Hi Han,
Thanks for getting back to me.
I am curious about the valid characters in a model name – I assume any
characters are valid as it is a quoted string in SQL. So $ could be in the
model name. I would think that the model would be determined then the model is
deployed, ( there could be other versions associated with authoring or
intermediate states of the model that never get deployed) – rather than
allocated by Flink if there is none.
I see https://github.com/onnx/onnx/blob/main/docs/Versioning.md supports
numbers or semantic versioning and 3 different types of versioning.
It would be interesting to see how champion challenger scenarios would play out
– when you try a new version of the model that might perform better.
I suggest having a new optional model-version keyword, which would seem to be a
cleaner way of specifying a model.
Kind regards, David.
From: Hao Li <h...@confluent.io.INVALID>
Date: Wednesday, 3 April 2024 at 18:58
To: dev@flink.apache.org <dev@flink.apache.org>
Subject: [EXTERNAL] Re: [DISCUSS] FLIP-437: Support ML Models in Flink SQL
Cross post David Radley's comments here from voting thread:
> I don’t think this counts as an objection, I have some comments. I should
have put this on the discussion thread earlier but have just got to this.
> - I suggest we can put a model version in the model resource. Versions
are notoriously difficult to add later; I don’t think we want to
proliferate differently named models as a model mutates. We may want to
work with non-latest models.
> - I see that the model name is the unique identifier. I realise this
would move away from the Oracle syntax – so may not be feasible short term;
but I wonder if we can have:
> - a uuid as the main identifier and the model name as an attribute.
> or
> - a namespace (or something like a system of origin)
> to help organise models with the same name.
> - does the model have an owner? I assume that Flink model resource is the
master of the model? I imagine in the future that a model that comes in via
a new connector could be kept up to date with the external model and would
not be allowed to be changed by anything other than the connector.
Thanks for the comments. I agree supporting the model version is important.
I think we could support versioning without changing the overall syntax by
appending version number/name to the model name. Catalog implementations
can handle the versions. For example,
CREATE MODEL `my-model$1`...
"$1" would imply it's version 1. If no version is provided, we can auto
increment the version if the model name exists already or create the first
version if the model name doesn't exist yet.
As for model ownership, I'm not entirely sure about the use case and how it
should be controlled. It could be controlled from the user side through
ACL/rbac or some way in the catalog I guess. Maybe we can follow up on this
as the requirement or use case becomes more clear.
Cross post David Moravek's comments from voting thread:
> My only suggestion would be to move Catalog changes into a separate
> interface to allow us to begin with lower stability guarantees. Existing
> Catalogs would be able to opt-in by implementing it. It's a minor thing
> though, overall the FLIP is solid and the direction is pretty exciting.
I think it's fine to move model related catalog changes to a separate
interface and let the current catalog interface extend it. As model support
will be built-in in Flink, the current catalog interface will need to
support model CRUD operations. For my own education, can you elaborate more
on how separate interface will allow us to begin with lower stability
guarantees?
Thanks,
Hao
On Thu, Mar 28, 2024 at 10:14 AM Hao Li <h...@confluent.io> wrote:
> Thanks Timo. I'll start a vote tomorrow if no further discussion.
>
> Thanks,
> Hao
>
> On Thu, Mar 28, 2024 at 9:33 AM Timo Walther <twal...@apache.org> wrote:
>
>> Hi everyone,
>>
>> I updated the FLIP according to this discussion.
>>
>> @Hao Li: Let me know if I made a mistake somewhere. I added some
>> additional explaning comments about the new PTF syntax.
>>
>> There are no further objections from my side. If nobody objects, Hao
>> feel free to start the voting tomorrow.
>>
>> Regards,
>> Timo
>>
>>
>> On 28.03.24 16:30, Jark Wu wrote:
>> > Thanks, Hao,
>> >
>> > Sounds good to me.
>> >
>> > Best,
>> > Jark
>> >
>> > On Thu, 28 Mar 2024 at 01:02, Hao Li <h...@confluent.io.invalid> wrote:
>> >
>> >> Hi Jark,
>> >>
>> >> I think we can start with supporting popular model providers such as
>> >> openai, azureml, sagemaker for remote models.
>> >>
>> >> Thanks,
>> >> Hao
>> >>
>> >> On Tue, Mar 26, 2024 at 8:15 PM Jark Wu <imj...@gmail.com> wrote:
>> >>
>> >>> Thanks for the PoC and updating,
>> >>>
>> >>> The final syntax looks good to me, at least it is a nice and concise
>> >> first
>> >>> step.
>> >>>
>> >>> SELECT f1, f2, label FROM
>> >>> ML_PREDICT(
>> >>> input => `my_data`,
>> >>> model => `my_cat`.`my_db`.`classifier_model`,
>> >>> args => DESCRIPTOR(f1, f2));
>> >>>
>> >>> Besides, what built-in models will we support in the FLIP? This might
>> be
>> >>> important
>> >>> because it relates to what use cases can run with the new Flink
>> version
>> >> out
>> >>> of the box.
>> >>>
>> >>> Best,
>> >>> Jark
>> >>>
>> >>> On Wed, 27 Mar 2024 at 01:10, Hao Li <h...@confluent.io.invalid>
>> wrote:
>> >>>
>> >>>> Hi Timo,
>> >>>>
>> >>>> Yeah. For `primary key` and `from table(...)` those are explicitly
>> >>> matched
>> >>>> in parser: [1].
>> >>>>
>> >>>>> SELECT f1, f2, label FROM
>> >>>> ML_PREDICT(
>> >>>> input => `my_data`,
>> >>>> model => `my_cat`.`my_db`.`classifier_model`,
>> >>>> args => DESCRIPTOR(f1, f2));
>> >>>>
>> >>>> This named argument syntax looks good to me. It can be supported
>> >> together
>> >>>> with
>> >>>>
>> >>>> SELECT f1, f2, label FROM ML_PREDICT(`my_data`,
>> >>>> `my_cat`.`my_db`.`classifier_model`,DESCRIPTOR(f1, f2));
>> >>>>
>> >>>> Sure. Will let you know once updated the FLIP.
>> >>>>
>> >>>> [1]
>> >>>>
>> >>>>
>> >>>
>> >>
>> https://github.com/confluentinc/flink/blob/release-1.18-confluent/flink-table/flink-sql-parser/src/main/codegen/includes/parserImpls.ftl#L814
>> >>>>
>> >>>> Thanks,
>> >>>> Hao
>> >>>>
>> >>>> On Tue, Mar 26, 2024 at 4:15 AM Timo Walther <twal...@apache.org>
>> >> wrote:
>> >>>>
>> >>>>> Hi Hao,
>> >>>>>
>> >>>>> > `TABLE(my_data)` and `MODEL(my_cat.my_db.classifier_model)`
>> >> doesn't
>> >>>>> > work since `TABLE` and `MODEL` are already key words
>> >>>>>
>> >>>>> This argument doesn't count. The parser supports introducing
>> keywords
>> >>>>> that are still non-reserved. For example, this enables using "key"
>> >> for
>> >>>>> both primary key and a column name:
>> >>>>>
>> >>>>> CREATE TABLE t (i INT PRIMARY KEY NOT ENFORCED)
>> >>>>> WITH ('connector' = 'datagen');
>> >>>>>
>> >>>>> SELECT i AS key FROM t;
>> >>>>>
>> >>>>> I'm sure we will introduce `TABLE(my_data)` eventually as this is
>> >> what
>> >>>>> the standard dictates. But for now, let's use the most compact
>> syntax
>> >>>>> possible which is also in sync with Oracle.
