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Posted to github@arrow.apache.org by GitBox <gi...@apache.org> on 2020/10/04 19:01:11 UTC
[GitHub] [arrow] jorgecarleitao commented on a change in pull request #8222: ARROW-10043: [Rust][DataFusion] Implement COUNT(DISTINCT col)
jorgecarleitao commented on a change in pull request #8222:
URL: https://github.com/apache/arrow/pull/8222#discussion_r499271330
##########
File path: rust/datafusion/src/physical_plan/aggregates.rs
##########
@@ -124,14 +126,30 @@ pub fn create_aggregate_expr(
let return_type = return_type(&fun, &arg_types)?;
- Ok(match fun {
- AggregateFunction::Count => {
+ Ok(match (fun, distinct) {
+ (AggregateFunction::Count, false) => {
Arc::new(expressions::Count::new(arg, name, return_type))
}
- AggregateFunction::Sum => Arc::new(expressions::Sum::new(arg, name, return_type)),
- AggregateFunction::Min => Arc::new(expressions::Min::new(arg, name, return_type)),
- AggregateFunction::Max => Arc::new(expressions::Max::new(arg, name, return_type)),
- AggregateFunction::Avg => Arc::new(expressions::Avg::new(arg, name, return_type)),
+ (AggregateFunction::Count, true) => {
+ Arc::new(distinct_expressions::DistinctCount::new(
+ arg_types,
+ args.clone(),
+ name,
+ return_type,
+ ))
+ }
+ (AggregateFunction::Sum, _) => {
Review comment:
What do you think of replacing `_` by `false` and return an error if the expression is not implemented, just to be on the safe side?
##########
File path: rust/datafusion/src/physical_plan/distinct_expressions.rs
##########
@@ -0,0 +1,203 @@
+// 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.
+
+//! Implementations for DISTINCT expressions, e.g. `COUNT(DISTINCT c)`
+
+use std::cell::RefCell;
+use std::convert::TryFrom;
+use std::fmt::Debug;
+use std::hash::Hash;
+use std::rc::Rc;
+use std::sync::Arc;
+
+use arrow::array::ArrayRef;
+use arrow::array::ListArray;
+use arrow::datatypes::{DataType, Field};
+
+use fnv::FnvHashSet;
+
+use crate::error::{ExecutionError, Result};
+use crate::physical_plan::group_scalar::GroupByScalar;
+use crate::physical_plan::{Accumulator, AggregateExpr, PhysicalExpr};
+use crate::scalar::ScalarValue;
+
+#[derive(Debug, PartialEq, Eq, Hash, Clone)]
+struct DistinctScalarValues(Vec<GroupByScalar>);
+
+fn format_state_name(name: &str, state_name: &str) -> String {
+ format!("{}[{}]", name, state_name)
+}
+
+/// Expression for a COUNT(DISTINCT) aggregation.
+#[derive(Debug)]
+pub struct DistinctCount {
+ /// Column name
+ name: String,
+ /// The DataType for the final count
+ data_type: DataType,
+ /// The DataType for each input argument
+ input_data_types: Vec<DataType>,
+ /// The input arguments
+ exprs: Vec<Arc<dyn PhysicalExpr>>,
+}
+
+impl DistinctCount {
+ /// Create a new COUNT(DISTINCT) aggregate function.
