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From xkrogen <xkro...@gmail.com>
Subject Re: [DISCUSS] SPIP: FunctionCatalog
Date Sat, 27 Feb 2021 00:47:51 GMT
> Correct me if I'm wrong, but it appears we've basically agreed upon the
APIs proposed in the SPIP (forget the naming part):

I don't think that's the case. Wenchen's proposal is that the *primary* API
is one discovered via reflection which detects methods by their types. The
InternalRow API would be the add-on for supporting varargs. This is in
opposition to Ryan's proposal which says the *primary* API is the
InternalRow API, with a reflective API being the add-on. This is important
because of Wenchen's point about forcing users to implement the InternalRow
API even if they prefer the reflective API.

> > I think that the InternalRow option is easier to build against because
it provides at least some type checking when accessing values from the
input row.
> I have a different opinion about this. If the input is string type but
the UDF implementation calls `row.getLong(0)`, *it returns wrong data*,
which is very bad. With the individual-parameters approach, if you
implement UDF with `def call(input: Long)` but the input is string type,
analyzer can detect it and fail the query.

I am in agreement with Wenchen on this point. I think we should consider
query-compile-time checks as nearly-as-good as Java-compile-time checks for
the purposes of safety. I believe that Wenchen's proposal will provide
*stronger
query-compile-time safety* (i.e. fewer runtime issues) at the expense of *less
Java-compile-time safety*, which seems like a good tradeoff. This also
pushes more complexity onto the Spark implementation side for the purposes
of reflectively discovering methods and applying casts as necessary, but
again, I see this as a good tradeoff for providing what seems to me to be a
more user-friendly (albeit slightly more "magical") API.

The biggest questions to me are whether the Spark-side implementation for
the reflective API will become too complex to implement well (one of the
strengths of the InternalRow API is its simplicity), and whether type
erasure will hurt the ability to do reasonable reflective discovery on
complex types. To resolve these, I would love to see a POC of Wenchen's
proposal.

I am supportive of moving forward with committing a version of the PR that
does *not* include the UDF APIs to make concrete progress towards this SPIP
while the discussion plays out, but I also feel Ryan's concern that the API
is too integral to the SPIP to move forward without it is reasonable. At
this time I'm not supportive of merging the PR as-is because I do not think
the API debate has been reasonably settled and it will inevitably be harder
to change later rather than getting right the first time.

Really appreciate the active and productive discussion on both sides here!
Thanks,
Erik

On Fri, Feb 26, 2021 at 3:38 PM Chao Sun <sunchao@apache.org> wrote:

> Correct me if I'm wrong, but it appears we've basically agreed upon the
> APIs proposed in the SPIP (forget the naming part):
>
> interface ScalarFunction extends BoundFunction<R> {
>   R produceResult(InternalRow args);
> }
> interface AggregateFunction<S, R> extends BoundFunction<R> {
>   S update(S state, InternalRow input);
> }
>
> together with the rest of the design such as FunctionCatalog and binding
> process.
>
> The argument at the moment seems to be whether we want to have
> SupportsInvoke or [Scalar|Aggregate]FunctionN alongside these, is that
> correct?
>  In order to move this forward, perhaps we can *merge the PR as it is* (maybe
> we'll need a vote?) and proceed to discuss these topics? We can also then
> present separate PRs on top of it, which can help a lot for people within
> this thread to provide comments.
>
> WDYT?
>
> Best,
> Chao
>
> On Wed, Feb 24, 2021 at 10:45 PM Wenchen Fan <cloud0fan@gmail.com> wrote:
>
>> I think there is one agreement between us: we need both the
>> individual-parameters and row-parameter APIs(your SupportsInvoke
>> proposal and my VarargsScalarFunction proposal). IIUC the argument now
>> is how to compose these 2 APIs.
>>
>> Your proposal is to put the row-parameter API in the base ScalaFunction
>> interface, with an optional SupportsInvoke interface for the
>> individual-parameters API. I don't like it because it promotes the
>> row-parameter API and forces users to implement it, even if the users want
>> to only use the individual-parameters API.
>>
>> My proposal is to leave the choice to the users. They can pick one from
>> ScalarFunction0, ScalarFunction1, ..., VarargsScalarFunction.
>>
>> More replies below:
>>
>> > We agree that ScalarFunction and AggregateFunction can optionally
>> define methods for Spark to directly call in codegen
>>
>> I don't think we agree with it. Whatever UDF API we choose at the end
>> (either individual-parameters or row-parameter), both non-codegen and
>> codegen code paths should just call these Java methods from the UDF API. It
>> doesn't make sense to have different UDF APIs for non-codegen and codegen.
>>
>> > The second option is to introduce 9 or more interfaces to break out the
>> fields of the input row, and an additional Object[] variation for varargs:
>>
>> My initial idea is to not have these 9 interfaces and fully rely on Java
>> reflection. We can do some benchmark, if reflection is not that slow, I
>> think we don't need to add these 9 interfaces. Preso UDF API takes the same
>> approach. And one correction: my proposal is to use InternalRow for
>> varargs UDF, not Object[].
>>
>> > Spark will need additional code to call the right method based on
>> input, so it will either have 10 wrapper classes or a big match statement
>>
>> You can take a look at the Spark ScalaUDF
>> <https://github.com/apache/spark/blob/v3.1.1-rc3/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUDF.scala>
>> expression. It has a big match statement for the non-codegen path, but the
>> codegen path is much simpler because we can generate the exact Java code to
>> call the specific UDF. I don't think it's a big problem, or we can use
>> reflection in the non-codegen path to avoid the big match statement.
>>
>> > Spark is always going to essentially call the raw interface with no
>> specific type parameters. As a result, incorrect types (like String) will
>> compile but fail at runtime with ClassCastException.
>>
>> You seem to keep ignoring my proposal that we can check the UDF function
>> signature at the analysis phase to make sure it matches the input types.
>> And with codegen Spark can call the specific function to avoid boxing
>> issues. If you missed my previous example, here is what the generated code
>> looks like:
>>
>> double input1 = ...;
>> double input2 = ...;
>> DoubleAdd udf = ...;
>> double res = udf.call(input1, input2);
>>
>> > I think that the InternalRow option is easier to build against because
>> it provides at least some type checking when accessing values from the
>> input row.
>>
>> I have a different opinion about this. If the input is string type but
>> the UDF implementation calls `row.getLong(0)`, *it returns wrong data*,
>> which is very bad. With the individual-parameters approach, if you
>> implement UDF with `def call(input: Long)` but the input is string type,
>> analyzer can detect it and fail the query.
>>
>> On Thu, Feb 25, 2021 at 6:48 AM Ryan Blue <rblue@netflix.com> wrote:
>>
>>> How functions are called is a really big element of this effort. I don’t
>>> want to get in a position where we’ve started committing changes without
>>> clear agreement on something so fundamental to the proposal. I’d like to
>>> make sure we’re in agreement with a vote on the SPIP before committing
>>> anything. That is, after all, the point of the SPIPs.
>>>
>>> If people think it would help to have an alternative API in a PR, then
>>> that’s fine with me.
>>>
>>> Since that PR suggestion is intended to make it easier to understand the
>>> technical details, I’ll try to summarize where we’re at now:
>>>
>>>    - We agree on the scope of adding FunctionCatalog to load functions
>>>    - We agree with the FunctionCatalog methods and the function binding
>>>    approach
>>>    - We agree that a bound function will be either a ScalarFunction or
>>>    an AggregateFunction (plus the mix-in PartialAggregateFunction)
>>>    - We agree that values should be passed should be Spark’s internal
>>>    representation to avoid translation
>>>    - We agree that ScalarFunction and AggregateFunction can optionally
>>>    define methods for Spark to directly call in codegen
>>>
>>> The disagreement is about how to call functions when codegen isn’t used
>>> or when the function needs to support variable-length argument lists. There
>>> are two options:
>>>
>>> The first option is for each function to have a method that accepts an
>>> InternalRow, from the proposed SPIP:
>>>
>>> interface ScalarFunction extends BoundFunction<R> {
>>>   R produceResult(InternalRow input);
>>> }
>>> interface AggregateFunction<S> extends BoundFunction<R> {
>>>   S update(S state, InternalRow input);
>>>   ...
>>> }
>>>
>>> The second option is to introduce 9 or more interfaces to break out the
>>> fields of the input row, and an additional Object[] variation for
>>> varargs:
>>>
>>> interface ScalarFunction1<T1> extends BoundFunction<R> {
>>>   R produceResult(T1 one);
>>> }
>>> interface ScalarFunction2<T1, T2> extends BoundFunction<R> {
>>>   R produceResult(T1 one, T2 two);
>>> }
>>> ... 8 more ScalarFunction interfaces
>>> interface ScalarFunctionVarargs extends BoundFunction<R> {
>>>   R produceResult(Object[] args);
>>> }
>>> interface AggregateFunction<S, T1> extends BoundFunction<R> {
>>>   S update(S state, T1 one);
>>> }
>>> interface AggregateFunction<S, T1, T2> extends BoundFunction<R> {
>>>   S update(S state, T1 one, T2 two);
>>> }
>>> ... 8 more AggregateFunction interfaces
>>> interface AggregateFunctionVarargs<S> extends BoundFunction<R> {
>>>   S update(S state, Object[] args);
>>> }
>>>
>>> Because this is for the non-invoke case, the two options have roughly
>>> the same performance characteristics.
