Optimistic locking is a concurrency control method used in database management systems to handle simultaneous transactions without locking the data during read operations. This approach assumes that conflicts between transactions are rare and allows multiple transactions to proceed without interference until the point of committing changes. Here is a detailed explanation of how optimistic locking works and its key characteristics:
How Optimistic Locking Works
Version Tracking: Each record in the database contains a version field, which could be a timestamp or an incrementing number. This version represents the state of the record at the time it was read.
Read Operation: When a transaction reads a record, it also reads the version number of that record.
Update Attempt: Before updating the record, the transaction checks the current version of the record against the version it initially read.
Version Check:
If the versions match: This indicates that no other transaction has modified the record since it was read. The update proceeds, and the version field is incremented.
If the versions do not match: This indicates that another transaction has modified the record. The update is aborted, and an optimistic locking exception is thrown, signaling a failed update due to concurrent modifications.
Advantages of Optimistic Locking
Reduced Locking Overhead: Since data is not locked during read operations, optimistic locking reduces the overhead associated with managing locks.
Improved Concurrency: It allows more transactions to proceed concurrently, which can enhance performance, especially in environments with a high number of read operations and fewer write operations.
Avoidance of Deadlocks: By not holding locks during the transaction, optimistic locking avoids the risk of deadlocks, which can occur in pessimistic locking scenarios.
Disadvantages of Optimistic Locking
Conflict Handling: Requires additional logic to handle conflicts when they occur. Applications must be designed to gracefully handle optimistic locking exceptions and resolve them, often by retrying the transaction.
Increased Complexity: The need to manage version checks and handle potent...
