In HSQLDB, triggers are database objects that automatically execute in response to specific events occurring within the database. The primary purpose of triggers is to enforce business rules, data integrity constraints, and automate complex database operations. Here are some key purposes and use cases of triggers in HSQLDB :


Enforcing Data Integrity :

* Triggers can enforce data integrity constraints that cannot be expressed using standard table constraints, such as cross-table validations or complex business rules.
* For example, you can use triggers to enforce referential integrity between related tables, validate input data, or apply custom business logic before data modification operations.


Auditing and Logging Changes :

* Triggers can be used to log changes made to specific tables or columns, providing an audit trail of data modifications for compliance, tracking, and debugging purposes.
* For example, you can create triggers to record who made changes to a table, when the changes were made, and what specific data was modified.


Implementing Complex Business Logic :

* Triggers allow you to implement complex business logic that spans multiple tables or involves conditional logic not easily expressed in SQL statements alone.
* For example, you can use triggers to automatically calculate derived values, enforce complex workflow rules, or trigger notifications based on specific database events.
Ensuring Data Consistency Across Tables :

* Triggers can maintain data consistency across related tables by automatically updating or synchronizing data in response to changes in other tables.
* For example, you can use triggers to propagate changes from a parent table to its child tables, enforce denormalization rules, or maintain calculated fields.


Validating Input Data :

* Triggers can validate input data before it is inserted, updated, or deleted in a table, ensuring that only valid and consistent data is stored in the database.
* For example, you can use triggers to enforce complex validation rules, perform data cleansing or normalization, or prevent certain types of data modifications based on predefined criteria.