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Posted to github@arrow.apache.org by GitBox <gi...@apache.org> on 2020/10/07 12:41:41 UTC
[GitHub] [arrow] drusso commented on a change in pull request #8222: ARROW-10043: [Rust][DataFusion] Implement COUNT(DISTINCT col)
drusso commented on a change in pull request #8222:
URL: https://github.com/apache/arrow/pull/8222#discussion_r500979025
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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:
See 213daa2.
##########
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:
See 15e6f0b.
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