Rachelint commented on code in PR #23055: URL: https://github.com/apache/datafusion/pull/23055#discussion_r3456276967
########## datafusion/physical-plan/src/aggregates/aggregate_hash_table/common.rs: ########## @@ -0,0 +1,406 @@ +// 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 std::marker::PhantomData; +use std::sync::Arc; + +use arrow::array::{ArrayRef, AsArray, new_null_array}; +use arrow::datatypes::SchemaRef; +use arrow::record_batch::RecordBatch; +use datafusion_common::{Result, internal_err}; +use datafusion_execution::memory_pool::proxy::VecAllocExt; +use datafusion_expr::{EmitTo, GroupsAccumulator}; +use datafusion_physical_expr::aggregate::AggregateFunctionExpr; + +use crate::PhysicalExpr; +use crate::aggregates::group_values::{GroupByMetrics, GroupValues, new_group_values}; +use crate::aggregates::order::GroupOrdering; +use crate::aggregates::row_hash::create_group_accumulator; +use crate::aggregates::{ + AggregateExec, PhysicalGroupBy, aggregate_expressions, evaluate_group_by, +}; + +/// Marker for raw rows -> partial state aggregation. +pub(in crate::aggregates) struct Partial; +/// Marker for raw rows -> partial state conversion without aggregation. +pub(in crate::aggregates) struct PartialSkip; +/// Marker for partial state -> final value aggregation. +pub(in crate::aggregates) struct Final; + +/// Grouped hash table shared by the partial and final paths. +/// +/// While building, it consumes input batches and updates group / accumulator +/// state. While outputting, it incrementally drains that state into output +/// batches. +/// +/// # Marker Type +/// `AggrMode` selects the aggregate semantics. +/// +/// e.g. `AggregateHashTable::<Partial>::new(...)` creates an aggregate hash table +/// for the partial hash aggregate stage, the input schema is raw rows and output +/// schema is intermediate states. +/// +/// It is a zero-sized compile-time marker, so each stage keeps its update logic +/// in a separate impl block, to make the behavior difference explicit. +pub(in crate::aggregates) struct AggregateHashTable<AggrMode> { + /// Grouping and accumulator-specific timing metrics. + pub(super) group_by_metrics: GroupByMetrics, + + /// Raw input schema, used to evaluate expressions and synthesize empty + /// grouping-set rows. + pub(super) input_schema: SchemaRef, + + /// Output schema: group columns followed by aggregate state or final values. + pub(super) output_schema: SchemaRef, + + /// Maximum rows per emitted output batch, from config `batch_size`. + pub(super) batch_size: usize, + + /// Lifecycle-specific state: building stage / outputting stage. + pub(super) state: AggregateHashTableState, + + pub(super) _mode: PhantomData<AggrMode>, +} + +pub(super) struct HashAggregateAccumulator { + /// Aggregate expression used to create a fresh accumulator for related + /// hash tables, such as the partial-skip table. + aggregate_expr: Arc<AggregateFunctionExpr>, + + /// Arguments to pass to this accumulator. + /// + /// Example: `CORR(x, y)` stores two expressions here, while `SUM(x)` stores one. + arguments: Vec<Arc<dyn PhysicalExpr>>, + + /// Optional `FILTER` expression for this accumulator. + /// + /// Example: `SUM(x) FILTER (WHERE x > 10)` stores the `x > 10` predicate. + filter: Option<Arc<dyn PhysicalExpr>>, + + /// Accumulator state for all groups for one aggregate expression. + accumulator: Box<dyn GroupsAccumulator>, +} + +pub(super) struct EvaluatedHashAggregateAccumulator { + pub(super) arguments: Vec<ArrayRef>, + pub(super) filter: Option<ArrayRef>, +} + +/// Evaluated all group by keys and accumulator args. +/// +/// e.g., `select k+1, sum(v*v) from t group by (k+1)`, this function evaluates +/// `k+1`, `v*v` +pub(super) struct