duongcongtoai commented on code in PR #17915: URL: https://github.com/apache/datafusion/pull/17915#discussion_r2414648804
########## datafusion/functions-aggregate-common/src/aggregate/array_agg.rs: ########## @@ -0,0 +1,491 @@ +// 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. + +//! Dedicated implementation of `GroupsAccumulator` for `array_agg` + +use std::iter::repeat_n; +use std::mem; +use std::sync::Arc; + +use arrow::array::{new_empty_array, Array, GenericListArray}; +use arrow::array::{ArrayRef, AsArray, BooleanArray}; +use arrow::buffer::OffsetBuffer; +use arrow::compute::kernels; +use arrow::datatypes::{ArrowNativeType, Field}; +use datafusion_common::{internal_datafusion_err, Result}; +use datafusion_expr_common::groups_accumulator::{EmitTo, GroupsAccumulator}; + +#[derive(Default, Clone)] +pub struct AggGroupAccumulator { + // [1,2,3] [4,5,6] + stacked_batches: Vec<ArrayRef>, + // address items of each group within the stacked_batches + // this is maintained to perform kernel::interleave + stacked_group_indices: Vec<( + /*group_number*/ usize, + /*array_number*/ usize, + /*offset_in_array*/ usize, + )>, + indice_sorted: bool, + max_group: usize, +} + +impl AggGroupAccumulator { + pub fn new() -> Self { + Self { + stacked_batches: vec![], + stacked_group_indices: vec![], + indice_sorted: false, + max_group: 0, + } + } + fn consume_stacked_batches( + &mut self, + emit_to: EmitTo, + ) -> Result<GenericListArray<i32>> { + // in the case of continous calls to function `evaluate` happen, + // (without any interleaving calls to `merge_batch` or `update_batch`) + // the first call will basically sort everything beforehand + // so the second one does not need to + if !self.indice_sorted { + self.indice_sorted = true; + self.stacked_group_indices.sort_by_key(|a| { + // TODO: array_agg with distinct and custom order can be implemented here + a.0 + }); + } + + let mut current_group = self.stacked_group_indices[0].0; + + // this is inclusive, zero-based + let stop_at_group = match emit_to { + EmitTo::All => self.max_group-1, + EmitTo::First(groups_taken) => groups_taken-1, + }; + let mut group_windows = + Vec::<i32>::with_capacity(self.max_group.min(stop_at_group) + 1); + group_windows.push(0); + let mut split_offset = None; + + // TODO: init with a good cap if possible via some stats during accumulation phase + let mut interleave_offsets = vec![]; + for (offset, (group_index, array_number, offset_in_array)) in + self.stacked_group_indices.iter().enumerate() + { + if *group_index > stop_at_group { + split_offset = Some(offset); + break; + } + if *group_index > current_group { + current_group = *group_index; + group_windows.push(offset as i32); + } + interleave_offsets.push((*array_number, *offset_in_array)); + } + if let Some(split_offset) = split_offset { + let mut tail_part = self.stacked_group_indices.split_off(split_offset); + mem::swap(&mut self.stacked_group_indices, &mut tail_part); + for item in self.stacked_group_indices.iter_mut() { + // shift down the number of group being taken + item.0 -= (stop_at_group+1) + } + + group_windows.push(split_offset as i32); + } else { + group_windows.push(self.stacked_group_indices.len() as i32); + mem::take(&mut self.stacked_group_indices); + }; + + let stacked_batches = self + .stacked_batches + .iter() + .map(|a| a.as_ref()) + .collect::<Vec<_>>(); + + let offsets_buffer = OffsetBuffer::new(group_windows.into()); + + // group indices like [1,1,1,2,2,2] + // backend_array like [a,b,c,d,e,f] + // offsets should be: [0,3,6] + // then result should be [a,b,c], [d,e,f] + + // backend_array is a flatten list of individual values before aggregation + let backend_array = + kernels::interleave::interleave(&stacked_batches, &interleave_offsets)?; + let dt = backend_array.data_type(); + let field = Arc::new(Field::new_list_field(dt.clone(), true)); + + let arr = + GenericListArray::<i32>::new(field, offsets_buffer, backend_array, None); + Ok(arr) + } +} + +impl GroupsAccumulator for AggGroupAccumulator { + // given the stacked_batch as: + // - batch1 [1,4,5,6,7] + // - batch2 [5,1,1,1,1] + + // and group_indices as + // indices g1: [(0,0), (1,1), (1,2) ...] + // indices g2: [] + // indices g3: [] + // indices g4: [(0,1)] + // each tuple represents (batch_index, and offset within the batch index) + // for example + // - (0,0) means the 0th item inside batch1, which is `1` + // - (1,1) means the 1th item inside batch2, which is `1` + fn update_batch( + &mut self, + values: &[ArrayRef], + group_indices: &[usize], + opt_filter: Option<&BooleanArray>, + total_num_groups: usize, + ) -> Result<()> { + let singular_col = values + .first() + .ok_or(internal_datafusion_err!("invalid agg input"))?; + + self.stacked_batches.push(Arc::clone(singular_col)); + let batch_index = self.stacked_batches.len() - 1; + + if let Some(filter) = opt_filter { + for (array_offset, (group_index, filter_value)) in + group_indices.iter().zip(filter.iter()).enumerate() + { + if let Some(true) = filter_value { + self.stacked_group_indices.push(( + *group_index, + batch_index, + array_offset, + )); + } + } + } else { + for (array_offset, group_index) in group_indices.iter().enumerate() { + self.stacked_group_indices.push(( + *group_index, + batch_index, + array_offset, + )); + } + } + self.indice_sorted = false; + self.max_group = self.max_group.max(total_num_groups); + Ok(()) + } + + fn evaluate(&mut self, emit_to: EmitTo) -> Result<ArrayRef> { + let arr = self.consume_stacked_batches(emit_to)?; + Ok(Arc::new(arr) as ArrayRef) + } + + // filtered_null_mask(opt_filter, &values); + fn state(&mut self, emit_to: EmitTo) -> Result<Vec<ArrayRef>> { + Ok(vec![self.evaluate(emit_to)?]) + } + + fn merge_batch( + &mut self, + values: &[ArrayRef], + group_indices: &[usize], + // for merge_batch which happens at final stage + // opt_filter will always be none + _opt_filter: Option<&BooleanArray>, + total_num_groups: usize, + ) -> Result<()> { + let singular_col = values + .first() + .ok_or(internal_datafusion_err!("invalid agg input"))?; + let list_arr = singular_col.as_list::<i32>(); + let new_array_number = self.stacked_batches.len(); + // TODO: the backed_arr contains redundant data + // make sure that flatten_group_index has the same length with backed_arr + let flatten_group_index = + group_indices + .iter() + .enumerate() + .flat_map(|(row, group_index)| { + let end = list_arr.value_offsets()[row + 1].as_usize(); + let start = list_arr.value_offsets()[row].as_usize(); + (start..end).map(|offset| (*group_index, new_array_number, offset)) + }); + self.stacked_group_indices.extend(flatten_group_index); + + let backed_arr = list_arr.values(); + self.stacked_batches.push(Arc::clone(backed_arr)); + self.indice_sorted = false; + self.max_group = self.max_group.max(total_num_groups); + Ok(()) + } + + fn size(&self) -> usize { + size_of_val(self) Review Comment: ``` for batch in batches { in_progress_file.append_batch(&batch)?; max_record_batch_size = max_record_batch_size.max(batch.get_sliced_size()?); } ``` max_record_batch_size decides the allocation during the spill merge, maybe this is the difference -- This is an automated message from the Apache Git Service. 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