Rollbacks and Re-Indexing Strategies

What happens if you accidentally delete your production collection? Or if a new embedding model turns out to be inaccurate? You need a Rollback Plan.

The Snapshot Method

Before any major change (re-indexing or model update), take a snapshot of your vector database directory.

# Example for local Chroma
tar -cvf backup_v1.tar ./current_db

Zero-Downtime Re-indexing

Use an Alias or Pointer in your code.

# In your config
CURRENT_COLLECTION = "prod_v2" # Switch to "prod_v1" to rollback instantly

Atomic Updates

When updating a document:

1. First, upsert the new vector.
2. Then, verify the metadata.
3. If it fails, restore from the previous_vector stored in your audit logs.

Handling Corruption

If an ingestion job crashes halfway, it might leave your index in a "Partial" state.

• Idempotency: Ensure that running the same ingestion job twice results in the same final state, without creating duplicate vectors.
• Cleaning: Always clear any partial or "temporary" collections after a successful migration.

The Disaster Recovery Runbook

Your team should have a document that explains:

• Where the backups are stored.
• How to restore the Chroma instance from a zip file.
• How to re-trigger the ingestion pipeline from the raw S3 data.

Exercises

1. Practice a manual "Rollback." Rename collection_v2 to collection_v1 and see if your app handles it.
2. Why is "Snapshotting" harder with a distributed cloud database like Pinecone?
3. What is the difference between a "Data Rollback" (restoring vectors) and a "Code Rollback" (restoring the Python script)?