MongoEngine/mongoengine
Mongoengine
MongoEngine est un mapper objet-document (ODM) Python qui traduit les enregistrements de base de données en objets pour fournir une interface orientée objet pour la persistance des données. Il sert de gestionnaire de documents et de validateur de schéma pour MongoDB, mappant les classes aux documents pour appliquer les types de données et les règles de validation.
Le projet fournit un système de queryset à chargement différé (lazy-loaded) pour filtrer, trier et agréger des collections en utilisant une syntaxe Pythonique. Il gère des structures de données complexes via des fonctionnalités telles que l'héritage de documents, la gestion récursive de documents imbriqués et la liaison d'objets basée sur des références.
La bibliothèque couvre de larges capacités, notamment la migration de schéma, la recherche plein texte et la gestion de fichiers binaires volumineux via le système de fichiers GridFS. Elle inclut également des outils pour l'optimisation des index de base de données, le profilage des performances des requêtes et des hooks de cycle de vie basés sur des signaux pour automatiser la logique lors des événements de document.
Features
- Object-Document Mappers - Translates Python classes into MongoDB documents to provide an object-oriented interface for data persistence.
- MongoDB Eloquent Adapters - Provides an object-document mapping layer for MongoDB, translating Python classes into database documents.
- Class-Based Schema Mappings - Provides a declarative class-based system to map Python objects to MongoDB collections with defined field types.
- Data Validation - Enforces data integrity through built-in or custom rules and field-level constraints before saving documents.
- Document Schema Enforcement - Defines blueprints for collections through mapping classes to documents with specified fields and indexes.
- Document Embedding - Supports nesting related data structures by treating embedded documents as dependent objects within a parent document.
- Automatic Object Resolution - Links separate documents through identifiers that are automatically resolved into full objects upon access.
- ODM Frameworks - Functions as an ODM framework that translates MongoDB records into Python objects.
- MongoDB Integrations - Provides a comprehensive interface for managing MongoDB documents, including embedded documents and references.
- MongoDB Schema Modeling - Enforces data integrity through a framework for defining document blueprints with strict types and custom validation rules.
- NoSQL Data Modeling - Implements a flexible NoSQL data model that supports complex Python data structures, inheritance, and relationships.
- NoSQL Database Querying - Provides a lazy-loaded queryset system for filtering, sorting, and aggregating MongoDB collections using Pythonic syntax.
- Object Persistence - Creates and updates documents in the database by persisting object attributes.
- Queryset Filtering Tools - Executes filters, sorting, and slicing on collections of documents to retrieve specific subsets of data.
- Document-Based Querying - Retrieves data using filters, operators, sorting, and aggregation to find specific documents.
- Collection-Based Inheritance - Stores different document types in a single collection while querying both specific subtypes and general superclasses.
- Lazy-Loaded Result Sets - Ships a chainable query interface that defers database execution until the results are explicitly accessed.
- Binary Large Object Storage - Manages large binary data using the MongoDB GridFS filesystem for efficient storage and streaming.
- Cascade Deletion Rules - Links documents together and defines automatic deletion rules for when a referenced document is removed.
- Database Indexing - Supports the creation of collection indexes with multi-field keys and background processing.
- Full Text Search - Enables full-text searches across documents with results ordered by relevance score.
- GridFS Integrations - Supports storage and retrieval of large binary files via the MongoDB GridFS filesystem.
- Query Optimizations - Improves data retrieval speeds through the use of indexes, query profiling, and efficient filtering.
- Schema Migrations - Updates existing database records to match changes in the object model such as adding new fields.
- Application Lifecycle Event Triggers - Executes automated business logic triggered by document lifecycle changes such as creation or deletion.
- Signal-Based Lifecycle Hooks - Provides signal-based hooks to trigger custom logic during document events like saving or deleting.
- Schema Registration - Uses Python metaclasses to automatically register document schemas and coordinate inheritance across database collections.
- Data Modeling and ODM - Document-object mapper for Python applications.
- Database Clients - Object-Document-Mapper for MongoDB.
- Database ORMs - Listed in the “Database ORMs” section of the Awesome Python awesome list.
- Database ORMs and ODMs - Document-Object Mapper for MongoDB.
- Object Relational Mappers - Object-document mapper for MongoDB.
- Language Drivers - ODM library for Python applications.