>> >>>>>
>> >>>>> TLDR: We allow identifiers as arguments for PTFs which are expanded
>> >>> with
>> >>>>> catalog and database if necessary. Those identifier arguments
>> >> translate
>> >>>>> to catalog lookups for table and models. The ML_ functions will make
>> >>>>> sure that the arguments are of correct type model or table.
>> >>>>>
>> >>>>> SELECT f1, f2, label FROM
>> >>>>> ML_PREDICT(
>> >>>>> input => `my_data`,
>> >>>>> model => `my_cat`.`my_db`.`classifier_model`,
>> >>>>> args => DESCRIPTOR(f1, f2));
>> >>>>>
>> >>>>> So this will allow us to also use in the future:
>> >>>>>
>> >>>>> SELECT * FROM poly_func(table1);
>> >>>>>
>> >>>>> Same support as Oracle [1]. Very concise.
>> >>>>>
>> >>>>> Let me know when you updated the FLIP for a final review before
>> >> voting.
>> >>>>>
>> >>>>> Do others have additional objections?
>> >>>>>
>> >>>>> Regards,
>> >>>>> Timo
>> >>>>>
>> >>>>> [1]
>> >>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://livesql.oracle.com/apex/livesql/file/content_HQK7TYEO0NHSJCDY3LN2ERDV6.html
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> On 25.03.24 23:40, Hao Li wrote:
>> >>>>>> Hi Timo,
>> >>>>>>
>> >>>>>>> Please double check if this is implementable with the current
>> >>> stack. I
>> >>>>>> fear the parser or validator might not like the "identifier"
>> >>> argument?
>> >>>>>>
>> >>>>>> I checked this, currently the validator throws an exception trying
>> >> to
>> >>>> get
>> >>>>>> the full qualifier name for `classifier_model`. But since
>> >>>>>> `SqlValidatorImpl` is implemented in Flink, we should be able to
>> >> fix
>> >>>>> this.
>> >>>>>> The only caveator is if not full model path is provided,
>> >>>>>> the qualifier is interpreted as a column. We should be able to
>> >>> special
>> >>>>>> handle this by rewriting the `ml_predict` function to add the
>> >> catalog
>> >>>> and
>> >>>>>> database name in `FlinkCalciteSqlValidator` though.
>> >>>>>>
>> >>>>>>> SELECT f1, f2, label FROM
>> >>>>>> ML_PREDICT(
>> >>>>>> TABLE `my_data`,
>> >>>>>> my_cat.my_db.classifier_model,
>> >>>>>> DESCRIPTOR(f1, f2))
>> >>>>>>
>> >>>>>> SELECT f1, f2, label FROM
>> >>>>>> ML_PREDICT(
>> >>>>>> input => TABLE `my_data`,
>> >>>>>> model => my_cat.my_db.classifier_model,
>> >>>>>> args => DESCRIPTOR(f1, f2))
>> >>>>>>
>> >>>>>> I verified these can be parsed. The problem is in validator for
>> >>>> qualifier
>> >>>>>> as mentioned above.
>> >>>>>>
>> >>>>>>> So the safest option would be the long-term solution:
>> >>>>>>
>> >>>>>> SELECT f1, f2, label FROM
>> >>>>>> ML_PREDICT(
>> >>>>>> input => TABLE(my_data),
>> >>>>>> model => MODEL(my_cat.my_db.classifier_model),
>> >>>>>> args => DESCRIPTOR(f1, f2))
>> >>>>>>
>> >>>>>> `TABLE(my_data)` and `MODEL(my_cat.my_db.classifier_model)` doesn't
>> >>>> work
>> >>>>>> since `TABLE` and `MODEL` are already key words in calcite used by
>> >>>>> `CREATE
>> >>>>>> TABLE`, `CREATE MODEL`. Changing to `model_name(...)` works and
>> >> will
>> >>> be
>> >>>>>> treated as a function.
>> >>>>>>
>> >>>>>> So I think
>> >>>>>>
>> >>>>>> SELECT f1, f2, label FROM
>> >>>>>> ML_PREDICT(
>> >>>>>> input => TABLE `my_data`,
>> >>>>>> model => my_cat.my_db.classifier_model,
>> >>>>>> args => DESCRIPTOR(f1, f2))
>> >>>>>> should be fine for now.
>> >>>>>>
>> >>>>>> For the syntax part:
>> >>>>>> 1). Sounds good. We can drop model task and model kind from the
>> >>>>> definition.
>> >>>>>> They can be deduced from the options.
>> >>>>>>
>> >>>>>> 2). Sure. We can add temporary model
>> >>>>>>
>> >>>>>> 3). Make sense. We can use `show create model <name>` to display
>> >> all
>> >>>>>> information and `describe model <name>` to show input/output schema
>> >>>>>>
>> >>>>>> Thanks,
>> >>>>>> Hao
>> >>>>>>
>> >>>>>> On Mon, Mar 25, 2024 at 3:21 PM Hao Li <h...@confluent.io> wrote:
>> >>>>>>
>> >>>>>>> Hi Ahmed,
>> >>>>>>>
>> >>>>>>> Looks like the feature freeze time for 1.20 release is June 15th.
>> >> We
>> >>>> can
>> >>>>>>> definitely get the model DDL into 1.20. For predict and evaluate
>> >>>>> functions,
>> >>>>>>> if we can't get into the 1.20 release, we can get them into the
>> >> 1.21
>> >>>>>>> release for sure.
>> >>>>>>>
>> >>>>>>> Thanks,
>> >>>>>>> Hao
>> >>>>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>> On Mon, Mar 25, 2024 at 1:25 AM Timo Walther <twal...@apache.org>
>> >>>>> wrote:
>> >>>>>>>
>> >>>>>>>> Hi Jark and Hao,
>> >>>>>>>>
>> >>>>>>>> thanks for the information, Jark! Great that the Calcite
>> >> community
>> >>>>>>>> already fixed the problem for us. +1 to adopt the simplified
>> >> syntax
>> >>>>>>>> asap. Maybe even before we upgrade Calcite (i.e. copy over
>> >>> classes),
>> >>>> if
>> >>>>>>>> upgrading Calcite is too much work right now?
>> >>>>>>>>
>> >>>>>>>> > Is `DESCRIPTOR` a must in the syntax?
>> >>>>>>>>
>> >>>>>>>> Yes, we should still stick to the standard as much as possible
>> >> and
>> >>>> all
>> >>>>>>>> vendors use DESCRIPTOR/COLUMNS for distinuishing columns vs.
>> >>> literal
>> >>>>>>>> arguments. So the final syntax of this discussion would be:
>> >>>>>>>>
>> >>>>>>>>
>> >>>>>>>> SELECT f1, f2, label FROM
>> >>>>>>>> ML_PREDICT(TABLE `my_data`, `classifier_model`,
>> >> DESCRIPTOR(f1,
>> >>>> f2))
>> >>>>>>>>
>> >>>>>>>> SELECT * FROM
>> >>>>>>>> ML_EVALUATE(TABLE `eval_data`, `classifier_model`,
>> >>> DESCRIPTOR(f1,
>> >>>>> f2))
>> >>>>>>>>
>> >>>>>>>> Please double check if this is implementable with the current
>> >>> stack.
>> >>>> I
>> >>>>>>>> fear the parser or validator might not like the "identifier"
>> >>>> argument?