+ pub fn new(
+ input_data_types: Vec<DataType>,
+ exprs: Vec<Arc<dyn PhysicalExpr>>,
+ name: String,
+ data_type: DataType,
+ ) -> Self {
+ Self {
+ input_data_types,
+ exprs,
+ name,
+ data_type,
+ }
+ }
+}
+
+impl AggregateExpr for DistinctCount {
+ fn field(&self) -> Result<Field> {
+ Ok(Field::new(&self.name, self.data_type.clone(), false))
+ }
+
+ fn state_fields(&self) -> Result<Vec<Field>> {
+ Ok(self
+ .input_data_types
+ .iter()
+ .map(|data_type| {
+ Field::new(
+ &format_state_name(&self.name, "count distinct"),
+ DataType::List(Box::new(data_type.clone())),
+ false,
+ )
+ })
+ .collect::<Vec<_>>())
+ }
+
+ fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
+ self.exprs.clone()
+ }
+
+ fn create_accumulator(&self) -> Result<Rc<RefCell<dyn Accumulator>>> {
+ Ok(Rc::new(RefCell::new(DistinctCountAccumulator {
+ values: FnvHashSet::default(),
+ data_types: self.input_data_types.clone(),
+ count_data_type: self.data_type.clone(),
+ })))
+ }
+}
+
+#[derive(Debug)]
+struct DistinctCountAccumulator {
+ values: FnvHashSet<DistinctScalarValues>,
+ data_types: Vec<DataType>,
+ count_data_type: DataType,
+}
+
+impl Accumulator for DistinctCountAccumulator {
+ fn update_batch(&mut self, arrays: &Vec<ArrayRef>) -> Result<()> {
+ for row in 0..arrays[0].len() {
+ let row_values = arrays
+ .iter()
+ .map(|array| ScalarValue::try_from_array(array, row))
+ .collect::<Result<Vec<_>>>()?;
+
+ self.update(&row_values)?;
+ }
+
+ Ok(())
+ }
+
+ fn update(&mut self, values: &Vec<ScalarValue>) -> Result<()> {
+ // If a row has a NULL, it is not included in the final count.
+ if !values.iter().any(|v| v.is_null()) {
+ self.values.insert(DistinctScalarValues(
+ values
+ .iter()
+ .map(GroupByScalar::try_from)
+ .collect::<Result<Vec<_>>>()?,
+ ));
+ }
+
+ Ok(())
+ }
+
+ fn merge(&mut self, states: &Vec<ScalarValue>) -> Result<()> {
+ self.update(states)
+ }
+
+ fn merge_batch(&mut self, states: &Vec<ArrayRef>) -> Result<()> {
+ let list_arrays = states
+ .iter()
+ .map(|state_array| {
+ state_array.as_any().downcast_ref::<ListArray>().ok_or(
+ ExecutionError::InternalError(
+ "Failed to downcast ListArray".to_string(),
+ ),
+ )
+ })
+ .collect::<Result<Vec<_>>>()?;
+
+ let values_arrays = list_arrays
+ .iter()
+ .map(|list_array| list_array.values())
+ .collect::<Vec<_>>();
+
+ self.update_batch(&values_arrays)
+ }
+
+ fn state(&self) -> Result<Vec<ScalarValue>> {
+ let mut cols_out = self
+ .data_types
+ .iter()
+ .map(|data_type| ScalarValue::List(Some(Vec::new()), data_type.clone()))
+ .collect::<Vec<_>>();
+
+ let mut cols_vec = cols_out
+ .iter_mut()
+ .map(|c| match c {
+ ScalarValue::List(Some(ref mut v), _) => v,
+ _ => unreachable!(),
+ })
+ .collect::<Vec<_>>();
+
+ self.values.iter().for_each(|distinct_values| {
+ distinct_values.0.iter().enumerate().for_each(
+ |(col_index, distinct_value)| {
+ cols_vec[col_index].push(ScalarValue::from(distinct_value));
+ },
+ )
+ });
+
+ Ok(cols_out)
+ }
+
+ fn evaluate(&self) -> Result<ScalarValue> {
+ match &self.count_data_type {
Review comment:
Given that this only returns `UInt64`, what do you think about removing the `count_data_type` from the constructor's argument?
##########
File path: rust/datafusion/src/physical_plan/group_scalar.rs
##########
@@ -0,0 +1,77 @@
+// 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.
+
+//! Defines scalars used to construct groups, ex. in GROUP BY clauses.
