The article examines common pitfalls of naïve ad insertion in short‑video feeds, explains how cursor‑based pagination prevents duplicate ads, and outlines a client‑side mixing architecture that isolates services, meets strict latency requirements, and ensures accurate ad billing.
This article explains what a feed stream is, why it exists, how it evolved from RSS to modern social feeds, classifies different feed models, outlines the challenges of real‑time delivery, and provides a detailed backend architecture, data structures, storage design, pagination strategy, and core publish‑read workflows to help engineers build reliable, high‑performance feed systems.
This article introduces feed streams, explains their evolution from RSS to modern social feeds, classifies them by aggregation logic and display, discusses challenges such as real‑time performance and massive data, and presents a backend architecture with data models, pagination, write/read diffusion, and core publishing/reading workflows.
The article surveys feed‑stream systems—from their RSS roots to modern social‑media timelines—explaining classifications, core terminology, and a high‑performance architecture that uses Redis ZSET inboxes, write‑ and read‑diffusion, soft‑delete consistency, and last‑id pagination to handle massive real‑time writes, big‑V fan‑out, and efficient publishing and reading.
This article explains the concepts, classifications, design choices, and Java‑Redis implementation of a scalable feed stream system for enterprises, covering initialization, push/pull update mechanisms, handling of new posts, deletions, and follow/unfollow actions.
To protect the outbox Redis cluster from extreme read amplification during hot events, the service adds a resident local cache for hot creators’ latest posts, using a threshold‑based list, change‑broadcast updates, and checksum verification, which achieved over 55% cache hits and cut peak Redis load by roughly 44% and CPU usage by 37%.
This article explains the concept of feed streams, compares push and pull implementation models, discusses storage options such as MySQL, Redis SortedSet and Cassandra, and presents optimization techniques including online‑push/offline‑pull strategies, pagination methods, and deep‑paging solutions for large‑scale systems.
This article explains the principles, data models, storage choices, synchronization strategies, metadata handling, and scaling considerations for building a high‑performance feed‑stream system that can support millions to billions of users, with practical guidance on using NoSQL or relational databases.
This article explains how to design a high‑performance feed‑stream system—covering product definition, data categories, storage options, synchronization modes, metadata handling, commenting, likes, search, sorting, deletion, and update—so you can build a solution that scales to millions or billions of users.
This article explains how to design a high‑performance, billion‑user feed stream system, covering product definition, data modeling, storage choices, synchronization modes, metadata handling, commenting, likes, search, sorting, deletion, updates, and practical architecture examples for different feed‑type applications.
This article explains the fundamentals and architecture of feed‑stream systems—defining feed data, outlining storage choices such as distributed NoSQL or MySQL, comparing push, pull, and hybrid synchronization models, handling metadata, search, ordering, and scaling considerations for billion‑user platforms.
This article explains how to design a high‑performance feed‑stream architecture—including product definition, data modeling, storage choices, synchronization modes, metadata handling, commenting, likes, sorting, search, and deletion—so that a system can support tens of millions to billions of users while remaining reliable and scalable.
The article details NetEase Open Courses’ feed‑stream architecture, describing how content ingestion, multi‑level filtering, vertically and horizontally split storage, Elasticsearch indexing, two‑tier caching, and micro‑service orchestration combine to deliver personalized, high‑availability course feeds while addressing scalability, consistency, and operational challenges.