>> >>>>>>>>
>> >>>>>>>> Make sure that also these variations are supported:
>> >>>>>>>>
>> >>>>>>>> SELECT f1, f2, label FROM
>> >>>>>>>> ML_PREDICT(
>> >>>>>>>> TABLE `my_data`,
>> >>>>>>>> my_cat.my_db.classifier_model,
>> >>>>>>>> DESCRIPTOR(f1, f2))
>> >>>>>>>>
>> >>>>>>>> SELECT f1, f2, label FROM
>> >>>>>>>> ML_PREDICT(
>> >>>>>>>> input => TABLE `my_data`,
>> >>>>>>>> model => my_cat.my_db.classifier_model,
>> >>>>>>>> args => DESCRIPTOR(f1, f2))
>> >>>>>>>>
>> >>>>>>>> It might be safer and more future proof to wrap a MODEL()
>> >> function
>> >>>>>>>> around it. This would be more in sync with the standard that
>> >>> actually
>> >>>>>>>> still requires to put a TABLE() around the input argument:
>> >>>>>>>>
>> >>>>>>>> ML_PREDICT(TABLE(`my_data`) PARTITIONED BY c1 ORDERED BY c1,
>> >> ....)
>> >>>>>>>>
>> >>>>>>>> So the safest option would be the long-term solution:
>> >>>>>>>>
>> >>>>>>>> SELECT f1, f2, label FROM
>> >>>>>>>> ML_PREDICT(
>> >>>>>>>> input => TABLE(my_data),
>> >>>>>>>> model => MODEL(my_cat.my_db.classifier_model),
>> >>>>>>>> args => DESCRIPTOR(f1, f2))
>> >>>>>>>>
>> >>>>>>>> But I'm fine with this if others have a strong opinion:
>> >>>>>>>>
>> >>>>>>>> SELECT f1, f2, label FROM
>> >>>>>>>> ML_PREDICT(
>> >>>>>>>> input => TABLE `my_data`,
>> >>>>>>>> model => my_cat.my_db.classifier_model,
>> >>>>>>>> args => DESCRIPTOR(f1, f2))
>> >>>>>>>>
>> >>>>>>>> Some feedback for the remainder of the FLIP:
>> >>>>>>>>
>> >>>>>>>> 1) Simplify catalog objects
>> >>>>>>>>
>> >>>>>>>> I would suggest to drop:
>> >>>>>>>> CatalogModel.getModelKind()
>> >>>>>>>> CatalogModel.getModelTask()
>> >>>>>>>>
>> >>>>>>>> A catalog object should fully resemble the DDL. And since the DDL
>> >>>> puts
>> >>>>>>>> those properties in the WITH clause, the catalog object should
>> >> the
>> >>>> same
>> >>>>>>>> (i.e. put them into the `getModelOptions()`). Btw renaming this
>> >>>> method
>> >>>>>>>> to just `getOptions()` for consistency should be good as well.
>> >>>>>>>> Internally, we can still provide enums for these frequently used
>> >>>>>>>> classes. Similar to what we do in `FactoryUtil` for other
>> >>> frequently
>> >>>>>>>> used options.
>> >>>>>>>>
>> >>>>>>>> Remove `getDescription()` and `getDetailedDescription()`. They
>> >>> were a
>> >>>>>>>> mistake for CatalogTable and should actually be deprecated. They
>> >>> got
>> >>>>>>>> replaced by `getComment()` which is sufficient.
>> >>>>>>>>
>> >>>>>>>> 2) CREATE TEMPORARY MODEL is not supported.
>> >>>>>>>>
>> >>>>>>>> This is an unnecessary restriction. We should support temporary
>> >>>>> versions
>> >>>>>>>> of these catalog objects as well for consistency. Adding support
>> >>> for
>> >>>>>>>> this should be straightforward.
>> >>>>>>>>
>> >>>>>>>> 3) DESCRIBE | DESC } MODEL
>> >>> [catalog_name.][database_name.]model_name
>> >>>>>>>>
>> >>>>>>>> I would suggest we support `SHOW CREATE MODEL` instead. Similar
>> >> to
>> >>>>> `SHOW
>> >>>>>>>> CREATE TABLE`, this should show all properties. If we support
>> >>>> `DESCRIBE
>> >>>>>>>> MODEL` it should only list the input parameters similar to
>> >>> `DESCRIBE
>> >>>>>>>> TABLE` only shows the columns (not the WITH clause).
>> >>>>>>>>
>> >>>>>>>> Regards,
>> >>>>>>>> Timo
>> >>>>>>>>
>> >>>>>>>>
>> >>>>>>>> On 23.03.24 13:17, Ahmed Hamdy wrote:
>> >>>>>>>>> Hi everyone,
>> >>>>>>>>> +1 for this proposal, I believe it is very useful to the
>> >> minimum,
>> >>> It
>> >>>>>>>> would
>> >>>>>>>>> be great even having "ML_PREDICT" and "ML_EVALUATE" as built-in
>> >>>> PTFs
>> >>>>> in
>> >>>>>>>>> this FLIP as discussed.
>> >>>>>>>>> IIUC this will be included in the 1.20 roadmap?
>> >>>>>>>>> Best Regards
>> >>>>>>>>> Ahmed Hamdy
>> >>>>>>>>>
>> >>>>>>>>>
>> >>>>>>>>> On Fri, 22 Mar 2024 at 23:54, Hao Li <h...@confluent.io.invalid>
>> >>>>> wrote:
>> >>>>>>>>>
>> >>>>>>>>>> Hi Timo and Jark,
>> >>>>>>>>>>
>> >>>>>>>>>> I agree Oracle's syntax seems concise and more descriptive. For
>> >>> the
>> >>>>>>>>>> built-in `ML_PREDICT` and `ML_EVALUATE` functions I agree with
>> >>> Jark
>> >>>>> we
>> >>>>>>>> can
>> >>>>>>>>>> support them as built-in PTF using `SqlTableFunction` for this
>> >>>> FLIP.
>> >>>>>>>> We can
>> >>>>>>>>>> have a different FLIP discussing user defined PTF and adopt
>> >> that
>> >>>>> later
>> >>>>>>>> for
>> >>>>>>>>>> model functions later. To summarize, the current proposed
>> >> syntax
>> >>> is
>> >>>>>>>>>>
>> >>>>>>>>>> SELECT f1, f2, label FROM TABLE(ML_PREDICT(TABLE `my_data`,
>> >>>>>>>>>> `classifier_model`, f1, f2))
>> >>>>>>>>>>
>> >>>>>>>>>> SELECT * FROM TABLE(ML_EVALUATE(TABLE `eval_data`,
>> >>>>> `classifier_model`,
>> >>>>>>>> f1,
>> >>>>>>>>>> f2))
>> >>>>>>>>>>
>> >>>>>>>>>> Is `DESCRIPTOR` a must in the syntax? If so, it becomes
>> >>>>>>>>>>
>> >>>>>>>>>> SELECT f1, f2, label FROM TABLE(ML_PREDICT(TABLE `my_data`,
>> >>>>>>>>>> `classifier_model`, DESCRIPTOR(f1), DESCRIPTOR(f2)))
>> >>>>>>>>>>
>> >>>>>>>>>> SELECT * FROM TABLE(ML_EVALUATE(TABLE `eval_data`,
>> >>>>> `classifier_model`,
>> >>>>>>>>>> DESCRIPTOR(f1), DESCRIPTOR(f2)))
>> >>>>>>>>>>
>> >>>>>>>>>> If Calcite supports dropping outer table keyword, it becomes
>> >>>>>>>>>>
>> >>>>>>>>>> SELECT f1, f2, label FROM ML_PREDICT(TABLE `my_data`,
>> >>>>>>>> `classifier_model`,
>> >>>>>>>>>> DESCRIPTOR(f1), DESCRIPTOR(f2))
>> >>>>>>>>>>
>> >>>>>>>>>> SELECT * FROM ML_EVALUATE(TABLE `eval_data`,
>> >> `classifier_model`,
>> >>>>>>>>>> DESCRIPTOR(
>> >>>>>>>>>> f1), DESCRIPTOR(f2))
>> >>>>>>>>>>
>> >>>>>>>>>> Thanks,
>> >>>>>>>>>> Hao
>> >>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>>> On Fri, Mar 22, 2024 at 9:16 AM Jark Wu <imj...@gmail.com>
>> >>> wrote:
>> >>>>>>>>>>
>> >>>>>>>>>>> Sorry, I mean we can bump the Calcite version if needed in
>> >> Flink
>> >>>>> 1.20.