EvaluatedAggregateBatch { + /// One entry per grouping set; each entry contains all evaluated group key + /// arrays for the current input batch. + pub(super) grouping_set_args: Vec<Vec<ArrayRef>>, + + /// Evaluated arguments and filters, one entry per aggregate expression. + pub(super) accumulator_args: Vec<EvaluatedHashAggregateAccumulator>, +} + +/// Hash table state while grouped aggregation is consuming input. +/// +/// This owns the coupled state for: +/// - evaluating group keys, +/// - interning each distinct group, +/// - mapping each input row to its group index, +/// - evaluating aggregate inputs, +/// - updating per-group accumulator state. +pub(super) struct BuildingHashTableState { + /// GROUP BY expressions evaluated for each input batch. + pub(super) group_by: Arc<PhysicalGroupBy>, + + /// Interned group keys. Accumulator state is stored separately by group index. + pub(super) group_values: Box<dyn GroupValues>, + + /// Group index for each row in the current input batch. + /// + /// Each value indexes into `group_values`, and the same index is used by every + /// accumulator to update that group's aggregate state. + pub(super) batch_group_indices: Vec<usize>, + + /// One item per aggregate expression. + /// + /// Example: `COUNT(x), SUM(y)` creates two items. Each item owns the input + /// expressions, optional filter, and accumulator state for all groups. + pub(super) accumulators: Vec<HashAggregateAccumulator>, +} + +pub(super) enum AggregateHashTableState { + Building(BuildingHashTableState), + Outputting(BuildingHashTableState), + Done, +} + +impl HashAggregateAccumulator { + fn new( + aggregate_expr: Arc<AggregateFunctionExpr>, + arguments: Vec<Arc<dyn PhysicalExpr>>, + filter: Option<Arc<dyn PhysicalExpr>>, + accumulator: Box<dyn GroupsAccumulator>, + ) -> Self { + Self { + aggregate_expr, + arguments, + filter, + accumulator, + } + } + + pub(super) fn empty_like(&self) -> Result<Self> { + let accumulator = create_group_accumulator(&self.aggregate_expr)?; + Ok(Self::new( + Arc::clone(&self.aggregate_expr), + self.arguments.clone(), + self.filter.clone(), + accumulator, + )) + } + + fn evaluate(&self, batch: &RecordBatch) -> Result<EvaluatedHashAggregateAccumulator> { + let arguments = self + .arguments + .iter() + .map(|expr| { + expr.evaluate(batch) + .and_then(|value| value.into_array(batch.num_rows())) + }) + .collect::<Result<_>>()?; + + let filter = self + .filter + .as_ref() + .map(|filter| { + filter + .evaluate(batch) + .and_then(|value| value.into_array(batch.num_rows())) + }) + .transpose()?; + + Ok(EvaluatedHashAggregateAccumulator { arguments, filter }) + } + + pub(super) fn update_batch( + &mut self, + values: &EvaluatedHashAggregateAccumulator, + group_indices: &[usize], + total_num_groups: usize, + ) -> Result<()> { + let filter = values.filter.as_ref().map(|filter| filter.as_boolean()); + self.accumulator.update_batch( + &values.arguments, + group_indices, + filter, + total_num_groups, + ) + } + + pub(super) fn merge_batch( + &mut self, + values: &EvaluatedHashAggregateAccumulator, + group_indices: &[usize], + total_num_groups: usize, + ) -> Result<()> { + debug_assert!(values.filter.is_none()); + self.accumulator + .merge_batch(&values.arguments, group_indices, total_num_groups) + } + + pub(super) fn evaluate_final(&mut self, emit_to: EmitTo) -> Result<ArrayRef> { + self.accumulator.evaluate(emit_to) + } + + pub(super) fn state(&mut self, emit_to: EmitTo) -> Result<Vec<ArrayRef>> { + self.accumulator.state(emit_to) + } + + pub(super) fn supports_convert_to_state(&self) -> bool { + self.accumulator.supports_convert_to_state() + } + + pub(super) fn convert_to_state( + &mut self, + values: &EvaluatedHashAggregateAccumulator, + ) -> Result<Vec<ArrayRef>> { + let