Stop trusting your database backups until you have tested a point-in-time restore

Technical Briefing | 7/26/2026

You probably have a cron job dumping your databases to a bucket somewhere. It runs every night, the exit code is zero, and you get an email if it fails. That is great for your peace of mind, but it is dangerous if you have never actually tried to pull that data back into a production instance. I once spent six hours failing to restore a massive PostgreSQL dump because of a hidden collation mismatch that only surfaced during the import. The backup wasn’t broken, but my assumption that it was ready for production definitely was.

Why your backup logs are lying to you

A successful exit code from pg_dump or mysqldump just means the utility managed to stream the data to a file. It tells you nothing about the health of the indexes, the integrity of the triggers, or whether the resulting SQL file is actually parseable by your version of the database engine. If you aren’t testing restores on a throwaway instance, you aren’t backing up data, you are just collecting expensive digital artifacts.

docker run --rm -v /path/to/backup:/tmp/backup postgres:15-alpine pg_restore -d postgres -C /tmp/backup/dump.sql

• Use a dedicated environment that mirrors your production schema exactly
• Verify that stored procedures and triggers recreate without errors
• Compare row counts of critical tables post-restore to ensure parity
• Check that your secondary indexes are actually being rebuilt during the import

The right approach is to script a weekly destructive test where you wipe a dev database and hydrate it from the latest backup. If the restore takes longer than your allowed downtime window, you need to rethink your strategy—maybe look into parallel restores or moving to block-level snapshots if the dataset size is the bottleneck. Stop waiting for a real emergency to discover that your recovery process is too slow to matter.