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Awesome GitHub RepositoriesAsynchronous Stream Processing Frameworks

Toolkits for composing asynchronous data pipelines using non-blocking operators and backpressure.

Distinct from Stream Combinators: The candidates are too narrow (combinators or specific traits); this represents the framework's primary identity.

Explore 9 awesome GitHub repositories matching programming languages & runtimes · Asynchronous Stream Processing Frameworks. Refine with filters or upvote what's useful.

Awesome Asynchronous Stream Processing Frameworks GitHub Repositories

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  • reactor/reactor-corereactor 的头像

    reactor/reactor-core

    5,224在 GitHub 上查看↗

    Reactor Core is a reactive programming toolkit and non-blocking foundation for composing asynchronous data pipelines on the JVM. It serves as an asynchronous stream processing framework and a backpressure management system, allowing developers to transform, filter, and combine sequences of events while regulating data flow between producers and consumers to prevent resource exhaustion. The library differentiates itself through a sophisticated concurrency scheduling system and demand-based flow control. It decouples signal processing from specific threads using a scheduler registry and provide

    Acts as a comprehensive non-blocking foundation for composing asynchronous data pipelines on the JVM.

    Javaasynchronousflowflux
    在 GitHub 上查看↗5,224
  • reactivex/rxgoReactiveX 的头像

    ReactiveX/RxGo

    5,091在 GitHub 上查看↗

    RxGo 是一个函数式响应式编程库,也是 ReactiveX 的 Go 语言实现。它作为一个异步流处理工具包,旨在利用可观察模式协调基于事件的程序和数据流。 该库支持构建异步处理管道,以转换、过滤和组合事件序列。其特色在于使用函数式操作符来组合这些管道,并提供了管理并发执行的机制。 该工具包涵盖了广泛的流编排功能,包括数据聚合、多流组合以及将流转换为静态数据结构。它内置了对错误恢复、用于调节数据生产速度的背压控制以及用于跨 CPU 核心并行处理的工作池的支持。

    Provides a comprehensive framework for composing asynchronous data pipelines with built-in backpressure and non-blocking operators.

    Goasyncasynchronousconcurrency
    在 GitHub 上查看↗5,091
  • reactivex/rxpyReactiveX 的头像

    ReactiveX/RxPY

    5,014在 GitHub 上查看↗

    RxPY 是一个 Python 函数式响应式编程库,也是 ReactiveX 的可观察对象库。它作为一个异步流处理器和事件驱动的协调框架,用于构建能够对状态变化或随时间变化的事件流做出反应的数据流水线。 该库提供了一套工具,用于使用可观察序列和操作符来编写异步和基于事件的程序。它通过使用可配置的调度器来管理并发、时序和订阅生命周期,从而脱颖而出。 该项目涵盖了广泛的流处理能力,包括数据聚合、过滤和组合。它提供了事件广播、序列缓冲和错误处理机制,以及用于协调可观察流与异步事件循环的工具。 通过虚拟时间模拟、大理石图建模和发射验证,该库提供了完善的测试和质量保证支持。

    Serves as a comprehensive framework for composing asynchronous data pipelines using non-blocking operators.

    Python
    在 GitHub 上查看↗5,014
  • reactive-streams/reactive-streams-jvmreactive-streams 的头像

    reactive-streams/reactive-streams-jvm

    4,875在 GitHub 上查看↗

    该项目为异步流处理提供了正式规范和一套标准 Java 接口。它定义了一个标准化的协议,用于在不同线程的发布者和订阅者之间传递元素序列,核心是 JVM 的响应式流(Reactive Streams)规范。 该项目通过提供一个通用 API 来实现互操作性,允许不同的异步流处理库协同工作。这是通过一组标准接口和桥接机制实现的,这些机制可以在不兼容的流规范之间进行转换。 该规范涵盖了非阻塞背压(Backpressure)协议,通过要求订阅者发出需求信号来调节数据流并防止系统过载。它还定义了流的生命周期,包括订阅管理、元素处理以及用于资源清理的基于信号的终止。 该项目包括一个用于验证流行为的框架,以根据背压和异步事件规则验证处理逻辑。

    Provides the foundational toolkit for composing asynchronous data pipelines using non-blocking operators and backpressure.

