convex-migrations

Zero-downtime Convex schema migrations are automated by processing data in batches using cursors, tracking progress in a dedicated migrations table, and using start, update, complete, and fail mutations to ensure deterministic, auditable schema evolution.

Batched data backfilling during Convex schema evolution uses chunked processing with cursors to avoid long-running tasks, while a centralized migrations table tracks progress and ensures data integrity across large production datasets.

Migration progress and failures for large Convex datasets are tracked using a dedicated migrations table with start, update, complete, and fail mutations, providing an auditable record and optional rollback hooks for handling errors.

Adding, removing, or renaming fields and indexes in Convex avoids production downtime by applying changes through batched, chunked processing with cursors, ensuring data integrity without blocking ongoing operations.

Batched Convex migrations require a dedicated migrations table and chunked processing with cursors to avoid long-running tasks, meaning migration execution depends on sequential batch completion and careful failure handling rather than instant schema switching.