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Remove Accumulator::update and Accumulator::merge #1582

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Jan 18, 2022
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Remove Accumulator::update and Accumulator::merge #1582

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2 changes: 1 addition & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@ the convenience of an SQL interface or a DataFrame API.

## Known Uses

Projects that adapt to or service as plugins to DataFusion:
Projects that adapt to or serve as plugins to DataFusion:

- [datafusion-python](https://github.com/datafusion-contrib/datafusion-python)
- [datafusion-java](https://github.com/datafusion-contrib/datafusion-java)
Expand Down
58 changes: 43 additions & 15 deletions datafusion-examples/examples/simple_udaf.rs
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Expand Up @@ -18,7 +18,7 @@
/// In this example we will declare a single-type, single return type UDAF that computes the geometric mean.
/// The geometric mean is described here: https://en.wikipedia.org/wiki/Geometric_mean
use datafusion::arrow::{
array::Float32Array, array::Float64Array, datatypes::DataType,
array::ArrayRef, array::Float32Array, array::Float64Array, datatypes::DataType,
record_batch::RecordBatch,
};

Expand Down Expand Up @@ -66,20 +66,6 @@ impl GeometricMean {
pub fn new() -> Self {
GeometricMean { n: 0, prod: 1.0 }
}
}

// UDAFs are built using the trait `Accumulator`, that offers DataFusion the necessary functions
// to use them.
impl Accumulator for GeometricMean {
// this function serializes our state to `ScalarValue`, which DataFusion uses
// to pass this state between execution stages.
// Note that this can be arbitrary data.
fn state(&self) -> Result<Vec<ScalarValue>> {
Ok(vec![
ScalarValue::from(self.prod),
ScalarValue::from(self.n),
])
}

// this function receives one entry per argument of this accumulator.
// DataFusion calls this function on every row, and expects this function to update the accumulator's state.
Expand Down Expand Up @@ -114,6 +100,20 @@ impl Accumulator for GeometricMean {
};
Ok(())
}
}

// UDAFs are built using the trait `Accumulator`, that offers DataFusion the necessary functions
// to use them.
impl Accumulator for GeometricMean {
// This function serializes our state to `ScalarValue`, which DataFusion uses
// to pass this state between execution stages.
// Note that this can be arbitrary data.
fn state(&self) -> Result<Vec<ScalarValue>> {
Ok(vec![
ScalarValue::from(self.prod),
ScalarValue::from(self.n),
])
}

// DataFusion expects this function to return the final value of this aggregator.
// in this case, this is the formula of the geometric mean
Expand All @@ -122,9 +122,37 @@ impl Accumulator for GeometricMean {
Ok(ScalarValue::from(value))
}

// DataFusion calls this function to update the accumulator's state for a batch
// of inputs rows. In this case the product is updated with values from the first column
// and the count is updated based on the row count
fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
if values.is_empty() {
return Ok(());
};
(0..values[0].len()).try_for_each(|index| {
let v = values
.iter()
.map(|array| ScalarValue::try_from_array(array, index))
.collect::<Result<Vec<_>>>()?;
self.update(&v)
})
}

// Optimization hint: this trait also supports `update_batch` and `merge_batch`,
// that can be used to perform these operations on arrays instead of single values.
// By default, these methods call `update` and `merge` row by row
fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
if states.is_empty() {
return Ok(());
};
(0..states[0].len()).try_for_each(|index| {
let v = states
.iter()
.map(|array| ScalarValue::try_from_array(array, index))
.collect::<Result<Vec<_>>>()?;
self.merge(&v)
})
}
}

#[tokio::main]
Expand Down
48 changes: 37 additions & 11 deletions datafusion/src/datasource/file_format/parquet.rs
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Expand Up @@ -36,6 +36,9 @@ use parquet::file::statistics::Statistics as ParquetStatistics;

use super::FileFormat;
use super::FileScanConfig;
use crate::arrow::array::{
BooleanArray, Float32Array, Float64Array, Int32Array, Int64Array,
};
use crate::arrow::datatypes::{DataType, Field};
use crate::datasource::object_store::{ObjectReader, ObjectReaderStream};
use crate::datasource::{create_max_min_accs, get_col_stats};
Expand All @@ -47,7 +50,6 @@ use crate::physical_plan::expressions::{MaxAccumulator, MinAccumulator};
use crate::physical_plan::file_format::ParquetExec;
use crate::physical_plan::ExecutionPlan;
use crate::physical_plan::{Accumulator, Statistics};
use crate::scalar::ScalarValue;