opt_filter = values.filter.as_ref().map(|filter| filter.as_boolean()); + self.accumulator + .convert_to_state(&values.arguments, opt_filter) + } + + pub(super) fn null_arguments( + &self, + input_schema: &SchemaRef, + ) -> Result<Vec<ArrayRef>> { + self.arguments + .iter() + .map(|expr| { + let data_type = expr.data_type(input_schema)?; + Ok(new_null_array(&data_type, 1)) + }) + .collect() + } +} + +impl AggregateHashTableState { + pub(super) fn building(&self) -> &BuildingHashTableState { + let Self::Building(state) = self else { + unreachable!("hash aggregate table is not building") + }; + state + } + + pub(super) fn building_mut(&mut self) -> &mut BuildingHashTableState { + let Self::Building(state) = self else { + unreachable!("hash aggregate table is not building") + }; + state + } +} + +/// Methods shared by all aggregate hash table modes. Review Comment: Seems move method impls near where it define may be clearer? ########## datafusion/physical-plan/src/aggregates/aggregate_hash_table/common.rs: ########## @@ -0,0 +1,406 @@ +// 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 std::marker::PhantomData; +use std::sync::Arc; + +use arrow::array::{ArrayRef, AsArray, new_null_array}; +use arrow::datatypes::SchemaRef; +use arrow::record_batch::RecordBatch; +use datafusion_common::{Result, internal_err}; +use datafusion_execution::memory_pool::proxy::VecAllocExt; +use datafusion_expr::{EmitTo, GroupsAccumulator}; +use datafusion_physical_expr::aggregate::AggregateFunctionExpr; + +use crate::PhysicalExpr; +use crate::aggregates::group_values::{GroupByMetrics, GroupValues, new_group_values}; +use crate::aggregates::order::GroupOrdering; +use crate::aggregates::row_hash::create_group_accumulator; +use crate::aggregates::{ + AggregateExec, PhysicalGroupBy, aggregate_expressions, evaluate_group_by, +}; + +/// Marker for raw rows -> partial state aggregation. +pub(in crate::aggregates) struct Partial; +/// Marker for raw rows -> partial state conversion without aggregation. +pub(in crate::aggregates) struct PartialSkip; +/// Marker for partial state -> final value aggregation. +pub(in crate::aggregates) struct Final; + +/// Grouped hash table shared by the partial and final paths. +/// +/// While building, it consumes input batches and updates group / accumulator +/// state. While outputting, it incrementally drains that state into output +/// batches. +/// +/// # Marker Type +/// `AggrMode` selects the aggregate semantics. +/// +/// e.g. `AggregateHashTable::<Partial>::new(...)` creates an aggregate hash table +/// for the partial hash aggregate stage, the input schema is raw rows and output +/// schema is intermediate states. +/// +/// It is a zero-sized compile-time marker, so each stage keeps its update logic +/// in a separate impl block, to make the behavior difference explicit. +pub(in crate::aggregates) struct AggregateHashTable<AggrMode> { + /// Grouping and accumulator-specific timing metrics. + pub(super) group_by_metrics: GroupByMetrics, + + /// Raw input schema, used to evaluate expressions and synthesize empty + /// grouping-set rows. + pub(super) input_schema: SchemaRef, + + /// Output schema: group columns followed by aggregate state or final values. + pub(super) output_schema: SchemaRef, + + /// Maximum rows per emitted output batch, from config `batch_size`. + pub(super) batch_size: usize, + + /// Lifecycle-specific state: building stage / outputting stage. + pub(super) state: AggregateHashTableState, + + pub(super) _mode: PhantomData<AggrMode>, +} + +pub(super) struct HashAggregateAccumulator { + /// Aggregate expression used to create a fresh accumulator for related + /// hash tables, such as the partial-skip table. + aggregate_expr: Arc<AggregateFunctionExpr>, + + /// Arguments to pass to this accumulator. + /// + /// Example: `CORR(x, y)` stores two expressions here, while `SUM(x)` stores one. + arguments: Vec<Arc<dyn PhysicalExpr>>, + + /// Optional `FILTER` expression for this accumulator. + /// + /// Example: `SUM(x) FILTER (WHERE x > 10)` stores the `x > 10` predicate. + filter: Option<Arc<dyn PhysicalExpr>>, + + /// Accumulator state for all groups for one aggregate expression. + accumulator: Box<dyn GroupsAccumulator>, +} + +pub(super) struct EvaluatedHashAggregateAccumulator { + pub(super) arguments: Vec<ArrayRef>, + pub(super) filter: Option<ArrayRef>, +} + +/// Evaluated all group by keys and accumulator args. +/// +/// e.g., `select k+1, sum(v*v) from t group by (k+1)`, this function evaluates +/// `k+1`, `v*v` +pub(super) struct EvaluatedAggregateBatch { + /// One entry per grouping set; each entry contains all evaluated group key + /// arrays for the current input batch. + pub(super) grouping_set_args: Vec<Vec<ArrayRef>>, + + /// Evaluated arguments and filters, one entry per aggregate expression. + pub(super) accumulator_args: Vec<EvaluatedHashAggregateAccumulator>, +} + +/// Hash table state while grouped aggregation is consuming input. +/// +/// This owns the coupled state for: +/// - evaluating group keys, +/// - interning each distinct group, +/// - mapping each input row to its group index, +/// - evaluating aggregate inputs, +/// - updating per-group accumulator state. +pub(super) struct BuildingHashTableState { + /// GROUP BY expressions evaluated for each input batch. + pub(super) group_by: Arc<PhysicalGroupBy>, + + /// Interned group keys. Accumulator state is stored separately by group index. + pub(super) group_values: Box<dyn GroupValues>, + + /// Group index for each row in the current input batch. + /// + /// Each value indexes into `group_values`, and the same index is used by every + /// accumulator to update that group's aggregate state. + pub(super) batch_group_indices: Vec<usize>, + + /// One item per aggregate expression. + /// + /// Example: `COUNT(x), SUM(y)` creates two items. Each item owns the input + /// expressions, optional filter, and accumulator state for all groups. + pub(super) accumulators: Vec<HashAggregateAccumulator>, +} + +pub(super) enum AggregateHashTableState { + Building(BuildingHashTableState), + Outputting(BuildingHashTableState), + Done, +} + +impl HashAggregateAccumulator { + fn new( + aggregate_expr: Arc<AggregateFunctionExpr>, + arguments: Vec<Arc<dyn PhysicalExpr>>, + filter: Option<Arc<dyn PhysicalExpr>>, + accumulator: Box<dyn GroupsAccumulator>, + ) -> Self { + Self { + aggregate_expr, + arguments, + filter, + accumulator, + } + } + + pub(super) fn empty_like(&self) -> Result<Self> { + let accumulator = create_group_accumulator(&self.aggregate_expr)?; + Ok(Self::new( + Arc::clone(&self.aggregate_expr), + self.arguments.clone(), + self.filter.clone(), + accumulator, + )) + } + + fn evaluate(&self, batch: &RecordBatch) -> Result<EvaluatedHashAggregateAccumulator> { + let arguments = self + .arguments + .iter() + .map(|expr| { + expr.evaluate(batch) + .and_then(|value| value.into_array(batch.num_rows())) + }) + .collect::<Result<_>>()?; + + let filter = self + .filter + .as_ref() + .map(|filter| { + filter + .evaluate(batch) + .and_then(|value| value.into_array(batch.num_rows())) + }) + .transpose()?; + + Ok(EvaluatedHashAggregateAccumulator { arguments, filter }) + } + + pub(super) fn update_batch( + &mut self, + values: &EvaluatedHashAggregateAccumulator, + group_indices: &[usize], + total_num_groups: usize, + ) -> Result<()> { + let filter = values.filter.as_ref().map(|filter| filter.as_boolean()); + self.accumulator.update_batch( + &values.arguments, + group_indices, + filter, + total_num_groups, + ) + } + + pub(super) fn merge_batch( + &mut self, + values: &EvaluatedHashAggregateAccumulator, + group_indices: &[usize], + total_num_groups: usize, + ) -> Result<()> { + debug_assert!