Jefffrey commented on code in PR #17988: URL: https://github.com/apache/datafusion/pull/17988#discussion_r2438329135
########## datafusion/functions-aggregate/src/percentile_cont.rs: ########## @@ -0,0 +1,839 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +use std::fmt::{Debug, Formatter}; +use std::mem::{size_of, size_of_val}; +use std::sync::Arc; + +use arrow::array::{ + ArrowNumericType, BooleanArray, ListArray, PrimitiveArray, PrimitiveBuilder, +}; +use arrow::buffer::{OffsetBuffer, ScalarBuffer}; +use arrow::{ + array::{Array, ArrayRef, AsArray}, + datatypes::{ + ArrowNativeType, DataType, Decimal128Type, Decimal256Type, Decimal32Type, + Decimal64Type, Field, FieldRef, Float16Type, Float32Type, Float64Type, + }, +}; + +use arrow::array::ArrowNativeTypeOp; + +use datafusion_common::{ + internal_datafusion_err, internal_err, not_impl_datafusion_err, plan_err, + DataFusionError, HashSet, Result, ScalarValue, +}; +use datafusion_expr::expr::{AggregateFunction, Sort}; +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, Expr, Signature, TypeSignature, + Volatility, +}; +use datafusion_expr::{EmitTo, GroupsAccumulator}; +use datafusion_functions_aggregate_common::aggregate::groups_accumulator::accumulate::accumulate; +use datafusion_functions_aggregate_common::aggregate::groups_accumulator::nulls::filtered_null_mask; +use datafusion_functions_aggregate_common::utils::Hashable; +use datafusion_macros::user_doc; +use datafusion_physical_expr_common::physical_expr::PhysicalExpr; + +/// Precision multiplier for linear interpolation calculations. +/// +/// This value of 1,000,000 was chosen to balance precision with overflow safety: +/// - Provides 6 decimal places of precision for the fractional component +/// - Small enough to avoid overflow when multiplied with typical numeric values +/// - Sufficient precision for most statistical applications +/// +/// The interpolation formula: `lower + (upper - lower) * fraction` +/// is computed as: `lower + ((upper - lower) * (fraction * PRECISION)) / PRECISION` +/// to avoid floating-point operations on integer types while maintaining precision. +const INTERPOLATION_PRECISION: usize = 1_000_000; + +create_func!(PercentileCont, percentile_cont_udaf); + +/// Computes the exact percentile continuous of a set of numbers +pub fn percentile_cont(order_by: Sort, percentile: Expr) -> Expr { + let expr = order_by.expr.clone(); + let args = vec![expr, percentile]; + + Expr::AggregateFunction(AggregateFunction::new_udf( + percentile_cont_udaf(), + args, + false, + None, + vec![order_by], + None, + )) +} + +#[user_doc( + doc_section(label = "General Functions"), + description = "Returns the exact percentile of input values, interpolating between values if needed.", + syntax_example = "percentile_cont(percentile) WITHIN GROUP (ORDER BY expression)", + sql_example = r#"```sql +> SELECT percentile_cont(0.75) WITHIN GROUP (ORDER BY column_name) FROM table_name; ++----------------------------------------------------------+ +| percentile_cont(0.75) WITHIN GROUP (ORDER BY column_name) | ++----------------------------------------------------------+ +| 45.5 | ++----------------------------------------------------------+ +``` + +An alternate syntax is also supported: +```sql +> SELECT percentile_cont(column_name, 0.75) FROM table_name; ++---------------------------------------+ +| percentile_cont(column_name, 0.75) | ++---------------------------------------+ +| 45.5 | ++---------------------------------------+ +```"#, + standard_argument(name = "expression", prefix = "The"), + argument( + name = "percentile", + description = "Percentile to compute. Must be a float value between 0 and 1 (inclusive)." + ) +)] +/// PERCENTILE_CONT aggregate expression. This uses an exact calculation and stores all values +/// in memory before computing the result. If an approximation is sufficient then +/// APPROX_PERCENTILE_CONT provides a much more efficient solution. +/// +/// If using the distinct variation, the memory usage will be similarly high