+
+use std::convert::{From, TryFrom};
+
+use crate::error::{ExecutionError, Result};
+use crate::scalar::ScalarValue;
+
+/// Enumeration of types that can be used in a GROUP BY expression (all primitives except
+/// for floating point numerics)
+#[derive(Debug, PartialEq, Eq, Hash, Clone)]
+pub(crate) enum GroupByScalar {
+ UInt8(u8),
+ UInt16(u16),
+ UInt32(u32),
+ UInt64(u64),
+ Int8(i8),
+ Int16(i16),
+ Int32(i32),
+ Int64(i64),
+ Utf8(String),
+}
+
+impl TryFrom<&ScalarValue> for GroupByScalar {
+ type Error = ExecutionError;
+
+ fn try_from(scalar_value: &ScalarValue) -> Result<Self> {
+ Ok(match scalar_value {
+ ScalarValue::Int8(Some(v)) => GroupByScalar::Int8(*v),
+ ScalarValue::Int16(Some(v)) => GroupByScalar::Int16(*v),
+ ScalarValue::Int32(Some(v)) => GroupByScalar::Int32(*v),
+ ScalarValue::Int64(Some(v)) => GroupByScalar::Int64(*v),
+ ScalarValue::UInt8(Some(v)) => GroupByScalar::UInt8(*v),
+ ScalarValue::UInt16(Some(v)) => GroupByScalar::UInt16(*v),
+ ScalarValue::UInt32(Some(v)) => GroupByScalar::UInt32(*v),
+ ScalarValue::UInt64(Some(v)) => GroupByScalar::UInt64(*v),
+ ScalarValue::Utf8(Some(v)) => GroupByScalar::Utf8(v.clone()),
+ _ => {
+ return Err(ExecutionError::InternalError(
+ "Cannot convert ScalarValue to GroupByScalar".to_string(),
Review comment:
What do you think adding the `DataType` that we were unable to convert, so that we have a more informative error message?
##########
File path: rust/datafusion/src/test/mod.rs
##########
@@ -135,6 +135,13 @@ pub fn format_batch(batch: &RecordBatch) -> Vec<String> {
}
let array = batch.column(column_index);
match array.data_type() {
+ DataType::Utf8 => s.push_str(
+ array
+ .as_any()
+ .downcast_ref::<array::StringArray>()
+ .unwrap()
+ .value(row_index),
+ ),
Review comment:
I think that I also miss a test in this module
##########
File path: rust/datafusion/src/physical_plan/distinct_expressions.rs
##########
@@ -0,0 +1,203 @@
+// 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.
+
+//! Implementations for DISTINCT expressions, e.g. `COUNT(DISTINCT c)`
+
+use std::cell::RefCell;
+use std::convert::TryFrom;
+use std::fmt::Debug;
+use std::hash::Hash;
+use std::rc::Rc;
+use std::sync::Arc;
+
+use arrow::array::ArrayRef;
+use arrow::array::ListArray;
+use arrow::datatypes::{DataType, Field};
+
+use fnv::FnvHashSet;
+
+use crate::error::{ExecutionError, Result};
+use crate::physical_plan::group_scalar::GroupByScalar;
+use crate::physical_plan::{Accumulator, AggregateExpr, PhysicalExpr};
+use crate::scalar::ScalarValue;
+
+#[derive(Debug, PartialEq, Eq, Hash, Clone)]
+struct DistinctScalarValues(Vec<GroupByScalar>);
+
+fn format_state_name(name: &str, state_name: &str) -> String {
+ format!("{}[{}]", name, state_name)
+}
+
+/// Expression for a COUNT(DISTINCT) aggregation.
+#[derive(Debug)]
+pub struct DistinctCount {
+ /// Column name
+ name: String,
+ /// The DataType for the final count
+ data_type: DataType,
+ /// The DataType for each input argument
+ input_data_types: Vec<DataType>,
+ /// The input arguments
+ exprs: Vec<Arc<dyn PhysicalExpr>>,
+}
+
+impl DistinctCount {
+ /// Create a new COUNT(DISTINCT) aggregate function.
