How do relational databases manage concurrent access?

Relational databases manage concurrent access primarily through transactions, isolation levels, and locking mechanisms. Transactions group operations into atomic units of work, ensuring that all changes succeed (commit) or fail (rollback) together. Isolation levels define how transactions interact with each other by controlling visibility of uncommitted changes. For example, the Read Committed level ensures a transaction only sees data committed before it started, preventing “dirty reads” (accessing uncommitted changes). Higher levels like Repeatable Read prevent non-repeatable reads (a row changing during a transaction), while Serializable enforces strict ordering to avoid conflicts. These settings balance consistency and performance—stricter isolation reduces concurrency but minimizes anomalies.

Locking is another core mechanism. Databases use locks to restrict access to data during writes or reads. Exclusive locks prevent other transactions from modifying or reading a row while it’s being updated, while shared locks allow concurrent reads but block writes. For instance, when a user updates a row in MySQL, the database places an exclusive lock on it, forcing other write requests to wait. Lock granularity varies—row-level locks (common in PostgreSQL) allow finer concurrency, while table-level locks (used in older systems) are simpler but limit throughput. Deadlocks can occur when transactions hold locks and wait for each other’s resources. Databases resolve this by aborting one transaction (via timeouts or deadlock detection) or requiring retries.

Multi-Version Concurrency Control (MVCC) is a popular alternative to locking. Instead of blocking access, MVCC maintains multiple versions of a row. Readers see a snapshot of the database at the time their transaction started, while writers create new versions. PostgreSQL uses this approach: a SELECT query reads from a consistent snapshot, avoiding conflicts with concurrent updates. This reduces contention but increases storage overhead and requires cleanup (e.g., PostgreSQL’s vacuum process). MVCC works well for read-heavy workloads but can lead to “phantom reads” (new rows appearing in subsequent queries) at lower isolation levels. Developers choose between locking and MVCC based on their application’s read/write patterns and consistency needs. For example, banking systems might prioritize strict locking, while analytics platforms favor MVCC for scalability.