MongoEngine/mongoengine
Mongoengine
MongoEngine este un object-document mapper Python care traduce înregistrările bazei de date în obiecte pentru a oferi o interfață orientată pe obiecte pentru persistența datelor. Servește ca manager de documente și validator de schemă pentru MongoDB, mapând clasele la documente pentru a impune tipuri de date și reguli de validare.
Proiectul oferă un sistem de queryset lazy-loaded pentru filtrarea, sortarea și agregarea colecțiilor folosind sintaxa Pythonic. Gestionează structuri de date complexe prin funcții precum moștenirea documentelor, gestionarea recursivă a documentelor încorporate și legarea obiectelor bazată pe referințe.
Biblioteca acoperă capabilități largi, inclusiv migrarea schemei, căutarea full-text și gestionarea fișierelor binare mari prin sistemul de fișiere GridFS. Include, de asemenea, instrumente pentru optimizarea indexului bazei de date, profilarea performanței interogărilor și hook-uri de ciclu de viață bazate pe semnale pentru a automatiza logica în timpul evenimentelor documentului.
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.
