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18647: Refactor approx_median signature & support f16 #23

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8e3676e
Refactor approx_median signature & support f16
Jefffrey Nov 12, 2025
d3630a4
clippy
Jefffrey Nov 12, 2025
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36 changes: 0 additions & 36 deletions datafusion/functions-aggregate-common/src/tdigest.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,6 @@
use arrow::datatypes::DataType;
use arrow::datatypes::Float64Type;
use datafusion_common::cast::as_primitive_array;
use datafusion_common::Result;
use datafusion_common::ScalarValue;
use std::cmp::Ordering;
use std::mem::{size_of, size_of_val};
Expand Down Expand Up @@ -61,41 +60,6 @@ macro_rules! cast_scalar_u64 {
};
}

/// This trait is implemented for each type a [`TDigest`] can operate on,
/// allowing it to support both numerical rust types (obtained from
/// `PrimitiveArray` instances), and [`ScalarValue`] instances.
pub trait TryIntoF64 {
/// A fallible conversion of a possibly null `self` into a [`f64`].
///
/// If `self` is null, this method must return `Ok(None)`.
///
/// If `self` cannot be coerced to the desired type, this method must return
/// an `Err` variant.
fn try_as_f64(&self) -> Result<Option<f64>>;
}

/// Generate an infallible conversion from `type` to an [`f64`].
macro_rules! impl_try_ordered_f64 {
($type:ty) => {
impl TryIntoF64 for $type {
fn try_as_f64(&self) -> Result<Option<f64>> {
Ok(Some(*self as f64))
}
}
};
}

impl_try_ordered_f64!(f64);
impl_try_ordered_f64!(f32);
impl_try_ordered_f64!(i64);
impl_try_ordered_f64!(i32);
impl_try_ordered_f64!(i16);
impl_try_ordered_f64!(i8);
impl_try_ordered_f64!(u64);
impl_try_ordered_f64!(u32);
impl_try_ordered_f64!(u16);
impl_try_ordered_f64!(u8);

/// Centroid implementation to the cluster mentioned in the paper.
#[derive(Debug, PartialEq, Clone)]
pub struct Centroid {
Expand Down
88 changes: 51 additions & 37 deletions datafusion/functions-aggregate/src/approx_median.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -19,16 +19,18 @@

use arrow::datatypes::DataType::{Float64, UInt64};
use arrow::datatypes::{DataType, Field, FieldRef};
use datafusion_common::types::NativeType;
use datafusion_functions_aggregate_common::noop_accumulator::NoopAccumulator;
use std::any::Any;
use std::fmt::Debug;
use std::sync::Arc;

use datafusion_common::{not_impl_err, plan_err, Result};
use datafusion_common::{not_impl_err, Result};
use datafusion_expr::function::{AccumulatorArgs, StateFieldsArgs};
use datafusion_expr::type_coercion::aggregates::NUMERICS;
use datafusion_expr::utils::format_state_name;
use datafusion_expr::{
Accumulator, AggregateUDFImpl, Documentation, Signature, Volatility,
Accumulator, AggregateUDFImpl, Coercion, Documentation, Signature, TypeSignature,
TypeSignatureClass, Volatility,
};
use datafusion_macros::user_doc;

