# Design a News Feed (Facebook-style)

Source: https://www.techinterview.org/post/3233460016/design-a-news-feed-facebook-style/
Updated: 2026-04-16 · techinterview.org

Design a news feed system like Facebook's. This is a richer problem than the Twitter feed design — Facebook's feed involves mixed media (photos, videos, links), privacy filtering, and a ranking model that goes far beyond chronological ordering.

## Requirements Clarification

- **Scale:** 3 billion users, 1.5 billion DAU. Average user has 500 friends.

- **Feed content:** Text posts, photos, videos, shared links, event RSVPs, life events.

- **Ranking:** Not chronological — ranked by relevance (EdgeRank/ML model). User can switch to "Most Recent."

- **Privacy:** Posts have visibility settings: Public, Friends, Friends of Friends, Custom, Only Me.

- **Read/write ratio:** Heavily read-biased. 1.5B users refresh feed × multiple times daily vs ~500M posts/day.

- **Latency:** Feed load < 200ms p99.

## Back-of-Envelope

- 500M posts/day → ~6,000 posts/sec

- 1.5B DAU × 10 feed loads/day = 15B feed views/day → ~175,000 feed reads/sec

- Average feed: 20 posts per load. Each post: ~2KB metadata + media URLs. Feed payload: ~40KB before media.

- Average user: 500 friends. A post fans out to 500 feeds → 6,000 × 500 = 3M feed insertions/sec at peak

3M insertions/sec is too expensive for pure fanout-on-write. Facebook uses a **hybrid fanout** approach.

## Fanout Strategy: The Hybrid Approach

Pure fanout-on-write (push model): when Alice posts, immediately write to all 500 friends' feed caches. Fast reads. Expensive for high-follower users — a celebrity with 5M friends generates 5M cache writes per post.

Pure fanout-on-read (pull model): don't precompute. When Bob loads his feed, fetch posts from all 500 friends and merge/rank. No write amplification. Very expensive reads — 500 DB queries per feed load.

**Facebook's hybrid:**

- Regular users (<5K friends): fanout-on-write. Posts pushed to friends' feed caches immediately.

- High-follower users (celebrities, pages): fanout-on-read. Their posts are not pre-pushed. When you load your feed, the feed service fetches their recent posts and merges.

- Cutoff is configurable, typically 5K–10K friends/followers.

## Architecture


```
Post Service → [Kafka] → Fanout Service → Feed Cache (Redis)
                                ↓
                         Privacy Filter
                                ↓
                    Feed Cache: user_id → [post_id list]

Feed Read Path:
User Request → Feed Service → Redis (precomputed feed)
                           → Merge with celebrity posts (pull)
                           → Ranking Service (ML model)
                           → Post Details Service (hydrate metadata)
                           → CDN (media URLs)
```


## Privacy Filtering

Privacy is the hardest part of Facebook's feed. A post with visibility "Friends of Friends" must be excluded from your feed if the mutual friend relationship was broken since the post was created.

Options:

- **Pre-filter at fanout time:** When writing to feed cache, check privacy at insertion. Stale if Alice changes her post visibility later — cached feeds won't update.

- **Post-filter at read time:** Always re-check privacy when loading feed. Correct, but expensive — N posts × privacy check per feed load.

- **Facebook's approach:** Pre-filter at fanout, with invalidation events. When post visibility changes, send an invalidation to affected caches.

## Feed Ranking

Facebook's original EdgeRank (2010) ranked posts by: Affinity (how close you are to the poster) × Weight (post type: video > photo > link > text) × Time decay. Modern feed ranking is a full ML model — a multi-task neural network that predicts engagement probability (like, comment, share, click) for each post-user pair.

In an interview, you don't need to design the full ML system. Say: "We run a ranking service that scores each candidate post against the user's engagement history. Top-K posts are returned. The ranking model is trained offline on engagement labels and served via a low-latency feature store + model serving layer."