(values.filter.is_none()); + self.accumulator + .merge_batch(&values.arguments, group_indices, total_num_groups) + } + + pub(super) fn evaluate_final(&mut self, emit_to: EmitTo) -> Result<ArrayRef> { Review Comment: And can just name it `evaluate` after renaming above. ########## datafusion/physical-plan/src/aggregates/aggregate_hash_table/partial_table.rs: ########## @@ -0,0 +1,270 @@ +// 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 std::collections::HashMap; +use std::marker::PhantomData; +use std::sync::Arc; + +use arrow::array::{ArrayRef, BooleanArray, new_null_array}; +use arrow::datatypes::SchemaRef; +use arrow::record_batch::RecordBatch; +use datafusion_common::{Result, assert_eq_or_internal_err, internal_err}; + +use crate::aggregates::group_values::new_group_values; +use crate::aggregates::order::GroupOrdering; +use crate::aggregates::{AggregateExec, group_id_array, max_duplicate_ordinal}; + +use super::common::{ + AggregateHashTable, AggregateHashTableState, BuildingHashTableState, + EvaluatedHashAggregateAccumulator, HashAggregateAccumulator, Partial, PartialSkip, + emit_to_for_batch_size, +}; + +/// Methods specific to the aggregate hash table used in the partial aggregation stage. +impl AggregateHashTable<Partial> { + pub(in crate::aggregates) fn new( + agg: &AggregateExec, + partition: usize, + output_schema: SchemaRef, + batch_size: usize, + ) -> Result<Self> { + Self::new_with_filters( + agg, + partition, + output_schema, + batch_size, + agg.filter_expr.iter().cloned().collect(), + ) + } + + /// Emits the next batch of aggregated group keys and aggregate states. + /// + /// The output batch size is determined by `self.batch_size`. + /// + /// Returns `Some(batch)` for each emitted batch, `None` when output is + /// exhausted, and an internal error if polled in the `Building` state. + pub(in crate::aggregates) fn next_output_batch( + &mut self, + ) -> Result<Option<RecordBatch>> { + let output_schema = Arc::clone(&self.output_schema); + let batch_size = self.batch_size; + match &mut self.state { + AggregateHashTableState::Outputting(state) => { + if state.group_values.is_empty() { + self.state = AggregateHashTableState::Done; + return Ok(None); + } + + let emit_to = + emit_to_for_batch_size(batch_size, state.group_values.len()); + let timer = self.group_by_metrics.emitting_time.timer(); + let mut output = state.group_values.emit(emit_to)?; + + for acc in state.accumulators.iter_mut() { + output.extend(acc.state(emit_to)?); + } + let done = state.group_values.is_empty(); + drop(timer); + + let batch = RecordBatch::try_new(output_schema, output)?; + debug_assert!(batch.num_rows() > 0); + if done { + self.state = AggregateHashTableState::Done; + } + Ok(Some(batch)) + } + AggregateHashTableState::Done => Ok(None), + AggregateHashTableState::Building(_) => { + internal_err!("next_output_batch must be called in the outputting state") + } + } + } + + pub(in crate::aggregates) fn can_skip_aggregation(&self) -> bool { + self.state + .building() + .accumulators + .iter() + .all(|acc| acc.supports_convert_to_state()) + } + + /// In skip-partial-aggregation optimization, when a decision has made to skip + /// partial stage, build a typed hash table only for aggregation state conversion + /// row-by-row. + pub(in crate::aggregates) fn partial_skip_table( + &self, + ) -> Result<AggregateHashTable<PartialSkip>> { + let state = self.state.building(); + let group_schema = state.group_by.group_schema(&self.input_schema)?; + let