How Pessimistic Locking works in PostgreSQL?

Pessimistic locking in PostgreSQL relies on explicit lock commands, allowing transactions to acquire locks on specific database objects. These locks prevent other transactions from accessing or modifying the locked objects until the system releases the lock.

Here’s how pessimistic locking works in PostgreSQL:

1. Lock Types: PostgreSQL provides multiple lock types, including row-level locks, table-level locks, and advisory locks. Row-level locks secure individual rows within a table, while table-level locks restrict access to the entire table. Advisory locks serve as application-defined locks for synchronization.

2. Lock Acquiring: Transactions acquire locks using the LOCK command or the SELECT ... FOR UPDATE statement. The LOCK command secures specific tables or rows, while SELECT ... FOR UPDATE restricts access to selected rows within a transaction. These commands allow specifying the lock mode, such as exclusive or shared, to control concurrency levels.

3. Lock Conflict Resolution: When a transaction attempts to acquire a lock on an object already locked by another transaction, PostgreSQL applies predefined conflict resolution rules. The system may make the requesting transaction wait until the lock is released or block or cancel it, depending on the lock mode and database configuration.

4. Lock Release: Transactions automatically release acquired locks when they commit or roll back. However, users can also release locks explicitly using the UNLOCK command.

Conclusion:

Pessimistic locking is typically used in situations where data consistency is critical, and conflicts need to be avoided. However, it can also lead to reduced concurrency and potential performance issues if locks are held for a long time, causing blocking of other transactions.

It’s important to note that PostgreSQL also provides an alternative approach called Multiversion Concurrency Control (MVCC), which is the default concurrency control mechanism. MVCC uses optimistic locking by allowing concurrent transactions to operate on the same data without blocking each other, thereby maximizing concurrency while ensuring data consistency.

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About Shiv Iyer 497 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.