>>>
>>> The first option has some advantages:
>>>
>>>    - It is simpler: there are few interfaces and Spark will always find
>>>    the right method
>>>    - Accessing a value returns a concrete type, so it is less
>>>    error-prone. I’ve given an example where this helps identify a problem with
>>>    an invoke method.
>>>
>>> The second option’s advantage is that users have values broken out into
>>> arguments. That is, if I understand Wenchen correctly here: “I don’t like
>>> the SupportsInvoke approach as it still promotes the row-parameter API. I
>>> think the individual-parameters API is better for UDF developers.”
>>>
>>> Disadvantages with the second option:
>>>
>>>    - There are 20+ more interfaces in the API
>>>    - Spark will need additional code to call the right method based on
>>>    input, so it will either have 10 wrapper classes or a big match statement
>>>    that calls each interface separately (see UDFRegistration
>>>    <https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala>
>>>    ).
>>>    - Spark is always going to essentially call the raw interface with
>>>    no specific type parameters. As a result, incorrect types (like
>>>    String) will compile but fail at runtime with ClassCastException.
>>>    - The varargs case will result in casting to expected types, which
>>>    could also fail with ClassCastException
>>>
>>> I think that the InternalRow option is easier to build against because
>>> it provides at least some type checking when accessing values from the
>>> input row. You get compile-time checks when using the wrong type like this: String
>>> val = input.getString(0); won’t compile.
>>>
>>> Another important thing to note is that although the original idea was
>>> to keep the individual parameter approach simple, Wenchen has already
>>> suggested passing arrays as Java arrays, like UTF8String[]. This adds
>>> to the complexity of the overall solution and requires matching multiple
>>> types. How would Spark know to pass UTF8String[] or ArrayData?
>>>
>>> If anyone disagrees with that summary, please point out where it’s
>>> incorrect. But barring a major misunderstanding, I think the choice is
>>> clear: the simpler approach that provides additional compile-time safety is
>>> the right way to go.
>>>
>>> On Tue, Feb 23, 2021 at 1:48 AM Wenchen Fan <cloud0fan@gmail.com> wrote:
>>>
>>>> +1, as I already proposed we can move forward with PRs
>>>>
>>>> > To move forward, how about we implement the function loading and
>>>> binding first? Then we can have PRs for both the individual-parameters (I
>>>> can take it) and row-parameter approaches, if we still can't reach a
>>>> consensus at that time and need to see all the details.
>>>>
>>>> Ryan, can we focus on the function loading and binding part and get it
>>>> committed first? I can also fork your branch and put everything together,
>>>> but that might be too big to review.
>>>>
>>>> On Tue, Feb 23, 2021 at 4:35 PM Dongjoon Hyun <dongjoon.hyun@gmail.com>
>>>> wrote:
>>>>
>>>>> I've been still supporting Ryan's SPIP (original PR and its extension
>>>>> proposal discussed here) because of its simplicity.
>>>>>
>>>>> According to this email thread context, I also understand the
>>>>> different perspectives like Hyukjin's concerns about having multiple ways
>>>>> and Wenchen's proposal and rationales.
>>>>>
>>>>> It looks like we need more discussion to reach an agreement. And the
>>>>> technical details become more difficult to track because this is an email
>>>>> thread.
>>>>>
>>>>> Although Ryan initially suggested discussing this on Apache email
>>>>> thread instead of the PR, can we have a PR to discuss?
>>>>>
>>>>> Especially, Wenchen, could you make your PR based on Ryan's PR?
>>>>>
>>>>> If we collect the scattered ideas into a single PR, that would be
>>>>> greatly helpful not only for further discussions, but also when we go on a
>>>>> vote on Ryan's PR or Wenchen's PR.
>>>>>
>>>>> Bests,
>>>>> Dongjoon.
>>>>>
>>>>>
>>>>> On Mon, Feb 22, 2021 at 1:16 AM Wenchen Fan <cloud0fan@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi Walaa,
>>>>>>
>>>>>> Thanks for sharing this! The type signature stuff is already covered
>>>>>> by the unbound UDF API, which specifies the input and output data types.
>>>>>> The problem is how to check the method signature of the bound UDF. As you
>>>>>> said, Java has type erasure and we can't check `List<String>` for example.
>>>>>>
>>>>>> My initial proposal is to do nothing and simply pass the Spark
>>>>>> ArrayData, MapData, InternalRow to the UDF. This requires the UDF
>>>>>> developers to ensure the type is matched, as they need to call something
>>>>>> like `array.getLong(index)` with the corrected type name. It's as worse as
>>>>>> the row-parameter version but seems fine as it only happens with nested
>>>>>> types. And the type check is still done for the first level (the method
>>>>>> signature must use ArrayData/MapData/InternalRow at least).
>>>>>>
>>>>>> We can allow more types in the future to make the type check better.
>>>>>> It might be too detailed for this discussion thread but just put a few
>>>>>> thoughts:
>>>>>> 1. Java array doesn't do type erasure. We can use UTF8String[] for
>>>>>> example if the input type is array of string.
>>>>>> 2. For struct type, we can allow Java beans/Scala case classes if the
>>>>>> field name and type match the type signature.
>>>>>> 3. For map type, it's actually struct<keys: array<key_type>, values:
>>>>>> array<value_type>>, so we can also allow Java beans/Scala case
>>>>>> classes here.
>>>>>>
>>>>>> The general idea is to use stuff that can retain nested type
>>>>>> information at compile-time, i.e. array, java bean, case classes.
>>>>>>
>>>>>> Thanks,
>>>>>> Wenchen
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Mon, Feb 22, 2021 at 3:47 PM Walaa Eldin Moustafa <
>>>>>> wa.moustafa@gmail.com> wrote:
>>>>>>
>>>>>>> Wenchen, in Transport, users provide the input parameter signatures
>>>>>>> and output parameter signature as part of the API. Compile-time checks are
>>>>>>> done by parsing the type signatures and matching them to the type tree
>>>>>>> received at compile-time. This also helps with inferring the concrete
>>>>>>> output type.
>>>>>>>
>>>>>>> The specification in the UDF API looks like this:
>>>>>>>
>>>>>>>   @Override
>>>>>>>   public List<String> getInputParameterSignatures() {
>>>>>>>     return ImmutableList.of(
>>>>>>>         "ARRAY(K)",
>>>>>>>         "ARRAY(V)"
>>>>>>>     );
>>>>>>>   }
>>>>>>>
>>>>>>>   @Override
>>>>>>>   public String getOutputParameterSignature() {
>>>>>>>     return "MAP(K,V)";
>>>>>>>   }
>>>>>>>
>>>>>>> The benefits of this type of type signature specification as opposed
>>>>>>> to inferring types from Java type signatures given in the Java method are:
>>>>>>>
>>>>>>>    - For nested types, Java type erasure eliminates the information
>>>>>>>    about nested types, so for something like my_function(List<String>
>>>>>>>    a1, List<Integer> a2), the value of both a1.class or a2.class is
>>>>>>>    just a List. However, we are planning to work around this in a
>>>>>>>    future version
>>>>>>>    <https://github.com/linkedin/transport/tree/transport-api-v1/transportable-udfs-examples/transportable-udfs-example-udfs/src/main/java/com/linkedin/transport/examples> in
>>>>>>>    the case of Array and Map types. Struct types are discussed in the next
>>>>>>>    point.
>>>>>>>    - Without pre-code-generation there is no single Java type
>>>>>>>    signature from which we can capture the Struct info. However, Struct info
>>>>>>>    can be expressed in type signatures of the above type, e.g., ROW(FirstName
>>>>>>>    VARCHAR, LastName VARCHAR).
>>>>>>>
>>>>>>> When a Transport UDF represents a Spark UDF, the type signatures are
>>>>>>> matched against Spark native types, i.e., org.apache.spark.sql.types.{ArrayType,
>>>>>>> MapType, StructType}, and primitive types. The function that
>>>>>>> parses/compiles type signatures is found in AbstractTypeInference
>>>>>>> <https://github.com/linkedin/transport/blob/master/transportable-udfs-type-system/src/main/java/com/linkedin/transport/typesystem/AbstractTypeInference.java>. This
>>>>>>> class represents the generic component that is common between all supported
>>>>>>> engines. Its Spark-specific extension is in SparkTypeInference
>>>>>>> <https://github.com/linkedin/transport/blob/master/transportable-udfs-spark/src/main/scala/com/linkedin/transport/spark/typesystem/SparkTypeInference.scala>.
>>>>>>> In the above example, at compile time, if the first Array happens to be of
>>>>>>> String element type, and the second Array happens to be of Integer element
>>>>>>> type, the UDF will communicate to the Spark analyzer that the output should
>>>>>>> be of type MapData<String, Integer> (i.e., based on what was seen
>>>>>>> in the input at compile time). The whole UDF becomes a Spark
>>>>>>> Expression
>>>>>>> <https://github.com/linkedin/transport/blob/master/transportable-udfs-spark/src/main/scala/com/linkedin/transport/spark/StdUdfWrapper.scala#L24>
>>>>>>> at the end of the day.
