Django bulk_update() Atomicity with batch_size

Based on the Django 5.1 documentation, when using bulk_update() with a specified batch_size, the operation is not automatically atomic across all batches.

When you specify a batch_size, Django breaks the update operation into multiple separate queries, with each batch being its own database transaction. This means:

1. If a later batch fails, any changes from successfully completed previous batches will remain in the database
2. There is no automatic rollback of previous batches if a subsequent batch fails
3. Each batch operates independently in terms of transaction handling

If you need the entire bulk_update() operation to be atomic (all-or-nothing), you'll need to explicitly wrap it in a transaction using Django's transaction management:

PYTHONCopied
from django.db import transaction

with transaction.atomic():
    MyModel.objects.bulk_update(objects_to_update, fields_to_update, batch_size=100)

With this approach, if any batch within the bulk_update() operation fails, all changes from previous batches will be rolled back, ensuring the atomicity of the entire operation.

The batch_size parameter is primarily designed to:

• Control memory usage when dealing with large datasets
• Work around database-specific limitations on the number of parameters in a query
• Optimize performance based on your specific database and dataset

Remember that bulk_update() has several caveats, including not calling model save() methods and not sending pre_save and post_save signals.