>> >>>>>>>>>>>
>> >>>>>>>>>>> On Fri, 22 Mar 2024 at 22:19, Jark Wu <imj...@gmail.com>
>> >> wrote:
>> >>>>>>>>>>>
>> >>>>>>>>>>>> Hi Timo,
>> >>>>>>>>>>>>
>> >>>>>>>>>>>> Introducing user-defined PTF is very useful in Flink, I'm +1
>> >>> for
>> >>>>>>>> this.
>> >>>>>>>>>>>> But I think the ML model FLIP is not blocked by this, because
>> >>> we
>> >>>>>>>>>>>> can introduce ML_PREDICT and ML_EVALUATE as built-in PTFs
>> >>>>>>>>>>>> just like TUMBLE/HOP. And support user-defined ML functions
>> >> as
>> >>>>>>>>>>>> a future FLIP.
>> >>>>>>>>>>>>
>> >>>>>>>>>>>> Regarding the simplified PTF syntax which reduces the outer
>> >>>> TABLE()
>> >>>>>>>>>>>> keyword,
>> >>>>>>>>>>>> it seems it was just supported[1] by the Calcite community
>> >> last
>> >>>>> month
>> >>>>>>>>>> and
>> >>>>>>>>>>>> will be
>> >>>>>>>>>>>> released in the next version (v1.37). The Calcite community
>> >> is
>> >>>>>>>>>> preparing
>> >>>>>>>>>>>> the
>> >>>>>>>>>>>> 1.37 release, so we can bump the version if needed in Flink
>> >>> 1.19.
>> >>>>>>>>>>>>
>> >>>>>>>>>>>> Best,
>> >>>>>>>>>>>> Jark
>> >>>>>>>>>>>>
>> >>>>>>>>>>>> [1]: https://issues.apache.org/jira/browse/CALCITE-6254
>> >>>>>>>>>>>>
>> >>>>>>>>>>>> On Fri, 22 Mar 2024 at 21:46, Timo Walther <
>> >> twal...@apache.org
>> >>>>
>> >>>>>>>> wrote:
>> >>>>>>>>>>>>
>> >>>>>>>>>>>>> Hi everyone,
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> this is a very important change to the Flink SQL syntax but
>> >> we
>> >>>>> can't
>> >>>>>>>>>>>>> wait until the SQL standard is ready for this. So I'm +1 on
>> >>>>>>>>>> introducing
>> >>>>>>>>>>>>> the MODEL concept as a first class citizen in Flink.
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> For your information: Over the past months I have already
>> >>> spent
>> >>>> a
>> >>>>>>>>>>>>> significant amount of time thinking about how we can
>> >> introduce
>> >>>>> PTFs
>> >>>>>>>> in
>> >>>>>>>>>>>>> Flink. I reserved FLIP-440[1] for this purpose and I will
>> >>> share
>> >>>> a
>> >>>>>>>>>>>>> version of this in the next 1-2 weeks.
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> For a good implementation of FLIP-440 and also FLIP-437, we
>> >>>> should
>> >>>>>>>>>>>>> evolve the PTF syntax in collaboration with Apache Calcite.
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> There are different syntax versions out there:
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> 1) Flink
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> SELECT * FROM
>> >>>>>>>>>>>>> TABLE(TUMBLE(TABLE Bid, DESCRIPTOR(bidtime), INTERVAL
>> >>> '10'
>> >>>>>>>>>> MINUTES));
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> 2) SQL standard
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> SELECT * FROM
>> >>>>>>>>>>>>> TABLE(TUMBLE(TABLE(Bid), DESCRIPTOR(bidtime), INTERVAL
>> >>> '10'
>> >>>>>>>>>>> MINUTES));
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> 3) Oracle
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> SELECT * FROM
>> >>>>>>>>>>>>> TUMBLE(Bid, COLUMNS(bidtime), INTERVAL '10'
>> MINUTES));
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> As you can see above, Flink does not follow the standard
>> >>>> correctly
>> >>>>>>>> as
>> >>>>>>>>>> it
>> >>>>>>>>>>>>> would need to use `TABLE()` but this is not provided by
>> >>> Calcite
>> >>>>> yet.
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> I really like the Oracle syntax[2][3] a lot. It reduces
>> >>>> necessary
>> >>>>>>>>>>>>> keywords to a minimum. Personally, I would like to discuss
>> >>> this
>> >>>>>>>> syntax
>> >>>>>>>>>>>>> in a separate FLIP and hope I will find supporters for:
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> SELECT * FROM
>> >>>>>>>>>>>>> TUMBLE(TABLE Bid, DESCRIPTOR(bidtime), INTERVAL '10'
>> >>>>> MINUTES);
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> If we go entirely with the Oracle syntax, as you can see in
>> >>> the
>> >>>>>>>>>> example,
>> >>>>>>>>>>>>> Oracle allows for passing identifiers directly. This would
>> >>> solve
>> >>>>> our
>> >>>>>>>>>>>>> problems for the MODEL as well:
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> SELECT f1, f2, label FROM ML_PREDICT(
>> >>>>>>>>>>>>> data => `my_data`,
>> >>>>>>>>>>>>> model => `classifier_model`,
>> >>>>>>>>>>>>> input => DESCRIPTOR(f1, f2));
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> Or we completely adopt the Oracle syntax:
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> SELECT f1, f2, label FROM ML_PREDICT(
>> >>>>>>>>>>>>> data => `my_data`,
>> >>>>>>>>>>>>> model => `classifier_model`,
>> >>>>>>>>>>>>> input => COLUMNS(f1, f2));
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> What do you think?
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> Happy to create a FLIP for just this syntax question and
>> >>>>> collaborate
>> >>>>>>>>>>>>> with the Calcite community on this. Supporting the syntax of
>> >>>>> Oracle
>> >>>>>>>>>>>>> shouldn't be too hard to convince at least as parser
>> >>> parameter.
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> Regards,
>> >>>>>>>>>>>>> Timo
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> [1]
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://cwiki.apache.org/confluence/display/FLINK/%5BWIP%5D+FLIP-440%3A+User-defined+Polymorphic+Table+Functions
>> >>>>>>>>>>>>> [2]
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/DBMS_TF.html#GUID-0F66E239-DE77-4C0E-AC76-D5B632AB8072
>> >>>>>>>>>>>>> [3]
>> >>>>>>>>>>>
>> >>>>>
>> https://oracle-base.com/articles/18c/polymorphic-table-functions-18c
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>> On 20.03.24 17:22, Mingge Deng wrote:
>> >>>>>>>>>>>>>> Thanks Jark for all the insightful comments.