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Walaa.
>>>>>>>
>>>>>>>
>>>>>>> On Sun, Feb 21, 2021 at 7:26 PM Wenchen Fan <cloud0fan@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> I think I have made it clear that it's simpler for the UDF
>>>>>>>> developers to deal with the input parameters directly, instead of getting
>>>>>>>> them from a row, as you need to provide the index and type (e.g.
>>>>>>>> row.getLong(0)). It's also coherent with the existing Spark
>>>>>>>> Scala/Java UDF APIs, so that Spark users will be more familiar with the
>>>>>>>> individual-parameters API.
>>>>>>>>
>>>>>>>> And I have explained it already that we can use reflection to make
>>>>>>>> sure the defined methods have the right types at query-compilation time.
>>>>>>>> It's better than leaving this problem to the UDF developers and asking them
>>>>>>>> to ensure the inputs are gotten from the row correctly with index and type.
>>>>>>>> If there are people from Presto/Transport, it will be great if you can
>>>>>>>> share how Presto/Transport do this check.
>>>>>>>>
>>>>>>>> I don't like 22 additional interfaces too, but if you look at the
>>>>>>>> examples I gave, the current Spark Java UDF
>>>>>>>> <https://github.com/apache/spark/tree/master/sql/core/src/main/java/org/apache/spark/sql/api/java> only
>>>>>>>> has 9 interfaces, and Transport
>>>>>>>> <https://github.com/linkedin/transport/tree/master/transportable-udfs-api/src/main/java/com/linkedin/transport/api/udf> has
>>>>>>>> 8. I think it's good enough and people can use
>>>>>>>> VarargsScalarFunction if they need to take more parameters or
>>>>>>>> varargs. It resolves your concern about doing reflection in the non-codegen
>>>>>>>> execution path that leads to bad performance, it also serves for
>>>>>>>> documentation purpose as people can easily see the number of UDF inputs and
>>>>>>>> their types by a quick glance.
>>>>>>>>
>>>>>>>> As I said, we need to investigate how to avoid boxing. Since you
>>>>>>>> are asking the question now, I spent sometime to think about it. I think
>>>>>>>> the DoubleAdd example is the way to go. For non-codegen code path,
>>>>>>>> we can just call the interface method. For the codegen code path, the
>>>>>>>> generated Java code would look like (omit the null check logic):
>>>>>>>>
>>>>>>>> double input1 = ...;
>>>>>>>> double input2 = ...;
>>>>>>>> DoubleAdd udf = ...;
>>>>>>>> double res = udf.call(input1, input2);
>>>>>>>>
>>>>>>>> Which invokes the primitive version automatically. AFAIK this is
>>>>>>>> also how Scala supports primitive type parameter (generate an extra
>>>>>>>> non-boxing version of the method). If the UDF doesn't have the primtive
>>>>>>>> version method, this code will just call the boxed version and still works.
>>>>>>>>
>>>>>>>> I don't like the SupportsInvoke approach as it still promotes the
>>>>>>>> row-parameter API. I think the individual-parameters API is better for UDF
>>>>>>>> developers. Can other people share your opinions about the API?
>>>>>>>>
>>>>>>>> On Sat, Feb 20, 2021 at 5:32 AM Ryan Blue <rblue@netflix.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> I don’t see any benefit to more complexity with 22 additional
>>>>>>>>> interfaces, instead of simply passing an InternalRow. Why not use
>>>>>>>>> a single interface with InternalRow? Maybe you could share your
>>>>>>>>> motivation?
>>>>>>>>>
>>>>>>>>> That would also result in strange duplication, where the
>>>>>>>>> ScalarFunction2 method is just the boxed version:
>>>>>>>>>
>>>>>>>>> class DoubleAdd implements ScalarFunction2<Double, Double, Double> {
>>>>>>>>>   @Override
>>>>>>>>>   Double produceResult(Double left, Double right) {
>>>>>>>>>     return left + right;
>>>>>>>>>   }
>>>>>>>>>
>>>>>>>>>   double produceResult(double left, double right) {
>>>>>>>>>     return left + right;
>>>>>>>>>   }
>>>>>>>>> }
>>>>>>>>>
>>>>>>>>> This would work okay, but would be awkward if you wanted to use
>>>>>>>>> the same implementation for any number of arguments, like a sum
>>>>>>>>> method that adds all of the arguments together and returns the result. It
>>>>>>>>> also isn’t great for varargs, since it is basically the same as the invoke
>>>>>>>>> case.
>>>>>>>>>
>>>>>>>>> The combination of an InternalRow method and the invoke method
>>>>>>>>> seems to be a good way to handle this to me. What is wrong with it? And,
>>>>>>>>> how would you solve the problem when implementations define methods with
>>>>>>>>> the wrong types? The InternalRow approach helps implementations
>>>>>>>>> catch that problem (as demonstrated above) and also provides a fallback
>>>>>>>>> when there is a but preventing the invoke optimization from working. That
>>>>>>>>> seems like a good approach to me.
>>>>>>>>>
>>>>>>>>> On Thu, Feb 18, 2021 at 11:31 PM Wenchen Fan <cloud0fan@gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> If people have such a big concern about reflection, we can follow
>>>>>>>>>> the current Spark Java UDF
>>>>>>>>>> <https://github.com/apache/spark/tree/master/sql/core/src/main/java/org/apache/spark/sql/api/java>
>>>>>>>>>> and Transport
>>>>>>>>>> <https://github.com/linkedin/transport/tree/master/transportable-udfs-api/src/main/java/com/linkedin/transport/api/udf>,
>>>>>>>>>> and create ScalarFuncion0[R], ScalarFuncion1[T1, R], etc. to
>>>>>>>>>> avoid reflection. But we may need to investigate how to avoid boxing with
>>>>>>>>>> this API design.
>>>>>>>>>>
>>>>>>>>>> To put a detailed proposal: let's have ScalarFuncion0,
>>>>>>>>>> ScalarFuncion1, ..., ScalarFuncion9 and VarargsScalarFunction.
>>>>>>>>>> At execution time, if Spark sees ScalarFuncion0-9, pass the
>>>>>>>>>> input columns to the UDF directly, one column one parameter. So string type
>>>>>>>>>> input is UTF8String, array type input is ArrayData. If Spark
>>>>>>>>>> sees VarargsScalarFunction, wrap the input columns with
>>>>>>>>>> InternalRow and pass it to the UDF.
>>>>>>>>>>
>>>>>>>>>> In general, if VarargsScalarFunction is implemented, the UDF
>>>>>>>>>> should not implement ScalarFuncion0-9. We can also define a
>>>>>>>>>> priority order to allow this. I don't have a strong preference here.
>>>>>>>>>>
>>>>>>>>>> What do you think?
>>>>>>>>>>
>>>>>>>>>> On Fri, Feb 19, 2021 at 1:24 PM Walaa Eldin Moustafa <
>>>>>>>>>> wa.moustafa@gmail.com> wrote:
>>>>>>>>>>
>>>>>>>>>>> I agree with Ryan on the questions around the expressivity of
>>>>>>>>>>> the Invoke method. It is not clear to me how the Invoke method can be used
>>>>>>>>>>> to declare UDFs with type-parameterized parameters. For example: a UDF to
>>>>>>>>>>> get the Nth element of an array (regardless of the Array element type) or a
>>>>>>>>>>> UDF to merge two Arrays (of generic types) to a Map.
>>>>>>>>>>>
>>>>>>>>>>> Also, to address Wenchen's InternalRow question, can we create a
>>>>>>>>>>> number of Function classes, each corresponding to a number of input
>>>>>>>>>>> parameter length (e.g., ScalarFunction1, ScalarFunction2, etc)?
>>>>>>>>>>>
>>>>>>>>>>> Thanks,
>>>>>>>>>>> Walaa.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Thu, Feb 18, 2021 at 6:07 PM Ryan Blue
>>>>>>>>>>> <rblue@netflix.com.invalid> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> I agree with you that it is better in many cases to directly
>>>>>>>>>>>> call a method. But it it not better in all cases, which is why I don’t
>>>>>>>>>>>> think it is the right general-purpose choice.
>>>>>>>>>>>>
>>>>>>>>>>>> First, if codegen isn’t used for some reason, the reflection
>>>>>>>>>>>> overhead is really significant. That gets much better when you have an
>>>>>>>>>>>> interface to call. That’s one reason I’d use this pattern:
>>>>>>>>>>>>
>>>>>>>>>>>> class DoubleAdd implements ScalarFunction<Double>, SupportsInvoke {
>>>>>>>>>>>>   Double produceResult(InternalRow row) {
>>>>>>>>>>>>     return produceResult(row.getDouble(0), row.getDouble(1));
>>>>>>>>>>>>   }
>>>>>>>>>>>>
>>>>>>>>>>>>   double produceResult(double left, double right) {
>>>>>>>>>>>>     return left + right;
>>>>>>>>>>>>   }
>>>>>>>>>>>> }
>>>>>>>>>>>>
>>>>>>>>>>>> There’s little overhead to adding the InternalRow variation,
>>>>>>>>>>>> but we could call it in eval to avoid the reflect overhead. To
>>>>>>>>>>>> the point about UDF developers, I think this is a reasonable cost.
