mongoengine vs pymongo: What are the differences?
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- Document Structure: In pymongo, you work directly with dictionaries to represent data, whereas in mongoengine, you define Document classes to represent data. This difference leads to a more structured and class-based approach in mongoengine compared to pymongo's dictionary-based approach.
- Data Validation: Mongoengine provides built-in data validation through document fields, while pymongo does not offer this functionality out of the box. With mongoengine, you can define field types, required fields, and validation rules, ensuring data integrity at the application level.
- Query Building: When querying data, pymongo uses a method-based approach where you construct queries using methods like find() and update(). On the other hand, mongoengine allows you to build queries using chained method calls, providing a more expressive and intuitive way to interact with the database.
- Object-Document Mapping (ODM): Mongoengine is an Object-Document Mapping (ODM) library, meaning it provides a higher level of abstraction that maps Python objects to MongoDB documents. In contrast, pymongo is a lower-level driver that directly interacts with the MongoDB database without any mapping of objects.
- Embedded Document Handling: Mongoengine supports embedded documents, allowing you to define nested structures within a document, thus facilitating the modeling of complex relationships in your data. Pymongo, however, lacks native support for embedded documents, requiring you to handle such relationships manually.
- Schema Definition: In mongoengine, you can define schemas for your documents using class attributes, making it easier to enforce a consistent structure across your data. Pymongo, being schema-less, does not impose any restrictions on the structure of your data, giving you more flexibility but requiring you to manage schema validation at the application level.
In Summary, mongoengine provides a more structured and feature-rich approach to interacting with MongoDB, offering benefits such as data validation, object-document mapping, and schema enforcement compared to pymongo's more low-level and unstructured operations.
