7 dépôts
Low-overhead processing of large-scale nested data collections using lazy operations.
Distinct from High Performance: Focuses on functional collection processing for memory efficiency, unlike candidates targeting hardware scalers or image processing.
Explore 7 awesome GitHub repositories matching data & databases · High-Performance Collection Processing. Refine with filters or upvote what's useful.
Hutool is a comprehensive suite of Java extensions designed to serve as a standard library extension. Its primary purpose is to reduce development boilerplate for common programming tasks and data manipulation through a collection of utility classes. The project provides specialized toolkits for database management using active record patterns and connection pooling, as well as network communication via a simplified HTTP client and asynchronous socket management. It includes security and identity capabilities such as symmetric and asymmetric encryption, image captcha generation, and JWT token
Implements Bloom filters for memory-efficient probabilistic membership verification.
Draft-js is a framework for building customizable rich text editors within React applications. It functions as a content editable framework that separates the underlying data model from the visual rendering layer, acting as a rich text content engine to manage complex text data and formatting. The project utilizes an immutable state management system to ensure consistent updates and predictable undo history. It manages editor state through persistent data structures, providing an immutable data state manager to prevent accidental mutation. The framework includes capabilities for high perform
Performs large-scale transformations and grouping on nested collections with minimal memory overhead.
EASTL is a C++ Standard Template Library implementation consisting of containers, iterators, and algorithms. It provides cross-platform data structures and a template-based algorithm library designed for use in resource-constrained game engine environments. The library focuses on game engine memory management, providing specialized utilities that ensure predictable memory allocation and high-performance access for real-time applications. These containers maintain consistent behavior across different operating systems and hardware platforms. The project covers high-performance C++ development
Provides high-performance containers for organizing and accessing data collections consistently across platforms.
RedisInsight is a graphical user interface and management tool for browsing, analyzing, and administering Redis databases. It provides a visual environment for exploring key-value data structures, managing database instances, and performing data analysis across different operating systems and deployments. The tool distinguishes itself by providing dedicated visual managers for complex operations, including a vector database manager for configuring embeddings and similarity searches, a query workbench for executing raw commands and Lua scripts, and a performance monitoring dashboard for tracki
Uses probabilistic algorithms to perform high-performance membership checks with significantly reduced memory overhead.
Pholcus is a distributed web crawling system designed for large-scale data scraping. It employs a master-worker distribution model to coordinate high-concurrency scraping tasks across a network of remote client nodes, enabling both horizontal and vertical data collection. The system features a hot-loadable rule engine that allows extraction and navigation logic to be updated at runtime without restarting the process. It handles dynamic content through headless browser integration and bypasses bot detection using proxy rotation, automated user authentication, and simulated human behavior. The
Uses static compiled code for high-performance scraping or dynamic files for hot-loading rules without restarting the system.
SpringSide 4 is an enterprise Java reference architecture and utility library built on the Spring Framework. It provides a pragmatic, best-practice application stack for building RESTful web services, web applications, and data access layers, along with a curated collection of high-performance utility classes for common operations like text, date, collection, reflection, concurrency, and I/O handling. The project distinguishes itself by combining a complete reference application scaffold with production-oriented infrastructure. It includes a JPA-based data access layer that automatically tran
Ships a curated collection of high-performance utility classes for common operations.
Cette bibliothèque fournit une collection de structures de données spécialisées pour le langage Swift qui étendent la bibliothèque standard avec des types de conteneurs avancés. Elle inclut des implémentations pour les files d'attente à double extrémité utilisant des tampons circulaires, des files d'attente de priorité basées sur des tas min-max, et un stockage efficace en mémoire de bit-set et bit-array pour les valeurs booléennes. Le projet propose des collections ordonnées qui maintiennent les éléments dans un ordre trié via des implémentations d'arbres B, ainsi que des ensembles et dictionnaires persistants qui utilisent des arbres préfixes compressés pour partager des données entre des copies mutées. Elle fournit également des conteneurs spécialisés qui préservent l'ordre d'insertion. La bibliothèque couvre une gamme de capacités, y compris la gestion mémoire de bas niveau pour les tampons C et les valeurs non copiables, le stockage à capacité fixe, et l'utilisation du hachage robin hood pour optimiser l'utilisation de la mémoire et les vitesses de recherche.
Provides a comprehensive suite of high-performance Swift data structures, including priority queues and deques.