Re: [DISCUSS] FLIP-38 Support python language in flink TableAPI

[Robert Bradshaw]

On Thu, Apr 25, 2019 at 6:04 AM jincheng sun  wrote:
>
> Hi Robert,
>
> In addition to the questions described by Dian, I also want to know what 
> difficult problems Py4j's solution will encounter in add UDF support, which 
> you mentioned as follows:
>
>> Using something like Py4j is an easy way to get up an running, especially 
>> for a very faithful API, but the instant one wants to add UDFs one hits a 
>> cliff of sorts (which is surmountable, but likely a lot harder than having 
>> gone the above approach).
>
> I appreciate if you can share more specific cases?

The orchestration involved in supporting UDFs is non-trivial. I think
it is true that a lot of effort can be saved by re-using significant
portions of the design, concepts, and even implementation we already
have for Beam, but still re-building it out of the individual pieces
(likely necessitated due to Py4j having hooked in at lower than the
DAG level) is likely harder (initially and on-going) than simply
leveraging the complete, working package.

> Dian Fu  于2019年4月25日周四 上午11:53写道：
>>
>> Thanks everyone for the discussion here.
>>
>> Regarding to the Java/Scala UDF and the built-in UDF to execute in the 
>> current Flink way (directly in JVM, not via RPC), I share the same thoughts 
>> with Max and Robert and I think it will not be a big problem. From the 
>> design doc, I guess the main reason to take the Py4J way instead of the DAG 
>> way at present is that DAG has some limitations in some scenarios such as 
>> interactive programing which may be a strong requirement for data scientist.
>>
>> > In addition (and I'll admit this is rather subjective) it seems to me one 
>> > of the primary values of a table-like API in a given language (vs. just 
>> > using (say) plain old SQL itself via a console) is the ability to embed it 
>> > in a larger pipeline, or at least drop in operations that are not (as) 
>> > naturally expressed in the "table way," including existing libraries. In 
>> > other words, a full SDK. The Py4j wrapping doesn't extend itself to such 
>> > integration nearly as easily.
>>
>>
>> Hi Robert, regarding to "a larger pipeline", do you mean translating a 
>> table-like API jobs from/to another kind of API job or embedding third-part 
>> libraries into a table-like API jobs via UDF? Could you kindly explain why 
>> this would be a problem for Py4J and will not be a problem if expressing the 
>> job with DAG?
>>
>> Thanks,
>> Dian
>>
>>
>> > 在 2019年4月25日，上午12:16，Robert Bradshaw  写道：
>> >
>> > Thanks for the meeting summary, Stephan. Sound like you covered a lot of 
>> > ground. Some more comments below, adding onto what Max has said.
>> >
>> > On Wed, Apr 24, 2019 at 3:20 PM Maximilian Michels > > > wrote:
>> > >
>> > > Hi Stephan,
>> > >
>> > > This is excited! Thanks for sharing. The inter-process communication
>> > > code looks like the most natural choice as a common ground. To go
>> > > further, there are indeed some challenges to solve.
>> >
>> > It certainly does make sense to share this work, though it does to me seem 
>> > like a rather low level to integrate at.
>> >
>> > > > => Biggest question is whether the language-independent DAG is 
>> > > > expressive enough to capture all the expressions that we want to map 
>> > > > directly to Table API expressions. Currently much is hidden in opaque 
>> > > > UDFs. Kenn mentioned the structure should be flexible enough to 
>> > > > capture more expressions transparently.
>> > >
>> > > Just to add some context how this could be done, there is the concept of
>> > > a FunctionSpec which is part of a transform in the DAG. FunctionSpec
>> > > contains a URN and with a payload. FunctionSpec can be either (1)
>> > > translated by the Runner directly, e.g. map to table API concepts or (2)
>> > > run a user-defined function with an Environment. It could be feasible
>> > > for Flink to choose the direct path, whereas Beam Runners would leverage
>> > > the more generic approach using UDFs. Granted, compatibility across
>> > > Flink and Beam would only work if both of the translation paths yielded
>> > > the same semantics.
>> >
>> > To elaborate a bit on this, Beam DAGs are built up by applying Transforms 
>> > (basically operations) to PColections (the equivalent of 
>> > dataset/datastream), but the key point here is that these transforms are 
>> > often composite operations that expand out into smaller subtransforms. 
>> > This expansion happens during pipeline construction, but with the recent 
>> > work on cross language pipelines can happen out of process. This is one 
>> > point of extendability. Secondly, and importantly, this composite 
>> > structure is preserved in the DAG, and so a runner is free to ignore the 
>> > provided expansion and supply its own (so long as semantically it produces 
>> > exactly the same output). These composite operations can be identified by 
>> > arbitrary URNs + payloads, and any runner that does not understand them 

