In this article, we will understand how OpenAI built such a system and the challenges they faced.

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OpenAI built a stream processing platform using Apache Flink (PyFlink) on Kubernetes to handle real-time data for AI model training and experimentation. The architecture addresses three key challenges: providing Python-first APIs for ML practitioners, handling cloud capacity constraints, and managing multi-primary Kafka clusters. The system features a control plane for multi-cluster failover, per-namespace isolation in Kubernetes, watchdog services for Kafka topology monitoring, and decoupled state management using RocksDB with highly available blob storage. Custom Kafka connectors enable reading from multiple primary clusters simultaneously while maintaining resilience during outages.

How OpenAI Uses Kubernetes And Apache Kafka for GenAI

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