group_values = new_group_values(group_schema, &GroupOrdering::None)?; + let accumulators = state + .accumulators + .iter() + .map(HashAggregateAccumulator::empty_like) + .collect::<Result<Vec<_>>>()?; + + Ok(AggregateHashTable { + group_by_metrics: self.group_by_metrics.clone(), + input_schema: Arc::clone(&self.input_schema), + output_schema: Arc::clone(&self.output_schema), + batch_size: self.batch_size, + state: AggregateHashTableState::Building(BuildingHashTableState { + group_by: Arc::clone(&state.group_by), + group_values, + batch_group_indices: Default::default(), + accumulators, + }), + _mode: PhantomData, + }) + } + + pub(in crate::aggregates) fn aggregate_batch( + &mut self, + batch: &RecordBatch, + ) -> Result<()> { + let evaluated_batch = self.evaluate_batch(batch)?; + let state = self.state.building_mut(); + + let timer = self.group_by_metrics.aggregation_time.timer(); + for group_values in &evaluated_batch.grouping_set_args { + state + .group_values + .intern(group_values, &mut state.batch_group_indices)?; + let group_indices = &state.batch_group_indices; + let total_num_groups = state.group_values.len(); + + for (acc, values) in state + .accumulators + .iter_mut() + .zip(evaluated_batch.accumulator_args.iter()) + { + acc.update_batch(values, group_indices, total_num_groups)?; + } + } + drop(timer); Review Comment: Explicit timer drop here seems can be removed, but not really matter. ########## datafusion/physical-plan/src/aggregates/aggregate_hash_table/common.rs: ########## @@ -0,0 +1,406 @@ +// 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 std::marker::PhantomData; +use std::sync::Arc; + +use arrow::array::{ArrayRef, AsArray, new_null_array}; +use arrow::datatypes::SchemaRef; +use arrow::record_batch::RecordBatch; +use datafusion_common::{Result, internal_err}; +use datafusion_execution::memory_pool::proxy::VecAllocExt; +use datafusion_expr::{EmitTo, GroupsAccumulator}; +use datafusion_physical_expr::aggregate::AggregateFunctionExpr; + +use crate::PhysicalExpr; +use crate::aggregates::group_values::{GroupByMetrics, GroupValues, new_group_values}; +use crate::aggregates::order::GroupOrdering; +use crate::aggregates::row_hash::create_group_accumulator; +use crate::aggregates::{ + AggregateExec, PhysicalGroupBy, aggregate_expressions, evaluate_group_by, +}; + +/// Marker for raw rows -> partial state aggregation. +pub(in crate::aggregates) struct Partial; +/// Marker for raw rows -> partial state conversion without aggregation. +pub(in crate::aggregates) struct PartialSkip; +/// Marker for partial state -> final value aggregation. +pub(in crate::aggregates) struct Final; + +/// Grouped hash table shared by the partial and final paths. +/// +/// While building, it consumes input batches and updates group / accumulator +/// state. While outputting, it incrementally drains that state into output +/// batches. +/// +/// # Marker Type +/// `AggrMode` selects the aggregate semantics. +/// +/// e.g. `AggregateHashTable::<Partial>::new(...)` creates an aggregate hash table +/// for the partial hash aggregate stage, the input schema is raw rows and output +/// schema is intermediate states. +/// +/// It is a zero-sized compile-time marker, so each stage keeps its update logic +/// in a separate impl block, to make the behavior difference explicit. +pub(in crate::aggregates) struct AggregateHashTable<AggrMode> { + /// Grouping and accumulator-specific timing metrics. + pub(super) group_by_metrics: GroupByMetrics, + + /// Raw input schema, used to evaluate expressions and synthesize empty + /// grouping-set rows. + pub(super) input_schema: SchemaRef, + + /// Output schema: group columns followed by aggregate state or final