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>> We have updated the proposal per our offline discussions:
>> >>>>>>>>>>>>>> 1. Model will be treated as a new relation in FlinkSQL.
>> >>>>>>>>>>>>>> 2. Include the common ML predict and evaluate functions
>> >> into
>> >>>> the
>> >>>>>>>>>> open
>> >>>>>>>>>>>>>> source flink to complete the user journey.
>> >>>>>>>>>>>>>> And we should be able to extend the calcite
>> >>>>> SqlTableFunction
>> >>>>>>>> to
>> >>>>>>>>>>>>> support
>> >>>>>>>>>>>>>> these two ML functions.
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>> Best,
>> >>>>>>>>>>>>>> Mingge
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>> On Mon, Mar 18, 2024 at 7:05 PM Jark Wu <imj...@gmail.com>
>> >>>>> wrote:
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> Hi Hao,
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>> I meant how the table name
>> >>>>>>>>>>>>>>> in window TVF gets translated to `SqlCallingBinding`.
>> >>> Probably
>> >>>>> we
>> >>>>>>>>>>> need
>> >>>>>>>>>>>>> to
>> >>>>>>>>>>>>>>> fetch the table definition from the catalog somewhere. Do
>> >> we
>> >>>>> treat
>> >>>>>>>>>>>>> those
>> >>>>>>>>>>>>>>> window TVF specially in parser/planner so that catalog is
>> >>>> looked
>> >>>>>>>> up
>> >>>>>>>>>>>>> when
>> >>>>>>>>>>>>>>> they are seen?
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> The table names are resolved and validated by Calcite
>> >>>>>>>> SqlValidator.
>> >>>>>>>>>>> We
>> >>>>>>>>>>>>>>> don' need to fetch from catalog manually.
>> >>>>>>>>>>>>>>> The specific checking logic of cumulate window happens in
>> >>>>>>>>>>>>>>>
>> >>>> SqlCumulateTableFunction.OperandMetadataImpl#checkOperandTypes.
>> >>>>>>>>>>>>>>> The return type of SqlCumulateTableFunction is defined in
>> >>>>>>>>>>>>>>> #getRowTypeInference() method.
>> >>>>>>>>>>>>>>> Both are public interfaces provided by Calcite and it
>> >> seems
>> >>>> it's
>> >>>>>>>>>> not
>> >>>>>>>>>>>>>>> specially handled in parser/planner.
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> I didn't try that, but my gut feeling is that the
>> >> framework
>> >>> is
>> >>>>>>>>>> ready
>> >>>>>>>>>>> to
>> >>>>>>>>>>>>>>> extend a customized TVF.
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>> For what model is, I'm wondering if it has to be datatype
>> >>> or
>> >>>>>>>>>>> relation.
>> >>>>>>>>>>>>>>> Can
>> >>>>>>>>>>>>>>> it be another kind of citizen parallel to
>> >>>>>>>>>>>>> datatype/relation/function/db?
>> >>>>>>>>>>>>>>> Redshift also supports `show models` operation, so it
>> >> seems
>> >>>> it's
>> >>>>>>>>>>>>> treated
>> >>>>>>>>>>>>>>> specially as well?
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> If it is an entity only used in catalog scope (e.g., show
>> >>> xxx,
>> >>>>>>>>>> create
>> >>>>>>>>>>>>> xxx,
>> >>>>>>>>>>>>>>> drop xxx), it is fine to introduce it.
>> >>>>>>>>>>>>>>> We have introduced such one before, called Module: "load
>> >>>>> module",
>> >>>>>>>>>>> "show
>> >>>>>>>>>>>>>>> modules" [1].
>> >>>>>>>>>>>>>>> But if we want to use Model in TVF parameters, it means it
>> >>> has
>> >>>>> to
>> >>>>>>>>>> be
>> >>>>>>>>>>> a
>> >>>>>>>>>>>>>>> relation or datatype, because
>> >>>>>>>>>>>>>>> that is what it only accepts now.
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> Thanks for sharing the reason of preferring TVF instead of
>> >>>>>>>> Redshift
>> >>>>>>>>>>>>> way. It
>> >>>>>>>>>>>>>>> sounds reasonable to me.
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> Best,
>> >>>>>>>>>>>>>>> Jark
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> [1]:
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/modules/
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>> On Fri, 15 Mar 2024 at 13:41, Hao Li
>> >>> <h...@confluent.io.invalid
>> >>>>>
>> >>>>>>>>>>> wrote:
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>> Hi Jark,
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>> Thanks for the pointer. Sorry for the confusion: I meant
>> >>> how
>> >>>>> the
>> >>>>>>>>>>> table
>> >>>>>>>>>>>>>>> name
>> >>>>>>>>>>>>>>>> in window TVF gets translated to `SqlCallingBinding`.
>> >>>> Probably
>> >>>>> we
>> >>>>>>>>>>>>> need to
>> >>>>>>>>>>>>>>>> fetch the table definition from the catalog somewhere. Do
>> >>> we
>> >>>>>>>> treat
>> >>>>>>>>>>>>> those
>> >>>>>>>>>>>>>>>> window TVF specially in parser/planner so that catalog is
>> >>>>> looked
>> >>>>>>>>>> up
>> >>>>>>>>>>>>> when
>> >>>>>>>>>>>>>>>> they are seen?
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>> For what model is, I'm wondering if it has to be datatype
>> >>> or
>> >>>>>>>>>>> relation.
>> >>>>>>>>>>>>>>> Can
>> >>>>>>>>>>>>>>>> it be another kind of citizen parallel to
>> >>>>>>>>>>>>> datatype/relation/function/db?
>> >>>>>>>>>>>>>>>> Redshift also supports `show models` operation, so it
>> >> seems
>> >>>>> it's
>> >>>>>>>>>>>>> treated
>> >>>>>>>>>>>>>>>> specially as well? The reasons I don't like Redshift's
>> >>> syntax
>> >>>>>>>> are:
>> >>>>>>>>>>>>>>>> 1. It's a bit verbose, you need to think of a model name
>> >> as
>> >>>>> well
>> >>>>>>>>>> as
>> >>>>>>>>>>> a
>> >>>>>>>>>>>>>>>> function name and the function name also needs to be
>> >>> unique.
>> >>>>>>>>>>>>>>>> 2. More importantly, prediction function isn't the only
>> >>>>> function
>> >>>>>>>>>>> that
>> >>>>>>>>>>>>> can
>> >>>>>>>>>>>>>>>> operate on models. There could be a set of inference
>> >>>> functions
>> >>>>>>>> [1]
>> >>>>>>>>>>> and
>> >>>>>>>>>>>>>>>> evaluation functions [2] which can operate on models.
>> >> It's
>> >>>> hard
>> >>>>>>>> to
>> >>>>>>>>>>>>>>> specify
>> >>>>>>>>>>>>>>>> all of them in model creation.
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>> [1]:
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-predict
>> >>>>>>>>>>>>>>>> [2]:
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>> Thanks,
>> >>>>>>>>>>>>>>>> Hao
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>> On Thu, Mar 14, 2024 at 8:18 PM Jark Wu <
>> >> imj...@gmail.com>
>> >>>>>>>> wrote:
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>> Hi Hao,
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>> Can you send me some pointers
>> >>>>>>>>>>>>>>>>> where the function gets the table information?
