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Design a News Feed (Social Feed) at Apple — System Design Interview Guide

The problem at Apple

Functional: users post content; followers see a ranked feed of recent posts; like/comment.
Non-functional: feed loads in <200ms, new posts appear within seconds, ranking is pluggable.
Scale: ~1B users, ~100M posts/day, read-heavy.
Consistency: feed ordering can be eventually consistent; a post may take a second to appear.

Out of scope (covered in detail below)

Ads injection, the ranking model training pipeline, content moderation, and A/B testing infra — deferred and discussed in the dedicated section.

At Apple, this problem is framed around craft, detail, and domain depth. Expect Apple-specific scale numbers and a focus on production readiness and the company's engineering bar.

Capacity estimation

Resource	Estimate	Notes
Posts/day	~100M	Each a small object
Feed reads/day	~billions	Read-heavy, ~100x writes
Feed cache (Redis)	~terabytes	Pre-computed feed lists
Max followers	~tens of millions	Celebrity problem

Architecture overview

Post -> fan-out -> feed cache -> read

+--------+  post   +-------------+  fan-out   +-------------------+
| Client |--------->| Post Service|----------->| Feed Cache (Redis)|
+--------+          +-------------+            | (sorted set)      |
                          |                   +-------------------+
                          v                            ^
                  +---------------+  follow               |
                  | Graph Service |----------------------+
                  +---------------+
                          |
                          v
                  +---------------+
                  | Fan-out Worker|  (async)
                  +---------------+

The fan-out worker pre-computes feeds; reads are a cached list fetch + batch object load.

The core decision: fan-out vs pull

Fan-out on write: on each post, push to every follower's pre-computed feed. Fast reads; expensive for celebrities.
Pull on read: fetch recent posts from followed users and rank on the fly. Cheap writes; heavier reads.
Hybrid: fan-out for normal users, pull for celebrities — the production answer at scale.

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