[System Design Tech Case Study Pulse #52] Facebook's News Feed Algorithm Marvel: How it Serves 2.9 Billion Daily Active Users Using PyTorch and Cassandra
Facebook's News Feed Algorithm is a engineering marvel, capable of serving personalized content to 2.9 billion daily active users with less than 50ms latency, leveraging PyTorch for machine learning and Cassandra for data storage. This sophisticated system forms the core of Facebook's user experience, delivering relevant and engaging content to users in real time.
Let me deep into how this system works, exploring the key components, technologies, and processes that enable such massive scale, low latency content delivery.
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System Overview
Daily Active Users (DAU): 2.9 billion
Posts processed daily: 4+ billion
Peak requests per second: 10 million+
Average feed generation time: < 50ms
ML model inference time: < 10ms
Cassandra read latency: < 5ms for 99% of queries
PyTorch models in production: 1,000+
Features considered per post: 100,000+
Data points processed daily: 100+ trillion
Global data centers: 15+
Edge locations: 100+
System availability: 99.99%
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