System Design HLD Example: News Feed (Home Timeline)

Scenario context

A news feed system builds personalized timelines by combining content publishing, graph relationships, and ranking. The scalability crux is the fan-out amplified write path: a single celebrity post can trigger 100M writes. A hybrid fan-out strategy—push for normal users, pull for celebrities—is the industry standard for 99.99% availability.

Imagine it’s 2013. Twitter is growing at a breakneck pace. Most users have…

This walkthrough focuses on The Katy Perry Problem.

Before making a design choice, clarify:

• Twitter is growing at a breakneck pace.
• Most users have a few hundred followers, and the system handles them easily by "pushing" new tweets into their home timelines at write-time.
• She has 100 million followers.

Concepts in play: system-design-hld-architectures, caching, graph-traversal-algorithms, event-driven-architecture

Stage 1 — News Feed: Use Cases, Actors, and Scale Requirements

• Actors - Publisher / Author: Creates and posts content (text, media).
• • Reader / Follower: Consumes a ranked home feed of followed users' posts.
• • System: Handles fan-out, ranking, and timeline materialization.
• Functional Requirements - Post Creation: Users can POST content with text/media.

Observable outcome: Functional Requirements - Post Creation: Users can POST content with text/media.

Stage 2 — Basics of News Feed Architecture

• At its heart, a news feed is a many-to-many relationship pipeline .
• Unlike a simple blog where one post is read by everyone, a feed is a personalized "inbox" for every user.
• Apache Kafka is the decoupling mechanism between post creation and fan-out.
• The posts.created topic is partitioned by author id , ensuring that all posts from one author are processed by the same fan-out worker partition and arrive in creation order.

Observable outcome: The posts.created topic is partitioned by author id , ensuring that all posts from one author are processed by the same fan-out worker partition and arrive in creation order.

End-to-end outcome

After working through the stages, you should be able to explain:

• The Celebrity Threshold Requires Continuous Calibration.
• Setting the celebrity threshold too low pushes too many accounts into pull fan-out, degrading read performance for mid-tier influencers whose follower lists still take seconds to query.
• • A news feed is a read-heavy, write-amplified system.
• The core design tension is between write-time cost (fan-out to millions of followers) and read-time cost (merging posts from all followed authors on every page load).

Alternatives considered

The source focuses on one primary design. A second architecture is not compared, so no alternative is invented here.

Failure and degraded modes

The source does not isolate a separate failure mode. Limits that matter for the scenario are called out in the stage decisions above.