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11 रिपॉजिटरी

Awesome GitHub RepositoriesEvent-Based Stream Processing

Processing of large data files as a sequence of events to minimize memory footprint.

Distinct from Streaming Processing: None of the candidates cover general event-based streaming for data serialization formats.

Explore 11 awesome GitHub repositories matching data & databases · Event-Based Stream Processing. Refine with filters or upvote what's useful.

Awesome Event-Based Stream Processing GitHub Repositories

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  • reactivex/rxgoReactiveX का अवतार

    ReactiveX/RxGo

    5,091GitHub पर देखें↗

    RxGo एक फ़ंक्शनल रिएक्टिव प्रोग्रामिंग लाइब्रेरी और Go भाषा के लिए ReactiveX का कार्यान्वयन है। यह ऑब्जर्वेबल पैटर्न का उपयोग करके इवेंट-आधारित प्रोग्राम्स और डेटा फ़्लो को कोऑर्डिनेट करने के लिए डिज़ाइन किया गया एक एसिंक्रोनस स्ट्रीम प्रोसेसिंग टूलकिट है। यह लाइब्रेरी एसिंक्रोनस प्रोसेसिंग पाइपलाइन्स के निर्माण को सक्षम बनाती है जो इवेंट सीक्वेंस को ट्रांसफ़ॉर्म, फ़िल्टर और संयोजित करती हैं। यह इन पाइपलाइन्स को कंपोज़ करने के लिए फ़ंक्शनल ऑपरेटर्स के उपयोग के माध्यम से खुद को अलग बनाती है और कॉन्करेंट निष्पादन को मैनेज करने के लिए तंत्र प्रदान करती है। यह टूलकिट डेटा एग्रीगेशन, मल्टी-स्ट्रीम कॉम्बिनेशन और स्ट्रीम्स को स्टैटिक डेटा स्ट्रक्चर्स में बदलने सहित स्ट्रीम ऑर्केस्ट्रेशन क्षमताओं की एक विस्तृत श्रृंखला को कवर करती है। इसमें एरर रिकवरी, डेटा उत्पादन गति को विनियमित करने के लिए बैकप्रेशर कंट्रोल, और CPU कोर पर प्रोसेसिंग को समानांतर करने के लिए वर्कर पूलिंग के लिए इन-बिल्ट सपोर्ट शामिल है।

    The library selects specific items from a stream based on predicates, time-based debouncing, or index positions.

    Goasyncasynchronousconcurrency
    GitHub पर देखें↗5,091
  • reactivex/rxpyReactiveX का अवतार

    ReactiveX/RxPY

    5,014GitHub पर देखें↗

    RxPY is a functional reactive programming library and a ReactiveX observable library for Python. It serves as an asynchronous stream processor and event-driven coordination framework used to build data pipelines that react to changes in state or streams of events over time. The library provides a toolkit for composing asynchronous and event-based programs using observable sequences and operators. It distinguishes itself through the use of configurable schedulers to manage concurrency, timing, and subscription lifecycles. The project covers a wide range of stream processing capabilities, incl

    Filters items from event streams based on predicates, indices, or timing criteria.

    Python
    GitHub पर देखें↗5,014
  • dropbox/leptondropbox का अवतार

    dropbox/lepton

    4,997GitHub पर देखें↗

    Lepton एक विशेष टूल और फाइल फॉर्मेट है जिसे JPEG इमेज डेटा के लॉसलेस कंप्रेशन और कुशल स्टोरेज के लिए डिज़ाइन किया गया है। यह एक लॉसलेस कंप्रेसर और स्टोरेज ऑप्टिमाइज़र के रूप में कार्य करता है जो मूल पिक्सेल डेटा को बदले बिना फाइल साइज को कम करता है, जिससे इमेज का बिट-परफेक्ट पुनर्निर्माण सुनिश्चित होता है। यह प्रोजेक्ट डिजिटल इमेज आर्काइव के लिए डिस्क स्पेस और नेटवर्क बैंडविड्थ की आवश्यकताओं को कम करने पर केंद्रित है। यह उच्च-गुणवत्ता वाले इमेज स्टोरेज को बनाए रखते हुए और कुल डेटा फुटप्रिंट को कम करने के लिए JPEG फाइलों के कंप्रेशन और डीकंप्रेशन दोनों की क्षमताएं प्रदान करता है। इसका कार्यान्वयन विभिन्न एंट्रॉपी एनकोडिंग और डेटा प्रोसेसिंग तकनीकों का उपयोग करता है, जिसमें अरिथमेटिक और हफमैन कोडिंग, प्रेडिक्टिव मॉडलिंग और स्ट्रीम-आधारित प्रोसेसिंग शामिल है। यह बड़े इमेज डेटा ब्लॉक की प्रोसेसिंग के लिए ऑप्टिमाइज़्ड ऐरे ऑपरेशंस को भी एकीकृत करता है।

    Processes large image files as a sequence of bytes to maintain a low memory footprint.

    C++compressioncompression-algorithmdecompression
    GitHub पर देखें↗4,997
  • effector/effectoreffector का अवतार

    effector/effector

    4,837GitHub पर देखें↗

    Effector is a reactive state management library designed for building complex, event-driven applications. It functions as a data flow engine that models application logic as a directed acyclic graph, ensuring that state updates propagate automatically through interconnected nodes. By utilizing atomic state updates and declarative unit composition, the library maintains data consistency and provides a predictable execution model for managing application state. The project distinguishes itself through its framework-agnostic architecture, which decouples business logic from user interface implem

    Filters data streams by applying conditional logic to event triggers before they reach target effects.

