When Soft Deletes Turn Into Ticketing Nightmares: A Real‑World Failure Story

Major Incident Warning

While working at an event‑ticketing company, the author created a pull request that migrated a seat‑locking mechanism—implemented with a soft‑delete flag—into a new database collection intended for deleted records.

The migration unintentionally removed the Paranoia library (https://github.com/rubysherpas/paranoia) that automatically filters out rows marked with a deleted_at timestamp. Because the automatic exclusion stopped working, the background job began processing records that were already marked as deleted, releasing seats that had already been sold and allowing the same seat to be sold repeatedly.

The bug manifested during a Shawn Mendes concert, where the same seat was sold to multiple customers. Hundreds of duplicate bookings required refunds, order cancellations, and apology emails, and a late‑night incident report was written.

Why Soft Deletes Are Problematic

Increased Complexity – Soft deletes spread a “deleted” flag throughout the schema, making queries more complicated and increasing the risk of exposing stale or sensitive data when writing custom SQL.

Indexes, unique constraints, and foreign‑key relationships must all account for the delete flag, leading to heavier maintenance and potential performance degradation, especially in high‑traffic environments.

Data Integrity Risks – Deleting a row that is still referenced by a foreign key triggers errors such as:

ERROR: delete on table "users" violates foreign key constraint "orders_user_id_fkey" on table "orders"
DETAIL: Key (id)=(456) is still referenced from table "orders".

Manually enforcing referential integrity in application code is error‑prone and adds significant overhead.

Safer Alternatives to Soft Deletes

One approach is to archive deleted rows into a separate history table before removal, preserving data for compliance or audit purposes without polluting the main tables.

For a more automated solution, use database‑level change tracking. PostgreSQL offers several strategies, detailed in the “Ultimate Guide to PostgreSQL Data Change Tracking” (https://blog.bemi.io/the-ultimate-guide-to-postgresql-data-change-tracking/). The open‑source project Bemi (https://github.com/BemiHQ/bemi) provides an ORM‑agnostic audit layer that records context‑rich change logs.

Bottom Line

Avoid soft deletes in production systems. Although they seem convenient, they act like a ticking time bomb that can explode into data‑loss incidents, performance issues, and complex maintenance. Prefer archival tables or audit‑trail mechanisms for a cleaner, safer, and more maintainable solution.