How LinkedIn Scaled User Restriction System to 5 Million Queries Per Second

LinkedIn's Community Abuse and Safety Application Layer (CASAL) is a multi-layered system designed to enforce user restrictions and maintain a safe, professional environment. Over three generations, LinkedIn evolved its enforcement infrastructure from a relational database-based system to a highly scalable, low-latency solution leveraging NoSQL databases, distributed caching, and advanced frameworks like DaVinci and Venice. The system now handles 5 million queries per second with ultra-low latency (<5 ms) and high availability (99.999%), ensuring real-time synchronization and efficient memory usage.

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Core Technical Concepts/Technologies Discussed

1. CASAL (Community Abuse and Safety Application Layer)
2. Machine Learning (ML) Models
3. Rule-Based Systems
4. Relational Databases (Oracle)
5. Server-Side and Client-Side Caching
6. Bloom Filters
7. NoSQL Distributed Systems (Espresso)