Jefffrey commented on code in PR #17988: URL: https://github.com/apache/datafusion/pull/17988#discussion_r2417089907
########## datafusion/sqllogictest/test_files/aggregate.slt: ########## Review Comment: Perhaps we should add some tests without the `WITHIN GROUP` clause, as well as a test with percentile of `0.4` for ascending and `0.6` for descending on the same column to show they should give the same result ########## 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 + } Review Comment: Would be nice to deduplicate this code with approx_percentile_cont; also I wonder if we could clean some of it up. `get_scalar_value` feels a bit hacky, I wonder if there is some other code that exists to do this for us already 🤔 Also the error message for percentile value needing to be a literal would probably be a plan error instead of NotImplemented as I'm not sure we ever plan to implement something like that (how would we pass an array instead of a scalar for that 🤔 ) -- This is an automated message from the Apache Git Service. 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