19 dépôts
Mechanisms for storing and retrieving intermediate computation results to accelerate data processing.
Distinct from Data Processing: Distinct from general data processing: focuses specifically on caching analytical results to ensure instantaneous responses.
Explore 19 awesome GitHub repositories matching data & databases · Result Caching. Refine with filters or upvote what's useful.
Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as Python code. It functions as a container-native engine that wraps individual tasks in isolated environments, ensuring consistent dependencies and resource allocation across diverse infrastructure. By utilizing a state-machine-based orchestration model, the system tracks execution progress through discrete transitions and persistent event logs to maintain reliable and observable task processing. The platform distinguishes itself through a decoupled worker-API architecture, which sep
Saves task outputs to external storage to enable result reuse and optimization of redundant computations.
Apollo Client is a GraphQL client library and data fetching framework used to request data from a GraphQL server and synchronize that state within a frontend application. It functions as a remote state manager and a local state management tool, allowing developers to define client-side schemas and resolvers for data that does not reside on a remote server. The project features a normalized GraphQL cache that identifies objects by ID to ensure referential equality and consistent data updates across different queries. It also includes a GraphQL API mocking tool to simulate server responses and
Stores fetched GraphQL query results locally to minimize network requests and improve data retrieval speed.
Apollo Client is a frontend GraphQL integration layer and client library used to fetch, manage, and cache data from a GraphQL server in web and mobile applications. It functions as a state management framework that synchronizes remote server data with local application state. The project provides a TypeScript wrapper for executing GraphQL queries and mutations, ensuring type-safe API integration with automatic validation and code completion. The library manages data fetching and synchronization between the backend and the user interface. It includes capabilities for caching GraphQL results l
Stores retrieved GraphQL data locally to improve loading speeds and minimize network overhead.
Plotly.py is a comprehensive framework for building production-ready data applications and interactive dashboards directly from Python code. It functions as both a high-performance visualization library for browser-based charts and a full-stack tool for transforming analytical scripts into responsive, web-based interfaces. By abstracting away the need for manual HTML or JavaScript, it allows developers to define complex layouts and functional logic using modular, reusable components. The framework distinguishes itself through a robust architecture that handles event orchestration and state sy
Caches complex analytical results to ensure instantaneous responses when performing heavy computations on large datasets.
Apollo Server is a spec-compliant JavaScript implementation for building GraphQL APIs that resolve queries and mutations based on a defined schema. It functions as a Node.js framework that integrates GraphQL functionality into various web frameworks and serverless environments through middleware. The project provides a federated GraphQL gateway that aggregates multiple distributed subgraphs into a single unified entry point. It includes a built-in interactive API sandbox for testing operations at the server endpoint and a schema registry client to automate the synchronization of API definitio
Implements query result storage based on cache-control hints to reduce redundant computation.
Dask est un framework de calcul parallèle et un planificateur de tâches distribué conçu pour mettre à l'échelle les flux de travail de science des données Python, des machines uniques aux grands clusters. Il fonctionne comme un gestionnaire de ressources de cluster qui orchestre la logique computationnelle en représentant les tâches et leurs dépendances sous forme de graphes acycliques dirigés. Cette architecture permet au système d'automatiser la distribution des charges de travail sur le matériel disponible tout en gérant des exigences d'exécution complexes. Le projet se distingue par un moteur d'évaluation paresseuse qui diffère les opérations sur les données jusqu'à ce qu'elles soient explicitement demandées, permettant une optimisation globale du graphe et une allocation efficace des ressources. Il intègre le déversement de données conscient de la mémoire pour éviter les plantages du système lors du traitement de jeux de données dépassant la mémoire disponible, et il utilise la fusion de graphes de tâches pour combiner des séquences d'opérations en étapes d'exécution uniques, minimisant la surcharge de planification et la communication entre nœuds. La plateforme fournit une surface de capacités complète pour l'analyse de données à grande échelle, incluant le support pour l'apprentissage automatique distribué, l'intégration du calcul haute performance et le traitement de données parallèle. Elle offre des outils étendus pour la gestion du cycle de vie des clusters, le profilage des performances et la surveillance en temps réel de l'exécution des tâches. Les utilisateurs peuvent déployer ces environnements sur diverses infrastructures, incluant le matériel local, les fournisseurs cloud, les systèmes conteneurisés et les clusters de calcul haute performance.
