How do relational databases handle data updates across multiple tables?

Relational databases manage data updates across multiple tables using transactions and constraints to maintain consistency. When you modify data in multiple tables, the database ensures all changes succeed together or fail together. This is achieved through ACID (Atomicity, Consistency, Isolation, Durability) properties. For example, if an e-commerce application updates an order table and reduces inventory in a product table, a transaction groups these operations. If either update fails (e.g., due to insufficient inventory), the entire transaction rolls back, leaving both tables unchanged. This prevents partial updates that could corrupt data relationships.

Foreign key constraints and cascading actions further automate multi-table updates. For instance, deleting a user in a users table might require deleting their related entries in an orders table. By defining a foreign key with ON DELETE CASCADE, the database automatically removes dependent records. Similarly, ON UPDATE CASCADE propagates changes to linked tables when a primary key changes. These constraints reduce manual coding but require careful setup to avoid unintended side effects. Developers must explicitly define relationships during schema design to leverage these features effectively.

Explicit transaction control using SQL commands like BEGIN TRANSACTION, COMMIT, and ROLLBACK provides granular control. For example, transferring funds between bank accounts involves deducting from one account and crediting another. Wrapping these steps in a transaction ensures both updates occur atomically. Databases also use locks to isolate transactions, preventing conflicting reads or writes during updates. While this can impact performance under high concurrency, it guarantees data integrity. Tools like savepoints allow partial rollbacks within transactions, enabling developers to handle complex workflows without losing all progress if a subset of operations fails.