Hi Aljoscha, Thank you for your valuable comments! I agree with you that there is some optimization space for existing API and can be applied to the python DataStream API implementation.
According to your comments, I have concluded them into the following parts: 1. SingleOutputStreamOperator and DataStreamSource. Yes, the SingleOutputStreamOperator and DataStreamSource are a bit redundant, so we can unify their APIs into DataStream to make it more clear. 2. The internal or low-level methods. - DataStream.get_id(): Has been removed in the FLIP wiki page. - DataStream.partition_custom(): Has been removed in the FLIP wiki page. - SingleOutputStreamOperator.can_be_parallel/forceNoParallel: Has been removed in the FLIP wiki page. Sorry for mistakenly making those internal methods public, we would not expose them to users in the Python API. 3. "declarative" Apis. - KeyedStream.sum/min/max/min_by/max_by: Has been removed in the FLIP wiki page. They could be well covered by Table API. 4. Spelling problems. - StreamExecutionEnvironment.from_collections. Should be from_collection(). - StreamExecutionEnvironment.generate_sequenece. Should be generate_sequence(). Sorry for the spelling error. 5. Predefined source and sink. As you said, most of the predefined sources are not suitable for production, we can ignore them in the new Python DataStream API. There is one exception that maybe I think we should add the print() since it is commonly used by users and it is very useful for debugging jobs. We can add comments for the API that it should never be used for production. Meanwhile, as you mentioned, a good alternative that always prints on the client should also be supported. For this case, maybe we can add the collect method and return an Iterator. With the iterator, uses can print the content on the client. This is also consistent with the behavior in Table API. 6. For Row. Do you mean that we should not expose the Row type in Python API? Maybe I haven't gotten your concerns well. We can use tuple type in Python DataStream to support Row. (I have updated the example section of the FLIP to reflect the design.) Highly appreciated for your suggestions again. Looking forward to your feedback. Best, Shuiqiang Aljoscha Krettek <aljos...@apache.org> 于2020年7月15日周三 下午5:58写道： > Hi, > > thanks for the proposal! I have some comments about the API. We should not > blindly copy the existing Java DataSteam because we made some mistakes with > that and we now have a chance to fix them and not forward them to a new API. > > I don't think we need SingleOutputStreamOperator, in the Scala API we just > have DataStream and the relevant methods from SingleOutputStreamOperator > are added to DataStream. Having this extra type is more confusing than > helpful to users, I think. In the same vain, I think we also don't need > DataStreamSource. The source methods can also just return a DataStream. > > There are some methods that I would consider internal and we shouldn't > expose them: > - DataStream.get_id(): this is an internal method > - DataStream.partition_custom(): I think adding this method was a mistake > because it's to low-level, I could be convinced otherwise > - DataStream.print()/DataStream.print_to_error(): These are questionable > because they print to the TaskManager log. Maybe we could add a good > alternative that always prints on the client, similar to the Table API > - DataStream.write_to_socket(): It was a mistake to add this sink on > DataStream it is not fault-tolerant and shouldn't be used in production > > - KeyedStream.sum/min/max/min_by/max_by: Nowadays, the Table API should > be used for "declarative" use cases and I think these methods should not be > in the DataStream API > - SingleOutputStreamOperator.can_be_parallel/forceNoParallel: these are > internal methods > > - StreamExecutionEnvironment.from_parallel_collection(): I think the > usability is questionable > - StreamExecutionEnvironment.from_collections -> should be called > from_collection > - StreamExecutionEnvironment.generate_sequenece -> should be called > generate_sequence > > I think most of the predefined sources are questionable: > - fromParallelCollection: I don't know if this is useful > - readTextFile: most of the variants are not useful/fault-tolerant > - readFile: same > - socketTextStream: also not useful except for toy examples > - createInput: also not useful, and it's legacy DataSet InputFormats > > I think we need to think hard whether we want to further expose Row in our > APIs. I think adding it to flink-core was more an accident than anything > else but I can see that it would be useful for Python/Java interop. > > Best, > Aljoscha > > > On Mon, Jul 13, 2020, at 04:38, jincheng sun wrote: > > Thanks for bring up this DISCUSS Shuiqiang! > > > > +1 for the proposal! > > > > Best, > > Jincheng > > > > > > Xingbo Huang <hxbks...@gmail.com> 于2020年7月9日周四 上午10:41写道： > > > > > Hi Shuiqiang, > > > > > > Thanks a lot for driving this discussion. > > > Big +1 for supporting Python DataStream. > > > In many ML scenarios, operating Object will be more natural than > operating > > > Table. > > > > > > Best, > > > Xingbo > > > > > > Wei Zhong <weizhong0...@gmail.com> 于2020年7月9日周四 上午10:35写道： > > > > > > > Hi Shuiqiang, > > > > > > > > Thanks for driving this. Big +1 for supporting DataStream API in > PyFlink! > > > > > > > > Best, > > > > Wei > > > > > > > > > > > > > 在 2020年7月9日，10:29，Hequn Cheng <he...@apache.org> 写道： > > > > > > > > > > +1 for adding the Python DataStream API and starting with the > stateless > > > > > part. > > > > > There are already some users that expressed their wish to have the > > > Python > > > > > DataStream APIs. Once we have the APIs in PyFlink, we can cover > more > > > use > > > > > cases for our users. > > > > > > > > > > Best, Hequn > > > > > > > > > > On Wed, Jul 8, 2020 at 11:45 AM Shuiqiang Chen < > acqua....@gmail.com> > > > > wrote: > > > > > > > > > >> Sorry, the 3rd link is broken, please refer to this one: Support > > > Python > > > > >> DataStream API > > > > >> < > > > > >> > > > > > > > > https://docs.google.com/document/d/1H3hz8wuk22-8cDBhQmQKNw3m1q5gDAMkwTDEwnj3FBI/edit > > > > >>> > > > > >> > > > > >> Shuiqiang Chen <acqua....@gmail.com> 于2020年7月8日周三 上午11:13写道： > > > > >> > > > > >>> Hi everyone, > > > > >>> > > > > >>> As we all know, Flink provides three layered APIs: the > > > > ProcessFunctions, > > > > >>> the DataStream API and the SQL & Table API. Each API offers a > > > different > > > > >>> trade-off between conciseness and expressiveness and targets > > > different > > > > >> use > > > > >>> cases[1]. > > > > >>> > > > > >>> Currently, the SQL & Table API has already been supported in > PyFlink. > > > > The > > > > >>> API provides relational operations as well as user-defined > functions > > > to > > > > >>> provide convenience for users who are familiar with python and > > > > relational > > > > >>> programming. > > > > >>> > > > > >>> Meanwhile, the DataStream API and ProcessFunctions provide more > > > generic > > > > >>> APIs to implement stream processing applications. The > > > ProcessFunctions > > > > >>> expose time and state which are the fundamental building blocks > for > > > any > > > > >>> kind of streaming application. > > > > >>> To cover more use cases, we are planning to cover all these APIs > in > > > > >>> PyFlink. > > > > >>> > > > > >>> In this discussion(FLIP-130), we propose to support the Python > > > > DataStream > > > > >>> API for the stateless part. For more detail, please refer to the > FLIP > > > > >> wiki > > > > >>> page here[2]. If interested in the stateful part, you can also > take a > > > > >>> look the design doc here[3] for which we are going to discuss in > a > > > > >> separate > > > > >>> FLIP. > > > > >>> > > > > >>> Any comments will be highly appreciated! > > > > >>> > > > > >>> [1] > https://flink.apache.org/flink-applications.html#layered-apis > > > > >>> [2] > > > > >>> > > > > >> > > > > > > > > https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=158866298 > > > > >>> [3] > > > > >>> > > > > >> > > > > > > > > https://docs.google.com/document/d/1H3hz8wuk228cDBhQmQKNw3m1q5gDAMkwTDEwnj3FBI/edit?usp=sharing > > > > >>> > > > > >>> Best, > > > > >>> Shuiqiang > > > > >>> > > > > >>> > > > > >>> > > > > >>> > > > > >> > > > > > > > > > > > > > >
