5 Repos
Treating live data sequences as collections to apply functional transformations in real-time.
Distinguishing note: None of the candidates cover the general capability of treating live streams as declarative collections
Explore 5 awesome GitHub repositories matching data & databases · Reactive Stream Processing. Refine with filters or upvote what's useful.
Rx.NET is a reactive programming framework and library for the .NET ecosystem used to compose asynchronous and event-based programs. It provides an observable data stream API that treats live sequences of information as collections, allowing developers to coordinate asynchronous event workflows using a declarative syntax. The project functions as an extension of language integrated query patterns to asynchronous streams. This allows for the filtering and transformation of asynchronous notifications and event sequences through the application of query operators. The framework covers asynchron
Allows live sequences of information to be treated as collections for real-time event response using a declarative model.
This is a non-blocking network client for Java applications that provides asynchronous HTTP request capabilities and bidirectional WebSocket connectivity. It leverages a Netty-based architecture to handle high volumes of network input and output. The library utilizes native transport abstractions such as Epoll, KQueue, and io_uring to reduce system latency. It supports HTTP/2 multiplexing to run multiple concurrent request-response streams over a single connection and provides automatic response body decompression for various formats. Core capabilities cover the management of complex network
Handles large request and response bodies by pushing data in chunks using reactive stream processing.
Reactor Core ist ein Toolkit für reaktive Programmierung und eine nicht-blockierende Grundlage für die Komposition asynchroner Datenpipelines auf der JVM. Es dient als Framework für asynchrone Stream-Verarbeitung und als Backpressure-Managementsystem, das es Entwicklern ermöglicht, Ereignissequenzen zu transformieren, zu filtern und zu kombinieren, während der Datenfluss zwischen Produzenten und Konsumenten reguliert wird, um Ressourcenerschöpfung zu vermeiden. Die Bibliothek differenziert sich durch ein ausgeklügeltes Concurrency-Scheduling-System und nachfragebasierte Flusskontrolle. Sie entkoppelt die Signalverarbeitung von spezifischen Threads unter Verwendung einer Scheduler-Registry und bietet Mechanismen für die kontextbewusste Propagierung unveränderlicher Metadaten über asynchrone Grenzen hinweg. Sie enthält zudem spezialisierte Tools für die Trace-Erfassung zur Assembly-Zeit und Virtual-Time-Scheduling, um das Testen zeitbasierter Operatoren zu erleichtern. Das Projekt deckt ein breites Spektrum an Funktionen ab, einschließlich funktionaler Datenverarbeitung für Sequenzaggregation und Windowing, einer Vielzahl von Fehlerbehebungsstrategien wie exponentiellem Backoff-Retry sowie Dienstprogrammen zur Überbrückung von Legacy-Callback- oder synchronen APIs in reaktive Streams. Zudem bietet es Instrumentierung für Pipeline-Monitoring und eine Suite von Test-Tools zur Verifizierung von Signalsequenzen.
Provides a complete toolkit for processing asynchronous sequences using non-blocking functional operators.
ZIO is a functional effect system for the JVM that models asynchronous and concurrent programs as pure, composable values with typed error handling and dependency injection. Its core identity is built on fiber-based concurrency, where lightweight, non-blocking fibers execute millions of concurrent tasks with structured lifecycle management, and a dual-channel error model that separates expected business failures from unexpected system defects at compile time. The system provides effect-typed dependency injection through a layer-based dependency graph, pull-based reactive stream processing with
Provides pull-based reactive streams with integrated backpressure and composable sinks.
This repository serves as an educational resource for implementing asynchronous programming patterns in Android applications using Kotlin Coroutines and Flow. It provides a collection of practical examples and unit tests designed to demonstrate how to manage background tasks, concurrent network requests, and reactive data streams while maintaining a responsive user interface. The project focuses on structured concurrency, offering patterns for organizing tasks into hierarchical scopes that automatically propagate cancellation and lifecycle signals. It emphasizes the integration of lifecycle-a
Defines data sequences as lazy streams that only execute production logic upon collection.