if the +/// cardinality is high as it stores all distinct values in memory before computing the +/// result, but if cardinality is low then memory usage will also be lower. +#[derive(PartialEq, Eq, Hash)] +pub struct PercentileCont { + signature: Signature, +} + +impl Debug for PercentileCont { + fn fmt(&self, f: &mut Formatter) -> std::fmt::Result { + f.debug_struct("PercentileCont") + .field("name", &self.name()) + .field("signature", &self.signature) + .finish() + } +} + +impl Default for PercentileCont { + fn default() -> Self { + Self::new() + } +} + +impl PercentileCont { + pub fn new() -> Self { + let mut variants = Vec::with_capacity(NUMERICS.len()); + // Accept any numeric value paired with a float64 percentile + for num in NUMERICS { + variants.push(TypeSignature::Exact(vec![num.clone(), DataType::Float64])); + } + Self { + signature: Signature::one_of(variants, Volatility::Immutable), + } + } + + fn create_accumulator(&self, args: AccumulatorArgs) -> Result<Box<dyn Accumulator>> { + let percentile = validate_percentile(&args.exprs[1])?; + + let is_descending = args + .order_bys + .first() + .map(|sort_expr| sort_expr.options.descending) + .unwrap_or(false); + + let percentile = if is_descending { + 1.0 - percentile + } else { + percentile + }; + + macro_rules! helper { + ($t:ty, $dt:expr) => { + if args.is_distinct { + Ok(Box::new(DistinctPercentileContAccumulator::<$t> { + data_type: $dt.clone(), + distinct_values: HashSet::new(), + percentile, + })) + } else { + Ok(Box::new(PercentileContAccumulator::<$t> { + data_type: $dt.clone(), + all_values: vec![], + percentile, + })) + } + }; + } + + let input_dt = args.exprs[0].data_type(args.schema)?; + match input_dt { + // For integer types, use Float64 internally since percentile_cont returns Float64 + DataType::Int8 + | DataType::Int16 + | DataType::Int32 + | DataType::Int64 + | DataType::UInt8 + | DataType::UInt16 + | DataType::UInt32 + | DataType::UInt64 => helper!(Float64Type, DataType::Float64), + DataType::Float16 => helper!(Float16Type, input_dt), + DataType::Float32 => helper!(Float32Type, input_dt), + DataType::Float64 => helper!(Float64Type, input_dt), + DataType::Decimal32(_, _) => helper!(Decimal32Type, input_dt), + DataType::Decimal64(_, _) => helper!(Decimal64Type, input_dt), + DataType::Decimal128(_, _) => helper!(Decimal128Type, input_dt), + DataType::Decimal256(_, _) => helper!(Decimal256Type, input_dt), + _ => Err(DataFusionError::NotImplemented(format!( + "PercentileContAccumulator not supported for {} with {}", + args.name, input_dt, + ))), + } + } +} + +fn get_scalar_value(expr: &Arc<dyn PhysicalExpr>) -> Result<ScalarValue> { + use arrow::array::RecordBatch; + use arrow::datatypes::Schema; + use datafusion_expr::ColumnarValue; + + let empty_schema = Arc::new(Schema::empty()); + let batch = RecordBatch::new_empty(Arc::clone(&empty_schema)); + if let ColumnarValue::Scalar(s) = expr.evaluate(&batch)? { + Ok(s) + } else { + internal_err!("Didn't expect ColumnarValue::Array") + } +} + +fn validate_percentile(expr: &Arc<dyn PhysicalExpr>) -> Result<f64> { + let percentile = match get_scalar_value(expr) + .map_err(|_| not_impl_datafusion_err!("Percentile value for 'PERCENTILE_CONT' must be a literal"))? { + ScalarValue::Float32(Some(value)) => { + value as f64 + } + ScalarValue::Float64(Some(value)) => { + value + } + sv => { + return plan_err!( + "Percentile value for 'PERCENTILE_CONT' must be Float32 or Float64 literal (got data type {})", + sv.data_type() + ) + } + }; + + // Ensure the percentile is between 0 and 1. + if !(0.0..=1.0).contains(&percentile) { + return plan_err!( + "Percentile value must be between 0.0 and 1.0 inclusive, {percentile} is invalid" + ); + } + Ok(percentile) +} + +impl AggregateUDFImpl for PercentileCont { + fn as_any(&self) -> &dyn std::any::Any { + self + } + + fn name(&self) -> &str { + "percentile_cont" + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> { + if !arg_types[0].is_numeric() { + return plan_err!