+ pub fn new(
+ input_data_types: Vec<DataType>,
+ exprs: Vec<Arc<dyn PhysicalExpr>>,
+ name: String,
+ data_type: DataType,
+ ) -> Self {
+ Self {
+ input_data_types,
+ exprs,
+ name,
+ data_type,
+ }
+ }
+}
+
+impl AggregateExpr for DistinctCount {
+ fn field(&self) -> Result<Field> {
+ Ok(Field::new(&self.name, self.data_type.clone(), false))
+ }
+
+ fn state_fields(&self) -> Result<Vec<Field>> {
+ Ok(self
+ .input_data_types
+ .iter()
+ .map(|data_type| {
+ Field::new(
+ &format_state_name(&self.name, "count distinct"),
+ DataType::List(Box::new(data_type.clone())),
+ false,
+ )
+ })
+ .collect::<Vec<_>>())
+ }
+
+ fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
+ self.exprs.clone()
+ }
+
+ fn create_accumulator(&self) -> Result<Rc<RefCell<dyn Accumulator>>> {
+ Ok(Rc::new(RefCell::new(DistinctCountAccumulator {
+ values: FnvHashSet::default(),
+ data_types: self.input_data_types.clone(),
+ count_data_type: self.data_type.clone(),
+ })))
+ }
+}
+
+#[derive(Debug)]
+struct DistinctCountAccumulator {
+ values: FnvHashSet<DistinctScalarValues>,
+ data_types: Vec<DataType>,
+ count_data_type: DataType,
+}
+
+impl Accumulator for DistinctCountAccumulator {
+ fn update_batch(&mut self, arrays: &Vec<ArrayRef>) -> Result<()> {
Review comment:
this expression seems similar to the default `update_batch` (implemented by the trait `Accumulator`). Do you think there's a way to generalize the `default` to take this `Accumulator` into account, so that we can DRY some code? (same for `merge_batch`)
##########
File path: rust/datafusion/src/physical_plan/distinct_expressions.rs
##########
@@ -0,0 +1,203 @@
+// 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.
+
+//! Implementations for DISTINCT expressions, e.g. `COUNT(DISTINCT c)`
+
+use std::cell::RefCell;
+use std::convert::TryFrom;
+use std::fmt::Debug;
+use std::hash::Hash;
+use std::rc::Rc;
+use std::sync::Arc;
+
+use arrow::array::ArrayRef;
+use arrow::array::ListArray;
+use arrow::datatypes::{DataType, Field};
+
+use fnv::FnvHashSet;
+
+use crate::error::{ExecutionError, Result};
+use crate::physical_plan::group_scalar::GroupByScalar;
+use crate::physical_plan::{Accumulator, AggregateExpr, PhysicalExpr};
+use crate::scalar::ScalarValue;
+
+#[derive(Debug, PartialEq, Eq, Hash, Clone)]
+struct DistinctScalarValues(Vec<GroupByScalar>);
+
+fn format_state_name(name: &str, state_name: &str) -> String {
+ format!("{}[{}]", name, state_name)
+}
+
+/// Expression for a COUNT(DISTINCT) aggregation.
+#[derive(Debug)]
+pub struct DistinctCount {
+ /// Column name
+ name: String,
+ /// The DataType for the final count
+ data_type: DataType,
+ /// The DataType for each input argument
+ input_data_types: Vec<DataType>,
+ /// The input arguments
+ exprs: Vec<Arc<dyn PhysicalExpr>>,
+}
+
+impl DistinctCount {
+ /// Create a new COUNT(DISTINCT) aggregate function.
