19 Repos
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 ist ein Framework für paralleles Rechnen und ein verteilter Task-Scheduler, der darauf ausgelegt ist, Python-Data-Science-Workflows von einzelnen Maschinen auf große Cluster zu skalieren. Es fungiert als Cluster-Ressourcenmanager, der die Berechnungslogik orchestriert, indem Aufgaben und deren Abhängigkeiten als gerichtete azyklische Graphen dargestellt werden. Diese Architektur ermöglicht es dem System, die Verteilung von Workloads auf verfügbare Hardware zu automatisieren und gleichzeitig komplexe Ausführungsanforderungen zu verwalten. Das Projekt zeichnet sich durch eine Lazy-Evaluation-Engine aus, die Datenoperationen verzögert, bis sie explizit angefordert werden, was eine globale Graphoptimierung und effiziente Ressourcenzuweisung ermöglicht. Es integriert speicherbewusstes Data-Spilling, um Systemabstürze bei der Verarbeitung von Datensätzen zu verhindern, die den verfügbaren Speicher überschreiten, und nutzt Task-Graph-Fusion, um Sequenzen von Operationen in einzelne Ausführungsschritte zu kombinieren, wodurch Scheduling-Overhead und Inter-Node-Kommunikation minimiert werden. Die Plattform bietet eine umfassende Oberfläche für die Datenanalyse im großen Maßstab, einschließlich Unterstützung für verteiltes maschinelles Lernen, Integration in das Hochleistungsrechnen und parallele Datenverarbeitung. Sie bietet umfangreiche Werkzeuge für das Cluster-Lebenszyklusmanagement, Performance-Profiling und die Echtzeitüberwachung der Aufgabenausführung. Benutzer können diese Umgebungen über verschiedene Infrastrukturen hinweg bereitstellen, einschließlich lokaler Hardware, Cloud-Anbietern, containerisierten Systemen und Hochleistungsrechner-Clustern.
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 ist ein serverseitiges Framework für den Aufbau und die Ausführung von GraphQL-APIs innerhalb von C#-Anwendungen. Es bietet ein umfassendes Toolkit für den Schema-Aufbau, eine föderierte Engine für verteilte Datengraphen und einen Subscription-Handler für die Verwaltung von Echtzeit-Datenströmen. Das Projekt zeichnet sich durch einen flexiblen Schema-Builder aus, der sowohl programmatische Code-First-Definitionen als auch deklarative Schema-First-Ansätze unter Verwendung der Standard-Schema-Definitionssprache unterstützt. Es enthält eine dedizierte Föderations-Engine, um Datengraphen in Subgraphen aufzuteilen und zu einem einheitlichen Gateway zusammenzuführen, sowie eine Data-Loader-Implementierung, die speziell darauf ausgelegt ist, das N+1-Abfrageproblem durch Batching und Caching zu lösen. Das Framework deckt ein breites Spektrum an operativen Funktionen ab, einschließlich Dependency-Injection-Integration für das Service-Lifetime-Management, Middleware-Pipelines für die Interzeption von Feldauflösungen und eine Ausführungspipeline, die mit Werttypen optimiert wurde, um Speicherallokationen zu reduzieren. Zudem bietet es Tools für die Analyse der Abfragekomplexität, Dokument-Caching und rollenbasierte Zugriffskontrolle zur Absicherung von API-Endpunkten. Die Unterstützung für Ahead-of-Time-Schema-Kompilierung ermöglicht es dem Framework, in Umgebungen ausgeführt zu werden, die dynamische Code-Generierung untersagen.
Converts GraphQL execution results and tracing metrics into JSON for transmission to clients.
GraphQL-Ruby ist eine Ruby-Bibliothek zum Erstellen von GraphQL-APIs mit einem stark typisierten Schema und einer dedizierten Query-Execution-Engine. Sie bietet ein umfassendes Framework zum Mappen von Anwendungsobjekten auf ein formales Typsystem, was strukturiertes Datenabrufen durch definierte Resolver ermöglicht. Das Projekt zeichnet sich durch fortschrittliche Performance- und Bereitstellungsmechanismen aus, darunter einen Data Loader für Batching und Caching zur Vermeidung von N+1-Abfragemustern. Es unterstützt leistungsstarke Datenbereitstellung durch inkrementelles Response-Streaming, verzögerte Abfrageantworten und paralleles Datenabrufen mittels Fibers. Zudem bietet es native Unterstützung für Relay-Konventionen, einschließlich spezialisierter Helfer für Connections und Objektidentifikation. Die Bibliothek deckt ein breites Spektrum an API-Management ab, einschließlich fein abgestufter Zugriffskontrolle, Schema-Versionierung zur Wahrung der Abwärtskompatibilität und Echtzeit-Updates via Subscriptions. Sie enthält zudem Traffic-Management-Tools zum Schutz von Serverressourcen, wie z. B. die Begrenzung der Abfragekomplexität und Request-Rate-Limiting. Entwicklung und Observability werden durch AST-Analysewerkzeuge, Execution-Tracing und spezialisierte Test-Utilities zur Verifizierung von Batch-Loading unterstützt.
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.