In pessimistic locking a resource is locked from the time it is first accessed in a transaction until the transaction is finished, making it inaccessible to other transactions during that time.
With optimistic locking, a resource is not actually locked when it is first accessed by a transaction. Instead, the state of the resource at the time when it would have been locked with the pessimistic locking approach is saved. Other transactions are able to concurrently access to the resource and the possibility of conflicting changes is possible. At commit time, when the resource is about to be updated in persistent storage, the state of the resource is read from storage again and compared to the state that was saved when the resource was first accessed in the transaction. If the two states differ, a conflicting update was made, and the transaction will be rolled back.
In the banking application example, the amount of an account is saved when the account is first accessed in a transaction. If the transaction changes the account amount, the amount is read from the store again just before the amount is about to be updated. If the amount has changed since the transaction began, the transaction will fail itself, otherwise the new amount is written to persistent storage.
Pessimistic locking has much better integrity than optimistic locking but requires you to be careful with your application design to avoid Deadlocks. To use pessimistic locking you need either a direct connection to the database (as would typically be the case in a two tier client server application) or an externally available transaction ID that can be used independently of the connection.
Optimistic locking is used when you don't expect many collisions. It costs less to do a normal operation but if the collision DOES occur you would pay a higher price to resolve it as the transaction is aborted.
Pessimistic locking is used when a collision is anticipated. The transactions which would violate synchronization are simply blocked.
To select proper locking mechanism you have to estimate the amount of reads and writes and plan accordingly
it should be said that optimistic locking tends to improve concurrency at the expense of predictability. Pessimistic locking tends to reduce concurrency, but is more predictable.
For exact data operations (like in banking) use pessimistic. It's essential that the data is accurately read, with no un-shown changes - the extra locking overhead is worth it.
Optimistic strategy is most useful for web applications where you do not necessarily maintain a connection to the database for your session. In this situation the client cannot actually maintain database locks as the connections are taken from a pool and you may not be using the same connection from one access to the next.
For optimistic locking every participant in data modification must agree in using this kind of locking. But if someone modifies the data without taking care about the version column, this will spoil the whole idea of the optimistic locking.
How to Perform Pessimistic Lock?
Pessimistic locking in six easy steps
The basics steps for pessimistic locking are as follows:
Create a transaction with an IsolationLevel of RepeatableRead.
Set the DataAdapter’s SelectCommand property to use the transaction you created.
Make the changes to the data.
Set DataAdapter’s Insert, Update, and Delete command properties to use the transaction you created.
Call the DataAdapter’s Update method.
Commit the transaction.
You can implement optimistic locks in your DB table in this way (this is how optimistic locking is done in Hibernate):
Add integer "version" column to your table.
Increase value of this column with each update of corresponding row.
To obtain lock, just read "version" value of row.
Add "version = obtained_version" condition to where clause of your update statement. Verify number of affected rows after update. If no rows were affected - someone has already modified your entry.
Your update should look like
UPDATE mytable SET name = 'Andy', version = 3 WHERE id = 1 and version = 2