("percentile_cont requires numeric input types"); + } + // PERCENTILE_CONT performs linear interpolation and should return a float type + // For integer inputs, return Float64 (matching PostgreSQL/DuckDB behavior) + // For float inputs, preserve the float type + match &arg_types[0] { + DataType::Float16 | DataType::Float32 | DataType::Float64 => { + Ok(arg_types[0].clone()) + } + DataType::Decimal32(_, _) + | DataType::Decimal64(_, _) + | DataType::Decimal128(_, _) + | DataType::Decimal256(_, _) => Ok(arg_types[0].clone()), + DataType::UInt8 + | DataType::UInt16 + | DataType::UInt32 + | DataType::UInt64 + | DataType::Int8 + | DataType::Int16 + | DataType::Int32 + | DataType::Int64 => Ok(DataType::Float64), + // Shouldn't happen due to signature check, but just in case + dt => plan_err!( + "percentile_cont does not support input type {}, must be numeric", + dt + ), + } + } + + fn state_fields(&self, args: StateFieldsArgs) -> Result<Vec<FieldRef>> { + //Intermediate state is a list of the elements we have collected so far + let input_type = args.input_fields[0].data_type().clone(); + // For integer types, we store as Float64 internally + let storage_type = match &input_type { + DataType::Int8 + | DataType::Int16 + | DataType::Int32 + | DataType::Int64 + | DataType::UInt8 + | DataType::UInt16 + | DataType::UInt32 + | DataType::UInt64 => DataType::Float64, + _ => input_type, + }; + + let field = Field::new_list_field(storage_type, true); + let state_name = if args.is_distinct { + "distinct_percentile_cont" + } else { + "percentile_cont" + }; + + Ok(vec![Field::new( + format_state_name(args.name, state_name), + DataType::List(Arc::new(field)), + true, + ) + .into()]) + } + + fn accumulator(&self, acc_args: AccumulatorArgs) -> Result<Box<dyn Accumulator>> { + self.create_accumulator(acc_args) + } + + fn groups_accumulator_supported(&self, args: AccumulatorArgs) -> bool { + !args.is_distinct + } + + fn create_groups_accumulator( + &self, + args: AccumulatorArgs, + ) -> Result<Box<dyn GroupsAccumulator>> { + let num_args = args.exprs.len(); + if num_args != 2 { + return internal_err!( + "percentile_cont should have 2 args, but found num args:{}", + args.exprs.len() + ); + } + + let percentile = validate_percentile(&args.exprs[1])?; + + let is_descending = args + .order_bys + .first() + .map(|sort_expr| sort_expr.options.descending) + .unwrap_or(false); + + let percentile = if is_descending { + 1.0 - percentile + } else { + percentile + }; + + macro_rules! helper { + ($t:ty, $dt:expr) => { + Ok(Box::new(PercentileContGroupsAccumulator::<$t>::new( + $dt, percentile, + ))) + }; + } + + let input_dt = args.exprs[0].data_type(args.schema)?; + match input_dt { + // For integer types, use Float64 internally since percentile_cont returns Float64 + DataType::Int8 + | DataType::Int16 + | DataType::Int32 + | DataType::Int64 + | DataType::UInt8 + | DataType::UInt16 + | DataType::UInt32 + | DataType::UInt64 => helper!(Float64Type, DataType::Float64), + DataType::Float16 => helper!(Float16Type, input_dt), + DataType::Float32 => helper!(Float32Type, input_dt), + DataType::Float64 => helper!(Float64Type, input_dt), + DataType::Decimal32(_, _) => helper!(Decimal32Type, input_dt), + DataType::Decimal64(_, _) => helper!(Decimal64Type, input_dt), + DataType::Decimal128(_, _) => helper!(Decimal128Type, input_dt), + DataType::Decimal256(_, _) => helper!(Decimal256Type, input_dt), + _ => Err(DataFusionError::NotImplemented(format!( + "PercentileContGroupsAccumulator not supported for {} with {}", + args.name, input_dt, + ))), + } + } + + fn supports_null_handling_clause(&self) -> bool { + false + } + + fn is_ordered_set_aggregate(&self) -> bool { + true + } + + fn documentation(&self) -> Option<&Documentation> { + self.doc() + } +} + +/// The percentile_cont accumulator accumulates the raw input values +/// as native types. +/// +/// The intermediate state is represented as a List of scalar values updated by +/// `merge_batch` and a `Vec` of native values that are converted to scalar values +/// in the final evaluation step so that we avoid expensive conversions and +/// allocations during `update_batch`. +struct PercentileContAccumulator<T: ArrowNumericType> { + data_type: DataType, + all_values: Vec<T::Native>, + percentile: f64, +} + +impl<T: ArrowNumericType> Debug for PercentileContAccumulator<T> { + fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result { + write!