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
>> >>>>>>>>>>>>>>
>> >>>>>>>>>>>>>
>> >>>>>>>>>>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>>
>> >>>>>>>>
>> >>>>>>>
>> >>>>>>
>> >>>>>
>> >>>>>
>> >>>
>> >>
>> >
>>
>>