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>> Here is the code of cumulate window type checking [1].
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>> Also is it possible to support <query_stmt> in
>> >>>>>>>>>>>>>>>>> window functions in addiction to table?
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>> Yes. It is not allowed in TVF.
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>> Thanks for the syntax links of other systems. The
>> >> reason I
>> >>>>>>>> prefer
>> >>>>>>>>>>> the
>> >>>>>>>>>>>>>>>>> Redshift way is
>> >>>>>>>>>>>>>>>>> that it avoids introducing Model as a relation or
>> >> datatype
>> >>>>>>>>>>>>> (referenced
>> >>>>>>>>>>>>>>>> as a
>> >>>>>>>>>>>>>>>>> parameter in TVF).
>> >>>>>>>>>>>>>>>>> Model is not a relation because it can be queried
>> >> directly
>> >>>>>>>> (e.g.,
>> >>>>>>>>>>>>>>> SELECT
>> >>>>>>>>>>>>>>>> *
>> >>>>>>>>>>>>>>>>> FROM model).
>> >>>>>>>>>>>>>>>>> I'm also confused about making Model as a datatype,
>> >>> because
>> >>>> I
>> >>>>>>>>>> don't
>> >>>>>>>>>>>>>>> know
>> >>>>>>>>>>>>>>>>> what class the
>> >>>>>>>>>>>>>>>>> model parameter of the eval method of
>> >>>>>>>>>> TableFunction/ScalarFunction
>> >>>>>>>>>>>>>>> should
>> >>>>>>>>>>>>>>>>> be. By defining
>> >>>>>>>>>>>>>>>>> the function with the model, users can directly invoke
>> >> the
>> >>>>>>>>>> function
>> >>>>>>>>>>>>>>>> without
>> >>>>>>>>>>>>>>>>> reference to the model name.
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>> Best,
>> >>>>>>>>>>>>>>>>> Jark
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>> [1]:
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://github.com/apache/flink/blob/d6c7eee8243b4fe3e593698f250643534dc79cb5/flink-table/flink-table-planner/src/main/java/org/apache/flink/table/planner/functions/sql/SqlCumulateTableFunction.java#L53
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>> On Fri, 15 Mar 2024 at 02:48, Hao Li
>> >>>> <h...@confluent.io.invalid
>> >>>>>>
>> >>>>>>>>>>>>> wrote:
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>> Hi Jark,
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>> Thanks for the pointers. It's very helpful.
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>> 1. Looks like `tumble`, `hopping` are keywords in
>> >> calcite
>> >>>>>>>>>> parser.
>> >>>>>>>>>>>>> And
>> >>>>>>>>>>>>>>>> the
>> >>>>>>>>>>>>>>>>>> syntax `cumulate(Table my_table, ...)` needs to get
>> >> table
>> >>>>>>>>>>>>> information
>> >>>>>>>>>>>>>>>>> from
>> >>>>>>>>>>>>>>>>>> catalog somewhere for type validation etc. Can you send
>> >>> me
>> >>>>> some
>> >>>>>>>>>>>>>>>> pointers
>> >>>>>>>>>>>>>>>>>> where the function gets the table information?
>> >>>>>>>>>>>>>>>>>> 2. The ideal syntax for model function I think would be
>> >>>>>>>>>>>>>>>> `ML_PREDICT(MODEL
>> >>>>>>>>>>>>>>>>>> <model_name>, {table <table_name> | (query_stmt) })`. I
>> >>>> think
>> >>>>>>>>>> with
>> >>>>>>>>>>>>>>>>> special
>> >>>>>>>>>>>>>>>>>> handling of the `ML_PREDICT` function in
>> >> parser/planner,
>> >>>>> maybe
>> >>>>>>>>>> we
>> >>>>>>>>>>>>> can
>> >>>>>>>>>>>>>>>> do
>> >>>>>>>>>>>>>>>>>> this like window functions. But to support `MODEL`
>> >>> keyword,
>> >>>>> we
>> >>>>>>>>>>> need
>> >>>>>>>>>>>>>>>>> calcite
>> >>>>>>>>>>>>>>>>>> parser change I guess. Also is it possible to support
>> >>>>>>>>>> <query_stmt>
>> >>>>>>>>>>>>> in
>> >>>>>>>>>>>>>>>>>> window functions in addiction to table?
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>> For the redshift syntax, I'm not sure the purpose of
>> >>>> defining
>> >>>>>>>>>> the
>> >>>>>>>>>>>>>>>>> function
>> >>>>>>>>>>>>>>>>>> name with the model. Is it to define the function
>> >>>>> input/output
>> >>>>>>>>>>>>>>> schema?
>> >>>>>>>>>>>>>>>> We
>> >>>>>>>>>>>>>>>>>> have the schema in our create model syntax and the
>> >>>>> `ML_PREDICT`
>> >>>>>>>>>>> can
>> >>>>>>>>>>>>>>>>> handle
>> >>>>>>>>>>>>>>>>>> it by getting model definition. I think our syntax is
>> >>> more
>> >>>>>>>>>> concise
>> >>>>>>>>>>>>> to
>> >>>>>>>>>>>>>>>>> have
>> >>>>>>>>>>>>>>>>>> a generic prediction function. I also did some research
>> >>> and
>> >>>>>>>> it's
>> >>>>>>>>>>> the
>> >>>>>>>>>>>>>>>>> syntax
>> >>>>>>>>>>>>>>>>>> used by Databricks `ai_query` [1], Snowflake `predict`
>> >>> [2],
>> >>>>>>>>>>> Azureml
>> >>>>>>>>>>>>>>>>>> `predict` [3].
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>> [1]:
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://docs.databricks.com/en/sql/language-manual/functions/ai_query.html
>> >>>>>>>>>>>>>>>>>> [2]:
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://github.com/Snowflake-Labs/sfguide-intro-to-machine-learning-with-snowpark-ml-for-python/blob/main/3_snowpark_ml_model_training_inference.ipynb?_fsi=sksXUwQ0
>> >>>>>>>>>>>>>>>>>> [3]:
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://learn.microsoft.com/en-us/sql/machine-learning/tutorials/quickstart-python-train-score-model?view=azuresqldb-mi-current
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>> Thanks,
>> >>>>>>>>>>>>>>>>>> Hao
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>> On Wed, Mar 13, 2024 at 8:57 PM Jark Wu <
>> >>> imj...@gmail.com>
>> >>>>>>>>>> wrote:
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>> Hi Mingge, Hao,
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>> Thanks for your replies.
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>> PTF is actually the ideal approach for model
>> >> functions,
>> >>>> and
>> >>>>>>>> we
>> >>>>>>>>>>> do
>> >>>>>>>>>>>>>>>>> have
>> >>>>>>>>>>>>>>>>>>> the plans to use PTF for
>> >>>>>>>>>>>>>>>>>>> all model functions (including prediction, evaluation
>> >>>> etc..)
>> >>>>>>>>>> once
>> >>>>>>>>>>>>>>> the
>> >>>>>>>>>>>>>>>>> PTF
>> >>>>>>>>>>>>>>>>>>> is supported in FlinkSQL
>> >>>>>>>>>>>>>>>>>>> confluent extension.
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>> It sounds that PTF is the ideal way and table function
>> >>> is
>> >>>> a
>> >>>>>>>>>>>>>>> temporary
>> >>>>>>>>>>>>>>>>>>> solution which will be dropped in the future.
