Evolution of Feed System Architecture for To Home Discovery Channel
This document outlines the architectural evolution of the Feed system for To Home Discovery Channel, addressing scalability challenges through database optimization, Elasticsearch integration, and caching strategies to manage billion-scale user data and dynamic content updates.
This document details the architectural evolution of the Feed system for To Home Discovery Channel, focusing on scalability solutions for handling billion-scale user data and dynamic content.
Initial implementations used database tables for user-store relationships and feed content, but faced performance issues with data volume growth.
Subsequent optimizations introduced Elasticsearch for distributed storage and parent-child document relationships, followed by caching strategies using jimdb to reduce latency and memory usage.
Further refinements included zset-based caching for sorted feed indices, reducing IO operations and memory overhead while maintaining data consistency through push/pull synchronization models.
The final architecture combines Elasticsearch for core data storage, jimdb for hot data caching, and zset structures for efficient feed indexing, achieving 60% memory reduction and improved system stability.
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