Re: [DISCUSS] FLIP-38 Support python language in flink TableAPI

[Robert Bradshaw]

On Thu, Apr 25, 2019 at 5:59 AM Dian Fu  wrote:
>
> Thanks everyone for the discussion here.
>
> Regarding to the Java/Scala UDF and the built-in UDF to execute in the 
> current Flink way (directly in JVM, not via RPC), I share the same thoughts 
> with Max and Robert and I think it will not be a big problem. From the design 
> doc, I guess the main reason to take the Py4J way instead of the DAG way at 
> present is that DAG has some limitations in some scenarios such as 
> interactive programing which may be a strong requirement for data scientist.

I definitely agree that interactive is  strong requirement for the
data scientist (and others). I don't think this is incompatible with
the DAG model, and something I want to see more of. (For one
exploration, see BeamPython's (still WIP) InteractiveRunner). There
are lots of interesting challenges here (e.g. sampling, partial
results, optimal caching of results vs. re-execution, especially in
the face of fusion) that would be worth working out together.

> In addition (and I'll admit this is rather subjective) it seems to me one of 
> the primary values of a table-like API in a given language (vs. just using 
> (say) plain old SQL itself via a console) is the ability to embed it in a 
> larger pipeline, or at least drop in operations that are not (as) naturally 
> expressed in the "table way," including existing libraries. In other words, a 
> full SDK. The Py4j wrapping doesn't extend itself to such integration nearly 
> as easily.
>
> Hi Robert, regarding to "a larger pipeline", do you mean translating a 
> table-like API jobs from/to another kind of API job or embedding third-part 
> libraries into a table-like API jobs via UDF? Could you kindly explain why 
> this would be a problem for Py4J and will not be a problem if expressing the 
> job with DAG?

I'm talking about anything one would want to do after
tableEnv.toDataSet() or before tableEnv.registerTable(...). Unless you
plan on also wrapping the DataSet/DataStream APIs too, which is a much
taller task. Let alone wrapping all the libraries one might want to
use that are built on these APIs.

If this is instead integrated at a higher level, you could swap back
and forth between the new Tables API and the existing Python SDK
(including libraries such as TFX, and cross langauge capabilities)
almost for free.

> 在 2019年4月25日，上午12:16，Robert Bradshaw  写道：
>
> Thanks for the meeting summary, Stephan. Sound like you covered a lot of 
> ground. Some more comments below, adding onto what Max has said.
>
> On Wed, Apr 24, 2019 at 3:20 PM Maximilian Michels  wrote:
> >
> > Hi Stephan,
> >
> > This is excited! Thanks for sharing. The inter-process communication
> > code looks like the most natural choice as a common ground. To go
> > further, there are indeed some challenges to solve.
>
> It certainly does make sense to share this work, though it does to me seem 
> like a rather low level to integrate at.
>
> > > => Biggest question is whether the language-independent DAG is expressive 
> > > enough to capture all the expressions that we want to map directly to 
> > > Table API expressions. Currently much is hidden in opaque UDFs. Kenn 
> > > mentioned the structure should be flexible enough to capture more 
> > > expressions transparently.
> >
> > Just to add some context how this could be done, there is the concept of
> > a FunctionSpec which is part of a transform in the DAG. FunctionSpec
> > contains a URN and with a payload. FunctionSpec can be either (1)
> > translated by the Runner directly, e.g. map to table API concepts or (2)
> > run a user-defined function with an Environment. It could be feasible
> > for Flink to choose the direct path, whereas Beam Runners would leverage
> > the more generic approach using UDFs. Granted, compatibility across
> > Flink and Beam would only work if both of the translation paths yielded
> > the same semantics.
>
> To elaborate a bit on this, Beam DAGs are built up by applying Transforms 
> (basically operations) to PColections (the equivalent of dataset/datastream), 
> but the key point here is that these transforms are often composite 
> operations that expand out into smaller subtransforms. This expansion happens 
> during pipeline construction, but with the recent work on cross language 
> pipelines can happen out of process. This is one point of extendability. 
> Secondly, and importantly, this composite structure is preserved in the DAG, 
> and so a runner is free to ignore the provided expansion and supply its own 
> (so long as semantically it produces exactly the same output). These 
> composite operations can be identified by arbitrary URNs + payloads, and any 
> runner that does not understand them simply uses the pre-provided expansion.
>
> The existing Flink runner operates on exactly this principle, translating 
> URNs for the leaf operations (Map, Flatten, ...) as well as some composites 
> it can do better (e.g. Reshard). It is intentionally easy to 

