Hi FangYong, Thanks for the comments. These are very good points, especially for multimodal / AI-oriented workloads.
1. For multimodal read/write APIs, the current intent is to keep this FLIP focused on the general DataFrame API surface, while using read_custom / write_custom as the escape hatch for connectors or formats that do not yet have dedicated helpers. For multimodal data specifically, I totally agree with you that more explicit APIs should be introduced, e.g. read_video_frames, etc. You may have noticed that currently multimodal data-types are also not included in this FLIP. I'd like to make it a separate FLIP for multimodal data support (once multimodal data-types support is ready which are under-discussion in FLIP-589 and FLIP-590). 2. For map_batches, I agree with your point. For the initial FLIP, we currently keep batch_size for simplicity. I think introducing memory-budget-based batching is a good direction which may be worth a separate FLIP to make sure it's well designed. 3. GPU resource configuration is also a very good point: It depends on some work from Yi Zhang. However, I believe we will introduce GPU support in Python DataFrame API soon. 4. For class-based processors in map / map_batches: It's planned to be supported. I have added examples in the corresponding sections. Regards, Dian On Thu, Jun 18, 2026 at 10:25 PM Dian Fu <[email protected]> wrote: > > Hi Zander, > > Thanks for the feedback! > > Replies inline. > > Relationship to FLIP-541: Good point. FLIP-541 took the approach of > evolving the Table API itself toward a more DataFrame-like style. In > practice, though, I found that it turned out to be hard to push > forward: the Table API already has a large API surface, and for > compatibility reasons the DataFrame-style additions would have to > coexist with the existing Table-style APIs. Mixing the two styles in > one API tends to make things more confusing for users rather than > less. So I think a more viable direction may be to keep the Table API > positioned as the API aligned with Flink's existing relational/Table > model and introduce a separate, dedicated DataFrame API alongside it. > That keeps each API internally consistent — Table API for the > Flink-native relational model, DataFrame API for the Python/DataFrame > mental model — instead of blending both into one surface. > > API parity: My overall take is that we should adopt a DataFrame-style > API design rather than 100% following Pandas DataFrame API. This is > also the direction taken by the newer generation of Python data > projects such as Polars, Daft, and Ray Data: they share the familiar > DataFrame mental model and ergonomics, but each adapts the API to its > own execution engine and semantics rather than mirroring pandas > exactly. We follow the same philosophy here — borrow the > widely-adopted ergonomics, but stay consistent with Flink's > streaming/distributed semantics. > > On the specific points: > 1. dropna/fillna vs drop_null/fill_null: I'd lean toward keeping > drop_null/fill_null. Flink's type system has a real NULL (and NaN is a > distinct float value) and actually Python has None, so the FLIP > separates them: drop_null/fill_null for NULL and drop_nan/fill_nan for > NaN. The pandas dropna name conflates the two. Polars made the same > NULL/NaN split with drop_nulls/fill_null + fill_nan, so this also has > precedent. > 2. show() / display(): Good point. Peeking at data is essential. Have > added show, __repr__, _repr_html_ and _repr_mimebundle_ in the section > "DataFrame — Conversion & Execution". > 3. sort(by, descending=False): I kept descending= rather than > ascending=. The default is ascending in all cases, matching > pandas/PySpark/Polars/Daft behavior — the only difference is the flag > name, and the flag name descending aligns with Polars/Daft/Ray. While > Pyspark/Pandas takes ascending as the flag name. For me, I slightly > tend to align with Polars/Daft/Ray since our motivation is multimodal > data processing and our users will be familiar with these projects. > However, I'm open to this. > 4. map() element-wise vs row-wise: You're right that pandas > DataFrame.map is element-wise (it's the renamed applymap), while ours > is row-wise. We intentionally follow the Ray Data model here (map = > per-row, map_batches = vectorized), which fits the enrichment/ML use > cases. The name `apply` isn't paired with map_batches and so I tend to > use `map` here. > 5. Index: Yes, this is deliberate — the API has no implicit > pandas-style index. Flink is a distributed/streaming engine with no > global row order, so a positional index isn't well-defined. Row/column > selection is done by column reference and boolean filtering instead. > 6. in_place: DataFrames here are lazy logical plans, not mutable > in-memory buffers, so every operation returns a new DataFrame — > there's nothing to mutate in place. > 7. to_json/from_json and .values(): JSON is supported for > sources/sinks via read_json/write_json. Is that what you want? > 8. Properties (df.shape/df.info/df.describe): Have added `describe()` > in section "13. DataFrame — Conversion & Execution". Regarding > `df.shape and df.info`: I left out for now since it will force data > processing for `df.shape` which seems not that useful and schema > inspection is already available via