Sync-over-Async: Bypassing Azure Service Bus Session Limits for AI Workloads
This article discusses a pattern to decouple slow AI workloads from legacy HTTP clients using Azure Service Bus without running into stateful bottlenecks.
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Why it matters
This pattern is crucial for building scalable, fault-tolerant architectures that can handle the demands of AI/ML workloads on legacy infrastructure.
Key Points
- 1Standard REST APIs are not designed for slow AI workloads, leading to issues like 504 Gateway Timeouts
- 2The
- 3 pattern uses a message broker to bridge synchronous HTTP requests to asynchronous backend processing
- 4Using Azure Service Bus Sessions causes scalability issues due to stateful routing across a distributed cluster
- 5The
- 6 pattern solves this by using dynamic subscriptions and explicit addressing to achieve horizontal scalability
Details
The article discusses the challenges of integrating slow AI workloads (like large language models) with legacy HTTP clients and infrastructure. Standard REST APIs are designed for speed, but AI tasks can take 45+ seconds to complete, leading to issues like 504 Gateway Timeouts and thundering herd problems. \n\nTo decouple the slow AI processing from the synchronous HTTP layer, the author introduces the
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