>>>>>>>>>>>>
>>>>>>>>>>>> Second, I think usability is better and helps avoid runtime
>>>>>>>>>>>> issues. Here’s an example:
>>>>>>>>>>>>
>>>>>>>>>>>> class StrLen implements ScalarFunction<Integer>, SupportsInvoke {
>>>>>>>>>>>>   Integer produceResult(InternalRow row) {
>>>>>>>>>>>>     return produceResult(row.getString(0));
>>>>>>>>>>>>   }
>>>>>>>>>>>>
>>>>>>>>>>>>   Integer produceResult(String str) {
>>>>>>>>>>>>     return str.length();
>>>>>>>>>>>>   }
>>>>>>>>>>>> }
>>>>>>>>>>>>
>>>>>>>>>>>> See the bug? I forgot to use UTF8String instead of String.
>>>>>>>>>>>> With the InternalRow method, I get a compiler warning because
>>>>>>>>>>>> getString produces UTF8String that can’t be passed to
>>>>>>>>>>>> produceResult(String). If I decided to implement length
>>>>>>>>>>>> separately, then we could still run the InternalRow version
>>>>>>>>>>>> and log a warning. The code would be slightly slower, but wouldn’t fail.
>>>>>>>>>>>>
>>>>>>>>>>>> There are similar situations with varargs where it’s better to
>>>>>>>>>>>> call methods that produce concrete types than to cast from
>>>>>>>>>>>> Object to some expected type.
>>>>>>>>>>>>
>>>>>>>>>>>> I think that using invoke is a great extension to the proposal,
>>>>>>>>>>>> but I don’t think that it should be the only way to call functions. By all
>>>>>>>>>>>> means, let’s work on it in parallel and use it where possible. But I think
>>>>>>>>>>>> we do need the fallback of using InternalRow and that it isn’t
>>>>>>>>>>>> a usability problem to include it.
>>>>>>>>>>>>
>>>>>>>>>>>> Oh, and one last thought is that we already have users that
>>>>>>>>>>>> call Dataset.map and use InternalRow. This would allow converting that code
>>>>>>>>>>>> directly to a UDF.
>>>>>>>>>>>>
>>>>>>>>>>>> I think we’re closer to agreeing here than it actually looks.
>>>>>>>>>>>> Hopefully you’ll agree that having the InternalRow method
>>>>>>>>>>>> isn’t a big usability problem.
>>>>>>>>>>>>
>>>>>>>>>>>> On Wed, Feb 17, 2021 at 11:51 PM Wenchen Fan <
>>>>>>>>>>>> cloud0fan@gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> I don't see any objections to the rest of the proposal
>>>>>>>>>>>>> (loading functions from the catalog, function binding stuff, etc.) and I
>>>>>>>>>>>>> assume everyone is OK with it. We can commit that part first.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Currently, the discussion focuses on the `ScalarFunction`
>>>>>>>>>>>>> API, where I think it's better to directly take the input columns as the
>>>>>>>>>>>>> UDF parameter, instead of wrapping the input columns with
>>>>>>>>>>>>> InternalRow and taking the InternalRow as the UDF parameter.
>>>>>>>>>>>>> It's not only for better performance, but also for ease of use. For
>>>>>>>>>>>>> example, it's easier for the UDF developer to write `input1 +
>>>>>>>>>>>>> input2` than `inputRow.getLong(0) + inputRow.getLong(1)`, as
>>>>>>>>>>>>> they don't need to specify the type and index by themselves (
>>>>>>>>>>>>> getLong(0)) which is error-prone.
>>>>>>>>>>>>>
>>>>>>>>>>>>> It does push more work to the Spark side, but I think it's
>>>>>>>>>>>>> worth it if implementing UDF gets easier. I don't think the work is very
>>>>>>>>>>>>> challenging, as we can leverage the infra we built for the expression
>>>>>>>>>>>>> encoder.
>>>>>>>>>>>>>
>>>>>>>>>>>>> I think it's also important to look at the UDF API from the
>>>>>>>>>>>>> user's perspective (UDF developers). How do you like the UDF API without
>>>>>>>>>>>>> considering how Spark can support it? Do you prefer the
>>>>>>>>>>>>> individual-parameters version or the row-parameter version?
>>>>>>>>>>>>>
>>>>>>>>>>>>> To move forward, how about we implement the function loading
>>>>>>>>>>>>> and binding first? Then we can have PRs for both the individual-parameters
>>>>>>>>>>>>> (I can take it) and row-parameter approaches, if we still can't reach a
>>>>>>>>>>>>> consensus at that time and need to see all the details.
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Thu, Feb 18, 2021 at 4:48 AM Ryan Blue
>>>>>>>>>>>>> <rblue@netflix.com.invalid> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks, Hyukjin. I think that's a fair summary. And I agree
>>>>>>>>>>>>>> with the idea that we should focus on what Spark will do by default.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I think we should focus on the proposal, for two reasons:
>>>>>>>>>>>>>> first, there is a straightforward path to incorporate Wenchen's suggestion
>>>>>>>>>>>>>> via `SupportsInvoke`, and second, the proposal is more complete: it defines
>>>>>>>>>>>>>> a solution for many concerns like loading a function and finding out what
>>>>>>>>>>>>>> types to use -- not just how to call code -- and supports more use cases
>>>>>>>>>>>>>> like varargs functions. I think we can continue to discuss the rest of the
>>>>>>>>>>>>>> proposal and be confident that we can support an invoke code path where it
>>>>>>>>>>>>>> makes sense.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Does everyone agree? If not, I think we would need to solve a
>>>>>>>>>>>>>> lot of the challenges that I initially brought up with the invoke idea. It
>>>>>>>>>>>>>> seems like a good way to call a function, but needs a real proposal behind
>>>>>>>>>>>>>> it if we don't use it via `SupportsInvoke` in the current proposal.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Tue, Feb 16, 2021 at 11:07 PM Hyukjin Kwon <
>>>>>>>>>>>>>> gurwls223@gmail.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Just to make sure we don’t move past, I think we haven’t
>>>>>>>>>>>>>>> decided yet:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>    - if we’ll replace the current proposal to Wenchen’s
>>>>>>>>>>>>>>>    approach as the default
>>>>>>>>>>>>>>>    - if we want to have Wenchen’s approach as an optional
>>>>>>>>>>>>>>>    mix-in on the top of Ryan’s proposal (SupportsInvoke)
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> From what I read, some people pointed out it as a
>>>>>>>>>>>>>>> replacement. Please correct me if I misread this discussion thread.
>>>>>>>>>>>>>>> As Dongjoon pointed out, it would be good to know rough ETA
>>>>>>>>>>>>>>> to make sure making progress in this, and people can compare more easily.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> FWIW, there’s the saying I like in the zen of Python
>>>>>>>>>>>>>>> <https://www.python.org/dev/peps/pep-0020/>:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> There should be one— and preferably only one —obvious way to
>>>>>>>>>>>>>>> do it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> If multiple approaches have the way for developers to do the
>>>>>>>>>>>>>>> (almost) same thing, I would prefer to avoid it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> In addition, I would prefer to focus on what Spark does by
>>>>>>>>>>>>>>> default first.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 2021년 2월 17일 (수) 오후 2:33, Dongjoon Hyun <
>>>>>>>>>>>>>>> dongjoon.hyun@gmail.com>님이 작성:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hi, Wenchen.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> This thread seems to get enough attention. Also, I'm
>>>>>>>>>>>>>>>> expecting more and more if we have this on the `master` branch because we
>>>>>>>>>>>>>>>> are developing together.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>     > Spark SQL has many active contributors/committers and
>>>>>>>>>>>>>>>> this thread doesn't get much attention yet.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> So, what's your ETA from now?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>     > I think the problem here is we were discussing some
>>>>>>>>>>>>>>>> very detailed things without actual code.
>>>>>>>>>>>>>>>>     > I'll implement my idea after the holiday and then we
>>>>>>>>>>>>>>>> can have more effective discussions.
>>>>>>>>>>>>>>>>     > We can also do benchmarks and get some real numbers.
>>>>>>>>>>>>>>>>     > In the meantime, we can continue to discuss other
>>>>>>>>>>>>>>>> parts of this proposal, and make a prototype if possible.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I'm looking forward to seeing your PR. I hope we can
>>>>>>>>>>>>>>>> conclude this thread and have at least one implementation in the `master`
>>>>>>>>>>>>>>>> branch this month (February).
