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Implement kurtosis_pop UDAF #12273

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merged 12 commits into from
Sep 4, 2024

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goldmedal
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@goldmedal goldmedal commented Aug 31, 2024

Which issue does this PR close?

Closes #12251 .

Rationale for this change

I followed the algorithm of the DuckDB implementation to implement this function. The behavior is the same but there are some precision issues for the double value.

I guess that it's also a part of #12250.

What changes are included in this PR?

Are these changes tested?

yes

Are there any user-facing changes?

@github-actions github-actions bot added sqllogictest SQL Logic Tests (.slt) proto Related to proto crate functions labels Aug 31, 2024
query R
SELECT kurtosis_pop(col) FROM VALUES (1), (10), (100), (10), (1) as tab(col);
----
0.194323231917
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I tried this function with the CLI

DataFusion CLI v41.0.0
> SELECT kurtosis_pop(col) FROM VALUES (1), (10), (100), (10), (1) as tab(col);
+-----------------------+
| kurtosis_pop(tab.col) |
+-----------------------+
| 0.19432323191699075   |
+-----------------------+

I'm not sure but I guess the sqllogicttest may do some rounds for the result.

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Yes sqllogictest will do the rounding according to https://github.com/apache/datafusion/tree/main/datafusion/sqllogictest

floating point values are rounded to the scale of "12",

Comment on lines +5873 to +5877
# The result is -1.153061224489787 actually
query R
SELECT kurtosis_pop(col) FROM VALUES (1), (2), (3), (2), (1) as tab(col);
----
-1.15306122449
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This result is different from DuckDB but I'm not sure why.

D SELECT kurtosis_pop(col) FROM VALUES (1), (2), (3), (2), (1) as tab(col);
┌────────────────────┐
│ kurtosis_pop(col)  │
│       double       │
├────────────────────┤
│ -1.153061224489769 │
└────────────────────┘

Comment on lines +171 to +175
let count_64 = 1_f64 / self.count as f64;
let m4 = count_64
* (self.sum_four - 4.0 * self.sum_cub * self.sum * count_64
+ 6.0 * self.sum_sqr * self.sum.powi(2) * count_64.powi(2)
- 3.0 * self.sum.powi(4) * count_64.powi(3));
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I followed the DuckDB way to get the divisor here.

https://github.com/duckdb/duckdb/blob/a706958d15a6fc7fd47d65d22de7deac63613458/src/core_functions/aggregate/distributive/kurtosis.cpp#L69

The result will same as DuckDB but it's different from Clickhouse.

I did some test to compare the behavior between DuckDB and Clickhouse:

DuckDB

D  SELECT kurtosis_pop(col) FROM VALUES (1), (10), (100), (10), (1) as tab(col);
┌─────────────────────┐
│  kurtosis_pop(col)  │
│       double        │
├─────────────────────┤
│ 0.19432323191699075 │
└─────────────────────┘

Clickhouse

:) SELECT kurtPop(value) FROM (SELECT arrayJoin([1, 10, 100, 10, 1]) AS value);

SELECT kurtPop(value)
FROM
(
    SELECT arrayJoin([1, 10, 100, 10, 1]) AS value
)

Query id: abdea377-40b1-4437-a87a-4814f11cc866

   ┌─────kurtPop(value)─┐
1. │ 3.1943232319169903 │
   └────────────────────┘

1 row in set. Elapsed: 0.002 sec. 

Because DuckDB's kurtosis_pop calculates the population kurtosis using Fisher's definition, which results in the excess kurtosis, i.e., the value minus 3, ClickHouse directly provides the population kurtosis value without subtracting 3.

