Hi everyone, I'd like to start a discussion on an umbrella FLIP[1] that lays out a direction for evolving Flink into a data engine that natively supports AI workloads.
The short version: user workloads are shifting from BI analytics to multimodal data processing centered on model inference, and this triggers cascading changes across the stack — multimodal data flowing through pipelines, heterogeneous CPU/GPU resources, vectorized execution, and inference tasks that run for seconds to minutes on Spot instances. The proposal sketches an evolution along five directions (development paradigm, data model, heterogeneous resources, execution engine, fault tolerance), decomposed into 11 sub-FLIPs organized into three layers: core runtime primitives, AI workload expression and execution, and production-grade operational guarantees. Most sub-FLIPs have no hard dependencies on each other and can be advanced in parallel. A note on scope, since it's an umbrella: - In scope here: whether the evolution directions are reasonable, whether each sub-FLIP's motivation and proposed approach are well-founded, and whether the boundaries and dependencies between sub-FLIPs are clear. - Out of scope here: detailed designs, API specifics, and implementation plans of individual sub-FLIPs — those will go through their own FLIPs. - Consensus criteria: agreement on the overall direction is sufficient for the umbrella to pass; passing it does not lock in any sub-FLIP's design — sub-FLIPs may still be adjusted, deferred, or withdrawn as they progress. All proposed changes are incremental — no existing API or behavior is removed or altered. Compatibility details are covered at the end of the document. Looking forward to your feedback on the overall direction and the layering. [1] https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=421957275 Thanks, Guowei