>>>>>>>>>>>>>>>> If you need more time (one month or longer), why don't we
>>>>>>>>>>>>>>>> have Ryan's suggestion in the `master` branch first and benchmark with your
>>>>>>>>>>>>>>>> PR later during Apache Spark 3.2 timeframe.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Bests,
>>>>>>>>>>>>>>>> Dongjoon.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Tue, Feb 16, 2021 at 9:26 AM Ryan Blue
>>>>>>>>>>>>>>>> <rblue@netflix.com.invalid> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Andrew,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The proposal already includes an API for aggregate
>>>>>>>>>>>>>>>>> functions and I think we would want to implement those right away.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Processing ColumnBatch is something we can easily extend
>>>>>>>>>>>>>>>>> the interfaces to support, similar to Wenchen's suggestion. The important
>>>>>>>>>>>>>>>>> thing right now is to agree on some basic functionality: how to look up
>>>>>>>>>>>>>>>>> functions and what the simple API should be. Like the TableCatalog
>>>>>>>>>>>>>>>>> interfaces, we will layer on more support through optional interfaces like
>>>>>>>>>>>>>>>>> `SupportsInvoke` or `SupportsColumnBatch`.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Tue, Feb 16, 2021 at 9:00 AM Andrew Melo <
>>>>>>>>>>>>>>>>> andrew.melo@gmail.com> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hello Ryan,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> This proposal looks very interesting. Would future goals
>>>>>>>>>>>>>>>>>> for this
>>>>>>>>>>>>>>>>>> functionality include both support for aggregation
>>>>>>>>>>>>>>>>>> functions, as well
>>>>>>>>>>>>>>>>>> as support for processing ColumnBatch-es (instead of
>>>>>>>>>>>>>>>>>> Row/InternalRow)?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks
>>>>>>>>>>>>>>>>>> Andrew
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Mon, Feb 15, 2021 at 12:44 PM Ryan Blue
>>>>>>>>>>>>>>>>>> <rblue@netflix.com.invalid> wrote:
>>>>>>>>>>>>>>>>>> >
>>>>>>>>>>>>>>>>>> > Thanks for the positive feedback, everyone. It sounds
>>>>>>>>>>>>>>>>>> like there is a clear path forward for calling functions. Even without a
>>>>>>>>>>>>>>>>>> prototype, the `invoke` plans show that Wenchen's suggested optimization
>>>>>>>>>>>>>>>>>> can be done, and incorporating it as an optional extension to this proposal
>>>>>>>>>>>>>>>>>> solves many of the unknowns.
>>>>>>>>>>>>>>>>>> >
>>>>>>>>>>>>>>>>>> > With that area now understood, is there any discussion
>>>>>>>>>>>>>>>>>> about other parts of the proposal, besides the function call interface?
>>>>>>>>>>>>>>>>>> >
>>>>>>>>>>>>>>>>>> > On Fri, Feb 12, 2021 at 10:40 PM Chao Sun <
>>>>>>>>>>>>>>>>>> sunchao@apache.org> wrote:
>>>>>>>>>>>>>>>>>> >>
>>>>>>>>>>>>>>>>>> >> This is an important feature which can unblock several
>>>>>>>>>>>>>>>>>> other projects including bucket join support for DataSource v2, complete
>>>>>>>>>>>>>>>>>> support for enforcing DataSource v2 distribution requirements on the write
>>>>>>>>>>>>>>>>>> path, etc. I like Ryan's proposals which look simple and elegant, with nice
>>>>>>>>>>>>>>>>>> support on function overloading and variadic arguments. On the other hand,
>>>>>>>>>>>>>>>>>> I think Wenchen made a very good point about performance. Overall, I'm
>>>>>>>>>>>>>>>>>> excited to see active discussions on this topic and believe the community
>>>>>>>>>>>>>>>>>> will come to a proposal with the best of both sides.
>>>>>>>>>>>>>>>>>> >>
>>>>>>>>>>>>>>>>>> >> Chao
>>>>>>>>>>>>>>>>>> >>
>>>>>>>>>>>>>>>>>> >> On Fri, Feb 12, 2021 at 7:58 PM Hyukjin Kwon <
>>>>>>>>>>>>>>>>>> gurwls223@gmail.com> wrote:
>>>>>>>>>>>>>>>>>> >>>
>>>>>>>>>>>>>>>>>> >>> +1 for Liang-chi's.
>>>>>>>>>>>>>>>>>> >>>
>>>>>>>>>>>>>>>>>> >>> Thanks Ryan and Wenchen for leading this.
>>>>>>>>>>>>>>>>>> >>>
>>>>>>>>>>>>>>>>>> >>>
>>>>>>>>>>>>>>>>>> >>> 2021년 2월 13일 (토) 오후 12:18, Liang-Chi Hsieh <
>>>>>>>>>>>>>>>>>> viirya@gmail.com>님이 작성:
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> Basically I think the proposal makes sense to me and
>>>>>>>>>>>>>>>>>> I'd like to support the
>>>>>>>>>>>>>>>>>> >>>> SPIP as it looks like we have strong need for the
>>>>>>>>>>>>>>>>>> important feature.
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> Thanks Ryan for working on this and I do also look
>>>>>>>>>>>>>>>>>> forward to Wenchen's
>>>>>>>>>>>>>>>>>> >>>> implementation. Thanks for the discussion too.
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> Actually I think the SupportsInvoke proposed by Ryan
>>>>>>>>>>>>>>>>>> looks a good
>>>>>>>>>>>>>>>>>> >>>> alternative to me. Besides Wenchen's alternative
>>>>>>>>>>>>>>>>>> implementation, is there a
>>>>>>>>>>>>>>>>>> >>>> chance we also have the SupportsInvoke for
>>>>>>>>>>>>>>>>>> comparison?
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> John Zhuge wrote
>>>>>>>>>>>>>>>>>> >>>> > Excited to see our Spark community rallying behind
>>>>>>>>>>>>>>>>>> this important feature!
>>>>>>>>>>>>>>>>>> >>>> >
>>>>>>>>>>>>>>>>>> >>>> > The proposal lays a solid foundation of minimal
>>>>>>>>>>>>>>>>>> feature set with careful
>>>>>>>>>>>>>>>>>> >>>> > considerations for future optimizations and
>>>>>>>>>>>>>>>>>> extensions. Can't wait to see
>>>>>>>>>>>>>>>>>> >>>> > it leading to more advanced functionalities like
>>>>>>>>>>>>>>>>>> views with shared custom
>>>>>>>>>>>>>>>>>> >>>> > functions, function pushdown, lambda, etc. It has
>>>>>>>>>>>>>>>>>> already borne fruit from
>>>>>>>>>>>>>>>>>> >>>> > the constructive collaborations in this thread.
>>>>>>>>>>>>>>>>>> Looking forward to
>>>>>>>>>>>>>>>>>> >>>> > Wenchen's prototype and further discussions
>>>>>>>>>>>>>>>>>> including the SupportsInvoke
>>>>>>>>>>>>>>>>>> >>>> > extension proposed by Ryan.
>>>>>>>>>>>>>>>>>> >>>> >
>>>>>>>>>>>>>>>>>> >>>> >
>>>>>>>>>>>>>>>>>> >>>> > On Fri, Feb 12, 2021 at 4:35 PM Owen O'Malley &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > owen.omalley@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt;
>>>>>>>>>>>>>>>>>> >>>> > wrote:
>>>>>>>>>>>>>>>>>> >>>> >
>>>>>>>>>>>>>>>>>> >>>> >> I think this proposal is a very good thing giving
>>>>>>>>>>>>>>>>>> Spark a standard way of
>>>>>>>>>>>>>>>>>> >>>> >> getting to and calling UDFs.
>>>>>>>>>>>>>>>>>> >>>> >>
>>>>>>>>>>>>>>>>>> >>>> >> I like having the ScalarFunction as the API to
>>>>>>>>>>>>>>>>>> call the UDFs. It is
>>>>>>>>>>>>>>>>>> >>>> >> simple, yet covers all of the polymorphic type
>>>>>>>>>>>>>>>>>> cases well. I think it
>>>>>>>>>>>>>>>>>> >>>> >> would
>>>>>>>>>>>>>>>>>> >>>> >> also simplify using the functions in other
>>>>>>>>>>>>>>>>>> contexts like pushing down
>>>>>>>>>>>>>>>>>> >>>> >> filters into the ORC & Parquet readers although
>>>>>>>>>>>>>>>>>> there are a lot of
>>>>>>>>>>>>>>>>>> >>>> >> details
>>>>>>>>>>>>>>>>>> >>>> >> that would need to be considered there.
>>>>>>>>>>>>>>>>>> >>>> >>
>>>>>>>>>>>>>>>>>> >>>> >> .. Owen
>>>>>>>>>>>>>>>>>> >>>> >>
>>>>>>>>>>>>>>>>>> >>>> >>
>>>>>>>>>>>>>>>>>> >>>> >> On Fri, Feb 12, 2021 at 11:07 PM Erik Krogen &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > ekrogen@.com
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt;
>>>>>>>>>>>>>>>>>> >>>> >> wrote:
>>>>>>>>>>>>>>>>>> >>>> >>
>>>>>>>>>>>>>>>>>> >>>> >>> I agree that there is a strong need for a
>>>>>>>>>>>>>>>>>> FunctionCatalog within Spark
>>>>>>>>>>>>>>>>>> >>>> >>> to
>>>>>>>>>>>>>>>>>> >>>> >>> provide support for shareable UDFs, as well as
>>>>>>>>>>>>>>>>>> make movement towards
>>>>>>>>>>>>>>>>>> >>>> >>> more
>>>>>>>>>>>>>>>>>> >>>> >>> advanced functionality like views which
>>>>>>>>>>>>>>>>>> themselves depend on UDFs, so I
>>>>>>>>>>>>>>>>>> >>>> >>> support this SPIP wholeheartedly.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> I find both of the proposed UDF APIs to be
>>>>>>>>>>>>>>>>>> sufficiently user-friendly
>>>>>>>>>>>>>>>>>> >>>> >>> and
>>>>>>>>>>>>>>>>>> >>>> >>> extensible. I generally think Wenchen's proposal
>>>>>>>>>>>>>>>>>> is easier for a user to
>>>>>>>>>>>>>>>>>> >>>> >>> work with in the common case, but has greater
>>>>>>>>>>>>>>>>>> potential for confusing
>>>>>>>>>>>>>>>>>> >>>> >>> and
>>>>>>>>>>>>>>>>>> >>>> >>> hard-to-debug behavior due to use of reflective
>>>>>>>>>>>>>>>>>> method signature
>>>>>>>>>>>>>>>>>> >>>> >>> searches.