+ pub fn new(
+ input_data_types: Vec<DataType>,
+ exprs: Vec<Arc<dyn PhysicalExpr>>,
+ name: String,
+ data_type: DataType,
+ ) -> Self {
+ Self {
+ input_data_types,
+ exprs,
+ name,
+ data_type,
+ }
+ }
+}
+
+impl AggregateExpr for DistinctCount {
+ fn field(&self) -> Result<Field> {
+ Ok(Field::new(&self.name, self.data_type.clone(), false))
+ }
+
+ fn state_fields(&self) -> Result<Vec<Field>> {
+ Ok(self
+ .input_data_types
+ .iter()
+ .map(|data_type| {
+ Field::new(
+ &format_state_name(&self.name, "count distinct"),
+ DataType::List(Box::new(data_type.clone())),
+ false,
+ )
+ })
+ .collect::<Vec<_>>())
+ }
+
+ fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
+ self.exprs.clone()
+ }
+
+ fn create_accumulator(&self) -> Result<Rc<RefCell<dyn Accumulator>>> {
+ Ok(Rc::new(RefCell::new(DistinctCountAccumulator {
+ values: FnvHashSet::default(),
+ data_types: self.input_data_types.clone(),
+ count_data_type: self.data_type.clone(),
+ })))
+ }
+}
+
+#[derive(Debug)]
+struct DistinctCountAccumulator {
+ values: FnvHashSet<DistinctScalarValues>,
+ data_types: Vec<DataType>,
+ count_data_type: DataType,
+}
+
+impl Accumulator for DistinctCountAccumulator {
+ fn update_batch(&mut self, arrays: &Vec<ArrayRef>) -> Result<()> {
+ for row in 0..arrays[0].len() {
+ let row_values = arrays
+ .iter()
+ .map(|array| ScalarValue::try_from_array(array, row))
+ .collect::<Result<Vec<_>>>()?;
+
+ self.update(&row_values)?;
+ }
+
+ Ok(())
+ }
+
+ fn update(&mut self, values: &Vec<ScalarValue>) -> Result<()> {
+ // If a row has a NULL, it is not included in the final count.
+ if !values.iter().any(|v| v.is_null()) {
+ self.values.insert(DistinctScalarValues(
+ values
+ .iter()
+ .map(GroupByScalar::try_from)
+ .collect::<Result<Vec<_>>>()?,
+ ));
+ }
+
+ Ok(())
+ }
+
+ fn merge(&mut self, states: &Vec<ScalarValue>) -> Result<()> {
+ self.update(states)
+ }
+
+ fn merge_batch(&mut self, states: &Vec<ArrayRef>) -> Result<()> {
+ let list_arrays = states
+ .iter()
+ .map(|state_array| {
+ state_array.as_any().downcast_ref::<ListArray>().ok_or(
+ ExecutionError::InternalError(
+ "Failed to downcast ListArray".to_string(),
+ ),
+ )
+ })
+ .collect::<Result<Vec<_>>>()?;
+
+ let values_arrays = list_arrays
+ .iter()
+ .map(|list_array| list_array.values())
+ .collect::<Vec<_>>();
+
+ self.update_batch(&values_arrays)
+ }
+
+ fn state(&self) -> Result<Vec<ScalarValue>> {
+ let mut cols_out = self
+ .data_types
+ .iter()
+ .map(|data_type| ScalarValue::List(Some(Vec::new()), data_type.clone()))
+ .collect::<Vec<_>>();
+
+ let mut cols_vec = cols_out
+ .iter_mut()
+ .map(|c| match c {
+ ScalarValue::List(Some(ref mut v), _) => v,
+ _ => unreachable!(),
+ })
+ .collect::<Vec<_>>();
+
+ self.values.iter().for_each(|distinct_values| {
+ distinct_values.0.iter().enumerate().for_each(
+ |(col_index, distinct_value)| {
+ cols_vec[col_index].push(ScalarValue::from(distinct_value));
+ },
+ )
+ });
+
+ Ok(cols_out)
+ }
+
+ fn evaluate(&self) -> Result<ScalarValue> {
+ match &self.count_data_type {
+ DataType::UInt64 => Ok(ScalarValue::UInt64(Some(self.values.len() as u64))),
+ t => {
+ return Err(ExecutionError::InternalError(format!(
+ "Invalid data type {:?} for count distinct aggregation",
+ t
+ )))
+ }
+ }
+ }
+}
Review comment:
I think I miss some tests here. Something like:
* one without nulls
* when with some nulls
* one with only nulls
* one with an empty batch
(you can take a look at `expressions.rs` for inspiration ^_^)
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