Re: [DISCUSS] FLIP-38 Support python language in flink TableAPI

[jincheng sun]

Hi Robert,

In addition to the questions described by Dian, I also want to know what
difficult problems Py4j's solution will encounter in add UDF support, which
you mentioned as follows:

Using something like Py4j is an easy way to get up an running, especially
> for a very faithful API, but the instant one wants to add UDFs one hits a
> cliff of sorts (which is surmountable, but likely a lot harder than having
> gone the above approach).


I appreciate if you can share more specific cases?

Thanks,
Jincheng

Dian Fu  于2019年4月25日周四 上午11:53写道：

> Thanks everyone for the discussion here.
>
> Regarding to the Java/Scala UDF and the built-in UDF to execute in the
> current Flink way (directly in JVM, not via RPC), I share the same thoughts
> with Max and Robert and I think it will not be a big problem. From the
> design doc, I guess the main reason to take the Py4J way instead of the DAG
> way at present is that DAG has some limitations in some scenarios such as
> interactive programing which may be a strong requirement for data scientist.
>
> > In addition (and I'll admit this is rather subjective) it seems to me
> one of the primary values of a table-like API in a given language (vs. just
> using (say) plain old SQL itself via a console) is the ability to embed it
> in a larger pipeline, or at least drop in operations that are not (as)
> naturally expressed in the "table way," including existing libraries. In
> other words, a full SDK. The Py4j wrapping doesn't extend itself to such
> integration nearly as easily.
>
>
> Hi Robert, regarding to "a larger pipeline", do you mean translating a
> table-like API jobs from/to another kind of API job or embedding third-part
> libraries into a table-like API jobs via UDF? Could you kindly explain why
> this would be a problem for Py4J and will not be a problem if expressing
> the job with DAG?
>
> Thanks,
> Dian
>
>
> > 在 2019年4月25日，上午12:16，Robert Bradshaw  写道：
> >
> > Thanks for the meeting summary, Stephan. Sound like you covered a lot of
> ground. Some more comments below, adding onto what Max has said.
> >
> > On Wed, Apr 24, 2019 at 3:20 PM Maximilian Michels  > wrote:
> > >
> > > Hi Stephan,
> > >
> > > This is excited! Thanks for sharing. The inter-process communication
> > > code looks like the most natural choice as a common ground. To go
> > > further, there are indeed some challenges to solve.
> >
> > It certainly does make sense to share this work, though it does to me
> seem like a rather low level to integrate at.
> >
> > > > => Biggest question is whether the language-independent DAG is
> expressive enough to capture all the expressions that we want to map
> directly to Table API expressions. Currently much is hidden in opaque UDFs.
> Kenn mentioned the structure should be flexible enough to capture more
> expressions transparently.
> > >
> > > Just to add some context how this could be done, there is the concept
> of
> > > a FunctionSpec which is part of a transform in the DAG. FunctionSpec
> > > contains a URN and with a payload. FunctionSpec can be either (1)
> > > translated by the Runner directly, e.g. map to table API concepts or
> (2)
> > > run a user-defined function with an Environment. It could be feasible
> > > for Flink to choose the direct path, whereas Beam Runners would
> leverage
> > > the more generic approach using UDFs. Granted, compatibility across
> > > Flink and Beam would only work if both of the translation paths yielded
> > > the same semantics.
> >
> > To elaborate a bit on this, Beam DAGs are built up by applying
> Transforms (basically operations) to PColections (the equivalent of
> dataset/datastream), but the key point here is that these transforms are
> often composite operations that expand out into smaller subtransforms. This
> expansion happens during pipeline construction, but with the recent work on
> cross language pipelines can happen out of process. This is one point of
> extendability. Secondly, and importantly, this composite structure is
> preserved in the DAG, and so a runner is free to ignore the provided
> expansion and supply its own (so long as semantically it produces exactly
> the same output). These composite operations can be identified by arbitrary
> URNs + payloads, and any runner that does not understand them simply uses
> the pre-provided expansion.
> >
> > The existing Flink runner operates on exactly this principle,
> translating URNs for the leaf operations (Map, Flatten, ...) as well as
> some composites it can do better (e.g. Reshard). It is intentionally easy
> to define and add new ones. This actually seems the easier approach (to me
> at least, but that's probably heavily influenced by what I'm familiar with
> vs. what I'm not).
> >
> > As for how well this maps onto the Flink Tables API, part of that
> depends on how much of the API is the operations themselves, and how much
> is concerning configuration/environment/etc. which is harder to talk about
> in an 