/// The default file exetension of parquet files
pub const DEFAULT_PARQUET_EXTENSION: &str = ".parquet";
Expand Down Expand Up @@ -132,15 +134,19 @@ fn summarize_min_max(
if let DataType::Boolean = fields[i].data_type() {
if s.has_min_max_set() {
if let Some(max_value) = &mut max_values[i] {
match max_value.update(&[ScalarValue::Boolean(Some(*s.max()))]) {
match max_value.update_batch(&[Arc::new(BooleanArray::from(
vec![Some(*s.max())],
))]) {
Ok(_) => {}
Err(_) => {
max_values[i] = None;
}
}
}
if let Some(min_value) = &mut min_values[i] {
match min_value.update(&[ScalarValue::Boolean(Some(*s.min()))]) {
match min_value.update_batch(&[Arc::new(BooleanArray::from(
vec![Some(*s.min())],
))]) {
Ok(_) => {}
Err(_) => {
min_values[i] = None;
Expand All @@ -154,15 +160,21 @@ fn summarize_min_max(
if let DataType::Int32 = fields[i].data_type() {
if s.has_min_max_set() {
if let Some(max_value) = &mut max_values[i] {
match max_value.update(&[ScalarValue::Int32(Some(*s.max()))]) {
match max_value.update_batch(&[Arc::new(Int32Array::from_value(
*s.max(),
1,
))]) {
Ok(_) => {}
Err(_) => {
max_values[i] = None;
}
}
}
if let Some(min_value) = &mut min_values[i] {
match min_value.update(&[ScalarValue::Int32(Some(*s.min()))]) {
match min_value.update_batch(&[Arc::new(Int32Array::from_value(
*s.min(),
1,
))]) {
Ok(_) => {}
Err(_) => {
min_values[i] = None;
Expand All @@ -176,15 +188,21 @@ fn summarize_min_max(
if let DataType::Int64 = fields[i].data_type() {
if s.has_min_max_set() {
if let Some(max_value) = &mut max_values[i] {
match max_value.update(&[ScalarValue::Int64(Some(*s.max()))]) {
match max_value.update_batch(&[Arc::new(Int64Array::from_value(
*s.max(),
1,
))]) {
Ok(_) => {}
Err(_) => {
max_values[i] = None;
}
}
}
if let Some(min_value) = &mut min_values[i] {
match min_value.update(&[ScalarValue::Int64(Some(*s.min()))]) {
match min_value.update_batch(&[Arc::new(Int64Array::from_value(
*s.min(),
1,
))]) {
Ok(_) => {}
Err(_) => {
min_values[i] = None;
Expand All @@ -198,15 +216,19 @@ fn summarize_min_max(
if let DataType::Float32 = fields[i].data_type() {
if s.has_min_max_set() {
if let Some(max_value) = &mut max_values[i] {
match max_value.update(&[ScalarValue::Float32(Some(*s.max()))]) {
match max_value.update_batch(&[Arc::new(Float32Array::from(
vec![Some(*s.max())],
))]) {
Ok(_) => {}
Err(_) => {
max_values[i] = None;
}
}
}
if let Some(min_value) = &mut min_values[i] {
match min_value.update(&[ScalarValue::Float32(Some(*s.min()))]) {
match min_value.update_batch(&[Arc::new(Float32Array::from(
vec![Some(*s.min())],
))]) {
Ok(_) => {}
Err(_) => {
min_values[i] = None;
Expand All @@ -220,15 +242,19 @@ fn summarize_min_max(
if let DataType::Float64 = fields[i].data_type() {
if s.has_min_max_set() {
if let Some(max_value) = &mut max_values[i] {
match max_value.update(&[ScalarValue::Float64(Some(*s.max()))]) {
match max_value.update_batch(&[Arc::new(Float64Array::from(
vec![Some(*s.max())],
))]) {
Ok(_) => {}
Err(_) => {
max_values[i] = None;
}
}
}
if let Some(min_value) = &mut min_values[i] {
match min_value.update(&[ScalarValue::Float64(Some(*s.min()))]) {
match min_value.update_batch(&[Arc::new(Float64Array::from(
vec![Some(*s.min())],
))]) {
Ok(_) => {}
Err(_) => {
min_values[i] = None;
Expand Down
4 changes: 2 additions & 2 deletions datafusion/src/datasource/mod.rs
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Expand Up @@ -71,7 +71,7 @@ pub async fn get_statistics_with_limit(

