1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186
// 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.
//! This module contains functions and structs supporting user-defined aggregate functions.
use fmt::Debug;
use std::any::Any;
use std::fmt;
use arrow::{
datatypes::Field,
datatypes::{DataType, Schema},
};
use super::{expressions::format_state_name, Accumulator, AggregateExpr};
use crate::physical_plan::PhysicalExpr;
use datafusion_common::{DataFusionError, Result};
pub use datafusion_expr::AggregateUDF;
use datafusion_physical_expr::aggregate::utils::down_cast_any_ref;
use std::sync::Arc;
/// Creates a physical expression of the UDAF, that includes all necessary type coercion.
/// This function errors when `args`' can't be coerced to a valid argument type of the UDAF.
pub fn create_aggregate_expr(
fun: &AggregateUDF,
input_phy_exprs: &[Arc<dyn PhysicalExpr>],
input_schema: &Schema,
name: impl Into<String>,
) -> Result<Arc<dyn AggregateExpr>> {
let input_exprs_types = input_phy_exprs
.iter()
.map(|arg| arg.data_type(input_schema))
.collect::<Result<Vec<_>>>()?;
Ok(Arc::new(AggregateFunctionExpr {
fun: fun.clone(),
args: input_phy_exprs.to_vec(),
data_type: (fun.return_type)(&input_exprs_types)?.as_ref().clone(),
name: name.into(),
}))
}
/// Physical aggregate expression of a UDAF.
#[derive(Debug)]
pub struct AggregateFunctionExpr {
fun: AggregateUDF,
args: Vec<Arc<dyn PhysicalExpr>>,
/// Output / return type of this aggregate
data_type: DataType,
name: String,
}
impl AggregateFunctionExpr {
/// Return the `AggregateUDF` used by this `AggregateFunctionExpr`
pub fn fun(&self) -> &AggregateUDF {
&self.fun
}
}
impl AggregateExpr for AggregateFunctionExpr {
/// Return a reference to Any that can be used for downcasting
fn as_any(&self) -> &dyn Any {
self
}
fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
self.args.clone()
}
fn state_fields(&self) -> Result<Vec<Field>> {
let fields = (self.fun.state_type)(&self.data_type)?
.iter()
.enumerate()
.map(|(i, data_type)| {
Field::new(
format_state_name(&self.name, &format!("{i}")),
data_type.clone(),
true,
)
})
.collect::<Vec<Field>>();
Ok(fields)
}
fn field(&self) -> Result<Field> {
Ok(Field::new(&self.name, self.data_type.clone(), true))
}
fn create_accumulator(&self) -> Result<Box<dyn Accumulator>> {
(self.fun.accumulator)(&self.data_type)
}
fn create_sliding_accumulator(&self) -> Result<Box<dyn Accumulator>> {
let accumulator = (self.fun.accumulator)(&self.data_type)?;
// Accumulators that have window frame startings different
// than `UNBOUNDED PRECEDING`, such as `1 PRECEEDING`, need to
// implement retract_batch method in order to run correctly
// currently in DataFusion.
//
// If this `retract_batches` is not present, there is no way
// to calculate result correctly. For example, the query
//
// ```sql
// SELECT
// SUM(a) OVER(ORDER BY a ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) AS sum_a
// FROM
// t
// ```
//
// 1. First sum value will be the sum of rows between `[0, 1)`,
//
// 2. Second sum value will be the sum of rows between `[0, 2)`
//
// 3. Third sum value will be the sum of rows between `[1, 3)`, etc.
//
// Since the accumulator keeps the running sum:
//
// 1. First sum we add to the state sum value between `[0, 1)`
//
// 2. Second sum we add to the state sum value between `[1, 2)`
// (`[0, 1)` is already in the state sum, hence running sum will
// cover `[0, 2)` range)
//
// 3. Third sum we add to the state sum value between `[2, 3)`
// (`[0, 2)` is already in the state sum). Also we need to
// retract values between `[0, 1)` by this way we can obtain sum
// between [1, 3) which is indeed the apropriate range.
//
// When we use `UNBOUNDED PRECEDING` in the query starting
// index will always be 0 for the desired range, and hence the
// `retract_batch` method will not be called. In this case
// having retract_batch is not a requirement.
//
// This approach is a a bit different than window function
// approach. In window function (when they use a window frame)
// they get all the desired range during evaluation.
if !accumulator.supports_retract_batch() {
return Err(DataFusionError::NotImplemented(format!(
"Aggregate can not be used as a sliding accumulator because \
`retract_batch` is not implemented: {}",
self.name
)));
}
Ok(accumulator)
}
fn name(&self) -> &str {
&self.name
}
}
impl PartialEq<dyn Any> for AggregateFunctionExpr {
fn eq(&self, other: &dyn Any) -> bool {
down_cast_any_ref(other)
.downcast_ref::<Self>()
.map(|x| {
self.name == x.name
&& self.data_type == x.data_type
&& self.fun == x.fun
&& self.args.len() == x.args.len()
&& self
.args
.iter()
.zip(x.args.iter())
.all(|(this_arg, other_arg)| this_arg.eq(other_arg))
})
.unwrap_or(false)
}
}