What practice should be followed to avoid race conditions in database design?

What practice should be followed to avoid race conditions in database design?

• A. Implement optimistic locking
• B. Allow unbounded queries
• C. Avoid data locking
• D. Delete old data regularly

Answer: (A)

Implementing optimistic locking is a crucial practice in database design to prevent race conditions. This technique allows multiple transactions to proceed without locking the data but checks for conflicts before committing changes. Optimistic locking works under the premise that conflicts are rare and therefore does not impose heavy restrictions on database access, which can enhance performance and user experience. When a user attempts to update a record, the system checks whether the record has been altered by another transaction since it was read. If it has, the update will be rejected, and the user can then retrieve the most current data to make an informed decision about how to proceed. This approach minimizes the chance of lost updates and ensures data integrity. The other practices mentioned do not effectively manage race conditions. Allowing unbounded queries can lead to performance issues and does not inherently address conflict prevention. Avoiding data locking may increase concurrency but does not provide a mechanism to handle conflicting updates. Regularly deleting old data has its own benefits for performance and storage management but does not prevent race conditions in transactions involving shared data.

Implementing optimistic locking is a crucial practice in database design to prevent race conditions. This technique allows multiple transactions to proceed without locking the data but checks for conflicts before committing changes. Optimistic locking works under the premise that conflicts are rare and therefore does not impose heavy restrictions on database access, which can enhance performance and user experience.

When a user attempts to update a record, the system checks whether the record has been altered by another transaction since it was read. If it has, the update will be rejected, and the user can then retrieve the most current data to make an informed decision about how to proceed. This approach minimizes the chance of lost updates and ensures data integrity.

The other practices mentioned do not effectively manage race conditions. Allowing unbounded queries can lead to performance issues and does not inherently address conflict prevention. Avoiding data locking may increase concurrency but does not provide a mechanism to handle conflicting updates. Regularly deleting old data has its own benefits for performance and storage management but does not prevent race conditions in transactions involving shared data.