Dandandan commented on code in PR #7192:
URL: https://github.com/apache/arrow-datafusion/pull/7192#discussion_r1284113078
##########
datafusion/core/src/physical_plan/aggregates/priority_queue.rs:
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@@ -0,0 +1,235 @@
+use uuid::Uuid;
+use std::collections::{BTreeMap, HashMap};
+use std::pin::Pin;
+use std::sync::Arc;
+use std::task::{Context, Poll};
+use arrow::row::{OwnedRow, RowConverter, SortField};
+use arrow::util::pretty::print_batches;
+use arrow_array::{RecordBatch};
+use arrow_schema::{DataType, SchemaRef, SortOptions};
+use futures::stream::{Stream, StreamExt};
+use hashbrown::HashSet;
+use datafusion_common::DataFusionError;
+use datafusion_execution::TaskContext;
+use datafusion_physical_expr::{PhysicalExpr};
+use crate::physical_plan::aggregates::{aggregate_expressions, AggregateExec,
evaluate_group_by, evaluate_many, group_schema, PhysicalGroupBy};
+use crate::physical_plan::{RecordBatchStream, SendableRecordBatchStream};
+use datafusion_common::Result;
+use datafusion_physical_expr::expressions::{Max, Min};
+
+pub(crate) struct GroupedPriorityQueueAggregateStream {
+ schema: SchemaRef,
+ input: SendableRecordBatchStream,
+ aggregate_arguments: Vec<Vec<Arc<dyn PhysicalExpr>>>,
+ group_by: PhysicalGroupBy,
+ group_converter: RowConverter,
+ value_converter: RowConverter, // TODO: use accumulators
+ group_to_val: HashMap<OwnedRow, OwnedRow>, // TODO: BTreeMap->BinaryHeap,
OwnedRow->Rows
+ val_to_group: BTreeMap<OwnedRow, HashSet<OwnedRow>>,
+ limit: Option<usize>,
+ id: Uuid,
+ descending: bool,
+}
+
+impl GroupedPriorityQueueAggregateStream {
+ pub fn new(
+ agg: &AggregateExec,
+ context: Arc<TaskContext>,
+ partition: usize,
+ limit: Option<usize>,
+ ) -> Result<Self> {
+ let agg_schema = Arc::clone(&agg.schema);
+ let agg_group_by = agg.group_by.clone();
+
+ let input = agg.input.execute(partition, Arc::clone(&context))?;
+
+ let aggregate_arguments = aggregate_expressions(
+ &agg.aggr_expr,
+ &agg.mode,
+ agg_group_by.expr.len(),
+ )?;
+
+ let (value_type, descending) = Self::get_minmax_desc(agg)
+ .ok_or_else(|| DataFusionError::Execution("Min/max
required".to_string()))?;
+ let sort = SortOptions { descending, nulls_first: false };
+ let value_fields = vec![SortField::new_with_options(value_type, sort)];
+ let value_converter = RowConverter::new(value_fields.clone())?;
+
+ // TODO: filters
+
+ let group_schema = group_schema(&agg_schema, agg_group_by.expr.len());
+ // Schema { fields: [Field { name: "trace_id", data_type: UInt64
+ println!("group_schema={group_schema:?}");
+ let group_fields = group_schema
+ .fields()
+ .iter()
+ .map(|f| SortField::new(f.data_type().clone()))
+ .collect::<Vec<_>>();
+ let group_converter = RowConverter::new(group_fields.clone())?;
+
+ Ok(GroupedPriorityQueueAggregateStream {
+ schema: agg_schema,
+ input,
+ aggregate_arguments,
+ group_by: agg_group_by,
+ group_to_val: Default::default(),
+ val_to_group: Default::default(),
+ limit,
+ group_converter,
+ value_converter,
+ id: Uuid::new_v4(),
+ descending,
+ })
+ }
+
+ pub fn get_minmax_desc(agg: &AggregateExec) -> Option<(DataType, bool)> {
+ let agg_expr = match agg.aggr_expr.as_slice() {
+ [] => return None,
Review Comment:
```suggestion
```
##########