Re: [DISCUSS] FLIP-38 Support python language in flink TableAPI

[Dian Fu]

Thanks everyone for the discussion here. 

Regarding to the Java/Scala UDF and the built-in UDF to execute in the current 
Flink way (directly in JVM, not via RPC), I share the same thoughts with Max 
and Robert and I think it will not be a big problem. From the design doc, I 
guess the main reason to take the Py4J way instead of the DAG way at present is 
that DAG has some limitations in some scenarios such as interactive programing 
which may be a strong requirement for data scientist.

> In addition (and I'll admit this is rather subjective) it seems to me one of 
> the primary values of a table-like API in a given language (vs. just using 
> (say) plain old SQL itself via a console) is the ability to embed it in a 
> larger pipeline, or at least drop in operations that are not (as) naturally 
> expressed in the "table way," including existing libraries. In other words, a 
> full SDK. The Py4j wrapping doesn't extend itself to such integration nearly 
> as easily. 


Hi Robert, regarding to "a larger pipeline", do you mean translating a 
table-like API jobs from/to another kind of API job or embedding third-part 
libraries into a table-like API jobs via UDF? Could you kindly explain why this 
would be a problem for Py4J and will not be a problem if expressing the job 
with DAG?

Thanks,
Dian


> 在 2019年4月25日，上午12:16，Robert Bradshaw  写道：
> 
> Thanks for the meeting summary, Stephan. Sound like you covered a lot of 
> ground. Some more comments below, adding onto what Max has said. 
> 
> On Wed, Apr 24, 2019 at 3:20 PM Maximilian Michels  > wrote:
> >
> > Hi Stephan,
> >
> > This is excited! Thanks for sharing. The inter-process communication
> > code looks like the most natural choice as a common ground. To go
> > further, there are indeed some challenges to solve.
> 
> It certainly does make sense to share this work, though it does to me seem 
> like a rather low level to integrate at. 
> 
> > > => Biggest question is whether the language-independent DAG is expressive 
> > > enough to capture all the expressions that we want to map directly to 
> > > Table API expressions. Currently much is hidden in opaque UDFs. Kenn 
> > > mentioned the structure should be flexible enough to capture more 
> > > expressions transparently.
> >
> > Just to add some context how this could be done, there is the concept of
> > a FunctionSpec which is part of a transform in the DAG. FunctionSpec
> > contains a URN and with a payload. FunctionSpec can be either (1)
> > translated by the Runner directly, e.g. map to table API concepts or (2)
> > run a user-defined function with an Environment. It could be feasible
> > for Flink to choose the direct path, whereas Beam Runners would leverage
> > the more generic approach using UDFs. Granted, compatibility across
> > Flink and Beam would only work if both of the translation paths yielded
> > the same semantics.
> 
> To elaborate a bit on this, Beam DAGs are built up by applying Transforms 
> (basically operations) to PColections (the equivalent of dataset/datastream), 
> but the key point here is that these transforms are often composite 
> operations that expand out into smaller subtransforms. This expansion happens 
> during pipeline construction, but with the recent work on cross language 
> pipelines can happen out of process. This is one point of extendability. 
> Secondly, and importantly, this composite structure is preserved in the DAG, 
> and so a runner is free to ignore the provided expansion and supply its own 
> (so long as semantically it produces exactly the same output). These 
> composite operations can be identified by arbitrary URNs + payloads, and any 
> runner that does not understand them simply uses the pre-provided expansion. 
> 
> The existing Flink runner operates on exactly this principle, translating 
> URNs for the leaf operations (Map, Flatten, ...) as well as some composites 
> it can do better (e.g. Reshard). It is intentionally easy to define and add 
> new ones. This actually seems the easier approach (to me at least, but that's 
> probably heavily influenced by what I'm familiar with vs. what I'm not). 
> 
> As for how well this maps onto the Flink Tables API, part of that depends on 
> how much of the API is the operations themselves, and how much is concerning 
> configuration/environment/etc. which is harder to talk about in an agnostic 
> way. 
> 
> Using something like Py4j is an easy way to get up an running, especially for 
> a very faithful API, but the instant one wants to add UDFs one hits a cliff 
> of sorts (which is surmountable, but likely a lot harder than having gone the 
> above approach). In addition (and I'll admit this is rather subjective) it 
> seems to me one of the primary values of a table-like API in a given language 
> (vs. just using (say) plain old SQL itself via a console) is the ability to 
> embed it in a larger pipeline, or at least drop in operations that are not 
> (as) naturally 