Stores frequently accessed task results in memory to accelerate operations while automatically evicting data to manage capacity.
Chainlit is a Python framework designed for building and deploying interactive, stateful conversational AI interfaces. It provides a backend-driven platform that connects language models and agent frameworks to a web-based chat frontend, managing the complexities of session state, message history, and real-time communication. The framework distinguishes itself by offering a component-based UI builder that allows developers to inject interactive widgets, rich media, and data visualizations directly into the chat stream. It supports the visualization of complex agent workflows, enabling users t
Stores intermediate computation results to accelerate future requests and minimize redundant processing.
eShopOnWeb is a reference application for ASP.NET Core that demonstrates a sample e-commerce site. It serves as a template for building scalable services using domain-driven design to separate business logic from infrastructure and data access. The project implements a decoupled messaging pattern through a request pipeline to separate web controllers from application logic. It utilizes a repository pattern to abstract data persistence and isolate the core application logic from the specific database storage mechanism. The application covers a broad surface of web capabilities, including user
Stores intermediate data lookup results on the server to reduce database overhead.
Gorse is a personalized recommendation engine server and machine learning pipeline designed to suggest items to users based on their behavior and preferences. It operates as a distributed system that separates training, candidate generation, and serving nodes to support high-throughput workloads. The system utilizes a multi-stage recommendation pipeline to refine results through retrieval, scoring, and reranking. It generates personalized suggestions using collaborative filtering, matrix factorization, and item-to-item similarity models, while also providing non-personalized and fallback reco
Stores intermediate computation results and final lists to reduce CPU load and improve response times.
urql is a GraphQL client library designed for fetching and managing data from a GraphQL API. It provides a system for handling GraphQL data fetching, state management, and integration with React components. The library is distinguished by a middleware pipeline architecture that allows the request-response flow to be modified through swappable exchanges. This enables the customization of the data layer, including the addition of custom business logic, request deduplication, and specialized fetching behaviors. The project covers a broad range of capabilities, including normalized caching to en
Implements a document-based caching mechanism specifically for GraphQL query results.
urql is a GraphQL client and data management tool used to execute GraphQL operations and synchronize data from remote servers within a software application. It functions as a mechanism for fetching, caching, and managing GraphQL data to maintain state across application views. The project features a pluggable middleware architecture and a normalized GraphQL cache. This allows for the insertion of custom logic into the request and response lifecycle to modify client behavior and the organization of responses by unique identifiers to ensure data consistency. The client provides capabilities fo
Maps specific GraphQL documents and variables to their responses to avoid redundant network requests.
Vaex is a high-performance Apache Arrow DataFrame library and out-of-core data processing engine designed to handle billion-row tabular datasets in Python. It functions as a lazy evaluation framework that defers computations and transformations until results are required, enabling the processing of datasets that exceed available system RAM by mapping files directly from disk. The project distinguishes itself as a tool for big data visualization and exploration, specifically integrated for use within interactive notebooks. It provides specialized capabilities for machine learning feature engin
Caches the output of expensive computations in memory or on disk to eliminate redundant processing.
ccv is a computer vision library written in C designed for high-performance visual analysis. It serves as a framework for image classification, object detection, and the identification of faces, pedestrians, and vehicles. The library distinguishes itself through hardware-accelerated vision and deep learning inference optimizations. It utilizes a quantized tensor processor to transform floating-point data into eight-bit integers and implements integer-quantized attention mechanisms to reduce memory bandwidth and increase data throughput. The project covers a broad range of capabilities, inclu
Stores intermediate processed image data to avoid repeating expensive transformations across multiple operations.
Apache Fesod is a lightweight Java library that wraps Apache POI to provide a streaming API for reading and writing large Excel files. Its core identity is a low-memory spreadsheet processor that prevents out-of-memory errors by handling data row by row, never loading an entire document into memory at once. The library distinguishes itself through a listener-driven event model that fires row-level events to user code as each row is parsed, enabling incremental processing. It also includes an object mapping layer that maps spreadsheet rows directly to Java objects using configurable column map
Caches partially processed data in a temporary store to avoid re-reading the same file region multiple times.
