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System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

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This is a hard difficulty System Design question, commonly asked during Onsite rounds at Apple.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Apple during technical interviews.

Design and scale a Yelp-like platform | Apple Interview Question

Quick Overview

This question evaluates system design and engineering skills including data modeling, indexing, geospatial and full-text search, caching, replication and sharding for a high-scale reviews platform, together with key generation, storage layout, redirects, rate limiting and analytics for a URL-shortening service, probing the ability to balance scalability, consistency, availability, and operational complexity. It falls under the System Design / Distributed Systems / Databases domain and is commonly asked to assess architectural reasoning and trade-off analysis, testing both conceptual high-level design and practical implementation considerations such as performance, fault tolerance, and operational constraints.

System Design: Yelp-like Local-Business Reviews Platform + URL Shortener

You are designing two systems:

A Yelp-like local-business reviews platform that supports millions of users, businesses with geocoordinates, categories, reviews, ratings, photos, and check-ins.
A high-scale URL-shortening service.

Part A — Yelp-like Reviews Platform

Define and discuss:

Core data model
- Entities: users, businesses (with geocoordinates), categories, reviews, ratings, photos, check-ins.
- Indexes for common queries.
Scaling reads
- Replication, caches, CDN, search indexes.
Scaling writes
- Sharding, queues, consistency (read-your-writes), backfills/migrations.
Search and geospatial queries
- Full-text, category filters, distance/radius queries, sorting.
Cache invalidation and performance
- What to cache, when/how to invalidate, pagination, hot keys, tail latency.

Part B — URL-Shortening Service

Outline a design covering:

Key/ID generation and collision handling (sequential vs random, length analysis).
Storage layout and data model.
Redirects, HTTP status codes, and CDN usage.
Rate limiting and abuse prevention.
Analytics and aggregation.
TTL/expiration, deletion, and alias reuse policy.
Custom aliases and namespace management.
Strategies to scale high read/write traffic (multi-region, cache, sharding).

Quick Overview

Part A — Yelp-like Reviews Platform

Define and discuss:

Core data model

Entities: users, businesses (with geocoordinates), categories, reviews, ratings, photos, check-ins.
Indexes for common queries.

Scaling reads

Replication, caches, CDN, search indexes.

Scaling writes

Sharding, queues, consistency (read-your-writes), backfills/migrations.

Search and geospatial queries

Full-text, category filters, distance/radius queries, sorting.

Cache invalidation and performance

What to cache, when/how to invalidate, pagination, hot keys, tail latency.

Part B — URL-Shortening Service

Outline a design covering:

Key/ID generation and collision handling (sequential vs random, length analysis).

Storage layout and data model.

Redirects, HTTP status codes, and CDN usage.

Rate limiting and abuse prevention.

Analytics and aggregation.

TTL/expiration, deletion, and alias reuse policy.

Custom aliases and namespace management.

Strategies to scale high read/write traffic (multi-region, cache, sharding).

Design and scale a Yelp-like platform

Quick Overview

System Design: Yelp-like Local-Business Reviews Platform + URL Shortener

Part A — Yelp-like Reviews Platform

Part B — URL-Shortening Service

Solution

Comments (0)

Design and scale a Yelp-like platform

Quick Overview

System Design: Yelp-like Local-Business Reviews Platform + URL Shortener

Part A — Yelp-like Reviews Platform

Part B — URL-Shortening Service

Solution

Comments (0)