    Java
    在 GitHub 上查看↗4,875
  • sofastack/sofa-rpcsofastack 的头像

    sofastack/sofa-rpc

    3,920在 GitHub 上查看↗

    Sofa-rpc 是一个高性能远程过程调用框架,旨在构建分布式 Java 应用程序。它作为管理分布式服务网格的工具包,提供 gRPC 通信层以及用于注册和定位远程服务实例的系统。 该框架具有一个网络安全层,实现了 TLS 加密和授权检查,以保护服务之间传输的数据。它利用可插拔协议层支持多种通信标准,确保灵活的点对点连接。 可靠性和流量管理通过断路器、客户端负载均衡和服务健康监控来处理。该系统还包括用于分布式请求追踪和响应式远程流处理的可观测性工具,以提高资源效率。 该框架提供 JSON 数据序列化工具,并通过连接池和服务发现注册中心管理远程连接。

    Supports remote invocations using asynchronous streams to increase throughput and resource efficiency.

    Javahessianhttp2protobuf
    在 GitHub 上查看↗3,920
  • ufal/whisper_streamingufal 的头像

    ufal/whisper_streaming

    3,642在 GitHub 上查看↗

    Whisper streaming 是一个自动化语音识别引擎,旨在将实时音频转换为文本。它作为一个基于网络的转录服务器,接收来自远程客户端的原始音频数据并实时返回增量文本结果。 该系统通过其增量处理音频流的能力脱颖而出,允许在捕获语音时进行即时转录和翻译。它结合了语音活动检测以将人类语音与背景噪声隔离,并利用滑动窗口缓冲来管理传入的音频片段,确保处理在长时间内保持高效和连贯。 除了实时转录外,该项目还支持远程音频处理,并提供使用预录文件模拟实时条件的工具。这些功能允许对语音识别模型进行基准测试,并在受控环境下测试延迟。该软件作为 Python 包分发。

    Decouples audio ingestion from transcription tasks using non-blocking queues to ensure continuous data flow.

    Python
    在 GitHub 上查看↗3,642
  • js-csp/js-cspjs-csp 的头像

    js-csp/js-csp

    2,322在 GitHub 上查看↗

    Js-csp is a concurrency library that implements communicating sequential processes with channels and generator-based routines for asynchronous programming in JavaScript. It provides a framework for building data processing pipelines and managing concurrent workflows, bringing Go-style channels and process coordination primitives to applications. The library coordinates message passing through buffered channels, stream mixing, multi-channel selection, and pub-sub broadcasting. Communication pathways support unbuffered rendezvous, fixed buffers, sliding windows, and dropping overflow. Operation

    Provides an asynchronous framework for building data processing pipelines and managing concurrent workflows.

    JavaScript
    在 GitHub 上查看↗2,322
  • level/levelLevel 的头像

    Level/level

    1,675在 GitHub 上查看↗

    Level is a database library that provides a unified interface for managing sorted key-value data. It functions as an abstraction layer that allows applications to store and retrieve binary information consistently across server-side environments and web browsers. The project utilizes a modular architecture that supports pluggable storage backends, enabling the system to adapt to different host environments while maintaining identical behavior. By organizing data in lexicographical order, it facilitates efficient range queries and ordered retrieval. The library handles large datasets through a

    Implements asynchronous stream processing to handle large datasets without blocking the main execution thread.

    JavaScriptabstract-levelbrowserselectron
    在 GitHub 上查看↗1,675
  • packtpublishing/learn-cuda-programmingPacktPublishing 的头像

    PacktPublishing/Learn-CUDA-Programming

    1,258在 GitHub 上查看↗

    This project serves as a comprehensive educational resource for learning parallel programming and high-performance computing using graphics processing units. It provides technical guidance on the fundamental paradigms required to offload computationally intensive tasks from a host system to specialized hardware accelerators. The materials cover the core methodologies for managing data-parallel operations, including the orchestration of memory between host and device spaces and the organization of threads into structured grids and blocks. It details the execution models necessary to distribute

    Overlaps data transfers and kernel execution using non-blocking queues to maximize hardware utilization.

    Cuda
    在 GitHub 上查看↗1,258
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  2. Programming Languages & Runtimes
  3. Asynchronous Stream Processing Frameworks

探索子标签

  • Reactive Remote StreamsNon-blocking streams for remote procedure calls to improve resource efficiency. **Distinct from Asynchronous Stream Processing Frameworks:** Specializes asynchronous stream processing for the context of remote RPC calls specifically.