However, if we change the code like

Suggested change
let count_64 = 1_f64 / self.count as f64;
let m4 = count_64
* (self.sum_four - 4.0 * self.sum_cub * self.sum * count_64
+ 6.0 * self.sum_sqr * self.sum.powi(2) * count_64.powi(2)
- 3.0 * self.sum.powi(4) * count_64.powi(3));
let count_64 = self.count as f64;
let m4 =
(self.sum_four - 4.0 * self.sum_cub * self.sum / count_64
+ 6.0 * self.sum_sqr * self.sum.powi(2) / count_64.powi(2)
- 3.0 * self.sum.powi(4) / count_64.powi(3)) / count_64;

The result will same as Clikhouse, 3.1943232319169903 - 3 = 0.1943232319169903

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We could follow DuckDB in this case

@goldmedal goldmedal marked this pull request as ready for review August 31, 2024 15:10
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Thank you, the implementation looks good to me.
I think it's a good idea to follow DuckDB's behavior

One thing to do is to update the function doc also https://github.com/apache/datafusion/blob/main/docs/source/user-guide/sql/aggregate_functions.md

}
}

impl Accumulator for KurtosisPopAccumulator {
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It would be great to add a link to the algorithm (something like wikipedia or duckdb's implementation)

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Thanks for reminding this. I have added the doc for KurtosisPopAccumulator and updated the function doc.

impl KurtosisPopFunction {
pub fn new() -> Self {
Self {
signature: Signature::numeric(1, Volatility::Immutable),

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I think user_defined with Float64 is more suitable here.

    fn coerce_types(&self, _arg_types: &[DataType]) -> Result<Vec<DataType>> {
        Ok(vec![DataType::Float64])
    }

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@goldmedal If we handle the coercion before the function, they will be coerced to f64, therefore we just need to deal with f64 only

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You can take #12275 as reference, we can have signature coercible(vec![Float64]) if this function expect any type that is coercible to f64.

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It looks great! Thanks

}

fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> {
if !arg_types[0].is_null() && !arg_types[0].is_numeric() {
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I guess we don't require additional check

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Indeed, we have the mechanism to coerce the type implicitly. The case will work after this check is removed.

query R
SELECT kurtosis_pop(col) FROM VALUES ('1'), ('10'), ('100'), ('10'), ('1') as tab(col);
----
0.194323231917


impl Accumulator for KurtosisPopAccumulator {
fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
let values = &cast(&values[0], &DataType::Float64)?;
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Suggested change
let values = &cast(&values[0], &DataType::Float64)?;
let array = values[0].as_primitive::<Float64Type>();
for value in array.iter().flatten() {
self.count += 1;
self.sum += value;
self.sum_sqr += value.powi(2);
self.sum_cub += value.powi(3);
self.sum_four += value.powi(4);
}

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you can also use as_float64_array or as_primitive_opt if you prefer Result than panic.

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@goldmedal goldmedal Sep 1, 2024

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It looks good. I prefer to use as_float64_array. However, I think the &cast can't be removed. We should cast from another type array to the float64 array first, then downcast to Float64Array by as_float64_array.

Comment on lines +171 to +175
let count_64 = 1_f64 / self.count as f64;
let m4 = count_64
* (self.sum_four - 4.0 * self.sum_cub * self.sum * count_64
+ 6.0 * self.sum_sqr * self.sum.powi(2) * count_64.powi(2)
- 3.0 * self.sum.powi(4) * count_64.powi(3));
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We could follow DuckDB in this case

@github-actions github-actions bot added the documentation Improvements or additions to documentation label Sep 1, 2024

impl Accumulator for KurtosisPopAccumulator {
fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
let values = &cast(&values[0], &DataType::Float64)?;
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I think we don't need the cast here? 🤔
The coercion is handled in Signature::Coercible

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Amazing! Thanks for the suggestion.

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👍

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Thanks @goldmedal @2010YOUY01

@jayzhan211 jayzhan211 merged commit 5ff5a6c into apache:main Sep 4, 2024
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@goldmedal goldmedal deleted the feature/12251-kurtosis_pop branch September 4, 2024 07:20
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Thanks @jayzhan211 @2010YOUY01

@alamb
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alamb commented Sep 25, 2024

I filed #12625 to propose moving kertosis_pop

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Support kurtosis_pop in Aggregation function
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