awesome-repositories.com
ब्लॉग
MCP
awesome-repositories.com

AI-संचालित खोज के साथ बेहतरीन ओपन-सोर्स रिपॉजिटरी खोजें।

एक्सप्लोर करेंक्यूरेटेड खोजेंओपन-सोर्स विकल्पसेल्फ-होस्टेड सॉफ्टवेयरब्लॉगसाइटमैप
प्रोजेक्टMCP सर्वरहमारे बारे मेंहम रैंकिंग कैसे करते हैंप्रेस
कानूनीगोपनीयताशर्तें
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

2 रिपॉजिटरी

Awesome GitHub RepositoriesStateful Processing Patterns

Architectural patterns for maintaining state across distributed task executions.

Distinguishing note: Focuses on stateful execution patterns rather than stateless functional transformations.

Explore 2 awesome GitHub repositories matching software engineering & architecture · Stateful Processing Patterns. Refine with filters or upvote what's useful.

Awesome Stateful Processing Patterns GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • ray-project/rayray-project का अवतार

    ray-project/ray

    42,895GitHub पर देखें↗

    Ray is a distributed computing framework designed to scale Python and Java applications across clusters by abstracting task scheduling and resource management. It functions as a resource-aware execution engine that manages task dependencies, placement, and fault tolerance across networked compute nodes. At its core, the system provides a stateful actor model, allowing developers to define classes that run in dedicated processes to maintain and mutate internal state across remote method calls. The framework distinguishes itself through a robust cross-language interoperability layer, enabling f

    Implements stateful transformations using classes to perform expensive setup operations exactly once per worker.

    Pythondata-sciencedeep-learningdeployment
    GitHub पर देखें↗42,895
  • apache/beamapache का अवतार

    apache/beam

    8,612GitHub पर देखें↗

    Apache Beam is a distributed data pipeline framework and unified data processing model designed to handle both bounded batch data and unbounded real-time streams. It provides a system for building scalable, data-parallel workflows that operate across compute clusters using a single programming model. The framework utilizes a cross-runner pipeline abstraction that decouples the data processing logic from the underlying execution backend, allowing the same pipeline to run on different distributed compute engines. It supports multi-language pipeline development by translating high-level code fro

    Maintains per-key state and timers across processing stages to enable complex aggregations and sessionization.

    Java
    GitHub पर देखें↗8,612
  1. Home
  2. Software Engineering & Architecture
  3. Stateful Processing Patterns