I think there are two aspects:
1. The actual mechanics of implementing functions
2. The actual library of udf functions (e.g. sin, cos, nullif, etc)
I agree 2 is not something that belongs naturally in the arrow project and
is better aligned with query engines
However I think 1 is worth considering.
As I understand it, the problem arrow_udf solves is avoiding some of the
boilerplate required to make vectorized udfs. So instead of writing a
special eval_gcd function like this
```
fn gcd(l: i64, r: i64) -> i64 {
// do gcd calculation
}
// implement vectorized version
fn eval_gcd(left: &ArrayRef, right: &ArrayRef) -> ArrayRef {
let left = left.as_primitive<Int64Type>();
let right = right.as_primitive<Int64Type>();
res = binary(left, right, |l, r| gcd(l, r));
Arc::new(res)
}
```
The user simply annotates the scalar function and have the library code gen
the array version
```
#[function("gcd(int64, int64) -> int64", output = "eval_gcd")]
fn gcd(l: i64, r: i64) -> i64 {
// do gcd calculation
}
```
We have a lot of boilerplate / non idea macro stuff in DataFusion that I
think this would help a lot.
Andrew
On Fri, Jun 28, 2024 at 3:08 PM Raphael Taylor-Davies
<r.taylordav...@googlemail.com.invalid> wrote:
> I wonder if the DataFusion project might be a more natural home for this
> functionality? UDFs are more of a query engine concept, whereas arrow-rs is
> more focused on purely physical execution?
>
> On 28 June 2024 19:41:39 BST, Runji Wang <wangrunji0...@163.com> wrote:
> >Hi Felipe,
> >
> >Vectorization will be applied whenever possible. When all input and
> output types of a function are primitive (int16, int32, int64, float32,
> float64) and do not involve any Option or Result, the macro will
> automatically generate code based on unary <
> https://docs.rs/arrow/latest/arrow/compute/fn.unary.html> or binary <
> https://docs.rs/arrow/latest/arrow/compute/fn.binary.html> kernels, which
> potentially allows for vectorization.
> >
> >Both examples you showed are not vectorized. The `div` function is due to
> the Result output, while `gcd` is due to the loop in its implementation.
> However, if the function is simple enough, like an `add` function:
> >
> >#[function("add(int, int) -> int")]
> >fn add(a: i32, b: i32) -> i32 {
> > a + b
> >}
> >
> >It can be auto-vectorized by llvm.
> >
> >Runji
> >
> >
> >On 2024/06/28 17:13:16 Felipe Oliveira Carvalho wrote:
> >> On Fri, Jun 28, 2024 at 11:07 AM Andrew Lamb <al...@influxdata.com>
> wrote:
> >> >
> >> > Hi Xuanwo,
> >> >
> >> > Sorry for the delay in responding. I think the ability to easily
> write
> >> > functions that "feel" like native functions in whatever language and
> be
> >> > able to generate arrow / vectorized versions of them is quite
> valuable.
> >> > This is my understanding of what this proposal is about.
> >>
> >> My understanding is that it's not vectorized. From the examples in
> >> risingwavelabs/arrow-udf, <https://github.com/risingwavelabs/arrow-udf>
> it
> >> looks like the macros generate code that gathers values from columns
> into
> >> local scalars that are passed as scalar parameters to user functions. Is
> >> the hope here that rustc/llvm will auto-vectorize the code?
> >>
> >> #[function("gcd(int, int) -> int")]
> >> fn gcd(mut a: i32, mut b: i32) -> i32 {
> >> while b != 0 {
> >> (a, b) = (b, a % b);
> >> }
> >> a
> >> }
> >>
> >> #[function("div(int, int) -> int")]
> >> fn div(x: i32, y: i32) -> Result<i32, &'static str> {
> >> if y == 0 {
> >> return Err("division by zero");
> >> }
> >> Ok(x / y)
> >> }
> >>
> >> > I left some additional comments on the markdown.
> >> >
> >> > One thing that might be worth doing is articulate some other potential
> >> > locations for where the code might go. One option, as I think you
> propose,
> >> > is to make its own repository. Another option could be to donate the
> code
> >> > and put the various language bindings in the same repo as the arrow
> >> > language implementations (e.g arrow-rs, arrow for python, etc) which
> would
> >> > likely make it easier to maintain and discover.
> >> >
> >> > I am curious about what other devs / users feel about this?
> >> >
> >> > Andrew
> >> >
> >> >
> >> >
> >> > On Thu, Jun 20, 2024 at 3:04 AM Xuanwo <xu...@apache.org> wrote:
> >> >
> >> > > Hello, everyone.