>>>>>>>>>>>>>>>>>> >>>> >>> The merits on both sides can hopefully be more
>>>>>>>>>>>>>>>>>> properly examined with
>>>>>>>>>>>>>>>>>> >>>> >>> code,
>>>>>>>>>>>>>>>>>> >>>> >>> so I look forward to seeing an implementation of
>>>>>>>>>>>>>>>>>> Wenchen's ideas to
>>>>>>>>>>>>>>>>>> >>>> >>> provide
>>>>>>>>>>>>>>>>>> >>>> >>> a more concrete comparison. I am optimistic that
>>>>>>>>>>>>>>>>>> we will not let the
>>>>>>>>>>>>>>>>>> >>>> >>> debate
>>>>>>>>>>>>>>>>>> >>>> >>> over this point unreasonably stall the SPIP from
>>>>>>>>>>>>>>>>>> making progress.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Thank you to both Wenchen and Ryan for your
>>>>>>>>>>>>>>>>>> detailed consideration and
>>>>>>>>>>>>>>>>>> >>>> >>> evaluation of these ideas!
>>>>>>>>>>>>>>>>>> >>>> >>> ------------------------------
>>>>>>>>>>>>>>>>>> >>>> >>> *From:* Dongjoon Hyun &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > dongjoon.hyun@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt;
>>>>>>>>>>>>>>>>>> >>>> >>> *Sent:* Wednesday, February 10, 2021 6:06 PM
>>>>>>>>>>>>>>>>>> >>>> >>> *To:* Ryan Blue &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > blue@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt;
>>>>>>>>>>>>>>>>>> >>>> >>> *Cc:* Holden Karau &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > holden@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt;; Hyukjin Kwon <
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > gurwls223@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> >>; Spark Dev List &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > dev@.apache
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt;; Wenchen Fan
>>>>>>>>>>>>>>>>>> >>>> >>> &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > cloud0fan@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt;
>>>>>>>>>>>>>>>>>> >>>> >>> *Subject:* Re: [DISCUSS] SPIP: FunctionCatalog
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> BTW, I forgot to add my opinion explicitly in
>>>>>>>>>>>>>>>>>> this thread because I was
>>>>>>>>>>>>>>>>>> >>>> >>> on the PR before this thread.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> 1. The `FunctionCatalog API` PR was made on May
>>>>>>>>>>>>>>>>>> 9, 2019 and has been
>>>>>>>>>>>>>>>>>> >>>> >>> there for almost two years.
>>>>>>>>>>>>>>>>>> >>>> >>> 2. I already gave my +1 on that PR last Saturday
>>>>>>>>>>>>>>>>>> because I agreed with
>>>>>>>>>>>>>>>>>> >>>> >>> the latest updated design docs and AS-IS PR.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> And, the rest of the progress in this thread is
>>>>>>>>>>>>>>>>>> also very satisfying to
>>>>>>>>>>>>>>>>>> >>>> >>> me.
>>>>>>>>>>>>>>>>>> >>>> >>> (e.g. Ryan's extension suggestion and Wenchen's
>>>>>>>>>>>>>>>>>> alternative)
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> To All:
>>>>>>>>>>>>>>>>>> >>>> >>> Please take a look at the design doc and the PR,
>>>>>>>>>>>>>>>>>> and give us some
>>>>>>>>>>>>>>>>>> >>>> >>> opinions.
>>>>>>>>>>>>>>>>>> >>>> >>> We really need your participation in order to
>>>>>>>>>>>>>>>>>> make DSv2 more complete.
>>>>>>>>>>>>>>>>>> >>>> >>> This will unblock other DSv2 features, too.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Bests,
>>>>>>>>>>>>>>>>>> >>>> >>> Dongjoon.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> On Wed, Feb 10, 2021 at 10:58 AM Dongjoon Hyun
>>>>>>>>>>>>>>>>>> &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > dongjoon.hyun@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt;
>>>>>>>>>>>>>>>>>> >>>> >>> wrote:
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Hi, Ryan.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> We didn't move past anything (both yours and
>>>>>>>>>>>>>>>>>> Wenchen's). What Wenchen
>>>>>>>>>>>>>>>>>> >>>> >>> suggested is double-checking the alternatives
>>>>>>>>>>>>>>>>>> with the implementation to
>>>>>>>>>>>>>>>>>> >>>> >>> give more momentum to our discussion.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Your new suggestion about optional extention
>>>>>>>>>>>>>>>>>> also sounds like a new
>>>>>>>>>>>>>>>>>> >>>> >>> reasonable alternative to me.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> We are still discussing this topic together and
>>>>>>>>>>>>>>>>>> I hope we can make a
>>>>>>>>>>>>>>>>>> >>>> >>> conclude at this time (for Apache Spark 3.2)
>>>>>>>>>>>>>>>>>> without being stucked like
>>>>>>>>>>>>>>>>>> >>>> >>> last time.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> I really appreciate your leadership in this
>>>>>>>>>>>>>>>>>> dicsussion and the moving
>>>>>>>>>>>>>>>>>> >>>> >>> direction of this discussion looks constructive
>>>>>>>>>>>>>>>>>> to me. Let's give some
>>>>>>>>>>>>>>>>>> >>>> >>> time
>>>>>>>>>>>>>>>>>> >>>> >>> to the alternatives.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Bests,
>>>>>>>>>>>>>>>>>> >>>> >>> Dongjoon.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> On Wed, Feb 10, 2021 at 10:14 AM Ryan Blue &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > blue@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt; wrote:
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> I don’t think we should so quickly move past the
>>>>>>>>>>>>>>>>>> drawbacks of this
>>>>>>>>>>>>>>>>>> >>>> >>> approach. The problems are significant enough
>>>>>>>>>>>>>>>>>> that using invoke is not
>>>>>>>>>>>>>>>>>> >>>> >>> sufficient on its own. But, I think we can add
>>>>>>>>>>>>>>>>>> it as an optional
>>>>>>>>>>>>>>>>>> >>>> >>> extension
>>>>>>>>>>>>>>>>>> >>>> >>> to shore up the weaknesses.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Here’s a summary of the drawbacks:
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>>    - Magic function signatures are error-prone
>>>>>>>>>>>>>>>>>> >>>> >>>    - Spark would need considerable code to help
>>>>>>>>>>>>>>>>>> users find what went
>>>>>>>>>>>>>>>>>> >>>> >>>    wrong
>>>>>>>>>>>>>>>>>> >>>> >>>    - Spark would likely need to coerce arguments
>>>>>>>>>>>>>>>>>> (e.g., String,
>>>>>>>>>>>>>>>>>> >>>> >>>    Option[Int]) for usability
>>>>>>>>>>>>>>>>>> >>>> >>>    - It is unclear how Spark will find the Java
>>>>>>>>>>>>>>>>>> Method to call
>>>>>>>>>>>>>>>>>> >>>> >>>    - Use cases that require varargs fall back to
>>>>>>>>>>>>>>>>>> casting; users will
>>>>>>>>>>>>>>>>>> >>>> >>>    also get this wrong (cast to String instead
>>>>>>>>>>>>>>>>>> of UTF8String)
>>>>>>>>>>>>>>>>>> >>>> >>>    - The non-codegen path is significantly slower
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> The benefit of invoke is to avoid moving data
>>>>>>>>>>>>>>>>>> into a row, like this:
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> -- using invoke
>>>>>>>>>>>>>>>>>> >>>> >>> int result = udfFunction(x, y)
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> -- using row
>>>>>>>>>>>>>>>>>> >>>> >>> udfRow.update(0, x); -- actual: values[0] = x;
>>>>>>>>>>>>>>>>>> >>>> >>> udfRow.update(1, y);
>>>>>>>>>>>>>>>>>> >>>> >>> int result = udfFunction(udfRow);
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> And, again, that won’t actually help much in
>>>>>>>>>>>>>>>>>> cases that require varargs.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> I suggest we add a new marker trait for
>>>>>>>>>>>>>>>>>> BoundMethod called
>>>>>>>>>>>>>>>>>> >>>> >>> SupportsInvoke.