>> >>>>>>>>>>>>>>>>>>> I'm not sure whether we can implement it using PTF in
>> >>>> Flink
>> >>>>>>>>>> SQL.
>> >>>>>>>>>>>>>>> But
>> >>>>>>>>>>>>>>>> we
>> >>>>>>>>>>>>>>>>>>> have implemented window
>> >>>>>>>>>>>>>>>>>>> functions using PTF[1]. And introduced a new window
>> >>>> function
>> >>>>>>>>>>>>>>> (called
>> >>>>>>>>>>>>>>>>>>> CUMULATE[2]) in Flink SQL based
>> >>>>>>>>>>>>>>>>>>> on this. I think it might work to use PTF and
>> >> implement
>> >>>>> model
>> >>>>>>>>>>>>>>>> function
>> >>>>>>>>>>>>>>>>>>> syntax like this:
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>> SELECT * FROM TABLE(ML_PREDICT(
>> >>>>>>>>>>>>>>>>>>> TABLE my_table,
>> >>>>>>>>>>>>>>>>>>> my_model,
>> >>>>>>>>>>>>>>>>>>> col1,
>> >>>>>>>>>>>>>>>>>>> col2
>> >>>>>>>>>>>>>>>>>>> ));
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>> Besides, did you consider following the way of AWS
>> >>>> Redshift
>> >>>>>>>>>> which
>> >>>>>>>>>>>>>>>>> defines
>> >>>>>>>>>>>>>>>>>>> model function with the model itself together?
>> >>>>>>>>>>>>>>>>>>> IIUC, a model is a black-box which defines input
>> >>>> parameters
>> >>>>>>>> and
>> >>>>>>>>>>>>>>>> output
>> >>>>>>>>>>>>>>>>>>> parameters which can be modeled into functions.
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>> Best,
>> >>>>>>>>>>>>>>>>>>> Jark
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>> [1]:
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/window-tvf/#session
>> >>>>>>>>>>>>>>>>>>> [2]:
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-145%3A+Support+SQL+windowing+table-valued+function#FLIP145:SupportSQLwindowingtablevaluedfunction-CumulatingWindows
>> >>>>>>>>>>>>>>>>>>> [3]:
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://github.com/aws-samples/amazon-redshift-ml-getting-started/blob/main/use-cases/bring-your-own-model-remote-inference/README.md#create-model
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>> On Wed, 13 Mar 2024 at 15:00, Hao Li
>> >>>>> <h...@confluent.io.invalid
>> >>>>>>>>>>>
>> >>>>>>>>>>>>>>>> wrote:
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>> Hi Jark,
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>> Thanks for your questions. These are good questions!
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>> 1. The polymorphism table function I was referring to
>> >>>>> takes a
>> >>>>>>>>>>>>>>> table
>> >>>>>>>>>>>>>>>>> as
>> >>>>>>>>>>>>>>>>>>>> input and outputs a table. So the syntax would be
>> >> like
>> >>>>>>>>>>>>>>>>>>>> ```
>> >>>>>>>>>>>>>>>>>>>> SELECT * FROM ML_PREDICT('model', (SELECT * FROM
>> >>>> my_table))
>> >>>>>>>>>>>>>>>>>>>> ```
>> >>>>>>>>>>>>>>>>>>>> As far as I know, this is not supported yet on Flink.
>> >>> So
>> >>>>>>>>>> before
>> >>>>>>>>>>>>>>>> it's
>> >>>>>>>>>>>>>>>>>>>> supported, one option for the predict function is
>> >> using
>> >>>>> table
>> >>>>>>>>>>>>>>>>> function
>> >>>>>>>>>>>>>>>>>>>> which can output multiple columns
>> >>>>>>>>>>>>>>>>>>>> ```
>> >>>>>>>>>>>>>>>>>>>> SELECT * FROM my_table, LATERAL VIEW
>> >>> (ML_PREDICT('model',
>> >>>>>>>>>> col1,
>> >>>>>>>>>>>>>>>>> col2))
>> >>>>>>>>>>>>>>>>>>>> ```
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>> 2. Good question. Type inference is hard for the
>> >>>>> `ML_PREDICT`
>> >>>>>>>>>>>>>>>>> function
>> >>>>>>>>>>>>>>>>>>>> because it takes a model name string as input. I can
>> >>>> think
>> >>>>> of
>> >>>>>>>>>>>>>>> three
>> >>>>>>>>>>>>>>>>>> ways
>> >>>>>>>>>>>>>>>>>>> of
>> >>>>>>>>>>>>>>>>>>>> doing type inference for it.
>> >>>>>>>>>>>>>>>>>>>> 1). Treat `ML_PREDICT` function as something
>> >>>> special
>> >>>>> and
>> >>>>>>>>>>>>>>> during
>> >>>>>>>>>>>>>>>>> sql
>> >>>>>>>>>>>>>>>>>>>> parsing or planning time, if it's encountered, we
>> >> need
>> >>> to
>> >>>>>>>> look
>> >>>>>>>>>>> up
>> >>>>>>>>>>>>>>>> the
>> >>>>>>>>>>>>>>>>>>> model
>> >>>>>>>>>>>>>>>>>>>> from the first argument which is a model name from
>> >>>> catalog.
>> >>>>>>>>>> Then
>> >>>>>>>>>>>>>>> we
>> >>>>>>>>>>>>>>>>> can
>> >>>>>>>>>>>>>>>>>>>> infer the input/output for the function.
>> >>>>>>>>>>>>>>>>>>>> 2). We can define a `model` keyword and use
>> >> that
>> >>> in
>> >>>>> the
>> >>>>>>>>>>>>>>> predict
>> >>>>>>>>>>>>>>>>>>> function
>> >>>>>>>>>>>>>>>>>>>> to indicate the argument refers to a model. So it's
>> >>> like
>> >>>>>>>>>>>>>>>>>>> `ML_PREDICT(model
>> >>>>>>>>>>>>>>>>>>>> 'my_model', col1, col2))`
>> >>>>>>>>>>>>>>>>>>>> 3). We can create a special type of table
>> >>> function
>> >>>>> maybe
>> >>>>>>>>>>>>>>> called
>> >>>>>>>>>>>>>>>>>>>> `ModelFunction` which can resolve the model type
>> >>>> inference
>> >>>>> by
>> >>>>>>>>>>>>>>>> special
>> >>>>>>>>>>>>>>>>>>>> handling it during parsing or planning time.
>> >>>>>>>>>>>>>>>>>>>> 1) is hacky, 2) isn't supported in Flink for
>> >> function,
>> >>> 3)
>> >>>>>>>>>> might
>> >>>>>>>>>>>>>>> be
>> >>>>>>>>>>>>>>>> a
>> >>>>>>>>>>>>>>>>>>>> good option.
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>> 3. I sketched the `ML_PREDICT` function for
>> >> inference.
>> >>>> But
>> >>>>>>>>>> there
>> >>>>>>>>>>>>>>>> are
>> >>>>>>>>>>>>>>>>>>>> limitations of the function mentioned in 1 and 2. So
>> >>>> maybe
>> >>>>> we
>> >>>>>>>>>>>>>>> don't
>> >>>>>>>>>>>>>>>>>> need
>> >>>>>>>>>>>>>>>>>>> to
>> >>>>>>>>>>>>>>>>>>>> introduce them as built-in functions until
>> >> polymorphism
>> >>>>> table
>> >>>>>>>>>>>>>>>>> function
>> >>>>>>>>>>>>>>>>>>> and
>> >>>>>>>>>>>>>>>>>>>> we can properly deal with type inference.