Re: [DISCUSS] FLIP-38 Support python language in flink TableAPI

[Robert Bradshaw]

Thanks for the meeting summary, Stephan. Sound like you covered a lot of
ground. Some more comments below, adding onto what Max has said.

On Wed, Apr 24, 2019 at 3:20 PM Maximilian Michels  wrote:
>
> Hi Stephan,
>
> This is excited! Thanks for sharing. The inter-process communication
> code looks like the most natural choice as a common ground. To go
> further, there are indeed some challenges to solve.

It certainly does make sense to share this work, though it does to me seem
like a rather low level to integrate at.

> > => Biggest question is whether the language-independent DAG is
expressive enough to capture all the expressions that we want to map
directly to Table API expressions. Currently much is hidden in opaque UDFs.
Kenn mentioned the structure should be flexible enough to capture more
expressions transparently.
>
> Just to add some context how this could be done, there is the concept of
> a FunctionSpec which is part of a transform in the DAG. FunctionSpec
> contains a URN and with a payload. FunctionSpec can be either (1)
> translated by the Runner directly, e.g. map to table API concepts or (2)
> run a user-defined function with an Environment. It could be feasible
> for Flink to choose the direct path, whereas Beam Runners would leverage
> the more generic approach using UDFs. Granted, compatibility across
> Flink and Beam would only work if both of the translation paths yielded
> the same semantics.

To elaborate a bit on this, Beam DAGs are built up by applying Transforms
(basically operations) to PColections (the equivalent of
dataset/datastream), but the key point here is that these transforms are
often composite operations that expand out into smaller subtransforms. This
expansion happens during pipeline construction, but with the recent work on
cross language pipelines can happen out of process. This is one point of
extendability. Secondly, and importantly, this composite structure is
preserved in the DAG, and so a runner is free to ignore the provided
expansion and supply its own (so long as semantically it produces exactly
the same output). These composite operations can be identified by arbitrary
URNs + payloads, and any runner that does not understand them simply uses
the pre-provided expansion.

The existing Flink runner operates on exactly this principle, translating
URNs for the leaf operations (Map, Flatten, ...) as well as some composites
it can do better (e.g. Reshard). It is intentionally easy to define and add
new ones. This actually seems the easier approach (to me at least, but
that's probably heavily influenced by what I'm familiar with vs. what I'm
not).

As for how well this maps onto the Flink Tables API, part of that depends
on how much of the API is the operations themselves, and how much is
concerning configuration/environment/etc. which is harder to talk about in
an agnostic way.

Using something like Py4j is an easy way to get up an running, especially
for a very faithful API, but the instant one wants to add UDFs one hits a
cliff of sorts (which is surmountable, but likely a lot harder than having
gone the above approach). In addition (and I'll admit this is rather
subjective) it seems to me one of the primary values of a table-like API in
a given language (vs. just using (say) plain old SQL itself via a console)
is the ability to embed it in a larger pipeline, or at least drop in
operations that are not (as) naturally expressed in the "table way,"
including existing libraries. In other words, a full SDK. The Py4j wrapping
doesn't extend itself to such integration nearly as easily.

But I really do understand the desire to not block immediate work (and
value) for a longer term solution.

> >  If the DAG is generic enough to capture the additional information, we
probably still need some standardization, so that all the different
language APIs represent their expressions the same way
>
> I wonder whether that's necessary as a first step. I think it would be
> fine for Flink to have its own way to represent API concepts in the Beam
> DAG which Beam Runners may not be able to understand. We could then
> successively add the capability for these transforms to run with Beam.
>
> >  Similarly, it makes sense to standardize the type system (and type
inference) as far as built-in expressions and their interaction with UDFs
are concerned. The Flink Table API and Blink teams found this to be
essential for a consistent API behavior. This would not prevent all-UDF
programs from still using purely binary/opaque types.
>
> Beam has a set of standard coders which can be used across languages. We
> will have to expand those to play well with Flink's:
>
https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/tableApi.html#data-types
>
> I think we will need to exchange more ideas to work out a model that
> will work for both Flink and Beam. A regular meeting could be helpful.