>>>>>>>>>>>>>>>>>> >>>> >>> If that interface is implemented, then Spark
>>>>>>>>>>>>>>>>>> will look for a method that
>>>>>>>>>>>>>>>>>> >>>> >>> matches the expected signature based on the
>>>>>>>>>>>>>>>>>> bound input type. If it
>>>>>>>>>>>>>>>>>> >>>> >>> isn’t
>>>>>>>>>>>>>>>>>> >>>> >>> found, Spark can print a warning and fall back
>>>>>>>>>>>>>>>>>> to the InternalRow call:
>>>>>>>>>>>>>>>>>> >>>> >>> “Cannot find udfFunction(int, int)”.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> This approach allows the invoke optimization,
>>>>>>>>>>>>>>>>>> but solves many of the
>>>>>>>>>>>>>>>>>> >>>> >>> problems:
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>>    - The method to invoke is found using the
>>>>>>>>>>>>>>>>>> proposed load and bind
>>>>>>>>>>>>>>>>>> >>>> >>>    approach
>>>>>>>>>>>>>>>>>> >>>> >>>    - Magic function signatures are optional and
>>>>>>>>>>>>>>>>>> do not cause runtime
>>>>>>>>>>>>>>>>>> >>>> >>>    failures
>>>>>>>>>>>>>>>>>> >>>> >>>    - Because this is an optional optimization,
>>>>>>>>>>>>>>>>>> Spark can be more strict
>>>>>>>>>>>>>>>>>> >>>> >>>    about types
>>>>>>>>>>>>>>>>>> >>>> >>>    - Varargs cases can still use rows
>>>>>>>>>>>>>>>>>> >>>> >>>    - Non-codegen can use an evaluation method
>>>>>>>>>>>>>>>>>> rather than falling back
>>>>>>>>>>>>>>>>>> >>>> >>>    to slow Java reflection
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> This seems like a good extension to me; this
>>>>>>>>>>>>>>>>>> provides a plan for
>>>>>>>>>>>>>>>>>> >>>> >>> optimizing the UDF call to avoid building a row,
>>>>>>>>>>>>>>>>>> while the existing
>>>>>>>>>>>>>>>>>> >>>> >>> proposal covers the other cases well and
>>>>>>>>>>>>>>>>>> addresses how to locate these
>>>>>>>>>>>>>>>>>> >>>> >>> function calls.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> This also highlights that the approach used in
>>>>>>>>>>>>>>>>>> DSv2 and this proposal is
>>>>>>>>>>>>>>>>>> >>>> >>> working: start small and use extensions to layer
>>>>>>>>>>>>>>>>>> on more complex
>>>>>>>>>>>>>>>>>> >>>> >>> support.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> On Wed, Feb 10, 2021 at 9:04 AM Dongjoon Hyun
>>>>>>>>>>>>>>>>>> &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > dongjoon.hyun@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt;
>>>>>>>>>>>>>>>>>> >>>> >>> wrote:
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Thank you all for making a giant move forward
>>>>>>>>>>>>>>>>>> for Apache Spark 3.2.0.
>>>>>>>>>>>>>>>>>> >>>> >>> I'm really looking forward to seeing Wenchen's
>>>>>>>>>>>>>>>>>> implementation.
>>>>>>>>>>>>>>>>>> >>>> >>> That would be greatly helpful to make a decision!
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> > I'll implement my idea after the holiday and
>>>>>>>>>>>>>>>>>> then we can have
>>>>>>>>>>>>>>>>>> >>>> >>> more effective discussions. We can also do
>>>>>>>>>>>>>>>>>> benchmarks and get some real
>>>>>>>>>>>>>>>>>> >>>> >>> numbers.
>>>>>>>>>>>>>>>>>> >>>> >>> > FYI: the Presto UDF API
>>>>>>>>>>>>>>>>>> >>>> >>> &lt;
>>>>>>>>>>>>>>>>>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fprestodb.io%2Fdocs%2Fcurrent%2Fdevelop%2Ffunctions.html&amp;data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060067978066%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&amp;sdata=iMWmHqqXPcT7EK%2Bovyzhy%2BZpU6Llih%2BwdZD53wvobmc%3D&amp;reserved=0&gt
>>>>>>>>>>>>>>>>>> ;
>>>>>>>>>>>>>>>>>> >>>> >>> also
>>>>>>>>>>>>>>>>>> >>>> >>> takes individual parameters instead of the row
>>>>>>>>>>>>>>>>>> parameter. I think this
>>>>>>>>>>>>>>>>>> >>>> >>> direction at least worth a try so that we can
>>>>>>>>>>>>>>>>>> see the performance
>>>>>>>>>>>>>>>>>> >>>> >>> difference. It's also mentioned in the design
>>>>>>>>>>>>>>>>>> doc as an alternative
>>>>>>>>>>>>>>>>>> >>>> >>> (Trino).
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Bests,
>>>>>>>>>>>>>>>>>> >>>> >>> Dongjoon.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> On Tue, Feb 9, 2021 at 10:18 PM Wenchen Fan &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > cloud0fan@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt; wrote:
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> FYI: the Presto UDF API
>>>>>>>>>>>>>>>>>> >>>> >>> &lt;
>>>>>>>>>>>>>>>>>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fprestodb.io%2Fdocs%2Fcurrent%2Fdevelop%2Ffunctions.html&amp;data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060067988024%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&amp;sdata=ZSBCR7yx3PpwL4KY9V73JG42Z02ZodqkjxC0LweHt1g%3D&amp;reserved=0&gt
>>>>>>>>>>>>>>>>>> ;
>>>>>>>>>>>>>>>>>> >>>> >>> also takes individual parameters instead of the
>>>>>>>>>>>>>>>>>> row parameter. I think
>>>>>>>>>>>>>>>>>> >>>> >>> this
>>>>>>>>>>>>>>>>>> >>>> >>> direction at least worth a try so that we can
>>>>>>>>>>>>>>>>>> see the performance
>>>>>>>>>>>>>>>>>> >>>> >>> difference. It's also mentioned in the design
>>>>>>>>>>>>>>>>>> doc as an alternative
>>>>>>>>>>>>>>>>>> >>>> >>> (Trino).
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> On Wed, Feb 10, 2021 at 10:18 AM Wenchen Fan &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > cloud0fan@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt; wrote:
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Hi Holden,
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> As Hyukjin said, following existing designs is
>>>>>>>>>>>>>>>>>> not the principle of DS
>>>>>>>>>>>>>>>>>> >>>> >>> v2
>>>>>>>>>>>>>>>>>> >>>> >>> API design. We should make sure the DS v2 API
>>>>>>>>>>>>>>>>>> makes sense. AFAIK we
>>>>>>>>>>>>>>>>>> >>>> >>> didn't
>>>>>>>>>>>>>>>>>> >>>> >>> fully follow the catalog API design from Hive
>>>>>>>>>>>>>>>>>> and I believe Ryan also
>>>>>>>>>>>>>>>>>> >>>> >>> agrees with it.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> I think the problem here is we were discussing
>>>>>>>>>>>>>>>>>> some very detailed things
>>>>>>>>>>>>>>>>>> >>>> >>> without actual code. I'll implement my idea
>>>>>>>>>>>>>>>>>> after the holiday and then
>>>>>>>>>>>>>>>>>> >>>> >>> we
>>>>>>>>>>>>>>>>>> >>>> >>> can have more effective discussions. We can also
>>>>>>>>>>>>>>>>>> do benchmarks and get
>>>>>>>>>>>>>>>>>> >>>> >>> some
>>>>>>>>>>>>>>>>>> >>>> >>> real numbers.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> In the meantime, we can continue to discuss
>>>>>>>>>>>>>>>>>> other parts of this
>>>>>>>>>>>>>>>>>> >>>> >>> proposal,
>>>>>>>>>>>>>>>>>> >>>> >>> and make a prototype if possible. Spark SQL has
>>>>>>>>>>>>>>>>>> many active
>>>>>>>>>>>>>>>>>> >>>> >>> contributors/committers and this thread doesn't
>>>>>>>>>>>>>>>>>> get much attention yet.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> On Wed, Feb 10, 2021 at 6:17 AM Hyukjin Kwon &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > gurwls223@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt; wrote:
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Just dropping a few lines. I remember that one
>>>>>>>>>>>>>>>>>> of the goals in DSv2 is
>>>>>>>>>>>>>>>>>> >>>> >>> to
>>>>>>>>>>>>>>>>>> >>>> >>> correct the mistakes we made in the current
>>>>>>>>>>>>>>>>>> Spark codes.
