Hi Guowei, This is an interesting proposal. I second Roberts questions. Some thoughts.
Layer 3 does not depend on layers 1 and 2 I think. At the high level I wonder, is the idea that Flink could become like an R ML pipeline or SPSS? It would be good to compare existing technology solutions and what benefits Flink will bring to these scenarios. FLIP-XXX: Supporting RpcOperator — Independently Deployed and Scaled RPC Service Operators - see Robert's comment. I assume this is a specialization of the async io operator for RPC. When you say deploying RPC services that are fully managed by the Flink runtime, where would these be deployed? If it is remote how would this work? It would be interesting to see some use cases where Flink would be deploying RPC services that it has created. FLIP-XXX: Multimodal Data Type System and Object Reference Mechanism I like the idea of adding these types - the interesting part will be the deser. FLIP-XXX: A More Pythonic DataFrame API for Python Users - this makes sense FLIP-XXX: Connector API for Multimodal Data Source/Sink - I assume this will be renamed new multimodal formats. Are there existing registries that these could be looked in - similar to schema registry - so we can bring in artifacts via metadata? FLIP-XXX: Built-in Multimodal Operators and AI Functions - I wonder if we could bring in existing implementation libraries and the new work would allow us to call them from Flink. i.e. not having to do them one call by call but library by library. FLIP-XXX: Columnar Data Transport and Processing Optimization - this seems a big change, events as columns rather than events as rows or CDC sequences. I assume this would not be exposed in SQL? kind regards, David. From: Robert Metzger <[email protected]> Date: Tuesday, 28 April 2026 at 07:38 To: [email protected] <[email protected]> Subject: [EXTERNAL] Re: [DISCUSS] FLIP-577: AI-Native Flink — An Umbrella Proposal for Multimodal Data Processing Hey Guowei, Thanks for the proposal. I just took a brief look, here are some high level questions: Regarding the RPC Operator: What is the difference to the async io operator we have already? "Connector API for Multimodal Data Source/Sink": Why do we need to touch the connector API for supporting multimodal data? Isn't this more of a formats concern? "Non-Disruptive Scaling for CPU Operators": How do you want to guarantee exactly-once on that kind of scaling? E.g. you need to somehow make a handover between the old and new new pipeline Overall, I find the proposal has some things which seem related to making Flink more AI native, but other changes seem orthogonal to that. For example the checkpoint or scaling changes are actually unrelated to AI, and just engine improvements. On Tue, Apr 28, 2026 at 5:48 AM Guowei Ma <[email protected]> wrote: > 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 > Unless otherwise stated above: IBM United Kingdom Limited Registered in England and Wales with number 741598 Registered office: Building C, IBM Hursley Office, Hursley Park Road, Winchester, Hampshire SO21 2JN
