8 dépôts
Techniques for nesting related data within parent documents.
Distinguishing note: Focuses on denormalization and nesting strategies.
Explore 8 awesome GitHub repositories matching data & databases · Document Embedding. Refine with filters or upvote what's useful.
RethinkDB is a distributed, document-oriented database designed to store and manage JSON-formatted data across scalable clusters. It utilizes a custom log-structured storage engine with B-Tree indexing to ensure high-performance disk I/O and data persistence. The system maintains high availability through automatic sharding and replication, employing a primary-replica voting consensus mechanism to handle node failures and ensure consistent cluster operations. A defining characteristic of the platform is its reactive changefeed engine, which allows applications to subscribe to live data update
RethinkDB models one-to-many relationships by nesting related data directly within a parent document to simplify retrieval and ensure atomic updates for smaller datasets.
JSON API is a set of industry standards for RESTful APIs that defines uniform protocols for resource serialization, error responses, and query parameters. It provides a specification for request and response payloads in JSON-based APIs to ensure consistency across endpoints. The specification focuses on reducing network requests through a structured resource serialization format and a standardized mechanism for embedding related resources into a single response. It utilizes a custom JSON media type for content negotiation and supports the definition of custom profiles to provide specialized i
Includes related resources alongside primary data in a single response using a reserved includes parameter.
This project is a MongoDB database driver and object-relational mapper that brings MongoDB support to the Laravel Eloquent model and query builder. It provides a NoSQL model mapper that allows MongoDB collections to be mapped to object-oriented models using the Active Record pattern. The integration enables the use of a fluent query builder for constructing queries and aggregation pipelines without writing raw database syntax. It supports schema-less model integration, allowing applications to manage unstructured data while maintaining compatibility with standard object-oriented patterns. Th
Supports storing related data directly within parent documents to reduce read lookups.
NPOI is a pure .NET library for reading and writing Microsoft Office files in both legacy binary (.xls) and modern OpenXML (.xlsx, .docx) formats, operating entirely without requiring Microsoft Office or COM interop. It runs on Windows and Linux under .NET Standard and .NET Framework runtimes, using only managed code to parse and generate Office documents. The library provides comprehensive spreadsheet capabilities, including creating, editing, and reading Excel workbooks in both .xls and .xlsx formats, with support for cell formatting, styles, and formulas. It includes a streaming row-by-row
Opens legacy .xls files by reading OLE2 compound storage structures.
fast_jsonapi est un sérialiseur d'objets Ruby conçu pour transformer des objets de données backend complexes en représentations JSON structurées. Il implémente spécifiquement le format JSON:API pour assurer un échange de données cohérent entre serveurs et clients. La bibliothèque fonctionne comme un générateur de document composé, permettant aux ressources liées d'être intégrées au sein d'une seule réponse pour minimiser les requêtes réseau. Elle utilise une définition de schéma basée sur les classes pour découpler les modèles de base de données internes de la représentation API publique. Le projet inclut un outil en ligne de commande pour générer du boilerplate de sérialiseur en scannant les schémas de base de données. La performance est gérée par la mise en cache de sortie basée sur les clés et des outils d'instrumentation qui suivent la latence d'exécution des processus de sérialisation et d'encodage.
Generates compound documents by embedding related resources in a flat structure within a single JSON response.
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.
Supports nesting related data structures by treating embedded documents as dependent objects within a parent document.
Practicalnode is a comprehensive educational resource and backend development framework for mastering server-side programming with Node.js. It provides a structured approach to building scalable network services, REST APIs, and real-time applications using asynchronous JavaScript. The project serves as a detailed implementation guide for several core backend patterns, including MongoDB data modeling and the construction of REST API development kits. It emphasizes a specific workflow for Docker containerization and offers a variety of strategies for managing user identity through stateless tok
Implements denormalization strategies by nesting related data as sub-documents within a single collection.
python-docx-template is a template engine for generating Microsoft Word documents by merging .docx files with data contexts using a logic-based markup syntax. It functions as a document automator that injects variables, images, and sub-documents into Word files while maintaining the original styling. The project uses a rendering system based on Jinja2 to apply template logic and filters to Office Open XML files. It allows for the creation of custom template filters to transform data during the rendering phase and includes a command line interface for producing documents by passing a template
Inserts the contents of another file or programmatically generated document into a template variable.