Optimistic Locking and Pessimistic Locking are two primary strategies used in database management systems to handle concurrency control, ensuring data integrity when multiple transactions access the same data simultaneously.
Optimistic Locking
Optimistic Locking, also known as Optimistic Concurrency Control (OCC), operates on the assumption that conflicts between transactions are rare. This method allows multiple transactions to proceed without locking the data resources they are using. Instead, it checks for conflicts only at the time of committing the changes. If a conflict is detected, the transaction is rolled back and can be retried. This approach is beneficial in environments with low data contention and high read volumes, as it minimizes the overhead associated with locking mechanisms and allows for higher throughput[1][2][6].
Key Characteristics:
Concurrency: Multiple transactions can read and modify data simultaneously.
Conflict Detection: Conflicts are detected at the commit phase.
Rollback and Retry: Transactions that encounter conflicts are rolled back and can be retried.
Use Case: Suitable for systems with low data contention and high read operations, such as web applications where users frequently read data but rarely update it[1][3][6].
Example:
In a scenario where two users attempt to update the same record, each transaction proceeds without locking the record. At the commit phase, the system checks if the record has been modified by another transaction. If a conflict is detected, one of the transactions is rolled back[2][6].
Pessimistic Locking
Pessimistic Locking, also known as Record Locking, assumes that conflicts between transactions are likely to occur. This method locks the data resources before any transaction reads or modifies them, preventing other transactions from accessing the locked data until the lock is released. This approach ensures that only one transaction can update the data at a time, thereby preventing conflicts but potentially reducing system performance due to the overhead of managing locks[1][2][7].
Key Characteristics:
Concurrency: Only one transaction can access and modify the locked data at a time.
Conflict Prevention: Conflicts are prevented by locking data resources before any operation.
Performance Impact: Can lead to performance bottlenecks in high-concurrency environments due to the overhead of managing locks.
Use Case: Suitable for systems with high data contention and critical data integrity requirements, such as financial systems where transactions must be processed sequentially to avoid inconsistencies[1][3][7].
Example:
In a scenario where two users attempt to update the same record...