values. + pub(super) output_schema: SchemaRef, + + /// Maximum rows per emitted output batch, from config `batch_size`. + pub(super) batch_size: usize, + + /// Lifecycle-specific state: building stage / outputting stage. + pub(super) state: AggregateHashTableState, + + pub(super) _mode: PhantomData<AggrMode>, +} + +pub(super) struct HashAggregateAccumulator { + /// Aggregate expression used to create a fresh accumulator for related + /// hash tables, such as the partial-skip table. + aggregate_expr: Arc<AggregateFunctionExpr>, + + /// Arguments to pass to this accumulator. + /// + /// Example: `CORR(x, y)` stores two expressions here, while `SUM(x)` stores one. + arguments: Vec<Arc<dyn PhysicalExpr>>, + + /// Optional `FILTER` expression for this accumulator. + /// + /// Example: `SUM(x) FILTER (WHERE x > 10)` stores the `x > 10` predicate. + filter: Option<Arc<dyn PhysicalExpr>>, + + /// Accumulator state for all groups for one aggregate expression. + accumulator: Box<dyn GroupsAccumulator>, +} + +pub(super) struct EvaluatedHashAggregateAccumulator { + pub(super) arguments: Vec<ArrayRef>, + pub(super) filter: Option<ArrayRef>, +} + +/// Evaluated all group by keys and accumulator args. +/// +/// e.g., `select k+1, sum(v*v) from t group by (k+1)`, this function evaluates +/// `k+1`, `v*v` +pub(super) struct EvaluatedAggregateBatch { + /// One entry per grouping set; each entry contains all evaluated group key + /// arrays for the current input batch. + pub(super) grouping_set_args: Vec<Vec<ArrayRef>>, + + /// Evaluated arguments and filters, one entry per aggregate expression. + pub(super) accumulator_args: Vec<EvaluatedHashAggregateAccumulator>, +} + +/// Hash table state while grouped aggregation is consuming input. +/// +/// This owns the coupled state for: +/// - evaluating group keys, +/// - interning each distinct group, +/// - mapping each input row to its group index, +/// - evaluating aggregate inputs, +/// - updating per-group accumulator state. +pub(super) struct BuildingHashTableState { + /// GROUP BY expressions evaluated for each input batch. + pub(super) group_by: Arc<PhysicalGroupBy>, + + /// Interned group keys. Accumulator state is stored separately by group index. + pub(super) group_values: Box<dyn GroupValues>, + + /// Group index for each row in the current input batch. + /// + /// Each value indexes into `group_values`, and the same index is used by every + /// accumulator to update that group's aggregate state. + pub(super) batch_group_indices: Vec<usize>, + + /// One item per aggregate expression. + /// + /// Example: `COUNT(x), SUM(y)` creates two items. Each item owns the input + /// expressions, optional filter, and accumulator state for all groups. + pub(super) accumulators: Vec<HashAggregateAccumulator>, +} + +pub(super) enum AggregateHashTableState { + Building(BuildingHashTableState), + Outputting(BuildingHashTableState), + Done, +} + +impl HashAggregateAccumulator { + fn new( + aggregate_expr: Arc<AggregateFunctionExpr>, + arguments: Vec<Arc<dyn PhysicalExpr>>, + filter: Option<Arc<dyn PhysicalExpr>>, + accumulator: Box<dyn GroupsAccumulator>, + ) -> Self { + Self { + aggregate_expr, + arguments, + filter, + accumulator, + } + } + + pub(super) fn empty_like(&self) -> Result<Self> { + let accumulator = create_group_accumulator(&self.aggregate_expr)?; + Ok(Self::new( + Arc::clone(&self.aggregate_expr), + self.arguments.clone(), + self.filter.clone(), + accumulator, + )) + } + + fn evaluate(&self, batch: &RecordBatch) -> Result<EvaluatedHashAggregateAccumulator> { Review Comment: How about name it `evaluate_acc_args` like `evaluate_group_by` ? -- This is an automated message from the Apache Git Service. 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