if let Some(max_value) = &mut max_values[i] {
if let Some(file_max) = cs.max_value.clone() {
match max_value.update(&[file_max]) {
match max_value.update_batch(&[file_max.to_array()]) {
Ok(_) => {}
Err(_) => {
max_values[i] = None;
Expand All @@ -82,7 +82,7 @@ pub async fn get_statistics_with_limit(

if let Some(min_value) = &mut min_values[i] {
if let Some(file_min) = cs.min_value.clone() {
match min_value.update(&[file_min]) {
match min_value.update_batch(&[file_min.to_array()]) {
Ok(_) => {}
Err(_) => {
min_values[i] = None;
Expand Down
50 changes: 43 additions & 7 deletions datafusion/src/physical_plan/distinct_expressions.rs
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Expand Up @@ -17,13 +17,13 @@

//! Implementations for DISTINCT expressions, e.g. `COUNT(DISTINCT c)`

use arrow::array::ArrayRef;
use arrow::datatypes::{DataType, Field};
use std::any::Any;
use std::fmt::Debug;
use std::hash::Hash;
use std::sync::Arc;

use arrow::datatypes::{DataType, Field};

use ahash::RandomState;
use std::collections::HashSet;

Expand Down Expand Up @@ -130,8 +130,7 @@ struct DistinctCountAccumulator {
state_data_types: Vec<DataType>,
count_data_type: DataType,
}

impl Accumulator for DistinctCountAccumulator {
impl DistinctCountAccumulator {
fn update(&mut self, values: &[ScalarValue]) -> Result<()> {
// If a row has a NULL, it is not included in the final count.
if !values.iter().any(|v| v.is_null()) {
Expand Down Expand Up @@ -165,7 +164,33 @@ impl Accumulator for DistinctCountAccumulator {
self.update(&row_values)
})
}
}

impl Accumulator for DistinctCountAccumulator {
fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
if values.is_empty() {
return Ok(());
};
(0..values[0].len()).try_for_each(|index| {
let v = values
.iter()
.map(|array| ScalarValue::try_from_array(array, index))
.collect::<Result<Vec<_>>>()?;
self.update(&v)
})
}
fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
if states.is_empty() {
return Ok(());
};
(0..states[0].len()).try_for_each(|index| {
let v = states
.iter()
.map(|array| ScalarValue::try_from_array(array, index))
.collect::<Result<Vec<_>>>()?;
self.merge(&v)
})
}
fn state(&self) -> Result<Vec<ScalarValue>> {
let mut cols_out = self
.state_data_types
Expand Down Expand Up @@ -317,9 +342,20 @@ mod tests {

let mut accum = agg.create_accumulator()?;

for row in rows.iter() {
accum.update(row)?
}
let cols = (0..rows[0].len())
.map(|i| {
rows.iter()
.map(|inner| inner[i].clone())
.collect::<Vec<ScalarValue>>()
})
.collect::<Vec<_>>();

let arrays: Vec<ArrayRef> = cols
.iter()
.map(|c| ScalarValue::iter_to_array(c.clone()))
.collect::<Result<Vec<ArrayRef>>>()?;

accum.update_batch(&arrays)?;

Ok((accum.state()?, accum.evaluate()?))
}
Expand Down
17 changes: 0 additions & 17 deletions datafusion/src/physical_plan/expressions/approx_distinct.rs
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Expand Up @@ -217,23 +217,6 @@ impl<T: Hash> TryFrom<&ScalarValue> for HyperLogLog<T> {

macro_rules! default_accumulator_impl {
() => {
fn update(&mut self, values: &[ScalarValue]) -> Result<()> {
self.update_batch(
values
.iter()
.map(|s| s.to_array() as ArrayRef)
.collect::<Vec<_>>()
.as_slice(),
)
}

fn merge(&mut self, states: &[ScalarValue]) -> Result<()> {
assert_eq!(1, states.len(), "expect only 1 element in the states");
let other = HyperLogLog::try_from(&states[0])?;
self.hll.merge(&other);
Ok(())
}

fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
assert_eq!(1, states.len(), "expect only 1 element in the states");
let binary_array = states[0].as_any().downcast_ref::<BinaryArray>().unwrap();
Expand Down
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