>> >>>>>>>>>>>>>>>>>>>> After that, defining a user-defined model function
>> >>> should
>> >>>>>>>> also
>> >>>>>>>>>>> be
>> >>>>>>>>>>>>>>>>>>>> straightforward.
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>> 4. For model types, do you mean 'remote', 'import',
>> >>>>> 'native'
>> >>>>>>>>>>>>>>> models
>> >>>>>>>>>>>>>>>>> or
>> >>>>>>>>>>>>>>>>>>>> other things?
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>> 5. We could support popular providers such as
>> >>> 'azureml',
>> >>>>>>>>>>>>>>>> 'vertexai',
>> >>>>>>>>>>>>>>>>>>>> 'googleai' as long as we support the `ML_PREDICT`
>> >>>> function.
>> >>>>>>>>>>> Users
>> >>>>>>>>>>>>>>>>>> should
>> >>>>>>>>>>>>>>>>>>> be
>> >>>>>>>>>>>>>>>>>>>> able to implement 3rd-party providers if they can
>> >>>>> implement a
>> >>>>>>>>>>>>>>>>> function
>> >>>>>>>>>>>>>>>>>>>> handling the input/output for the provider.
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>> I think for the model functions, there are still
>> >>>>> dependencies
>> >>>>>>>>>> or
>> >>>>>>>>>>>>>>>>> hacks
>> >>>>>>>>>>>>>>>>>> we
>> >>>>>>>>>>>>>>>>>>>> need to sort out as a built-in function. Maybe we can
>> >>>>>>>> separate
>> >>>>>>>>>>>>>>> that
>> >>>>>>>>>>>>>>>>> as
>> >>>>>>>>>>>>>>>>>> a
>> >>>>>>>>>>>>>>>>>>>> follow up if we want to have it built-in and focus on
>> >>> the
>> >>>>>>>>>> model
>> >>>>>>>>>>>>>>>>> syntax
>> >>>>>>>>>>>>>>>>>>> for
>> >>>>>>>>>>>>>>>>>>>> this FLIP?
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>> Thanks,
>> >>>>>>>>>>>>>>>>>>>> Hao
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>> On Tue, Mar 12, 2024 at 10:33 PM Jark Wu <
>> >>>> imj...@gmail.com
>> >>>>>>
>> >>>>>>>>>>>>>>> wrote:
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>> Hi Minge, Chris, Hao,
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>> Thanks for proposing this interesting idea. I think
>> >>> this
>> >>>>> is
>> >>>>>>>> a
>> >>>>>>>>>>>>>>>> nice
>> >>>>>>>>>>>>>>>>>> step
>> >>>>>>>>>>>>>>>>>>>>> towards
>> >>>>>>>>>>>>>>>>>>>>> the AI world for Apache Flink. I don't know much
>> >> about
>> >>>>>>>> AI/ML,
>> >>>>>>>>>>>>>>> so
>> >>>>>>>>>>>>>>>> I
>> >>>>>>>>>>>>>>>>>> may
>> >>>>>>>>>>>>>>>>>>>> have
>> >>>>>>>>>>>>>>>>>>>>> some stupid questions.
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>> 1. Could you tell more about why polymorphism table
>> >>>>> function
>> >>>>>>>>>>>>>>>> (PTF)
>> >>>>>>>>>>>>>>>>>>>> doesn't
>> >>>>>>>>>>>>>>>>>>>>> work and do we have plan to use PTF as model
>> >>> functions?
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>> 2. What kind of object does the model map to in
>> >> SQL? A
>> >>>>>>>>>> relation
>> >>>>>>>>>>>>>>>> or
>> >>>>>>>>>>>>>>>>> a
>> >>>>>>>>>>>>>>>>>>> data
>> >>>>>>>>>>>>>>>>>>>>> type?
>> >>>>>>>>>>>>>>>>>>>>> It looks like a data type because we use it as a
>> >>>> parameter
>> >>>>>>>> of
>> >>>>>>>>>>>>>>> the
>> >>>>>>>>>>>>>>>>>> table
>> >>>>>>>>>>>>>>>>>>>>> function.
>> >>>>>>>>>>>>>>>>>>>>> If it is a data type, how does it cooperate with
>> >> type
>> >>>>>>>>>>>>>>>> inference[1]?
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>> 3. What built-in model functions will we support?
>> >> How
>> >>> to
>> >>>>>>>>>>>>>>> define a
>> >>>>>>>>>>>>>>>>>>>>> user-defined model function?
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>> 4. What built-in model types will we support? How to
>> >>>>> define
>> >>>>>>>> a
>> >>>>>>>>>>>>>>>>>>>> user-defined
>> >>>>>>>>>>>>>>>>>>>>> model type?
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>> 5. Regarding the remote model, what providers will
>> >> we
>> >>>>>>>>>> support?
>> >>>>>>>>>>>>>>>> Can
>> >>>>>>>>>>>>>>>>>>> users
>> >>>>>>>>>>>>>>>>>>>>> implement
>> >>>>>>>>>>>>>>>>>>>>> 3rd-party providers except OpenAI?
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>> Best,
>> >>>>>>>>>>>>>>>>>>>>> Jark
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>> [1]:
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/functions/udfs/#type-inference
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>> On Wed, 13 Mar 2024 at 05:55, Hao Li
>> >>>>>>>>>> <h...@confluent.io.invalid
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>> wrote:
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>> Hi, Dev
>> >>>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>> Mingge, Chris and I would like to start a
>> >> discussion
>> >>>>> about
>> >>>>>>>>>>>>>>>>>> FLIP-437:
>> >>>>>>>>>>>>>>>>>>>>>> Support ML Models in Flink SQL.
>> >>>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>> This FLIP is proposing to support machine learning
>> >>>> models
>> >>>>>>>> in
>> >>>>>>>>>>>>>>>>> Flink
>> >>>>>>>>>>>>>>>>>>> SQL
>> >>>>>>>>>>>>>>>>>>>>>> syntax so that users can CRUD models with Flink SQL
>> >>> and
>> >>>>> use
>> >>>>>>>>>>>>>>>>> models
>> >>>>>>>>>>>>>>>>>> on
>> >>>>>>>>>>>>>>>>>>>>> Flink
>> >>>>>>>>>>>>>>>>>>>>>> to do prediction with Flink data. The FLIP also
>> >>>> proposes
>> >>>>>>>> new
>> >>>>>>>>>>>>>>>>> model
>> >>>>>>>>>>>>>>>>>>>>> entities
>> >>>>>>>>>>>>>>>>>>>>>> and changes to catalog interface to support model
>> >>> CRUD
>> >>>>>>>>>>>>>>>> operations
>> >>>>>>>>>>>>>>>>>> in
>> >>>>>>>>>>>>>>>>>>>>>> catalog.
>> >>>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>> For more details, see FLIP-437 [1]. Looking forward
>> >>> to
>> >>>>> your
>> >>>>>>>>>>>>>>>>>> feedback.
>> >>>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>> [1]
>> >>>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-437%3A+Support+ML+Models+in+Flink+SQL
>> >>>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>> Thanks,
>> >>>>>>>>>>>>>>>>>>>>>> Minge, Chris & Hao
>> >>>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>>
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>>
>> >>>>>>>>
>> >>>>>>>>
>> >>>>>>
>> >>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>
>> >
>>
>>
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