Design a personalized news feed system that pushes items to users and also supports pull-based consumption. Requirements: 100M MAU, 1M publishers, 200k writes/s, 2M reads/s, p99 < 200 ms; support follow/mute/blocks, deduplication, diversity and freshness constraints, daily notification caps, and content retractions. Provide external API designs with request/response schemas and idempotency for: publish, subscribe/unsubscribe, get_feed (with pagination and consistent cursors), ack_consume, retract, and feedback logging. Choose between fan-out-on-write vs. fan-out-on-read and justify; describe storage (hot cache, cold store), event streaming, ranking feature pipeline, and online inference. Address abuse/spam controls, multi-region replication, backfill and replay, GDPR deletion, rate limiting, SLOs/observability, and failure modes (e.g., partial outages). Finally, outline how you would run online experiments on ranking while ensuring user-level traffic consistency and safe rollouts.

This question evaluates system design, scalable API design, and machine-learning ranking competencies for personalized content delivery, focusing on ingestion, candidate generation, ranking, and operational guarantees.

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This is a hard difficulty Machine Learning question, commonly asked during Onsite rounds at Yelp.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Yelp during technical interviews.

Design a News Feed with APIs | Yelp Interview Question

Q: Design a News Feed with APIs

This question evaluates system design, scalable API design, and machine-learning ranking competencies for personalized content delivery, focusing on ingestion, candidate generation, ranking, and operational guarantees.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Onsite rounds at Yelp.

Q: What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Yelp during technical interviews.

Personalized News Feed System Design (Push + Pull)

Context

You are designing a large-scale personalized news feed for a consumer application. The feed must support both push (server-initiated delivery/notifications) and pull (client-initiated fetch) consumption patterns, with strong ranking, safety, and operational guarantees.

Scale and Latency Targets

Users: 100M monthly active users (MAU)
Publishers: 1M
Ingestion: 200k writes/sec
Reads: 2M reads/sec
Latency: p99 < 200 ms for feed reads

Functional Requirements

Social graph and controls:
- Follow/unfollow
- Mute and block (user-level and publisher-level)
Feed quality:
- Deduplication (exact and near-duplicate)
- Diversity (content type/source/topic)
- Freshness/recency constraints
Push channel safeguards:
- Daily notification caps per user
Content lifecycle:
- Content retractions (publisher-initiated takedown)

API Design Deliverables

Design external APIs with request/response schemas and idempotency for:

publish
subscribe (follow)/unsubscribe
get_feed (pagination + consistent cursors)
ack_consume
retract
feedback logging

For each, provide:

Endpoint path and method
Request and response JSON schemas
Idempotency strategy

Architecture Choices and Justification

Choose fan-out-on-write vs. fan-out-on-read (or hybrid). Justify with the given scale and SLOs.
Describe storage layers: hot cache/timeline store vs. cold storage.
Describe event streaming topology.
Ranking pipeline: candidate generation, features, filtering, scoring, re-ranking, and online inference.

Reliability, Safety, and Operations

Abuse/spam controls
Multi-region replication and read locality
Backfill and replay strategy
GDPR deletion and data lifecycle
Rate limiting and quotas
SLOs, observability, on-call signals
Failure modes and graceful degradation (including partial outages)

Experimentation

How to run online experiments on ranking while ensuring user-level traffic consistency across sessions and pagination
Safe rollout plans and guardrails

Functional Requirements

Social graph and controls:

Follow/unfollow
Mute and block (user-level and publisher-level)

Feed quality:

Deduplication (exact and near-duplicate)
Diversity (content type/source/topic)
Freshness/recency constraints

Push channel safeguards:

Daily notification caps per user

Content lifecycle:

Content retractions (publisher-initiated takedown)

API Design Deliverables

Design external APIs with request/response schemas and idempotency for:

publish

subscribe (follow)/unsubscribe

get_feed (pagination + consistent cursors)

ack_consume

retract

feedback logging

For each, provide:

Endpoint path and method

Request and response JSON schemas

Idempotency strategy

Architecture Choices and Justification

Choose fan-out-on-write vs. fan-out-on-read (or hybrid). Justify with the given scale and SLOs.

Describe storage layers: hot cache/timeline store vs. cold storage.

Describe event streaming topology.

Ranking pipeline: candidate generation, features, filtering, scoring, re-ranking, and online inference.

Design a News Feed with APIs

Quick Overview

Personalized News Feed System Design (Push + Pull)

Context

Scale and Latency Targets

Functional Requirements

API Design Deliverables

Architecture Choices and Justification

Reliability, Safety, and Operations

Experimentation

Solution

Submit Your Answer to Earn 20XP

Design a News Feed with APIs

Quick Overview

Personalized News Feed System Design (Push + Pull)

Context

Scale and Latency Targets

Functional Requirements

API Design Deliverables

Architecture Choices and Justification

Reliability, Safety, and Operations

Experimentation

Solution

Submit Your Answer to Earn 20XP