df.schema. > > Execution model / low-level trigger: I like the escape-hatch idea. > Today an execution can be triggered via > to_pandas()/to_list()/collect()/show(). Could you elaborate on what > specific API you have in mind? > > EDA / common use cases: The primary use cases I envision are ML/AI > enrichment (this is to support FLIP-577: AI-Native Flink — An Umbrella > Proposal for Multimodal Data Processing). EDA is supported > (show/describe/to_pandas) but I'd keep the heavier EDA helpers minimal > and grow them based on demand. > > Thanks again and looking forward to collaborating! > > Regards, > Dian > > On Thu, Jun 18, 2026 at 8:50 PM Dian Fu <[email protected]> wrote: > > > > Hi Cole, > > > > Thanks for the feedback. Both suggestions make sense for me, and I've > > updated the FLIP. > > > > 1. kwargs aliasing in .agg: see section "6. DataFrame — Aggregation" > > 2. Attribute-style column references: see section "15. DataFrame — > > Column Access". > > > > Thanks again! > > > > Regards, > > Dian > > > > On Thu, Jun 18, 2026 at 11:06 AM Yong Fang <[email protected]> wrote: > > > > > > Thanks Fu Dian for initiating this discussion and +1 for the total design. > > > > > > I have several comments for this flip: > > > > > > 1. In multi-modal data processing scenarios, the operations are mostly > > > reading and writing files, images, audio and video. From the current API > > > perspective, these operations can only be added manually in read_custom > > > and > > > write_custom, how are the read/write APIs for these types designed? > > > > > > 2. Currently, batch_size is controlled by row count in map_batches. > > > However, the per-row size of multimodal data varies dramatically — a > > > single > > > 4K image can be up to 20 MB, while a piece of text may only be 100 B. > > > Splitting batches purely by row count may lead to OOM. Should we support > > > splitting batches by memory budget too? > > > > > > 3. GPU computing is a common requirement in multimodal processing, I don't > > > seem to see any related information in this set of APIs such as > > > map/map_batches and ect. How can we set GPU/CPU resources and > > > specifications for them and udfs? > > > > > > 4. In addition, users may need to load local models within map/map_batches > > > for data processing. The current APIs only support the callback format. > > > Should class types also be supported? This way, I/O and other operations > > > only need to be executed once for a class instance. > > > > > > Best, > > > FangYong > > > > > > On Thu, Jun 18, 2026 at 7:13 AM Zander Matheson <[email protected]> > > > wrote: > > > > > > > Very nice proposal, Dian Fu, thank you for putting this together! > > > > > > > > It feels like it supersedes FLIP-541 > > > > < > > > > https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=378473217 > > > > > > > > > since > > > > it will solve many of the problems we initially discussed related to > > > > making > > > > Flink more Pythonic. > > > > > > > > I had some questions and comments on the flip shared below. Overall I > > > > really like the design you have proposed and look forward to > > > > collaborating > > > > hopefully! > > > > > > > > *Pandas/PySpark/Polars API Parity* > > > > What are your thoughts on parity with some of the established APIs. I > > > > see > > > > it is mentioned in the FLIP to both lower the barrier to entry for > > > > Python > > > > users who are familiar with Dataframe APIs, while also having a > > > > non-goal of > > > > providing full compatibility with Pandas, Polars etc. Below are a > > > > couple of > > > > dataframe api common that we could potentially more closely align to. > > > > > > > > 1. dropna and fillna instead of drop_null/fill_null. Python does not > > > > have a Null concept, so it might make sense to keep the drop_na used > > > > in > > > > other dataframe libraries? > > > > 2. show(), .display(). How to peek at the data? This is common > > > > patter in > > > > development > > > > 3. sort(by, descending=False). This boolean flag is opposite what is > > > > used in other libraries. > > > > 4. map() in Dataframe land is an element-wise computation, do we > > > > want to > > > > break with that? I believe apply() is more similar. That being said, > > > > map as > > > > intended from map reduce is more akin to the row-wise model shown. > > > > More > > > > a > > > > question than anything else. > > > > 5. Index - I didn’t see any mention to indices in the document, is > > > > this > > > > something we are explicitly avoiding? I have often use indices for > > > > selecting groups of rows or columns for manipulation. > > > > 6. In_place - this may not be possible with the execution style, but > > > > is > > > > there a possibility of having in_place booleans for things like > > > > unique > > > > or > > > > sort where the output can either be done in place or the output needs > > > > to be > > > > assigned to a new dataframe. > > > > 7. to_json/from_json and .values(). > > > > 8. Properties - Do we want to add some of the niceties of > > > > spark/pandas > > > > here if possible? df.shape, df.info, df.describe > > > > > > > > > > > > *Execution Model*I like the idea of having triggers for the execution on > > > > write and materialization (to_pandas etc.). This is probably the meat of > > > > the problem for