Key ranking signals:

- Affinity score between user and poster (interaction frequency, mutual friends)

- Post type and recency

- Predicted engagement probability (from ML model)

- Content diversity (don't show 5 posts from the same friend in a row)

## Feed Cache Design


```
Redis sorted set per user:
  Key: feed:{user_id}
  Members: post_id (encoded as string)
  Score: ranking_score (float, computed at fanout time for fast reads)

Feed size: cap at 1,000 posts per user in cache (pagination handles older)
TTL: 7 days (inactive users' feeds expire; rebuilt on next load)
```


On feed read: fetch top-N post_ids from Redis → batch-fetch post metadata from Post Service → filter privacy → re-rank if using dynamic ranking signals → paginate.

## Media Handling

Photos and videos are not stored in the feed cache — only their IDs. At read time, the feed service returns media URLs pointing to CDN (Akamai, Facebook's own CDN). Video thumbnails are pre-generated at multiple resolutions. Video streaming uses HLS/DASH from Facebook's video processing pipeline (similar to YouTube's transcoding pipeline).

## Feed Pagination

Cursor-based pagination, not offset. Why: offset pagination requires scanning and skipping rows, which is O(n). Cursor-based: store the last-seen ranking_score as the cursor. "Give me posts with score less than {cursor}." O(1) lookup in the sorted set.

## Key Differences from Twitter Feed Design

| Aspect | Twitter/X | Facebook News Feed |
| --- | --- | --- |
| Default sort | Chronological + algorithmic | Algorithmic (EdgeRank/ML) |
| Privacy | Public by default | Complex visibility rules per post |
| Content types | Tweets (text/media) | Posts, events, life events, pages |
| Celebrity problem | Fan-out on read for all | Hybrid by follower count |
| Engagement | Likes/RTs/replies | Reactions, comments, shares, saves |

## Interview Follow-ups

- How do you handle feed freshness — a user posts and expects to see it in their own feed immediately?

- How would you design the "Memories" feature (posts from N years ago)?

- How do you prevent the ranking model from creating filter bubbles?

- Your fanout service falls behind during a viral event. How do you handle backpressure?

- How do you handle a user who suddenly gains 10M followers (goes viral)?

## Related System Design Topics
- [Caching Strategies](https://www.techinterview.org/post/3233459956/caching-strategies/) — the Redis sorted set feed cache is the read path's foundation
- [Message Queues](https://www.techinterview.org/post/3233459957/message-queues/) — Kafka fan-out pipeline from post creation to feed cache insertion
- [Database Sharding](https://www.techinterview.org/post/3233459955/database-sharding/) — sharding user and post data across the feed graph
- [Design Twitter / Social Feed](https://www.techinterview.org/post/3233462958/system-design-social-media-feed-system/) — compare fanout-on-write vs read; Facebook's hybrid approach
- [Load Balancing](https://www.techinterview.org/post/3233459958/load-balancing/) — distributing feed read requests across feed service nodes

## Companies That Ask This System Design Question

This problem type commonly appears in interviews at:

- [Meta Interview Guide](https://www.techinterview.org/post/3233460272/meta-interview-guide-2026-facebook-instagram-whatsapp-engineering/)

- [Twitter/X Interview Guide](https://www.techinterview.org/post/3233460305/twitter-x-interview-guide-2026-timeline-algorithms-real-time-search-and-content-at-scale/)

- [LinkedIn Interview Guide](https://www.techinterview.org/post/3233460278/linkedin-interview-guide-2026-social-graph-engineering-feed-ranking-and-professional-network-scale/)

- [HubSpot Interview Guide](https://www.techinterview.org/post/3233460314/hubspot-interview-guide-2026-crm-engineering-marketing-automation-and-multi-tenant-saas/)

*See our [company interview guides](https://www.techinterview.org/category/company-guides/) for full interview process, compensation, and preparation tips.*