convex-migrations

Convex schema migrations can be executed without downtime by using batch processing and a reusable runner pattern to coordinate field changes, data backfills, and progress tracking across large datasets during live deployments.

Backfilling new fields in a Convex database is best handled through staged migrations that process data in batches, track progress, and ensure idempotent execution to avoid data loss and minimize disruption to production traffic.

To safely rename or remove fields in Convex, use staged migrations that maintain explicit migration state and error handling, ensuring the old field is only removed after the new field is fully populated and verified.

Yes, large batch data migrations in Convex can be executed using a migration runner pattern that processes records incrementally, tracks progress across the dataset, and handles errors explicitly to ensure safe rollout.

You need to update indexes during Convex schema evolution when adding, removing, or renaming fields that impact query performance, ensuring scalable queries are maintained alongside the structural data changes.