+1, I think this would be really good for both this effort and general
collaboration 

Re: [DISCUSS] FLIP-38 Support python language in flink TableAPI

[Maximilian Michels]

Hi Stephan,

This is excited! Thanks for sharing. The inter-process communication 
code looks like the most natural choice as a common ground. To go 
further, there are indeed some challenges to solve.



=> Biggest question is whether the language-independent DAG is expressive 
enough to capture all the expressions that we want to map directly to Table API 
expressions. Currently much is hidden in opaque UDFs. Kenn mentioned the structure 
should be flexible enough to capture more expressions transparently.


Just to add some context how this could be done, there is the concept of 
a FunctionSpec which is part of a transform in the DAG. FunctionSpec 
contains a URN and with a payload. FunctionSpec can be either (1) 
translated by the Runner directly, e.g. map to table API concepts or (2) 
run a user-defined function with an Environment. It could be feasible 
for Flink to choose the direct path, whereas Beam Runners would leverage 
the more generic approach using UDFs. Granted, compatibility across 
Flink and Beam would only work if both of the translation paths yielded 
the same semantics.



 If the DAG is generic enough to capture the additional information, we 
probably still need some standardization, so that all the different language 
APIs represent their expressions the same way


I wonder whether that's necessary as a first step. I think it would be 
fine for Flink to have its own way to represent API concepts in the Beam 
DAG which Beam Runners may not be able to understand. We could then 
successively add the capability for these transforms to run with Beam.



 Similarly, it makes sense to standardize the type system (and type inference) 
as far as built-in expressions and their interaction with UDFs are concerned. 
The Flink Table API and Blink teams found this to be essential for a consistent 
API behavior. This would not prevent all-UDF programs from still using purely 
binary/opaque types.


Beam has a set of standard coders which can be used across languages. We 
will have to expand those to play well with Flink's: 
https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/tableApi.html#data-types


I think we will need to exchange more ideas to work out a model that 
will work for both Flink and Beam. A regular meeting could be helpful.


Thanks,
Max

On 23.04.19 21:23, Stephan Ewen wrote:

Hi all!

Below are my notes on the discussion last week on how to collaborate 
between Beam and Flink.
The discussion was between Tyler, Kenn, Luke, Ahmed, Xiaowei, Shaoxuan, 
Jincheng, and me.


This represents my understanding of the discussion, please augment this 
where I missed something or where your conclusion was different.


Best,
Stephan

===

*Beams Python and Portability Framework*

   - Portability core to Beam
   - Language independent dataflow DAG that is defined via ProtoBuf
   - DAG can be generated from various languages (Java, Python, Go)
   - The DAG describes the pipelines and contains additional parameters 
to describe each operator, and contains artifacts that need to be 
deployed / executed as part of an operator execution.
   - Operators execute in language-specific containers, data is 
exchanged between the language-specific container and the runner 
container (JVM) via gRPC.


*Flink's desiderata for Python API*

   - Python API should mirror Java / Scala Table API
   - All relational expressions that correspond to built-in functions 
should be translated to corresponding expressions in the Table API. That 
way the planner generated Java code for the data types and built-in 
expressions, meaning no Python code is necessary during execution

   - UDFs should be supported and run similarly as in Beam's approach
   - Python programs should be similarly created and submitted/deployed 
as Java / Scala programs (CLI, web, containerized, etc.)


*Consensus to share inter-process communication code*

   - Crucial code for robust setup and high performance data exchange 
across processes
   - The code for the SDK harness, the artifact boostrapping, and the 
data exchange make sense to share.
   - Ongoing discussion whether this can be a dedicated module with slim 
dependencies in Beam


*Potential Long Term Perspective: Share language-independent DAG 
representation*


   - Beam's language independent DAG could become a standard 
representation used in both projects
   - Flink would need an way to receive that DAG, map it to the Table 
API, execute it from there
   - The DAG would need to have a standardized representation of 
functions and expressions that then get mapped to Table API expressions 
to let the planner optimize those and generate Java code for those
   - Similar as UDFs are supported in the Table API, there would be 
additional "external UDFs" that would go through the above mentioned 
inter-process communication layer


   - _Advantages:_
     => Flink and Beam could share more language bindings
     => Flink 

Re: [DISCUSS] FLIP-38 Support python language in flink TableAPI

[jincheng sun]

Hi Stephan，

Thanks for your summary, from the points of my view, we are on the same
page about the conclusion of the discussion!