Expand Down Expand Up @@ -57,20 +59,11 @@ make_udaf_expr_and_func!(
```"#,
standard_argument(name = "expression",)
)]
#[derive(PartialEq, Eq, Hash)]
#[derive(Debug, PartialEq, Eq, Hash)]
pub struct ApproxMedian {
signature: Signature,
}

impl Debug for ApproxMedian {
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
f.debug_struct("ApproxMedian")
.field("name", &self.name())
.field("signature", &self.signature)
.finish()
}
}

impl Default for ApproxMedian {
fn default() -> Self {
Self::new()
Expand All @@ -81,33 +74,53 @@ impl ApproxMedian {
/// Create a new APPROX_MEDIAN aggregate function
pub fn new() -> Self {
Self {
signature: Signature::uniform(1, NUMERICS.to_vec(), Volatility::Immutable),
signature: Signature::one_of(
vec![
TypeSignature::Coercible(vec![Coercion::new_exact(
TypeSignatureClass::Integer,
)]),
TypeSignature::Coercible(vec![Coercion::new_implicit(
TypeSignatureClass::Float,
vec![TypeSignatureClass::Decimal],
NativeType::Float64,
)]),
],
Volatility::Immutable,
),
}
}
}

impl AggregateUDFImpl for ApproxMedian {
/// Return a reference to Any that can be used for downcasting
fn as_any(&self) -> &dyn Any {
self
}

fn state_fields(&self, args: StateFieldsArgs) -> Result<Vec<FieldRef>> {
Ok(vec![
Field::new(format_state_name(args.name, "max_size"), UInt64, false),
Field::new(format_state_name(args.name, "sum"), Float64, false),
Field::new(format_state_name(args.name, "count"), UInt64, false),
Field::new(format_state_name(args.name, "max"), Float64, false),
Field::new(format_state_name(args.name, "min"), Float64, false),
Field::new_list(
format_state_name(args.name, "centroids"),
Field::new_list_field(Float64, true),
false,
),
]
.into_iter()
.map(Arc::new)
.collect())
if args.input_fields[0].data_type().is_null() {
Ok(vec![Field::new(
format_state_name(args.name, self.name()),
DataType::Null,
true,
)
.into()])
} else {
Ok(vec![
Field::new(format_state_name(args.name, "max_size"), UInt64, false),
Field::new(format_state_name(args.name, "sum"), Float64, false),
Field::new(format_state_name(args.name, "count"), UInt64, false),
Field::new(format_state_name(args.name, "max"), Float64, false),
Field::new(format_state_name(args.name, "min"), Float64, false),
Field::new_list(
format_state_name(args.name, "centroids"),
Field::new_list_field(Float64, true),
false,
),
]
.into_iter()
.map(Arc::new)
.collect())
}
}

fn name(&self) -> &str {
Expand All @@ -119,9 +132,6 @@ impl AggregateUDFImpl for ApproxMedian {
}

fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> {
if !arg_types[0].is_numeric() {
return plan_err!("ApproxMedian requires numeric input types");
}
Ok(arg_types[0].clone())
}

Expand All @@ -132,10 +142,14 @@ impl AggregateUDFImpl for ApproxMedian {
);
}

Ok(Box::new(ApproxPercentileAccumulator::new(
0.5_f64,
acc_args.expr_fields[0].data_type().clone(),
)))
if acc_args.expr_fields[0].data_type().is_null() {
Ok(Box::new(NoopAccumulator::default()))
} else {
Ok(Box::new(ApproxPercentileAccumulator::new(
0.5_f64,
acc_args.expr_fields[0].data_type().clone(),
)))
}
}

fn documentation(&self) -> Option<&Documentation> {
Expand Down
83 changes: 21 additions & 62 deletions datafusion/functions-aggregate/src/approx_percentile_cont.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -16,11 +16,11 @@
// under the License.

use std::any::Any;
use std::fmt::{Debug, Formatter};
use std::fmt::Debug;
use std::mem::size_of_val;
use std::sync::Arc;

use arrow::array::Array;
use arrow::array::{Array, Float16Array};
use arrow::compute::{filter, is_not_null};
use arrow::datatypes::FieldRef;
use arrow::{
Expand All @@ -42,9 +42,7 @@ use datafusion_expr::{
Accumulator, AggregateUDFImpl, Documentation, Expr, Signature, TypeSignature,
Volatility,
};
use datafusion_functions_aggregate_common::tdigest::{
TDigest, TryIntoF64, DEFAULT_MAX_SIZE,
};
use datafusion_functions_aggregate_common::tdigest::{TDigest, DEFAULT_MAX_SIZE};
use datafusion_macros::user_doc;
use datafusion_physical_expr_common::physical_expr::PhysicalExpr;

Expand Down Expand Up @@ -121,20 +119,11 @@ An alternate syntax is also supported:
description = "Number of centroids to use in the t-digest algorithm. _Default is 100_. A higher number results in more accurate approximation but requires more memory."
)
)]
#[derive(PartialEq, Eq, Hash)]
#[derive(Debug, PartialEq, Eq, Hash)]
pub struct ApproxPercentileCont {
signature: Signature,
}

impl Debug for ApproxPercentileCont {
