LinkedIn's restriction enforcement system, CASAL, combines machine learning models, rule-based systems, and human reviews to prevent abuse and ensure a safe environment. Initially reliant on relational databases, LinkedIn faced challenges in scaling, leading to the adoption of server-side and client-side caching, and eventually NoSQL systems like Espresso. The third generation introduced off-heap memory and tools like DaVinci and Venice to enhance performance and resilience. Each iteration focused on improving scalability, reducing latency, and maintaining data consistency while supporting a growing user base.
2 Comments
Sort: