How do SQL transactions handle concurrency?

SQL transactions handle concurrency by ensuring that multiple operations can occur simultaneously without compromising data integrity. This is primarily achieved through isolation levels and locking mechanisms, which control how transactions interact with shared data. The goal is to prevent conflicts like dirty reads (reading uncommitted data), non-repeatable reads (data changing during a transaction), and phantom reads (new rows appearing during a transaction). Databases use these techniques to balance performance and consistency based on the needs of the application.

Isolation levels define the degree to which transactions are shielded from each other. The four standard levels, from least to most strict, are Read Uncommitted, Read Committed, Repeatable Read, and Serializable. For example, at Read Committed (the default in many databases), a transaction can only read data that has been committed by other transactions. This prevents dirty reads but allows non-repeatable reads. At the Serializable level, transactions are fully isolated, behaving as if they executed one after another. Locks play a key role here: shared locks allow concurrent reads but block writes, while exclusive locks block both reads and writes. For instance, if Transaction A updates a row, it places an exclusive lock, preventing Transaction B from modifying that row until A commits or rolls back.

Multi-Version Concurrency Control (MVCC) is another common approach, used by databases like PostgreSQL and MySQL’s InnoDB. Instead of locking rows, MVCC allows transactions to access snapshots of data as it existed at the start of the transaction. This lets reads proceed without blocking writes, improving performance. For example, if Transaction A updates a user’s balance while Transaction B reads it, B sees the pre-update balance until A commits. Conflicts are resolved at commit time: if two transactions modify the same row, the second one may be rolled back. Deadlocks (where transactions wait indefinitely for each other’s locks) are handled by aborting one transaction automatically. Developers can influence behavior by choosing isolation levels or using explicit locking (e.g., SELECT FOR UPDATE), but the database manages most complexity internally.