SQL locks are mechanisms databases use to manage concurrent access to data, ensuring consistency and preventing conflicts when multiple transactions interact with the same resources. When a transaction (like a query or update) accesses data, the database assigns a lock to control how other transactions can interact with that data. The two primary lock types are shared locks (for reads) and exclusive locks (for writes). Shared locks allow multiple transactions to read data simultaneously but block writes, while exclusive locks prevent both reads and writes by other transactions until released. This ensures that conflicting operations, like two transactions trying to update the same row, don’t corrupt data.
Locks operate at different levels of granularity, such as row-level, page-level, or table-level. For example, if Transaction A updates a row in a users table, it acquires an exclusive lock on that row. Transaction B attempting to read the same row might be blocked until Transaction A commits and releases the lock. Databases also use lock escalation (e.g., upgrading many row locks to a table lock) to reduce overhead. Lock behavior depends on the database’s isolation level. For instance, in Read Committed isolation, shared locks are released immediately after a read, while in Repeatable Read, they’re held until the transaction ends. Developers must understand these settings to balance consistency and performance.
However, locks can introduce challenges like deadlocks. A deadlock occurs when two transactions each hold a lock the other needs, creating a standstill. For example, Transaction A locks Row 1 and tries to lock Row 2, while Transaction B locks Row 2 and tries to lock Row 1. Databases detect deadlocks by monitoring wait chains and typically kill one transaction to resolve the issue. To minimize problems, developers should keep transactions short, avoid unnecessary locks, and design queries to access resources in a consistent order. Proper indexing and choosing the right isolation level (e.g., Read Committed vs. Serializable) also help optimize locking behavior without sacrificing data integrity.