> >> > >
> >> > > I start this thread to disscuss the donation of a User-Defined
> Function
> >> > > Framework for Apache Arrow.
> >> > >
> >> > > Feel free to review and leave your comments here. For live review,
> >> please
> >> > > visit:
> >> > >
> >> > > https://hackmd.io/@xuanwo/apache-arrow-udf
> >> > >
> >> > > The original content also pasted here for a quick reading:
> >> > >
> >> > > ------
> >> > >
> >> > > ## Abstract
> >> > >
> >> > > Arrow UDF is a User-Defined Function Framework for Apache Arrow.
> >> > >
> >> > > ## Proposal
> >> > >
> >> > > Arrow UDF allows user to easily create and run user-defined
> functions
> >> > > (UDF) in Rust, Python, Java or JavaScript based on Apache Arrow. The
> >> > > functions can be executed natively, or in WebAssembly, or in a
> remote
> >> > > server via Arrow Flight.
> >> > >
> >> > > Arrow UDF was originally designed to be used by the RisingWave
> project
> >> but
> >> > > is now being used by Databend and several database startups.
> >> > >
> >> > > We believe that the Arrow UDF project will provide diversity value
> to
> >> the
> >> > > entire Arrow community.
> >> > >
> >> > > ## Background
> >> > >
> >> > > Arrow UDF is being developed by an open-source community from day
> one
> >> and
> >> > > is owned by RisingWaveLabs. The project has been launched in
> December
> >> 2023.
> >> > >
> >> > > ## Initial Goals
> >> > >
> >> > > By transferring ownership of the project to the Apache Arrow, Arrow
> UDF
> >> > > expects to ensure its neutrality and further encourage and
> facilitate
> >> the
> >> > > adoption of Arrow UDF by the community.
> >> > >
> >> > > ## Current Status
> >> > >
> >> > > Contributors: 5
> >> > >
> >> > > Users:
> >> > >
> >> > > - [RisingWave]: A Distributed SQL Database for Stream Processing.
> >> > > - [Databend]: An open-source cloud data warehouse that serves as a
> >> > > cost-effective alternative to Snowflake.
> >> > >
> >> > > ## Documentation
> >> > >
> >> > > The document of Arrow UDF is hosted at
> >> > > https://docs.rs/arrow-udf/latest/arrow_udf/.
> >> > >
> >> > > ## Initial Source
> >> > >
> >> > > The project currently holds a GitHub repository and multiple
> packages:
> >> > >
> >> > > - https://github.com/risingwavelabs/arrow-udf
> >> > >
> >> > > Rust:
> >> > >
> >> > > - https://crates.io/arrow-udf/
> >> > > - https://crates.io/arrow-udf-python/
> >> > > - https://crates.io/arrow-udf-js/
> >> > > - https://crates.io/arrow-udf-js-deno/
> >> > > - https://crates.io/arrow-udf-wasm/
> >> > >
> >> > > Python:
> >> > >
> >> > > - https://pypi.org/project/arrow-udf/
> >> > >
> >> > > Those packge will retain its name, while the repository will be
> moved to
> >> > > apache org.
> >> > >
> >> > > ## Required Resources
> >> > >
> >> > > ### Mailing Lists
> >> > >
> >> > > We can reuse the existing mailing lists that arrow have.
> >> > >
> >> > > ### Git Repositories
> >> > >
> >> > > From
> >> > >
> >> > > - https://github.com/risingwavelabs/arrow-udf
> >> > >
> >> > > To
> >> > >
> >> > > - https://gitbox.apache.org/asf/repos/arrow-udf
> >> > > - https://github.com/apache/arrow-udf
> >> > >
> >> > > ### Issue Tracking
> >> > >
> >> > > The project would like to continue using GitHub Issues.
> >> > >
> >> > > ### Other Resources
> >> > >
> >> > > The project has already chosen GitHub actions as continuous
> integration
> >> > > tools.
> >> > >
> >> > > ## Initial Committers
> >> > >
> >> > > - Runji Wang wangrunji0...@163.com
> >> > > - Giovanny Gutiérrez
> >> > > - sundy-li sund...@apache.org
> >> > > - Xuanwo xua...@apache.org
> >> > > - Max Justus Spransy maxjus...@gmail.com
> >> > >
> >> > > [RisingWave]: https://github.com/risingwavelabs/risingwave
> >> > > [Databend]: https://github.com/datafuselabs/databend
> >> > >
> >> > > Xuanwo
> >> > >
> >>
