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tschellenbach/Stream-Framework

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4,746 نجوم·529 تفرعات·Python·13 مشاهداتgetstream.io↗

Stream Framework

Stream-Framework هي مكتبة Python لبناء تدفقات نشاط قابلة للتوسع، وخلاصات أخبار، وأنظمة إشعارات. تعمل كمحرك لتدفق النشاط يدير توزيع وتخزين واسترجاع تدفقات الأحداث الزمنية لقواعد مستخدمين كبيرة.

يستخدم إطار العمل مزيجاً من Cassandra وRedis لتوفير معمارية خلاصة قابلة للتوسع، مع استخدام التخزين المؤقت في الذاكرة للاسترجاع منخفض التأخير والتخزين الموزع لضمان التوافر العالي. يتميز بآلية توزيع غير متزامنة (Fan-out) لتوزيع الأنشطة على خلاصات المتابعين المتعددة وطبقة مزامنة في الوقت الفعلي تدفع التحديثات للعملاء عبر اتصالات WebSocket.

يتضمن النظام أدوات لتنسيق المحتوى المخصص، مما يسمح بترتيب وتصفية الخلاصات بناءً على اهتمامات المستخدم وشعبيته. كما يوفر مجموعة أدوات للمشاركة الاجتماعية لتنفيذ التعليقات المترابطة، والتفاعلات، والإشارات المرجعية، والتصويت، إلى جانب تعديل المحتوى المدعوم بالذكاء الاصطناعي لاكتشاف الوسائط الضارة والإبلاغ عنها في الوقت الفعلي.

تغطي القدرات الإضافية تكامل الاتصال في الوقت الفعلي للصوت والفيديو، وتسليم إشعارات الدفع، ومحرك استعلام عن النشاط لاكتشاف المحتوى بدقة.

Features

  • Activity Feed Engines - Functions as an activity stream engine managing the distribution and retrieval of chronological event feeds.
  • Activity Stream Engines - Provides the core engine for constructing and managing scalable social activity streams using Cassandra and Redis.
  • Community Engagement Tools - Offers a set of tools for implementing social interactions like threaded comments, voting, and reactions.
  • Personalized Feed Orchestrators - Includes a personalized feed orchestrator to rank content based on popularity and user interests.
  • Social Interactions - Provides a social interaction framework for reactions, threaded comments, bookmarks, and polls.
  • Personalized Feed Retrievers - Provides low-latency retrieval of recent activities to render personalized newsfeeds for specific users.
  • Feed Storage Frameworks - Implements a specialized framework using Cassandra and Redis to build scalable news feeds and notification systems.
  • Redis Caching Layers - Uses Redis caching layers to provide low-latency retrieval of recent activities for active users.
  • Wide-Column Stores - Utilizes Cassandra wide-column stores for scalable and high-availability storage of activity data.
  • Real-Time Data Synchronization - Implements real-time synchronization of feed views via WebSocket connections to eliminate manual page refreshing.
  • Real-time Notification Broadcasters - Provides a synchronization layer that pushes real-time activity updates and alerts to clients via WebSockets.
  • Fan-Out Patterns - Implements asynchronous fan-out patterns to distribute a single activity to multiple follower feeds.
  • WebSocket Synchronization - Uses WebSocket connections to maintain real-time synchronization between the client interface and activity feeds.
  • Social Feed Architectures - Provides a scalable social feed architecture capable of handling high volumes of activity across distributed environments.
  • Real-time Sync Engines - Ships a real-time synchronization engine using WebSockets to push activity updates to clients.
  • Modular AI Pipelines - Employs modular AI pipelines to process voice and vision data through pluggable classifiers.
  • Feed Content Filtering - Provides tools for filtering and removing specific activities from feeds based on defined criteria.
  • Content Discovery Systems - Curates custom discovery feeds based on user interests, popularity, and location to surface relevant content.
  • Custom Content Type Definitions - Allows the definition of custom content types to represent posts, photos, videos, or polls in feeds.
  • Cloud Push Notification Deliveries - Triggers cloud-based push notifications to user devices when new activities or social interactions occur.
  • Scalable Push Notification Platforms - Provides a horizontally scalable platform for broadcasting activity alerts and notifications to large user bases.
  • Request Token Validators - Implements request token validators to manage permissions for viewing and interacting with private streams.
  • Access Tokens - Generates secure server-side access tokens to authorize client access to activity streams.
  • Content Moderation - Uses AI to automatically detect and flag harmful text, image, video, and audio content in real time.
  • Content Moderation Policies - Allows the definition of rules to determine how content is filtered or flagged across communication channels.
  • Activity Feed Querying - Provides a dedicated query engine for precise content discovery and retrieval of specific activities or feeds.
  • Rule-Based Filters - Applies rule-based filters to curate personalized content or exclude specific items from user feeds.
  • Collaborative Commenting Systems - Implements collaborative commenting systems with support for threading, voting, and mentions.
  • In-App Messaging Components - Provides tools and pre-built components for creating real-time in-app messaging experiences with custom layouts.
  • Activity Streams - System for building notification and activity feeds.

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الأسئلة الشائعة

ما هي وظيفة tschellenbach/stream-framework؟

Stream-Framework هي مكتبة Python لبناء تدفقات نشاط قابلة للتوسع، وخلاصات أخبار، وأنظمة إشعارات. تعمل كمحرك لتدفق النشاط يدير توزيع وتخزين واسترجاع تدفقات الأحداث الزمنية لقواعد مستخدمين كبيرة.

ما هي الميزات الرئيسية لـ tschellenbach/stream-framework؟

الميزات الرئيسية لـ tschellenbach/stream-framework هي: Activity Feed Engines, Activity Stream Engines, Community Engagement Tools, Personalized Feed Orchestrators, Social Interactions, Personalized Feed Retrievers, Feed Storage Frameworks, Redis Caching Layers.

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