datafusion/core/src/physical_plan/aggregates/priority_queue.rs:
##########
@@ -0,0 +1,235 @@
+use uuid::Uuid;
+use std::collections::{BTreeMap, HashMap};
+use std::pin::Pin;
+use std::sync::Arc;
+use std::task::{Context, Poll};
+use arrow::row::{OwnedRow, RowConverter, SortField};
+use arrow::util::pretty::print_batches;
+use arrow_array::{RecordBatch};
+use arrow_schema::{DataType, SchemaRef, SortOptions};
+use futures::stream::{Stream, StreamExt};
+use hashbrown::HashSet;
+use datafusion_common::DataFusionError;
+use datafusion_execution::TaskContext;
+use datafusion_physical_expr::{PhysicalExpr};
+use crate::physical_plan::aggregates::{aggregate_expressions, AggregateExec,
evaluate_group_by, evaluate_many, group_schema, PhysicalGroupBy};
+use crate::physical_plan::{RecordBatchStream, SendableRecordBatchStream};
+use datafusion_common::Result;
+use datafusion_physical_expr::expressions::{Max, Min};
+
+pub(crate) struct GroupedPriorityQueueAggregateStream {
+ schema: SchemaRef,
+ input: SendableRecordBatchStream,
+ aggregate_arguments: Vec<Vec<Arc<dyn PhysicalExpr>>>,
+ group_by: PhysicalGroupBy,
+ group_converter: RowConverter,
+ value_converter: RowConverter, // TODO: use accumulators
+ group_to_val: HashMap<OwnedRow, OwnedRow>, // TODO: BTreeMap->BinaryHeap,
OwnedRow->Rows
+ val_to_group: BTreeMap<OwnedRow, HashSet<OwnedRow>>,
+ limit: Option<usize>,
+ id: Uuid,
+ descending: bool,
+}
+
+impl GroupedPriorityQueueAggregateStream {
+ pub fn new(
+ agg: &AggregateExec,
+ context: Arc<TaskContext>,
+ partition: usize,
+ limit: Option<usize>,
+ ) -> Result<Self> {
+ let agg_schema = Arc::clone(&agg.schema);
+ let agg_group_by = agg.group_by.clone();
+
+ let input = agg.input.execute(partition, Arc::clone(&context))?;
+
+ let aggregate_arguments = aggregate_expressions(
+ &agg.aggr_expr,
+ &agg.mode,
+ agg_group_by.expr.len(),
+ )?;
+
+ let (value_type, descending) = Self::get_minmax_desc(agg)
+ .ok_or_else(|| DataFusionError::Execution("Min/max
required".to_string()))?;
+ let sort = SortOptions { descending, nulls_first: false };
+ let value_fields = vec![SortField::new_with_options(value_type, sort)];
+ let value_converter = RowConverter::new(value_fields.clone())?;
+
+ // TODO: filters
+
+ let group_schema = group_schema(&agg_schema, agg_group_by.expr.len());
+ // Schema { fields: [Field { name: "trace_id", data_type: UInt64
+ println!("group_schema={group_schema:?}");
+ let group_fields = group_schema
+ .fields()
+ .iter()
+ .map(|f| SortField::new(f.data_type().clone()))
+ .collect::<Vec<_>>();
+ let group_converter = RowConverter::new(group_fields.clone())?;
+
+ Ok(GroupedPriorityQueueAggregateStream {
+ schema: agg_schema,
+ input,
+ aggregate_arguments,
+ group_by: agg_group_by,
+ group_to_val: Default::default(),
+ val_to_group: Default::default(),
+ limit,
+ group_converter,
+ value_converter,
+ id: Uuid::new_v4(),
+ descending,
+ })
+ }
+
+ pub fn get_minmax_desc(agg: &AggregateExec) -> Option<(DataType, bool)> {
+ let agg_expr = match agg.aggr_expr.as_slice() {
+ [] => return None,
+ [expr] => expr,
+ _ => return None,
+ };
+ let binding = agg_expr.expressions();
+ let expr = match binding.as_slice() {
+ [] => return None,
Review Comment:
```suggestion
```
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