soft-delete

To implement a soft delete pattern, you need database schema modifications and application-level logic to manage deleted_at timestamps. This approach enables data recovery and maintains audit trails by marking records as deleted instead of permanently removing them.

Restoring accidentally deleted records is handled through a restoration workflow that uses the deleted_at timestamp. This soft delete mechanism maintains audit trails and preserves referential integrity, allowing you to recover data within a defined period like GDPR compliance windows.

Filtering soft deleted records in TypeScript and Python requires application-level logic to exclude entries where the deleted_at timestamp is set. This ensures active queries ignore recovered or archived data while maintaining the audit trail.

Yes, implementing a soft delete pattern requires database schema modifications to add columns like deleted_at. You must also update application-level logic across TypeScript/JavaScript and Python environments to handle filtering, restoration, and permanent deletion workflows.

The best way to permanently delete records after a soft delete recovery period is through a dedicated permanent deletion workflow. This process removes the database entries entirely once the deleted_at timestamp indicates the compliance or recovery window has expired.

You should not use a soft delete approach when data privacy regulations demand immediate and permanent removal of records. Because soft delete preserves data for audit trails and recovery, it fails to meet strict erasure requirements unless paired with a scheduled permanent deletion workflow.