance, if a transaction is updated on one node, the consensus algorithm ensures that other nodes are informed of this change and can quickly reach an agreement about its validity. This coordinated approach is essential for maintaining data consistency and reliability across distributed environments, especially in applications requiring high availability and fault tolerance.