Optimistic concurrency control is the more complex of the two concurrency control methods. A transaction beginning is timestamped, a process is run, and the change is validated. If another transaction completed after this transaction’s start time, this transaction is aborted. In other words, the original record is unavailable because someone got to it first and completed the transaction. The risk here is a dirty read, as it is possible for more than one person to have access to a record at a time. Change validation is done at the end of the transaction block.
Conservative (or pessimistic) concurrency control is akin to checking out a book at the library, and is the simpler of the two methods. Once a transaction begins, the record is locked, and no one else can modify it. In the library example, I would go to the library to check out a book (record) to read it (modify it); if it is there (no one has initiated a change), I may check it out. If the book (record) is not there (someone has locked it, modifying it), I cannot check it out. It is a first-come, first-served method that ensures no two people have concurrent access to a record at a time.
Each has its risks and rewards. Optimistic concurrency control tends to be used in environments without much contention for a single record of truth. It allows a higher volume of transactions per hour. However, as the name implies, the method essentially hopes for the best then deals with the problem if and when it arises. On the other hand, pessimistic concurrency control virtually guarantees that all transactions will be executed correctly and that the database is stable. It is a simpler decision tree: either abort if locked or commit if unlocked. All the drawbacks of pessimistic concurrency control lie in timing: fewer transactions per hour and limited access to the data depending on the number of users making transactions.
One specific advantage of optimistic locking, that isn’t always thought of immediately, is evident in the scenario when a user cannot maintain a consistent connection to the database. Assume for a moment that a user locks a table in a remote database for updating and the connection is severed (either through server reset, ISP woes, et cetera). The user reconnects and is back in the database. However, the previous session was not properly closed, so we have a phantom user with the record still open.
Connolly, T. & Begg, C. (2015). Database Systems – Database Systems: A Practical Approach to Design, Implementation, and Management (6th ed.). London, UK: Pearson.