When it comes to database management, ensuring data consistency and preventing concurrent updates is crucial. Row-level locking is a vital concept that helps achieve this by controlling access to specific rows or records in a table. In this article, we'll delve into the world of row-level locking and explore its benefits and challenges.

Understanding row-level locking is essential for managing API keys, user accounts, and other critical data. Without proper row-level locking, concurrent updates can lead to race conditions, data inconsistencies, and even conflicts that disrupt the intended workflow. By implementing row-level locking with Django Rest Framework's transaction module and ORM method, developers can ensure that multiple transactions access and modify the same table concurrently without blocking or waiting for each other.

However, row-level locking also comes with its own set of challenges, such as deadlocks, increased overhead, and potential lock escalation. Deadlocks occur when two or more transactions are waiting for each other to release locks, leading to a circular wait that cannot be resolved without intervention. Increased overhead refers to the need for more system resources (memory and CPU) to manage row-level locks, which can become resource-intensive in high-concurrency environments.

Source: https://dev.to/sten/understanding-row-level-locking-in-databases-2c8j