Dry-Run Database Migrations on a Clone to Prevent Data Loss

说明与用途

标准测试无法检测数据库迁移过程中的数据丢失。建议在带有代表性数据的克隆生产模式上对迁移进行“试运行”，通过比较行数、空值率和约束等指标的快照，验证正向和反向迁移。

问题

Standard test suites validate the post-migration schema but cannot detect data loss or corruption that occurs during the migration process, especially when AI-generated migrations are used. Staging environments often lack representative data, making them poor substitutes for verifying migration safety.

结果

The workflow provides a mechanical verification method to ensure database migrations are reversible and do not cause silent data loss. It shifts the focus from 'does the app work with the new schema?' to 'can we safely revert if something goes wrong?'

工作流

• Clone the production schema and load a representative data sample (e.g., a few thousand rows per table).
• Snapshot the baseline metrics: record row counts, null rates, and constraint counts before any migration.
• Apply the 'up' migration and snapshot the metrics again.
• Apply the 'down' migration and snapshot the metrics a third time.
• Compare all three snapshots to ensure the 'up' migration changes only intended fields and the 'down' migration restores the baseline exactly.

证据

A green test suite is the wrong tool for judging an AI-generated migration, because tests run against the post-migration schema and never observe the intermediate states where data disappears.

来源

The key property is not that the down migration executes without error; it is that the schema and data return to the baseline state.

来源