Implementation of optimistic locking using check and set

Optimistic Locking and Check and Set (CAS):

Optimistic locking is a technique used to handle concurrent updates in a distributed system. The idea is to allow multiple clients to read data concurrently, and when they want to update the data, they first check if the data has been modified by any other client before proceeding with the update.

The Check and Set (CAS) pattern is a common method for implementing optimistic locking. It involves the following steps:

• The client reads the current value of the data it wants to update.
• The client calculates the new value and tries to set it if the current value matches the value it initially read.
• If the current value has changed since the client read it, the update is rejected, and the client needs to handle the conflict (retry or inform the user).

Implementing Optimistic Locking using WATCH and CAS in Redis:

In Redis, you can implement optimistic locking using the WATCH command along with a transaction. Here’s is the steps to implement optimistic locking using WATCH AND CAS in Redis:

• WATCH the Key: Before starting the transaction, use the WATCH command to monitor the key you’re going to update.
• Read the Current Value: Read the current value of the key. This is the value you’ll use to calculate the new value.
• Start a Transaction (MULTI-EXEC): Start a transaction block using the MULTI command.
• Calculate the New Value: Calculate the new value based on the current value and your business logic.
• Compare and Set (CAS): Use the SET command within the transaction to update the key’s value if and only if the value has not changed since you initially read it.
• Execute the Transaction (EXEC): Execute the transaction using the EXEC command.
• Handle Transaction Result: If the EXEC command returns a null response, it means that the data has been modified by another client between the WATCH and EXEC commands. Handle this case by either retrying the operation or informing the user.

Remember, optimistic locking using CAS is a powerful way to handle concurrency, but you should carefully design your application’s logic and error handling to ensure data consistency and proper user experience in case of conflicts. It’s important to note that Redis transactions do not support rollback like traditional relational database transactions. If an error occurs during the execution of a transaction, you will need to handle the error and revert any side effects manually.

Redis transactions allow you to group multiple commands into a single atomic operation. An atomic operation means that all the commands within a transaction are executed together, ensuring that either all of them succeed or none of them do. Redis transactions use a concept called “MULTI/EXEC” to initiate and commit the transaction.