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Mastering Optimistic Locking with SQL UPDATE Queries in PostgreSQL

Discover how to effectively handle version conflicts and entity not found scenarios using `Optimistic Locking` in PostgreSQL UPDATE queries. --- This video is based on the question https://stackoverflow.com/q/77807803/ asked by the user 'Myles McDonnell' ( https://stackoverflow.com/u/1022177/ ) and on the answer https://stackoverflow.com/a/77808997/ provided by the user 'Erwin Brandstetter' ( https://stackoverflow.com/u/939860/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Optimistic Lock in UPDATE query Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Mastering Optimistic Locking with SQL UPDATE Queries in PostgreSQL When working with databases, especially in multi-user environments, it's crucial to handle concurrent updates to avoid unexpected results or data inconsistencies. One common approach to dealing with this is known as Optimistic Locking. In this post, we'll explore how you can implement optimistic locking in your PostgreSQL UPDATE queries by identifying three distinct outcomes: successful update, version conflict, or entity not found. Understanding the Problem Imagine you have a database table called vessel_clearance, and you want to update a vessel's status. However, you want your update query to indicate whether the operation was successful or if there was an issue such as a version conflict or the entity not being found. This can be particularly challenging because: Concurrency: Multiple users might try to update the same record simultaneously. Version Conflicts: If the record has been updated since you last read it, you need to handle this scenario gracefully. Entity Not Found: There is always a chance that the entity you are trying to update doesn't exist in the table. The Solution: Using a CTE for Update Query To address this challenge, we can utilize a Common Table Expression (CTE) combined with a cleverly structured SQL statement. Below is the complete SQL query to achieve the desired outcomes: [[See Video to Reveal this Text or Code Snippet]] Breaking Down the Query The CTE (WITH clause): We start by declaring a CTE named upd. In this section, we attempt to update the vessel_clearance record. The UPDATE statement modifies the status, last_updated_at, and expiry fields where the record's ID matches vesselclearanceid and the current version timestamp matches current_version_ts. The RETURNING 1 clause indicates that we’re primarily concerned with whether or not any row was updated. The Outer SELECT: After executing the CTE, we leverage a SELECT statement to determine the outcome of our update. The COALESCE function checks the results of our update: If no conflicts occur and the update is successful, it returns 'UPDATE_APPLIED'. If there is a version conflict (the record was modified after we last read it), we query the vessel_clearance table to see if the entity still exists. If it does, it returns 'VERSION_CONFLICT'. If the entity doesn't exist at all or no rows were affected by the update, it defaults to 'ENTITY_NOT_FOUND'. Benefits of This Approach Single Statement Execution: The above logic all runs in a single SQL statement, ensuring no race conditions due to concurrent access. Simultaneous View: Both the CTE and the outer SELECT see the same snapshot of the table, enhancing data reliability during updates. Clear Outcomes: This structure allows for easy understanding of the outcome of the operation without needing multiple queries or transaction handling. Conclusion Incorporating optimistic locking in your PostgreSQL UPDATE queries not only safeguards against data mishaps but also streamlines your database transactions. With just a single SQL statement employing a CTE, you can adeptly distinguish between successful updates, version conflicts, and entities that weren't found, allowing for better error handling and user experience. Implement this solution in your applications to enhance data integrity and confidence in your database operations.