How does a relational database handle concurrency?

Relational databases handle concurrency by ensuring multiple transactions can operate simultaneously without conflicting with each other, while maintaining data integrity. This is primarily achieved through mechanisms like locking, multi-version concurrency control (MVCC), and transaction isolation levels. These techniques balance the need for performance with consistency, allowing developers to control how strictly conflicts are prevented based on their application’s requirements.

One common approach is locking, where the database restricts access to data during a transaction. For example, when a transaction updates a row, it might place an exclusive lock, blocking other transactions from modifying that row until the lock is released. Shared locks allow multiple transactions to read data but prevent writes until all readers finish. While effective, excessive locking can lead to bottlenecks or deadlocks. Databases often include deadlock detection to resolve these by aborting one transaction. For instance, SQL Server uses lock escalation to minimize overhead, switching from row-level to table-level locks when many small locks accumulate.

Another key method is multi-version concurrency control (MVCC), which avoids locks by maintaining multiple versions of data. When a transaction reads data, it sees a snapshot from the start of the transaction, even if other transactions modify the data later. This allows reads and writes to occur without blocking each other. PostgreSQL uses MVCC by default, storing versioned rows and cleaning up obsolete versions via vacuum processes. MVCC improves performance for read-heavy workloads but adds storage overhead. Isolation levels like Read Committed or Repeatable Read determine which snapshot a transaction uses, balancing consistency and performance.

Finally, relational databases use transaction isolation levels to define how transactions interact. The ANSI SQL standard defines four levels: Read Uncommitted, Read Committed, Repeatable Read, and Serializable. For example, Read Committed ensures a transaction only reads committed data, preventing “dirty reads,” while Serializable isolates transactions completely, often using locks or MVCC to simulate serial execution. Developers choose levels based on their needs—e.g., a reporting tool might use Read Committed for speed, while a banking system might require Serializable for strict consistency. Each level trades off between concurrency, performance, and the risk of anomalies like phantom reads.