Dandandan commented on code in PR #21561: URL: https://github.com/apache/datafusion/pull/21561#discussion_r3098475972
########## datafusion/functions-aggregate-common/src/aggregate/count_distinct/groups.rs: ########## @@ -0,0 +1,178 @@ +// 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 arrow::array::{ + ArrayRef, AsArray, BooleanArray, Int64Array, ListArray, PrimitiveArray, +}; +use arrow::buffer::OffsetBuffer; +use arrow::datatypes::{ArrowPrimitiveType, Field}; +use datafusion_common::HashSet; +use datafusion_common::hash_utils::RandomState; +use datafusion_expr_common::groups_accumulator::{EmitTo, GroupsAccumulator}; +use std::hash::Hash; +use std::mem::size_of; +use std::sync::Arc; + +use crate::aggregate::groups_accumulator::accumulate::accumulate; + +pub struct PrimitiveDistinctCountGroupsAccumulator<T: ArrowPrimitiveType> +where + T::Native: Eq + Hash, +{ + seen: HashSet<(usize, T::Native), RandomState>, + counts: Vec<i64>, +} + +impl<T: ArrowPrimitiveType> PrimitiveDistinctCountGroupsAccumulator<T> +where + T::Native: Eq + Hash, +{ + pub fn new() -> Self { + Self { + seen: HashSet::default(), + counts: Vec::new(), + } + } +} + +impl<T: ArrowPrimitiveType> Default for PrimitiveDistinctCountGroupsAccumulator<T> +where + T::Native: Eq + Hash, +{ + fn default() -> Self { + Self::new() + } +} + +impl<T: ArrowPrimitiveType + Send + std::fmt::Debug> GroupsAccumulator + for PrimitiveDistinctCountGroupsAccumulator<T> +where + T::Native: Eq + Hash, +{ + fn update_batch( + &mut self, + values: &[ArrayRef], + group_indices: &[usize], + opt_filter: Option<&BooleanArray>, + total_num_groups: usize, + ) -> datafusion_common::Result<()> { + debug_assert_eq!(values.len(), 1); + self.counts.resize(total_num_groups, 0); + let arr = values[0].as_primitive::<T>(); + accumulate(group_indices, arr, opt_filter, |group_idx, value| { + if self.seen.insert((group_idx, value)) { + self.counts[group_idx] += 1; + } + }); + Ok(()) + } + + fn evaluate(&mut self, emit_to: EmitTo) -> datafusion_common::Result<ArrayRef> { + let counts = emit_to.take_needed(&mut self.counts); + + match emit_to { + EmitTo::All => { + self.seen.clear(); + } + EmitTo::First(n) => { + let mut remaining = HashSet::default(); + for (group_idx, value) in self.seen.drain() { + if group_idx >= n { + remaining.insert((group_idx - n, value)); + } + } + self.seen = remaining; + } + } + + Ok(Arc::new(Int64Array::from(counts))) + } + + fn state(&mut self, emit_to: EmitTo) -> datafusion_common::Result<Vec<ArrayRef>> { + let num_emitted = match emit_to { + EmitTo::All => self.counts.len(), + EmitTo::First(n) => n, + }; + + let mut group_values: Vec<Vec<T::Native>> = vec![Vec::new(); num_emitted]; + + if matches!(emit_to, EmitTo::All) { + for (group_idx, value) in self.seen.drain() { + group_values[group_idx].push(value); + } + self.counts.clear(); + } else { + let mut remaining = HashSet::default(); + for (group_idx, value) in self.seen.drain() { + if group_idx < num_emitted { + group_values[group_idx].push(value); + } else { + remaining.insert((group_idx - num_emitted, value)); + } + } + self.seen = remaining; + let _ = emit_to.take_needed(&mut self.counts); + } + + let mut offsets = vec![0i32]; + let mut all_values = Vec::new(); + for values in &group_values { + all_values.extend(values.iter().copied()); + offsets.push(all_values.len() as i32); + } + + let values_array = Arc::new(PrimitiveArray::<T>::from_iter_values(all_values)); + let list_array = ListArray::new( + Arc::new(Field::new_list_field(T::DATA_TYPE, true)), + OffsetBuffer::new(offsets.into()), + values_array, + None, + ); + + Ok(vec![Arc::new(list_array)]) + } + + fn merge_batch( + &mut self, + values: &[ArrayRef], + group_indices: &[usize], + _opt_filter: Option<&BooleanArray>, + total_num_groups: usize, + ) -> datafusion_common::Result<()> { + debug_assert_eq!(values.len(), 1); + self.counts.resize(total_num_groups, 0); + let list_array = values[0].as_list::<i32>(); + + for (row_idx, &group_idx) in group_indices.iter().enumerate() { + let inner = list_array.value(row_idx); Review Comment: Something like this should be slightly faster: ``` let inner_values = inner.values(); let offsets = list_array.offsets(); for (row_idx, &group_idx) in group_indices.iter().enumerate() { let start = offsets[row_idx] as usize; let end = offsets[row_idx + 1] as usize; for &value in &inner_values[start..end] { if self.seen.insert((group_idx, value)) { self.counts[group_idx] += 1; } } } ``` -- This is an automated message from the Apache Git Service. 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