    TypeScriptbusiness-logiceffectorevent-driven
    GitHub पर देखें↗4,837
  • riemann/riemannriemann का अवतार

    riemann/riemann

    4,266GitHub पर देखें↗

    Riemann is a Clojure-based event stream processor and real-time analytics engine. It functions as a network telemetry pipeline and extensible event router that ingests, transforms, and routes event data from distributed systems. The system uses a domain-specific language to compute metrics and statistical patterns over continuous streams, enabling network trend analysis and real-time alerting. It supports dynamic plugin loading from the classpath and allows for live configuration reloading without interrupting active event streams. Capabilities include centralized telemetry aggregation, even

    Transforms event streams using complex operations including linear prediction, rate calculation, and event coalescing.

    Clojure
    GitHub पर देखें↗4,266
  • theturtle32/websocket-nodetheturtle32 का अवतार

    theturtle32/WebSocket-Node

    3,784GitHub पर देखें↗

    WebSocket-Node is a server-side implementation of the WebSocket protocol for Node.js environments. It serves as a framework for establishing persistent, bidirectional communication channels and low-latency data exchange between clients and servers. The project provides a secure socket implementation using transport layer security and includes an integrated client for establishing outbound encrypted connections. It utilizes a formal protocol-state machine and an event-driven connection framework to manage high-concurrency network streams. The framework covers server-side infrastructure includ

    Processes network data as a continuous stream of events to minimize memory overhead during high concurrency.

    JavaScript
    GitHub पर देखें↗3,784
  • bruin-data/ingestrbruin-data का अवतार

    bruin-data/ingestr

    3,714GitHub पर देखें↗

    ingestr is a command-line tool for copying and syncing data between different database engines and third-party platforms without writing custom code. It functions as an ETL pipeline utility that extracts data from diverse sources and loads it into destinations. The tool features a schema-agnostic data loader that maps source fields to destination columns dynamically, removing the need for predefined static table definitions. It also operates as an incremental data synchronizer, updating destination tables by appending new records or merging changes to maintain current datasets. The system pr

    Processes large datasets in small chunks to maintain a low memory footprint during data transfer.

    Go
    GitHub पर देखें↗3,714
  • aaubry/yamldotnetaaubry का अवतार

    aaubry/YamlDotNet

    2,807GitHub पर देखें↗

    YamlDotNet is a YAML serialization library and data mapping tool for .NET. It functions as a parser and generator that converts between .NET objects and YAML formatted text for data storage and configuration. The project provides capabilities for YAML document parsing and data object mapping. It transforms YAML streams into low-level representations or high-level object models for programmatic analysis and converts structured data objects back into valid YAML strings. The library covers general data serialization and configuration file management, allowing application data structures to be m

    Allows reading large YAML files as a sequence of events to avoid loading entire documents into memory.

    C#dotnetparserserialization
    GitHub पर देखें↗2,807
  • pulldown-cmark/pulldown-cmarkpulldown-cmark का अवतार

    pulldown-cmark/pulldown-cmark

    2,610GitHub पर देखें↗

    pulldown-cmark is a pull-parsing library that transforms Markdown text into a stream of events based on the CommonMark specification. It functions as an event-based processor that represents document structure as an iterator of events rather than a concrete syntax tree, serving as both a parser and a renderer to convert Markdown into HTML strings. The library is designed for memory efficiency by processing text as a stream to minimize resource usage. It supports programmatic document transformation, allowing users to map or filter the event stream before final rendering. The project includes

    Processes Markdown as an event stream to ensure low memory overhead and high speed.

    Rustcommonmarkmarkdownparser
    GitHub पर देखें↗2,610
  • reactphp/socketreactphp का अवतार

    reactphp/socket

    1,285GitHub पर देखें↗

    This library provides a framework for building event-driven, non-blocking network applications in PHP. It enables the development of asynchronous TCP and TLS servers and clients that manage multiple concurrent connections without stalling the main execution thread. By utilizing an event-loop architecture, the library handles network operations and data exchange through asynchronous streams, ensuring that the application remains responsive during high-throughput tasks. The project distinguishes itself through its integration with operating system primitives, such as raw file descriptors and Un

    Manages high-throughput data exchange by processing incoming and outgoing network chunks incrementally through non-blocking, event-based interfaces.

    PHPphpreactphpserver-socket
    GitHub पर देखें↗1,285
  • mrsuichuan/data-warehouse-learningMrSuiChuan का अवतार

    MrSuiChuan/data-warehouse-learning

    1,154GitHub पर देखें↗

    Data warehouse learning is a reference implementation of a real-time stream processing system and open-source data lakehouse architecture. It combines stream processing engines, open lakehouse formats, and analytical data warehouses into a complete e-commerce data warehouse system built for both offline and real-time analytics pipelines. The project implements hybrid data warehouse architectures utilizing multi-layer storage models and stream-batch processing pipelines. It features change data capture pipelines that stream database transaction logs into messaging systems, progressive data tra

    Applies progressive transformations across staging, dimensional, and summary layers using stream processing queries.

    Javadatartdinkydolphinscheduler
    GitHub पर देखें↗1,154
  1. Home
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  3. Event-Based Stream Processing

सब-टैग एक्सप्लोर करें

  • Batch-Based Stream ProcessingProcessing large datasets in small chunks to minimize memory usage during transfer. **Distinct from Event-Based Stream Processing:** Distinct from Event-Based Stream Processing: focuses on fixed-size chunking for memory efficiency rather than event-driven triggers.
  • Event Stream Filtering1 सब-टैगSelecting specific items from an event stream based on predicates, indices, or timing. **Distinct from Event-Based Stream Processing:** Distinct from Event-Based Stream Processing as it focuses specifically on the filtering logic rather than the overall processing architecture.