Understanding PyMongo and MongoDB: Is insert_many Transactional?

Описание: Explore how `insert_many` works in PyMongo and MongoDB, its transaction behavior, and how to handle bad data during bulk inserts effectively.
---
This video is based on the question https://stackoverflow.com/q/66414955/ asked by the user 'NealWalters' ( https://stackoverflow.com/u/160245/ ) and on the answer https://stackoverflow.com/a/66415007/ provided by the user 'D. SM' ( https://stackoverflow.com/u/9540925/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: is PyMongo / MongoDB insert_many transactional?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding PyMongo and MongoDB: Is insert_many Transactional?

When working with databases, especially in applications that demand high performance and speed, developers often turn to bulk insert operations. In the case of MongoDB and its Python interface, PyMongo, a common operation they utilize is insert_many. However, questions arise regarding the transactional nature of these operations. Specifically, when dealing with large datasets that may include bad data, it's crucial to understand how these bulk inserts function when errors occur. Let's delve into this topic to clarify how insert_many behaves in PyMongo and MongoDB.

The Problem at Hand

Suppose you have a large CSV file segmented into parts for better upload efficiency. You are leveraging multiprocessing in Python to simultaneously upload chunks of 3000 records using insert_many. However, you suspect that some rows contain bad data, and you want to know the implications of this on your bulk insert operation:

What happens if one of the rows fails?

Do previous rows still get inserted?

How can you manage erroneous data during the uploading process?

These questions highlight the importance of understanding MongoDB's bulk write operations and their behavior regarding transactions.

How insert_many Works

When you perform an insert_many operation in PyMongo, you can set the ordered option, which directly influences how the operation handles errors. Here’s a breakdown of its behavior:

1. Ordered vs. Unordered Inserts

Ordered Inserts (default):

When you set ordered to true, if MongoDB encounters an error with any document in the insert operation (say, the 1000th row has bad data), it stops the entire operation.

This means that none of the previously inserted documents will be rolled back, but the operation fails on the first encountered error. Only the rows prior to the error (the first 999) are inserted successfully.

Unordered Inserts:

If you set ordered to false, MongoDB will continue processing subsequent documents even if it encounters errors in others.

In this scenario, only the bad-document rows will fail, while the valid ones will still be inserted, leading to a situation where, for example, 2999 rows may be stored correctly, minus the defective one.

2. Transactional Nature of Bulk Writes

It’s critical to note that bulk writes with insert_many are not considered transactional in the strictest sense. Here’s what you should keep in mind:

Partial Writes: A failure in the bulk operation does not rollback any previously inserted documents. Thus, even if an error occurs, the records that were successfully written before the error remains in the database.

MongoDB Transactions: If you need strict transactional behavior (where either all documents are inserted successfully or none at all), you should consider utilizing MongoDB's transaction feature. This allows you to group multiple operations into a single transaction, ensuring atomicity.

Strategies for Handling Bad Data

Given the nuances of insert_many, what strategies can you implement to manage bad data effectively?

1. Try/Except Blocks

Wrap your insert_many operation in a try/except block to catch errors and debug which specific rows are causing issues. If the bulk insert fails, you can proceed to insert the documents one at a time.

2. Data Validation

Implement validation logic before performing insert operations. This could involve checking for data types, required fields, or other business rules relevant to your application before hitting the database.

3. Use Logging

Consider using logging to record which inserts were successful and which failed, so that you can review them later and track down problematic entries.

Conclusion

Understanding how insert_many