Hi Timo, Yeah, that’s a cool idea. Hopefully if this kind of type inference proves useful enough and captures enough of the type usage people need, it could be extended to elsewhere to make working with types in PyFlink feel a bit more natural (unless you need to be specific and granular).
Mika > On 31. Aug 2026, at 17:47, Timo Theusner via dev <[email protected]> wrote: > > Thank you Mika for the FLIP! I like the proposal and it would improve the > current PyFlink experience. > As a follow-up, we could think about other use cases for the type hints. > For example, when building Schemas importing `pyflink.table.typehints` and > `pyflink.table.DataTypes` might be confusing. > > Br, > Timo > > On Fri, Aug 28, 2026 at 4:17 AM Zander Matheson <[email protected] > <mailto:[email protected]>> > wrote: > >> This would be a significantly improved devex and I love the idea! Thanks >> for proposing, Mika. >> >> On Wed, Aug 26, 2026 at 3:45 AM Mika Naylor <[email protected]> wrote: >> >>> Hey everyone! >>> >>> I would like to kick off a discussion on FLIP-609: Type Inference for >>> Python User Defined Functions[1]. >>> >>> While working with Python UDFs, I often noticed I was doing some >> duplicate >>> effort around type hinting - in one place >>> so the planner knows the Flink specific input/output types of the UDF, >> and >>> also on the function itself so I could have >>> an extra layer of checking my type assumptions/flow using type checking >>> tools like mypy. I also noticed that there was >>> a bit of friction in doing this, especially since the form of specifying >>> input types through the UDF constructor was >>> necessarily disconnected from the actual function arguments the types >>> referred to. >>> >>> This FLIP proposes to add a type hints -> Flink types inference layer for >>> UDFs, so that users in ideal cases should >>> only have to annotate their function using native Python type hints, and >>> we can infer the input/output Flink types from >>> those. In more complex cases, where users want to specify a specific >> Flink >>> type rather than a Python type, I also propose >>> to add some shadow types that wrap the Flink types in a corresponding >>> Python type, so that both type checking works, >>> and the Flink specific type hints are bound to the actual arguments, >>> rather than just the argument positions via the udf >>> decorator. So that a user could do the following: >>> >>> from dataclasses import dataclass >>> from typing import Optional >>> from pyflink.table import udf >>> from pyflink.table.typehints import TinyInt, SmallInt, Decimal >>> >>> Money = Decimal(18, 2) >>> >>> @dataclass >>> class PricingResult: >>> final_price: Money >>> discount_applied: bool >>> tier: TinyInt >>> >>> @udf() >>> def apply_discount( >>> price: Money, >>> discount_pct: Optional[SmallInt], >>> tier: TinyInt, >>> ) -> PricingResult: >>> pct = discount_pct or 0 >>> discount = price * pct / 100 >>> return PricingResult( >>> final_price=price - discount, >>> discount_applied=pct > 0, >>> tier=tier, >>> ) >>> >>> Would love any thoughts or feedback the community might have on this >>> proposal! >>> >>> Kind regards, >>> Mika Naylor >>> >>> [1] >>> >> https://urldefense.com/v3/__https://cwiki.apache.org/confluence/spaces/FLINK/pages/449286339/FLIP-609*Type*Inference*for*Python*User*Defined*Functions__;KysrKysrKw!!Ayb5sqE7!pS7rgUV6JteB7ZRfw1GM-bBQkZZJPcmPH8jGatn0u52fueih7VIxW_v3HuZxne1FGTT7Uh21ofDzmpPtOU1IX01Dig$ >>> >>> >>> >> > > > -- > > Timo Theusner > > Senior Software Engineer > > [email protected] <mailto:[email protected]> > > <https://confluent.io <https://confluent.io/>> > > Follow us: [image: RSS] > <https://www.confluent.io/blog/?utm_source=footer&utm_medium=email&utm_campaign=ch.email-signature_type.community_content.blog>[image: > LinkedIn] > <https://www.linkedin.com/company/confluent/posts/?feedView=all>[image: > YouTube] <https://www.youtube.com/@Confluent>[image: Confluent Slack > Community] > <https://confluentcommunity.slack.com/join/shared_invite/zt-3az13rpkx-CMUmebJjiFwCn65VOaSEPA#/shared-invite/email> > > <https://confluent.io/data-monster>