fn fmt(&self, f: &mut Formatter) -> std::fmt::Result {
f.debug_struct("ApproxPercentileCont")
.field("name", &self.name())
.field("signature", &self.signature)
.finish()
}
}

impl Default for ApproxPercentileCont {
fn default() -> Self {
Self::new()
Expand Down Expand Up @@ -197,6 +186,7 @@ impl ApproxPercentileCont {
| DataType::Int16
| DataType::Int32
| DataType::Int64
| DataType::Float16
| DataType::Float32
| DataType::Float64 => {
if let Some(max_size) = tdigest_max_size {
Expand Down Expand Up @@ -376,83 +366,51 @@ impl ApproxPercentileAccumulator {
match values.data_type() {
DataType::Float64 => {
let array = downcast_value!(values, Float64Array);
Ok(array
.values()
.iter()
.filter_map(|v| v.try_as_f64().transpose())
.collect::<Result<Vec<_>>>()?)
Ok(array.values().iter().copied().collect::<Vec<_>>())
}
DataType::Float32 => {
let array = downcast_value!(values, Float32Array);
Ok(array.values().iter().map(|v| *v as f64).collect::<Vec<_>>())
}
DataType::Float16 => {
let array = downcast_value!(values, Float16Array);
Ok(array
.values()
.iter()
.filter_map(|v| v.try_as_f64().transpose())
.collect::<Result<Vec<_>>>()?)
.map(|v| v.to_f64())
.collect::<Vec<_>>())
}
DataType::Int64 => {
let array = downcast_value!(values, Int64Array);
Ok(array
.values()
.iter()
.filter_map(|v| v.try_as_f64().transpose())
.collect::<Result<Vec<_>>>()?)
Ok(array.values().iter().map(|v| *v as f64).collect::<Vec<_>>())
}
DataType::Int32 => {
let array = downcast_value!(values, Int32Array);
Ok(array
.values()
.iter()
.filter_map(|v| v.try_as_f64().transpose())
.collect::<Result<Vec<_>>>()?)
Ok(array.values().iter().map(|v| *v as f64).collect::<Vec<_>>())
}
DataType::Int16 => {
let array = downcast_value!(values, Int16Array);
Ok(array
.values()
.iter()
.filter_map(|v| v.try_as_f64().transpose())
.collect::<Result<Vec<_>>>()?)
Ok(array.values().iter().map(|v| *v as f64).collect::<Vec<_>>())
}
DataType::Int8 => {
let array = downcast_value!(values, Int8Array);
Ok(array
.values()
.iter()
.filter_map(|v| v.try_as_f64().transpose())
.collect::<Result<Vec<_>>>()?)
Ok(array.values().iter().map(|v| *v as f64).collect::<Vec<_>>())
}
DataType::UInt64 => {
let array = downcast_value!(values, UInt64Array);
Ok(array
.values()
.iter()
.filter_map(|v| v.try_as_f64().transpose())
.collect::<Result<Vec<_>>>()?)
Ok(array.values().iter().map(|v| *v as f64).collect::<Vec<_>>())
}
DataType::UInt32 => {
let array = downcast_value!(values, UInt32Array);
Ok(array
.values()
.iter()
.filter_map(|v| v.try_as_f64().transpose())
.collect::<Result<Vec<_>>>()?)
Ok(array.values().iter().map(|v| *v as f64).collect::<Vec<_>>())
}
DataType::UInt16 => {
let array = downcast_value!(values, UInt16Array);
Ok(array
.values()
.iter()
.filter_map(|v| v.try_as_f64().transpose())
.collect::<Result<Vec<_>>>()?)
Ok(array.values().iter().map(|v| *v as f64).collect::<Vec<_>>())
}
DataType::UInt8 => {
let array = downcast_value!(values, UInt8Array);
Ok(array
.values()
.iter()
.filter_map(|v| v.try_as_f64().transpose())
.collect::<Result<Vec<_>>>()?)
Ok(array.values().iter().map(|v| *v as f64).collect::<Vec<_>>())
}
e => internal_err!(
"APPROX_PERCENTILE_CONT is not expected to receive the type {e:?}"
Expand Down Expand Up @@ -495,6 +453,7 @@ impl Accumulator for ApproxPercentileAccumulator {
DataType::UInt16 => ScalarValue::UInt16(Some(q as u16)),
DataType::UInt32 => ScalarValue::UInt32(Some(q as u32)),
DataType::UInt64 => ScalarValue::UInt64(Some(q as u64)),
DataType::Float16 => ScalarValue::Float16(Some(half::f16::from_f64(q))),
DataType::Float32 => ScalarValue::Float32(Some(q as f32)),
DataType::Float64 => ScalarValue::Float64(Some(q)),
v => unreachable!("unexpected return type {}", v),
Expand Down
10 changes: 10 additions & 0 deletions datafusion/sqllogictest/test_files/aggregate.slt
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Original file line number Diff line number Diff line change
Expand Up @@ -910,6 +910,16 @@ SELECT approx_median(col_f64_nan) FROM median_table
----
NaN

query RT
select approx_median(arrow_cast(col_f32, 'Float16')), arrow_typeof(approx_median(arrow_cast(col_f32, 'Float16'))) from median_table;
----
2.75 Float16

query ?T
select approx_median(NULL), arrow_typeof(approx_median(NULL)) from median_table;
----
NULL Null

# median decimal
statement ok
create table t(c decimal(10, 4)) as values (0.0001), (0.0002), (0.0003), (0.0004), (0.0005), (0.0006);
Expand Down
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