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.

14m read timeFrom blog.bytebytego.com
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Supercharge Cursor and Claude with your team’s knowledge (Sponsored)Help us Make ByteByteGo Newsletter BetterChallengesArchitecture Deep DivePyFlink: Python-Friendly StreamingKafka Connector DesignHigh-Availability and FailoverConclusionSPONSOR US

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