Darshan Hiranandani : How to mitigate risks of data loss in MongoDB when changing int to double?

Darshan_Hiranandani · 2024-05-30T11:42:40.819Z

Hi, I’m Darshan Hiranandani, Could you please provide detailed suggestions on how to effectively mitigate the risks of data loss when converting integer fields to double in MongoDB? Your insights into best practices, potential pitfalls, and recommended approaches would be greatly appreciated by the community. Thank you for sharing your expertise

Azri · 2024-07-09T15:39:45.341Z

Hi @Darshan_Hiranandani. When converting a field from integer to double, there are a few things that you can look out for:

Double has a smaller range than integer. 9007199254740992 is the largest number that can be represented as a double (9007199254740992.0). Any larger than that could cause a precision issue. This isn’t exactly “data loss” but “data corruption” as the data is still there but some data that is above threshold will no longer be precise/usable. Otherwise, it should be fine.
It is best to create a backup of the collection/database before doing such a conversion/migration. Be prepared to restore the backup in case something goes wrong.
It is advisable to test the conversion in a pre-production environment with a large enough sample data. The result can then be tested against the application in pre-production to ensure the right behavior.
Be aware of potentially making the field a mixed type of int and double. MongoDB allows having a field with mixed types and the ability to query documents based on a $type operator. This may be important to handle in the application to avoid unexpected behaviors or errors. For example, there may be some parts of the application that still creates documents with the field in integer after the conversion is done, or calculations that expect an integer instead of double.

In a conversion scenario like this, there’s generally less of a risk of data loss and more of data corruption and errors. Such a conversion needs to be planned accordingly with how the field/data is used in the application.

If you have more details to share, perhaps there can be a more specific answer for your situation. Otherwise, hope this helps.

steevej · 2024-07-09T17:13:33.111Z

I especially like that clarification:

Azri:

This isn’t exactly “data loss” but “data corruption” as the data is still there but some data that is above threshold will no longer be precise/usable.

steevej · 2024-07-10T14:31:43.936Z

As suggested by @Azri and that it makes sense, I have moved your thread from Generative AI to Working with Data.