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tschellenbach avatar

tschellenbach/Stream-Framework

0
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4,746 stars·529 forks·Python·22 viewsgetstream.io↗

Stream Framework

Stream-Framework is a Python library for building scalable activity streams, news feeds, and notification systems. It functions as an activity stream engine that manages the distribution, storage, and retrieval of chronological event streams for large user bases.

The framework utilizes a combination of Cassandra and Redis to provide a scalable feed architecture, employing in-memory caching for low-latency retrieval and distributed storage for high availability. It features an asynchronous fan-out mechanism to distribute activities to multiple follower feeds and a real-time synchronization layer that pushes updates to clients via WebSocket connections.

The system includes tools for personalized content orchestration, allowing for the ranking and filtering of feeds based on user interests and popularity. It also provides a social engagement toolkit for implementing threaded comments, reactions, bookmarks, and voting, alongside AI-powered content moderation to detect and flag harmful media in real time.

Additional capabilities cover real-time communication integration for voice and video, push notification delivery, and an activity query engine for precise content discovery.

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.

Star history

Star history chart for tschellenbach/stream-frameworkStar history chart for tschellenbach/stream-framework

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Frequently asked questions

What does tschellenbach/stream-framework do?

Stream-Framework is a Python library for building scalable activity streams, news feeds, and notification systems. It functions as an activity stream engine that manages the distribution, storage, and retrieval of chronological event streams for large user bases.

What are the main features of tschellenbach/stream-framework?

The main features of tschellenbach/stream-framework are: Activity Feed Engines, Activity Stream Engines, Community Engagement Tools, Personalized Feed Orchestrators, Social Interactions, Personalized Feed Retrievers, Feed Storage Frameworks, Redis Caching Layers.

What are some open-source alternatives to tschellenbach/stream-framework?

Open-source alternatives to tschellenbach/stream-framework include: czy0729/bangumi — Bangumi is a cross-platform mobile application that serves as an anime and manga tracker, a community client for… baserow/baserow — Baserow is a self-hosted, no-code relational database platform built on PostgreSQL. It provides a spreadsheet-like… apachecn/interview — This project is a comprehensive knowledge base and study resource designed for mastering technical interviews. It… cluic/wxauto — wxauto is a Python library and bot framework designed for the programmatic control of the WeChat Windows desktop… terry-mao/goim — goim. water8394/flink-recommandsystem-demo — This project is a real-time product recommendation engine built on Apache Flink. It functions as a streaming…