I completely agree that we can divide the support of the Python Table API
into short-term and long-term goals, and the design of short-term goals
should be smoothly upgraded to long-term goals.
And we will also continue to communicate with the Beam community to achieve
the long-term goals.

We hope is that Flink 1.9 can support the Python Table API, so I am
preparing to create FLIP-38 in Flink Confluence and preparing to open the
first PR of Python Table API. Of course, we can continue the discussion in
the google doc and mail thread for the design that does not reach
consensus. Is that makes sense to you?

Regards,
Jincheng

Stephan Ewen  于2019年4月24日周三 上午3:24写道：

> Hi all!
>
> Below are my notes on the discussion last week on how to collaborate
> between Beam and Flink.
> The discussion was between Tyler, Kenn, Luke, Ahmed, Xiaowei, Shaoxuan,
> Jincheng, and me.
>
> This represents my understanding of the discussion, please augment this
> where I missed something or where your conclusion was different.
>
> Best,
> Stephan
>
> ===
>
> *Beams Python and Portability Framework*
>
>   - Portability core to Beam
>   - Language independent dataflow DAG that is defined via ProtoBuf
>   - DAG can be generated from various languages (Java, Python, Go)
>   - The DAG describes the pipelines and contains additional parameters to
> describe each operator, and contains artifacts that need to be deployed /
> executed as part of an operator execution.
>   - Operators execute in language-specific containers, data is exchanged
> between the language-specific container and the runner container (JVM) via
> gRPC.
>
> *Flink's desiderata for Python API*
>
>   - Python API should mirror Java / Scala Table API
>   - All relational expressions that correspond to built-in functions
> should be translated to corresponding expressions in the Table API. That
> way the planner generated Java code for the data types and built-in
> expressions, meaning no Python code is necessary during execution
>   - UDFs should be supported and run similarly as in Beam's approach
>   - Python programs should be similarly created and submitted/deployed as
> Java / Scala programs (CLI, web, containerized, etc.)
>
> *Consensus to share inter-process communication code*
>
>   - Crucial code for robust setup and high performance data exchange
> across processes
>   - The code for the SDK harness, the artifact boostrapping, and the data
> exchange make sense to share.
>   - Ongoing discussion whether this can be a dedicated module with slim
> dependencies in Beam
>
> *Potential Long Term Perspective: Share language-independent DAG
> representation*
>
>   - Beam's language independent DAG could become a standard representation
> used in both projects
>   - Flink would need an way to receive that DAG, map it to the Table API,
> execute it from there
>   - The DAG would need to have a standardized representation of functions
> and expressions that then get mapped to Table API expressions to let the
> planner optimize those and generate Java code for those
>   - Similar as UDFs are supported in the Table API, there would be
> additional "external UDFs" that would go through the above mentioned
> inter-process communication layer
>
>   - *Advantages:*
> => Flink and Beam could share more language bindings
> => Flink would execute Beam portability programs fast, without
> intermediate abstraction and directly in the JVM for many operators.
>  Abstraction is necessary around UDFs and to bridge between
> serializers / coders, etc.
>
>   - *Open issues:*
> => Biggest question is whether the language-independent DAG is
> expressive enough to capture all the expressions that we want to map
> directly to Table API expressions. Currently much is hidden in opaque UDFs.
> Kenn mentioned the structure should be flexible enough to capture more
> expressions transparently.
>
> => If the DAG is generic enough to capture the additional information,
> we probably still need some standardization, so that all the different
> language APIs represent their expressions the same way
> => Similarly, it makes sense to standardize the type system (and type
> inference) as far as built-in expressions and their interaction with UDFs
> are concerned. The Flink Table API and Blink teams found this to be
> essential for a consistent API behavior. This would not prevent all-UDF
> programs from still using purely binary/opaque types.
>
>  =>  We need to create a Python API that follows the same structure as
> Flink's Table API that produces the language-independent DAG
>
> *Short-term approach in Flink*
>
>   - Goal is to not block Flink's Python effort on the long term approach
> and the necessary design and evolution of the language-independent DAG.
>   - Depending on what the 

Re: [DISCUSS] FLIP-38 Support python language in flink TableAPI

[Stephan Ewen]

Hi all!