Vue Apollo is a GraphQL client library for Vue.js that integrates Apollo GraphQL queries and mutations into Vue components with reactive data binding. It provides a reactive data layer that automatically updates Vue component state when GraphQL query results change, and supports server-side rendering by prefetching queries during SSR to deliver fully populated HTML on initial page load. The library allows GraphQL queries and mutations to be declared directly inside Vue component options using the apollo property, keeping data dependencies co-located with the UI. It wraps Apollo Client's nor
Refreshes the user interface automatically when GraphQL query results change, removing the need for manual refetching.
GraphQL.NET est un framework côté serveur pour construire et exécuter des API GraphQL au sein d'applications C#. Il fournit une boîte à outils complète pour la construction de schémas, un moteur fédéré pour les graphes de données distribués et un gestionnaire d'abonnements pour gérer les flux de données en temps réel. Le projet se distingue par un constructeur de schéma flexible qui prend en charge à la fois les définitions programmatiques code-first et les approches déclaratives schema-first utilisant le langage de définition de schéma standard. Il inclut un moteur de fédération dédié pour diviser les graphes de données en sous-graphes et les composer en une passerelle unifiée, ainsi qu'une implémentation de chargeur de données (data loader) spécifiquement conçue pour résoudre le problème de requête N+1 via le traitement par lots et la mise en cache. Le framework couvre un large éventail de capacités opérationnelles, notamment l'intégration de l'injection de dépendances pour la gestion du cycle de vie des services, des pipelines de middleware pour l'interception de la résolution de champs, et un pipeline d'exécution optimisé avec des types de valeur pour réduire les allocations mémoire. Il fournit également des outils pour l'analyse de la complexité des requêtes, la mise en cache des documents et le contrôle d'accès basé sur les rôles pour sécuriser les endpoints de l'API. La prise en charge de la compilation de schéma en avance de phase (ahead-of-time) permet au framework de s'exécuter dans des environnements qui interdisent la génération de code dynamique.
Converts GraphQL execution results and tracing metrics into JSON for transmission to clients.
GraphQL-Ruby est une bibliothèque Ruby pour construire des API GraphQL avec un schéma fortement typé et un moteur d'exécution de requêtes dédié. Elle fournit un framework complet pour mapper les objets de l'application à un système de types formel, permettant une récupération de données structurée via des résolveurs définis. Le projet se distingue par des mécanismes avancés de performance et de livraison, incluant un data loader pour le batching et le cache afin d'éviter les patterns de requêtes N+1. Il supporte la livraison de données haute performance via le streaming de réponses incrémentales, les réponses de requêtes différées et la récupération de données en parallèle utilisant des fibers. De plus, il offre un support natif pour les conventions Relay, incluant des helpers spécialisés pour les connexions et l'identification d'objets. La bibliothèque couvre une large surface de gestion d'API, incluant un contrôle d'accès granulaire, le versioning de schéma pour maintenir la rétrocompatibilité et des mises à jour en temps réel via des abonnements. Elle inclut également des outils de gestion de trafic pour protéger les ressources serveur, tels que la limitation de complexité des requêtes et le rate limiting. Le développement et l'observabilité sont supportés par des outils d'analyse AST, le traçage d'exécution et des utilitaires de test spécialisés pour la vérification du chargement par lots.
Stores field results associated with specific objects to avoid redundant computations.
Apollo Kotlin is a strongly-typed GraphQL client and code generation library designed for Kotlin and JVM applications. It functions as a comprehensive development tool that transforms GraphQL schema definitions and query documents into type-safe models during the build process, ensuring that data access errors are identified at compile time rather than at runtime. The project distinguishes itself through its multiplatform runtime abstraction, which allows developers to share data fetching and caching logic across Android, iOS, and desktop environments. It provides a normalized local caching s
Stores query results in local storage to minimize network requests and ensure data remains accessible offline.
Chartbrew is a self-hosted business intelligence platform and data visualization engine designed to transform raw data from SQL databases and external API endpoints into interactive charts and dashboards. It serves as a tool for building analytics dashboards that monitor business metrics and KPIs through a privately hosted environment. The platform distinguishes itself with an embedded analytics workflow, allowing users to generate secure, time-limited shared links and iframes to display private charts on external websites. It also provides programmatic chart generation via API and integrates
Utilizes Redis to cache processed dataset results, reducing latency for external API requests.