tschellenbach/stream-framework

Stream Framework is a Python library, which allows you to build news feed, activity streams and notification systems using Cassandra and/or Redis. The...

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Summary Information

Updated 40 minutes ago
Added to GitGenius on September 14th, 2026
Created on August 8th, 2012
Open Issues & Pull Requests: 76 (+0)
GitHub issues: Enabled
Number of forks: 528
Total Stargazers: 4,744 (+0)
Total Subscribers: 209 (+0)

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Detailed Description

Stream Framework is a Python library that allows you to build activity streams, newsfeeds, and notification systems using Cassandra and Redis as backing stores.

The library addresses the challenge of efficiently storing and retrieving feed data at scale. It provides abstractions for common feed patterns including activity streams like those on Github, Twitter-style newsfeeds, Instagram or Pinterest-style feeds, Facebook-style newsfeeds, and notification systems. The framework handles the complexity of managing feed data across distributed storage backends, allowing developers to focus on application logic rather than the underlying infrastructure for feed generation and retrieval.

Stream Framework suits Python projects that need to build feed-based systems and have the operational capacity to manage Cassandra and Redis deployments. The library requires explicit installation of dependencies for the storage backend you intend to use, with separate installation options for Redis, Cassandra, or both. The README references a commercial cloud service offering from the same authors that provides similar feed functionality through a REST API without requiring self-hosted infrastructure management, though it does not make detailed comparisons between the library and that service.

The project maintains documentation and example applications to support adoption. The codebase includes references to academic and industry approaches to feed system architecture, drawing on published work from Twitter, Etsy, LinkedIn, Facebook, and other organizations that have tackled feed scaling challenges.