Below are my notes on the discussion last week on how to collaborate
between Beam and Flink.
The discussion was between Tyler, Kenn, Luke, Ahmed, Xiaowei, Shaoxuan,
Jincheng, and me.

This represents my understanding of the discussion, please augment this
where I missed something or where your conclusion was different.

Best,
Stephan

===

*Beams Python and Portability Framework*

  - Portability core to Beam
  - Language independent dataflow DAG that is defined via ProtoBuf
  - DAG can be generated from various languages (Java, Python, Go)
  - The DAG describes the pipelines and contains additional parameters to
describe each operator, and contains artifacts that need to be deployed /
executed as part of an operator execution.
  - Operators execute in language-specific containers, data is exchanged
between the language-specific container and the runner container (JVM) via
gRPC.

*Flink's desiderata for Python API*

  - Python API should mirror Java / Scala Table API
  - All relational expressions that correspond to built-in functions should
be translated to corresponding expressions in the Table API. That way the
planner generated Java code for the data types and built-in expressions,
meaning no Python code is necessary during execution
  - UDFs should be supported and run similarly as in Beam's approach
  - Python programs should be similarly created and submitted/deployed as
Java / Scala programs (CLI, web, containerized, etc.)

*Consensus to share inter-process communication code*

  - Crucial code for robust setup and high performance data exchange across
processes
  - The code for the SDK harness, the artifact boostrapping, and the data
exchange make sense to share.
  - Ongoing discussion whether this can be a dedicated module with slim
dependencies in Beam

*Potential Long Term Perspective: Share language-independent DAG
representation*

  - Beam's language independent DAG could become a standard representation
used in both projects
  - Flink would need an way to receive that DAG, map it to the Table API,
execute it from there
  - The DAG would need to have a standardized representation of functions
and expressions that then get mapped to Table API expressions to let the
planner optimize those and generate Java code for those
  - Similar as UDFs are supported in the Table API, there would be
additional "external UDFs" that would go through the above mentioned
inter-process communication layer

  - *Advantages:*
=> Flink and Beam could share more language bindings
=> Flink would execute Beam portability programs fast, without
intermediate abstraction and directly in the JVM for many operators.
 Abstraction is necessary around UDFs and to bridge between
serializers / coders, etc.

  - *Open issues:*
=> Biggest question is whether the language-independent DAG is
expressive enough to capture all the expressions that we want to map
directly to Table API expressions. Currently much is hidden in opaque UDFs.
Kenn mentioned the structure should be flexible enough to capture more
expressions transparently.

=> If the DAG is generic enough to capture the additional information,
we probably still need some standardization, so that all the different
language APIs represent their expressions the same way
=> Similarly, it makes sense to standardize the type system (and type
inference) as far as built-in expressions and their interaction with UDFs
are concerned. The Flink Table API and Blink teams found this to be
essential for a consistent API behavior. This would not prevent all-UDF
programs from still using purely binary/opaque types.

 =>  We need to create a Python API that follows the same structure as
Flink's Table API that produces the language-independent DAG

*Short-term approach in Flink*

  - Goal is to not block Flink's Python effort on the long term approach
and the necessary design and evolution of the language-independent DAG.
  - Depending on what the outcome of above investigation is, Flink may
initially go with a simple approach to map the Python Table API to the the
Java Table API via Py4J, as outlined in FLIP-38:
https://docs.google.com/document/d/1ybYt-0xWRMa1Yf5VsuqGRtOfJBz4p74ZmDxZYg3j_h8



On Tue, Apr 23, 2019 at 4:14 AM jincheng sun 
wrote:

> Hi everyone,
>
> Thank you for all of your feedback and comments in google doc!
>
> I have updated the google doc and add the UDFs part. For a short summary:
>
>   - Python TableAPI - Flink introduces a set of Python Table API Interfaces
> which align with Flink Java Table API. It uses Py4j framework to
> communicate between Python VM  and Java VM.
>   - Python User-defined functions - IMO. Flink supports the communication
> framework of UDFs, we will try to reuse the existing achievements of Beam
> as much as possible, and do our best for this. The first step is
>   to solve the above interface definition problem, which turns `
> WindowedValue` into `T`