SQL transactions handle concurrency through mechanisms like locking, isolation levels, and the use of transactions themselves to ensure data integrity and consistency. When multiple transactions are executed simultaneously, there is a risk that they could interfere with one another, leading to issues like lost updates, temporary inconsistencies, or even data corruption. To manage this, SQL databases implement different strategies to control how transactions interact with the data and with each other.
One common approach to handling concurrency is through locking. When a transaction needs to modify data, it will request a lock on that data, which prevents other transactions from reading or writing to the same data until the first transaction is finished and the lock is released. For example, if Transaction A is updating a row in a table, it will place a lock on that row. Transaction B, which might be trying to read or update the same row, will have to wait until Transaction A completes. This ensures that the transactions do not conflict but can lead to contention and delays if many transactions request locks on the same resources.
Another important aspect of managing concurrency is the concept of isolation levels. SQL databases allow developers to choose different isolation levels, which define the extent to which the operations of one transaction are isolated from others. The most commonly used isolation levels are Read Uncommitted, Read Committed, Repeatable Read, and Serializable. For instance, if a developer sets the isolation level to Serializable, it ensures that transactions operate as if they are executed one after another, completely isolating them. However, this can decrease performance due to increased locking. Conversely, with Read Committed, transactions could read uncommitted changes from other transactions, which may allow for increased concurrency but at the risk of encountering inconsistencies. Understanding these trade-offs is crucial for developers when designing applications that require reliable data handling.