creating a dataframe API that feels like the other > > > > commonly > > > > used dataframe APIs. The balance between write and providing the > > > > statement_set to make the writes coordinated seems nice. But is there > > > > also > > > > potentially a world where we want to expose the ability to arbitrarily > > > > trigger an execution? Say for Exploratory Data Analysis in a notebook? > > > > This > > > > seems possible with to_pandas and to_list etc., but maybe having a lower > > > > level primitive that could be called could be a good escape hatch? > > > > > > > > > > > > *Exploratory Data Analysis*One of the biggest use cases for dataframes > > > > is > > > > exploratory data analysis. Is this something we want to encourage with > > > > this > > > > FLIP? It might make sense to add some of the EDA methods for that > > > > purpose. > > > > See above, show/display and execution trigger. > > > > > > > > *Common Use Cases* > > > > Related to the EDA note above, I am curious what you envision as the > > > > most > > > > common use cases for this API. > > > > > > > > Thanks, > > > > > > > > Zander > > > > > > > > On Wed, Jun 17, 2026 at 2:42 AM Cole Bailey via dev > > > > <[email protected]> > > > > wrote: > > > > > > > > > Thanks Dian, > > > > > > > > > > There is a lot of good work here that aligns with what we have been > > > > > brainstorming for a better PyFlink experience. > > > > > > > > > > One ergonomic suggestion I'd love to see included is supporting > > > > > pythonic > > > > > aliasing via kwargs within `.agg` similar to what is already outlined > > > > > in > > > > > `with_columns` or `select`: > > > > > > > > > > The example would instead look like this: > > > > > > > > > > df.group_by("dept").agg( > > > > > avg_salary=col("salary").avg(), > > > > > headcount=col("id").count(), > > > > > ) > > > > > > > > > > > > > > > Another nice-to-have would be flexibility in column referencing, I > > > > > see 2 > > > > > variations scattered throughout the FLIP: > > > > > > > > > > col("age") > > > > > > > > > > df["age"] > > > > > > > > > > > > > > > Both of these make sense, I think we should also consider supporting > > > > > attr > > > > > style column references since these can be reused across the lambda or > > > > > subscript filtering examples already in the FLIP: > > > > > > > > > > df.age > > > > > > > > > > > > > > > That would then give us this representative example: > > > > > > > > > > df.group_by("dept").agg( > > > > > avg_salary=df.salary.avg(), > > > > > headcount=df.id.count(), > > > > > ) > > > > > > > > > > > > > > > Cheers, > > > > > Cole > > > > > > > > > > > > > > > > > > > > On Wed, Jun 17, 2026 at 6:12 AM Dian Fu <[email protected]> wrote: > > > > > > > > > > > Hi all, > > > > > > > > > > > > I would like to start a discussion about FLIP-591: Introducing > > > > > > Python > > > > > > DataFrame API in PyFlink [1]. > > > > > > > > > > > > This FLIP is to a sub-FLIP of the broader direction discussed in > > > > > > FLIP-577 (AI-Native Flink — An Umbrella Proposal for Multimodal Data > > > > > > Processing) [2]. This FLIP proposes a new public Python module, > > > > > > `pyflink.dataframe`, as a DataFrame-style API on top of the existing > > > > > > PyFlink Table API. The goal is not to introduce a new execution > > > > > > model, > > > > > > but to provide a more natural Python-facing entry point for users > > > > > > coming from the broader Python data ecosystem, while preserving > > > > > > Flink > > > > > > semantics and execution capabilities. > > > > > > > > > > > > The proposal focuses on: > > > > > > - Designing a Python-friendly DataFrame API for PyFlink, > > > > > > including > > > > > > the API shape itself, a more user-friendly DataType design, unified > > > > > > configuration, reduced TableEnvironment boilerplate, and a practical > > > > > > multiple-sink model for end-to-end pipelines > > > > > > - Providing ergonomic support for row-oriented Python > > > > > > transformations, including map / map_batches style operations for > > > > > > enrichment, feature engineering, and AI/ML workloads > > > > > > - Exposing concurrency configuration so that expensive Python > > > > > > stages can be scaled independently, making it easier to build > > > > > > practical jobs directly with the DataFrame API > > > > > > - Supporting Arrow as a first-class batch format for efficient > > > > > > interoperability with the Python ecosystem > > > > > > > > > > > > The Design Decisions section discusses the main design > > > > > > considerations > > > > > > behind the proposal and may be a useful place to pay extra attention > > > > > > when reviewing it. > > > > > > > > > > > > Looking forward to your feedback! > > > > > > > > > > > > Regards, > > > > > > Dian > > > > > > > > > > > > [1] > > > > > > > > > > > > > > > https://urldefense.com/v3/__https://cwiki.apache.org/confluence/display/FLINK/FLIP-591*3A*Introducing*Python*DataFrame*API*in*PyFlink__;JSsrKysrKw!!Ayb5sqE7!t1Rd2wTS8LFWmjw7srNbCUz4lZ5NXo__BnGTzGFeJ5BeO4T4tOCZ1hCNysc10NKuLUuegGThcr5ksMSizWPE4Qo$ > > > > > > [2] > > > > > > > > > > > > > > > https://urldefense.com/v3/__https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=421957275__;!!Ayb5sqE7!t1Rd2wTS8LFWmjw7srNbCUz4lZ5NXo__BnGTzGFeJ5BeO4T4tOCZ1hCNysc10NKuLUuegGThcr5ksMSiab5Dp28$ > > > > > > > > > > > > > > >
