Mini Design Breakdowns Hard

Question 1

Design Tinder (Dating App)Design a Web Crawler (e.g., Googlebot)

Answer 1

URL Frontier → Manage discovered but unvisited URLs

Politeness & Rate Limiting → Avoid overloading servers

Distributed Crawling → Scale across multiple machines

Deep Dives
How do you prevent duplicate crawling? → Use Bloom filters
How do you prioritize important pages? → Implement PageRank scoring

Question 2

Design Facebook Live Comments

Answer 2

Real-Time Streaming → Deliver new comments instantly

Comment Ranking Algorithm → Prioritize high-engagement comments

Spam Detection & Moderation → Prevent abuse in large streams

How do you handle comment bursts in viral streams? → Use comment batching

How do you prevent spam without slowing down real users? → AI-based content filtering

Question 3

Design Robinhood (Stock Trading App)

Answer 3

Key Components

Real-Time Stock Price Updates → Stream data from exchanges

Order Matching Engine → Process buy/sell orders efficiently

Fraud & Insider Trading Detection → Monitor suspicious activity

🔍 Deep Dives
How do you handle high-frequency trading? → Use low-latency messaging protocols

How do you prevent manipulation of stock prices? → Implement circuit breakers

Question 4

Designing Facebook’s Advertising System

Answer 4

Ad Inventory & Campaign Management → Store and manage ads from advertisers

User Targeting & Personalization → Show relevant ads based on user data

Real-Time Bidding (RTB) System → Determine which ad wins the auction

Ad Ranking Algorithm → Prioritize ads based on quality and engagement

Fraud Detection & Click Verification → Prevent fake clicks and bot activity

Analytics & Reporting Dashboard → Track impressions, conversions, and ROI

Deep Dives for Senior+
1️⃣ How do you handle real-time ad bidding at scale?

Facebook uses a Vickrey auction model, where advertisers bid for ad slots, but the winner pays the second-highest price.

The bidding process needs to be ultra-fast (~10-50ms) to avoid latency in user experience.

Distributed processing using Apache Kafka + Flink helps process bids in real time.

💡 Optimization Strategies

Pre-filter low-quality ads to reduce compute load

Use Redis/Memcached to cache recent bidding data

Implement batch processing for high-volume queries