5 real Kafka interview questions asked at LinkedIn. Upvoted by engineers who cleared the loop. Covers System Design, Technical rounds.
LinkedIn
Hard
System Design round
Distributed SystemsKafkaElasticsearch
Design LinkedIn's "People You May Know" feature.
How do you compute second and third-degree connections for 900 million users efficiently?
Discuss graph storage (adjacency list vs matrix), batch vs real-time computation, and how to refresh recommendations when someone adds a new connection.
↑ 65 upvotes · 63 engineers asked this · SDE2
LinkedIn
Hard
System Design round
Distributed SystemsElasticsearchKafka
Design LinkedIn's job recommendation engine.
Given a user's profile, skills, and activity, rank the most relevant open positions.
Cover candidate retrieval, feature engineering (skills overlap, company affinity, recency), and how you A/B test ranking changes at scale.
↑ 52 upvotes · 52 engineers asked this · SDE2
LinkedIn
Hard
System Design round
KafkaRedisDistributed Systems
Design LinkedIn's feed. Each user sees posts from their connections, pages they follow, and sponsored content. Posts must be ranked by relevance. Cover: fan-out-on-write vs fan-out-on-read, feed generation for users with 30k connections, and freshness.
↑ 67 upvotes · 44 engineers asked this · SDE2
LinkedIn
Hard
Technical round
KafkaJavaDistributed Systems
LinkedIn uses Kafka extensively. Explain how you would design the Kafka topic structure for LinkedIn's activity events (profile view, connection request, message sent). Cover: topic naming, partitioning strategy, consumer group design, and exactly-once semantics.
↑ 56 upvotes · 35 engineers asked this · SDE2
LinkedIn
Hard
System Design round
KafkaDistributed SystemsElasticsearch
Design LinkedIn's InMail spam filtering system. Premium users can message strangers. How do you detect and block spam campaigns while allowing legitimate cold outreach? Cover: sender reputation, content analysis, and feedback loops.
↑ 48 upvotes · 30 engineers asked this · SDE2
Design LinkedIn's "People You May Know" feature.
Design LinkedIn's "People You May Know" feature.
How do you compute second and third-degree connections for 900 million users efficiently?
Discuss graph storage (adjacency list vs matrix), batch vs real-time computation, and how to refresh recommendations when someone adds a new connection.
Design LinkedIn's job recommendation engine.
Design LinkedIn's job recommendation engine.
Given a user's profile, skills, and activity, rank the most relevant open positions.
Cover candidate retrieval, feature engineering (skills overlap, company affinity, recency), and how you A/B test ranking changes at scale.
Design LinkedIn's feed. Each user sees posts from their connections, pages they follow, and sponsored content. Posts must be ranked by relevance. Cover: fan-out-on-write vs fan-out-on-read, feed generation for users with 30k connections, and freshness.
Design LinkedIn's feed. Each user sees posts from their connections, pages they follow, and sponsored content. Posts must be ranked by relevance. Cover: fan-out-on-write vs fan-out-on-read, feed generation for users with 30k connections, and freshness.
LinkedIn uses Kafka extensively. Explain how you would design the Kafka topic structure for LinkedIn's activity events (profile view, connection request, message sent). Cover: topic naming, partitioning strategy, consumer group design, and exactly-once semantics.
LinkedIn uses Kafka extensively. Explain how you would design the Kafka topic structure for LinkedIn's activity events (profile view, connection request, message sent). Cover: topic naming, partitioning strategy, consumer group design, and exactly-once semantics.
Design LinkedIn's InMail spam filtering system. Premium users can message strangers. How do you detect and block spam campaigns while allowing legitimate cold outreach? Cover: sender reputation, content analysis, and feedback loops.
Design LinkedIn's InMail spam filtering system. Premium users can message strangers. How do you detect and block spam campaigns while allowing legitimate cold outreach? Cover: sender reputation, content analysis, and feedback loops.