( + f, + "PercentileContAccumulator({}, percentile={})", + self.data_type, self.percentile + ) + } +} + +impl<T: ArrowNumericType> Accumulator for PercentileContAccumulator<T> { + fn state(&mut self) -> Result<Vec<ScalarValue>> { + // Convert `all_values` to `ListArray` and return a single List ScalarValue + + // Build offsets + let offsets = + OffsetBuffer::new(ScalarBuffer::from(vec![0, self.all_values.len() as i32])); + + // Build inner array + let values_array = PrimitiveArray::<T>::new( + ScalarBuffer::from(std::mem::take(&mut self.all_values)), + None, + ) + .with_data_type(self.data_type.clone()); + + // Build the result list array + let list_array = ListArray::new( + Arc::new(Field::new_list_field(self.data_type.clone(), true)), + offsets, + Arc::new(values_array), + None, + ); + + Ok(vec![ScalarValue::List(Arc::new(list_array))]) + } + + fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> { + // Cast to target type if needed (e.g., integer to Float64) + let values = if values[0].data_type() != &self.data_type { + arrow::compute::cast(&values[0], &self.data_type)? + } else { + Arc::clone(&values[0]) + }; + + let values = values.as_primitive::<T>(); + self.all_values.reserve(values.len() - values.null_count()); + self.all_values.extend(values.iter().flatten()); + Ok(()) + } + + fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> { + let array = states[0].as_list::<i32>(); + for v in array.iter().flatten() { + self.update_batch(&[v])? + } + Ok(()) + } + + fn evaluate(&mut self) -> Result<ScalarValue> { + let d = std::mem::take(&mut self.all_values); + let value = calculate_percentile::<T>(d, self.percentile); + ScalarValue::new_primitive::<T>(value, &self.data_type) + } + + fn size(&self) -> usize { + size_of_val(self) + self.all_values.capacity() * size_of::<T::Native>() + } +} + +/// The percentile_cont groups accumulator accumulates the raw input values +/// +/// For calculating the exact percentile of groups, we need to store all values +/// of groups before final evaluation. +/// So values in each group will be stored in a `Vec<T>`, and the total group values +/// will be actually organized as a `Vec<Vec<T>>`. +/// +#[derive(Debug)] +struct PercentileContGroupsAccumulator<T: ArrowNumericType + Send> { + data_type: DataType, + group_values: Vec<Vec<T::Native>>, + percentile: f64, +} + +impl<T: ArrowNumericType + Send> PercentileContGroupsAccumulator<T> { + pub fn new(data_type: DataType, percentile: f64) -> Self { + Self { + data_type, + group_values: Vec::new(), + percentile, + } + } +} + +impl<T: ArrowNumericType + Send> GroupsAccumulator + for PercentileContGroupsAccumulator<T> +{ + fn update_batch( + &mut self, + values: &[ArrayRef], + group_indices: &[usize], + opt_filter: Option<&BooleanArray>, + total_num_groups: usize, + ) -> Result<()> { + // For ordered-set aggregates, we only care about the ORDER BY column (first element) + // The percentile parameter is already stored in self.percentile + + // Cast to target type if needed (e.g., integer to Float64) + let values_array = if values[0].data_type() != &self.data_type { + arrow::compute::cast(&values[0], &self.data_type)? + } else { + Arc::clone(&values[0]) + }; + + let values = values_array.as_primitive::<T>(); + + // Push the `not nulls + not filtered` row into its group + self.group_values.resize(total_num_groups, Vec::new()); + accumulate( + group_indices, + values, + opt_filter, + |group_index, new_value| { + self.group_values[group_index].push(new_value); + }, + ); + + Ok(()) + } + + fn merge_batch( + &mut self, + values: &[ArrayRef], + group_indices: &[usize], + // Since aggregate filter should be applied in partial stage, in final stage there should be no filter + _opt_filter: Option<&BooleanArray>, + total_num_groups: usize, + ) -> Result<()> { + assert_eq!