4 مستودعات
Libraries and drivers for interacting with document-oriented database systems.
Distinguishing note: None of the candidates were provided; this captures document-specific CRUD operations.
Explore 4 awesome GitHub repositories matching data & databases · Document Database Clients. Refine with filters or upvote what's useful.
TypeORM is an object-relational mapper for TypeScript and JavaScript that bridges the gap between object-oriented application code and relational database tables. It provides a comprehensive data persistence layer that allows developers to define database entities using class decorators or configuration objects, enabling seamless interaction with data through object-oriented patterns. The project distinguishes itself through a flexible architecture that supports both the data mapper and repository patterns, alongside a fluent query builder that translates high-level method calls into platform
TypeORM executes CRUD operations and advanced query operators using specialized managers and repositories designed for document-based storage.
The MongoDB Node.js Driver is a programmatic interface and NoSQL database client used to manage document storage and execute operations within a MongoDB database. It serves as an asynchronous database interface and connection manager that enables Node.js applications to integrate with MongoDB servers. The project implements client-side field encryption to secure sensitive data and queries locally before transmission. It also provides a BSON serialization library to convert JavaScript objects into a binary format for efficient storage and network transmission. The driver covers a broad range
Implements a comprehensive client driver for managing document-oriented data and indexes via a binary wire protocol.
FlutterFire is a collection of official plugins that integrate Firebase backend services into Flutter applications. It serves as a backend-as-a-service integration library, providing client-side wrappers for cloud authentication, databases, storage, and monitoring services. The project enables the integration of serverless backend logic and real-time data synchronization using NoSQL documents and state synchronization. It also provides capabilities for generative AI integration, including large language models, image generation, and local machine learning model management. The suite covers a
Implements a client for synchronizing NoSQL document data and real-time state across devices.
Vespa is a distributed search engine, vector database, and machine learning ranking engine. It serves as an AI search platform designed to handle large-scale document indexing and complex query processing across a cluster of nodes, combining keyword retrieval with high-dimensional embedding storage for semantic similarity search. The platform distinguishes itself by integrating machine learning models directly into the search pipeline to perform real-time inference and ranking. It converts these models into ranking expressions to score and order results based on relevance, while providing a s
Provides a standard library of specialized clients for sending and managing documents within the distributed search system.