>>>>>>>>>>>>>>>>>> >>>> >>> It would not have much point if we will happen
>>>>>>>>>>>>>>>>>> to just follow and mimic
>>>>>>>>>>>>>>>>>> >>>> >>> what Spark currently does. It might just end up
>>>>>>>>>>>>>>>>>> with another copy of
>>>>>>>>>>>>>>>>>> >>>> >>> Spark
>>>>>>>>>>>>>>>>>> >>>> >>> APIs, e.g. Expression (internal) APIs. I
>>>>>>>>>>>>>>>>>> sincerely would like to avoid
>>>>>>>>>>>>>>>>>> >>>> >>> this
>>>>>>>>>>>>>>>>>> >>>> >>> I do believe we have been stuck mainly due to
>>>>>>>>>>>>>>>>>> trying to come up with a
>>>>>>>>>>>>>>>>>> >>>> >>> better design. We already have an ugly picture
>>>>>>>>>>>>>>>>>> of the current Spark APIs
>>>>>>>>>>>>>>>>>> >>>> >>> to
>>>>>>>>>>>>>>>>>> >>>> >>> draw a better bigger picture.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> 2021년 2월 10일 (수) 오전 3:28, Holden Karau &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > holden@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt;님이 작성:
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> I think this proposal is a good set of
>>>>>>>>>>>>>>>>>> trade-offs and has existed in the
>>>>>>>>>>>>>>>>>> >>>> >>> community for a long period of time. I
>>>>>>>>>>>>>>>>>> especially appreciate how the
>>>>>>>>>>>>>>>>>> >>>> >>> design
>>>>>>>>>>>>>>>>>> >>>> >>> is focused on a minimal useful component, with
>>>>>>>>>>>>>>>>>> future optimizations
>>>>>>>>>>>>>>>>>> >>>> >>> considered from a point of view of making sure
>>>>>>>>>>>>>>>>>> it's flexible, but actual
>>>>>>>>>>>>>>>>>> >>>> >>> concrete decisions left for the future once we
>>>>>>>>>>>>>>>>>> see how this API is used.
>>>>>>>>>>>>>>>>>> >>>> >>> I
>>>>>>>>>>>>>>>>>> >>>> >>> think if we try and optimize everything right
>>>>>>>>>>>>>>>>>> out of the gate, we'll
>>>>>>>>>>>>>>>>>> >>>> >>> quickly get stuck (again) and not make any
>>>>>>>>>>>>>>>>>> progress.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> On Mon, Feb 8, 2021 at 10:46 AM Ryan Blue &lt;
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > blue@
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> > &gt; wrote:
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Hi everyone,
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> I'd like to start a discussion for adding a
>>>>>>>>>>>>>>>>>> FunctionCatalog interface to
>>>>>>>>>>>>>>>>>> >>>> >>> catalog plugins. This will allow catalogs to
>>>>>>>>>>>>>>>>>> expose functions to Spark,
>>>>>>>>>>>>>>>>>> >>>> >>> similar to how the TableCatalog interface allows
>>>>>>>>>>>>>>>>>> a catalog to expose
>>>>>>>>>>>>>>>>>> >>>> >>> tables. The proposal doc is available here:
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> https://docs.google.com/document/d/1PLBieHIlxZjmoUB0ERF-VozCRJ0xw2j3qKvUNWpWA2U/edit
>>>>>>>>>>>>>>>>>> >>>> >>> &lt;
>>>>>>>>>>>>>>>>>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.google.com%2Fdocument%2Fd%2F1PLBieHIlxZjmoUB0ERF-VozCRJ0xw2j3qKvUNWpWA2U%2Fedit&amp;data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060067988024%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&amp;sdata=Kyth8%2FhNUZ6GXG2FsgcknZ7t7s0%2BpxnDMPyxvsxLLqE%3D&amp;reserved=0&gt
>>>>>>>>>>>>>>>>>> ;
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Here's a high-level summary of some of the main
>>>>>>>>>>>>>>>>>> design choices:
>>>>>>>>>>>>>>>>>> >>>> >>> * Adds the ability to list and load functions,
>>>>>>>>>>>>>>>>>> not to create or modify
>>>>>>>>>>>>>>>>>> >>>> >>> them in an external catalog
>>>>>>>>>>>>>>>>>> >>>> >>> * Supports scalar, aggregate, and partial
>>>>>>>>>>>>>>>>>> aggregate functions
>>>>>>>>>>>>>>>>>> >>>> >>> * Uses load and bind steps for better error
>>>>>>>>>>>>>>>>>> messages and simpler
>>>>>>>>>>>>>>>>>> >>>> >>> implementations
>>>>>>>>>>>>>>>>>> >>>> >>> * Like the DSv2 table read and write APIs, it
>>>>>>>>>>>>>>>>>> uses InternalRow to pass
>>>>>>>>>>>>>>>>>> >>>> >>> data
>>>>>>>>>>>>>>>>>> >>>> >>> * Can be extended using mix-in interfaces to add
>>>>>>>>>>>>>>>>>> vectorization, codegen,
>>>>>>>>>>>>>>>>>> >>>> >>> and other future features
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> There is also a PR with the proposed API:
>>>>>>>>>>>>>>>>>> >>>> >>> https://github.com/apache/spark/pull/24559/files
>>>>>>>>>>>>>>>>>> >>>> >>> &lt;
>>>>>>>>>>>>>>>>>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fapache%2Fspark%2Fpull%2F24559%2Ffiles&amp;data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060067988024%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&amp;sdata=t3ZCqffdsrmCY3X%2FT8x1oMjMcNUiQ0wQNk%2ByAXQx1Io%3D&amp;reserved=0&gt
>>>>>>>>>>>>>>>>>> ;
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> Let's discuss the proposal here rather than on
>>>>>>>>>>>>>>>>>> that PR, to get better
>>>>>>>>>>>>>>>>>> >>>> >>> visibility. Also, please take the time to read
>>>>>>>>>>>>>>>>>> the proposal first. That
>>>>>>>>>>>>>>>>>> >>>> >>> really helps clear up misconceptions.
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> --
>>>>>>>>>>>>>>>>>> >>>> >>> Ryan Blue
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> --
>>>>>>>>>>>>>>>>>> >>>> >>> Twitter: https://twitter.com/holdenkarau
>>>>>>>>>>>>>>>>>> >>>> >>> &lt;
>>>>>>>>>>>>>>>>>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftwitter.com%2Fholdenkarau&amp;data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060067997978%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&amp;sdata=fVfSPIyazuUYv8VLfNu%2BUIHdc3ePM1AAKKH%2BlnIicF8%3D&amp;reserved=0&gt
>>>>>>>>>>>>>>>>>> ;
>>>>>>>>>>>>>>>>>> >>>> >>> Books (Learning Spark, High Performance Spark,
>>>>>>>>>>>>>>>>>> etc.):
>>>>>>>>>>>>>>>>>> >>>> >>> https://amzn.to/2MaRAG9
>>>>>>>>>>>>>>>>>> >>>> >>> &lt;
>>>>>>>>>>>>>>>>>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Famzn.to%2F2MaRAG9&amp;data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060067997978%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&amp;sdata=NbRl9kK%2B6Wy0jWmDnztYp3JCPNLuJvmFsLHUrXzEhlk%3D&amp;reserved=0&gt
>>>>>>>>>>>>>>>>>> ;
>>>>>>>>>>>>>>>>>> >>>> >>> YouTube Live Streams:
>>>>>>>>>>>>>>>>>> https://www.youtube.com/user/holdenkarau
>>>>>>>>>>>>>>>>>> >>>> >>> &lt;
>>>>>>>>>>>>>>>>>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.youtube.com%2Fuser%2Fholdenkarau&amp;data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060068007935%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&amp;sdata=OWXOBELzO3hBa2JI%2FOSBZ3oNyLq0yr%2FGXMkNn7bqYDM%3D&amp;reserved=0&gt
>>>>>>>>>>>>>>>>>> ;
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>> --
>>>>>>>>>>>>>>>>>> >>>> >>> Ryan Blue
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >>>
>>>>>>>>>>>>>>>>>> >>>> >
>>>>>>>>>>>>>>>>>> >>>> > --
>>>>>>>>>>>>>>>>>> >>>> > John Zhuge
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>> --
>>>>>>>>>>>>>>>>>> >>>> Sent from:
>>>>>>>>>>>>>>>>>> http://apache-spark-developers-list.1001551.n3.nabble.com/
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> ---------------------------------------------------------------------
>>>>>>>>>>>>>>>>>> >>>> To unsubscribe e-mail:
>>>>>>>>>>>>>>>>>> dev-unsubscribe@spark.apache.org
>>>>>>>>>>>>>>>>>> >>>>
>>>>>>>>>>>>>>>>>> >
>>>>>>>>>>>>>>>>>> >
>>>>>>>>>>>>>>>>>> > --
>>>>>>>>>>>>>>>>>> > Ryan Blue
>>>>>>>>>>>>>>>>>> > Software Engineer
>>>>>>>>>>>>>>>>>> > Netflix
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> ---------------------------------------------------------------------
>>>>>>>>>>>>>>>>>> To unsubscribe e-mail: dev-unsubscribe@spark.apache.org
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> --
>>>>>>>>>>>>>>>>> Ryan Blue
>>>>>>>>>>>>>>>>> Software Engineer
>>>>>>>>>>>>>>>>> Netflix
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> --
>>>>>>>>>>>>>> Ryan Blue
>>>>>>>>>>>>>> Software Engineer
>>>>>>>>>>>>>> Netflix
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> --
>>>>>>>>>>>> Ryan Blue
>>>>>>>>>>>> Software Engineer
>>>>>>>>>>>> Netflix
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>> Ryan Blue
>>>>>>>>> Software Engineer
>>>>>>>>> Netflix
>>>>>>>>>
>>>>>>>>
>>>
>>> --
>>> Ryan Blue
>>> Software Engineer
>>> Netflix
>>>
>>

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