RFC: Make it easier to call window functions via expression API (and add example)

[alamb]

before polishing this PR up I wanted to get some feedback on the approach

Which issue does this PR close?

Related to https://github.com/apache/arrow-datafusion/issues/5781 Closes: https://github.com/apache/arrow-datafusion/issues/6747

Rationale for this change

@mustafasrepo suggested adding an example for DataFrame::window here: https://github.com/apache/arrow-datafusion/pull/6703#discussion_r1238403292

When I tried to do so it turns out that creating an Expr::Window is quite challenging, so I took a step back and tried to clean up the API a little

What changes are included in this PR?

1. Change WindowExpr to be a builder style interface and thus making it easier to construct Expr::WindowFunction variants. This is modeled after the CaseBuilder
2. Add the lead, lag, etc. in expr_fns to follow the model of the other types of functions

Are these changes tested?

Yes

TOOD: I need to write tests for all the code in expr_fns which I will add to https://github.com/apache/arrow-datafusion/blob/main/datafusion/core/tests/dataframe/dataframe_functions.rs if people like this basic approach

Are there any user-facing changes?

yes, the APIs to create window functions are improved

[timsaucer]

As a new user, I like this change very much. I found creating lead and lag functions to be difficult to use. Suppose I wanted to call lead but provide the other two optional arguments - shift offset and default value. Would the best way to do that with this proposal to create the WindowFunction directly?

let single_lead = lead(col("mycol"));
let two_lead_with_default = expr::WindowFunction::new(BuiltInWindowFunction::Lead, vec![col("mycol"), lit(2), lit(mydefault)]);

[alamb]

As a new user, I like this change very much. I found creating lead and lag functions to be difficult to use. Suppose I wanted to call lead but provide the other two optional arguments - shift offset and default value. Would the best way to do that with this proposal to create the WindowFunction directly?

I think it would look something like this

let single_lead = lead(col("mycol"))
  .
let two_lead_with_default = lead.call(col("mycol"))
  .offset(lit(2))
  .partition_by(lit(mydefault)]))
  .build()

[timsaucer]

I'm looking at the arguments we end up passing. I think cume_dist takes no arguments. Lead and lag take anywhere between 1 and 3. nth_value takes two. I think it might be easier to use with something like

pub fn lead(arg: Expr, offset: Option<i64>, default: Option<ScalarValue>) -> expr::WindowFunction {
    let offset = lit(ScalarValue::Int64(offset));
    let default_value = match default {
        Some(v) => lit(v),
        None => lit(ScalarValue::Null),
    };
    expr::WindowFunction::new(BuiltInWindowFunction::Lead, vec![arg, offset, default_value])
}

and for nth value it's easier

pub fn nth_value(arg: Expr, n: i64) -> expr::WindowFunction {
    expr::WindowFunction::new(BuiltInWindowFunction::NthValue, vec![arg, lit(n)])
}

[alamb]

I think it might be easier to use with something like

That makes sense to me @timsaucer -- thank you for the feedback.

I don't think I will have time to work on this PR / issue for a while, but I tried to give it some visibility in https://github.com/apache/datafusion/issues/10395 and hopefully we can find someone else who is interested in helping make it work

[alamb]

(or of course if you have the time it would be an amazing contribution 🙏 )

[timsaucer]

I'm willing to work on this, but I'd like to wrap up the examples I have going into datafusion-python first. I think those will have more impact. I only have an hour or so a day to work on this, so I can't promise how fast I'll get to it.

[alamb]

I'm willing to work on this, but I'd like to wrap up the examples I have going into datafusion-python first. I think those will have more impact. I only have an hour or so a day to work on this, so I can't promise how fast I'll get to it.

Thank you very much 🙏

I totally understand re time constraints. Maybe we'll find some other people to help too. One can hope

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[alamb]

We can revive this PR / its API when someone has time to work on it

In case anyone is following along, @jayzhan211 added a really nice trait for working with aggregate functions. Maybe we can do something similar for window functions eventually

https://github.com/apache/datafusion/blob/e693ed7a3c3b36405f0a34887e6f8b49d4e97152/datafusion/expr/src/udaf.rs#L614-L653

[timsaucer]

I should have time to work on this near the end of the week or early next week. I'm in nearing the end of a large PR for datafusion-python and can dive back on this topic in once that's up for review.

[alamb]

Thanks @timsaucer -- looking forward to it.

I think the most useful part of this PR are the doc examples