(values.len(), 1, "one argument to merge_batch"); + + let input_group_values = values[0].as_list::<i32>(); + + // Ensure group values big enough + self.group_values.resize(total_num_groups, Vec::new()); + + // Extend values to related groups + group_indices + .iter() + .zip(input_group_values.iter()) + .for_each(|(&group_index, values_opt)| { + if let Some(values) = values_opt { + let values = values.as_primitive::<T>(); + self.group_values[group_index].extend(values.values().iter()); + } + }); + + Ok(()) + } + + fn state(&mut self, emit_to: EmitTo) -> Result<Vec<ArrayRef>> { + // Emit values + let emit_group_values = emit_to.take_needed(&mut self.group_values); + + // Build offsets + let mut offsets = Vec::with_capacity(self.group_values.len() + 1); + offsets.push(0); + let mut cur_len = 0_i32; + for group_value in &emit_group_values { + cur_len += group_value.len() as i32; + offsets.push(cur_len); + } + let offsets = OffsetBuffer::new(ScalarBuffer::from(offsets)); + + // Build inner array + let flatten_group_values = + emit_group_values.into_iter().flatten().collect::<Vec<_>>(); + let group_values_array = + PrimitiveArray::<T>::new(ScalarBuffer::from(flatten_group_values), None) + .with_data_type(self.data_type.clone()); + + // Build the result list array + let result_list_array = ListArray::new( + Arc::new(Field::new_list_field(self.data_type.clone(), true)), + offsets, + Arc::new(group_values_array), + None, + ); + + Ok(vec![Arc::new(result_list_array)]) + } + + fn evaluate(&mut self, emit_to: EmitTo) -> Result<ArrayRef> { + // Emit values + let emit_group_values = emit_to.take_needed(&mut self.group_values); + + // Calculate percentile for each group + let mut evaluate_result_builder = + PrimitiveBuilder::<T>::new().with_data_type(self.data_type.clone()); + for values in emit_group_values { + let value = calculate_percentile::<T>(values, self.percentile); + evaluate_result_builder.append_option(value); + } + + Ok(Arc::new(evaluate_result_builder.finish())) + } + + fn convert_to_state( + &self, + values: &[ArrayRef], + opt_filter: Option<&BooleanArray>, + ) -> Result<Vec<ArrayRef>> { + assert_eq!(values.len(), 1, "one argument to merge_batch"); + + // Cast to target type if needed (e.g., integer to Float64) + let values_array = if values[0].data_type() != &self.data_type { + arrow::compute::cast(&values[0], &self.data_type)? + } else { + Arc::clone(&values[0]) + }; + + let input_array = values_array.as_primitive::<T>(); + + // Directly convert the input array to states, each row will be + // seen as a respective group. + // For detail, the `input_array` will be converted to a `ListArray`. + // And if row is `not null + not filtered`, it will be converted to a list + // with only one element; otherwise, this row in `ListArray` will be set + // to null. + + // Reuse values buffer in `input_array` to build `values` in `ListArray` + let values = PrimitiveArray::<T>::new(input_array.values().clone(), None) + .with_data_type(self.data_type.clone()); + + // `offsets` in `ListArray`, each row as a list element + let offset_end = i32::try_from(input_array.len()).map_err(|e| { + internal_datafusion_err!( + "cast array_len to i32 failed in convert_to_state of group percentile_cont, err:{e:?}" + ) + })?; + let offsets = (0..=offset_end).collect::<Vec<_>>(); + // Safety: The offsets vector is constructed as a sequential range from 0 to input_array.len(), + // which guarantees all OffsetBuffer invariants: + // 1. Offsets are monotonically increasing (each element is prev + 1) + // 2. No offset exceeds the values array length (max offset = input_array.len()) + // 3. First offset is 0 and last offset equals the total length + // Therefore new_unchecked is safe to use here. + let offsets = unsafe { OffsetBuffer::new_unchecked(ScalarBuffer::from(offsets)) }; + + // `nulls` for converted `ListArray` + let nulls = filtered_null_mask(opt_filter, input_array); + + let converted_list_array = ListArray::new( + Arc::new(Field::new_list_field(self.data_type.clone(), true)), + offsets, + Arc::new(values), + nulls, + ); + + Ok(vec![Arc::new(converted_list_array)]) + } + + fn supports_convert_to_state(&self) -> bool { + true + } + + fn size(&self) -> usize { + self.group_values + .iter() + .map(|values| values.capacity() * size_of::<T::Native>()) + .sum::<usize>() + // account for size of self.group_values too + + self.group_values.capacity() * size_of::<Vec<T::Native>>() + } +} + +/// The distinct percentile_cont accumulator accumulates the raw input values +/// using a HashSet to eliminate duplicates. +/// +/// The intermediate state is represented as a List of scalar values updated by +/// `merge_batch` and a `Vec` of `ArrayRef` that are converted to scalar values +/// in the final evaluation step so that we avoid expensive conversions and +/// allocations during `update_batch`. +struct DistinctPercentileContAccumulator<T: ArrowNumericType> { + data_type: DataType, + distinct_values: HashSet<Hashable<T::Native>>, + percentile: f64, +} + +impl<T: ArrowNumericType> Debug for DistinctPercentileContAccumulator<T> { + fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result { + write!( + f, + "DistinctPercentileContAccumulator({}, percentile={})", + self.data_type, self.percentile + ) + } +} + +impl<T: ArrowNumericType> Accumulator for DistinctPercentileContAccumulator<T> { + fn state(&mut self) -> Result<Vec<ScalarValue>> { + let all_values = self + .distinct_values + .iter() + .map(|x| ScalarValue::new_primitive::<T>(Some(x.0), &self.data_type)) + .collect::<Result<Vec<_>>>()?; + + let arr = ScalarValue::new_list_nullable(&all_values, &self.data_type); + Ok(vec![ScalarValue::List(arr)]) + } + + fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> { + if values.is_empty() { + return Ok(()); + } + + // Cast to target type if needed (e.g., integer to Float64) + let values = if values[0].data_type() != &self.data_type { + arrow::compute::cast(&values[0], &self.data_type)? + } else { + Arc::clone(&values[0]) + }; + + let array = values.as_primitive::<T>(); + match array.nulls().filter(|x| x.null_count() > 0) { + Some(n) => { + for idx in n.valid_indices() { + self.distinct_values.insert(Hashable(array.value(idx))); + } + } + None => array.values().iter().for_each(|x| { + self.distinct_values.insert(Hashable(*x)); + }), + } + Ok(()) + } + + fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> { + let array = states[0].as_list::<i32>(); + for v in array.iter().flatten() { + self.update_batch(&[v])? + } + Ok(()) + } + + fn evaluate(&mut self) -> Result<ScalarValue> { + let d = std::mem::take(&mut self.distinct_values) + .into_iter() + .map(|v| v.0) + .collect::<Vec<_>>(); + let value = calculate_percentile::<T>(d, self.percentile); + ScalarValue::new_primitive::<T>(value, &self.data_type) + } + + fn size(&self) -> usize { + size_of_val(self) + self.distinct_values.capacity() * size_of::<T::Native>() + } +} + +/// Calculate the percentile value for a given set of values. +/// This function performs an exact calculation by sorting all values. +/// +/// The percentile is calculated using linear interpolation between closest ranks. +/// For percentile p and n values: +/// - If p * (n-1) is an integer, return the value at that position +/// - Otherwise, interpolate between the two closest values +fn calculate_percentile<T: ArrowNumericType>( + mut values: Vec<T::Native>, + percentile: f64, +) -> Option<T::Native> { + let cmp = |x: &T::Native, y: &T::Native| x.compare(*y); + + let len = values.len(); + if len == 0 { + None + } else if len == 1 { + Some(values[0]) + } else if percentile == 0.0 { + // Get minimum value + values.sort_by(cmp); + Some(values[0]) + } else if percentile == 1.0 { + // Get maximum value + values.sort_by(cmp); + Some(values[len - 1]) Review Comment: Raised as #18108 -- This is an automated message from the Apache Git Service. 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