spark-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Ryan Blue <rb...@netflix.com.INVALID>
Subject Re: [DISCUSS] SPIP: FunctionCatalog
Date Wed, 24 Feb 2021 22:48:17 GMT
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

Mime
View raw message