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Materialize dictionaries in group keys (#7647)
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Given that group keys inherently have few repeated values, especially
when grouping on a single column, the use of dictionary encoding is
unlikely to be yielding significant returns
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qrilka committed Nov 21, 2023
1 parent 58483fb commit 2b07114
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Showing 4 changed files with 39 additions and 38 deletions.
15 changes: 3 additions & 12 deletions datafusion/core/tests/path_partition.rs
Original file line number Diff line number Diff line change
Expand Up @@ -168,9 +168,9 @@ async fn parquet_distinct_partition_col() -> Result<()> {
assert_eq!(min_limit, resulting_limit);

let s = ScalarValue::try_from_array(results[0].column(1), 0)?;
let month = match extract_as_utf(&s) {
Some(month) => month,
s => panic!("Expected month as Dict(_, Utf8) found {s:?}"),
let month = match s {
ScalarValue::Utf8(Some(month)) => month,
s => panic!("Expected month as Utf8 found {s:?}"),
};

let sql_on_partition_boundary = format!(
Expand All @@ -191,15 +191,6 @@ async fn parquet_distinct_partition_col() -> Result<()> {
Ok(())
}

fn extract_as_utf(v: &ScalarValue) -> Option<String> {
if let ScalarValue::Dictionary(_, v) = v {
if let ScalarValue::Utf8(v) = v.as_ref() {
return v.clone();
}
}
None
}

#[tokio::test]
async fn csv_filter_with_file_col() -> Result<()> {
let ctx = SessionContext::new();
Expand Down
27 changes: 4 additions & 23 deletions datafusion/physical-plan/src/aggregates/group_values/row.rs
Original file line number Diff line number Diff line change
Expand Up @@ -17,22 +17,18 @@

use crate::aggregates::group_values::GroupValues;
use ahash::RandomState;
use arrow::compute::cast;
use arrow::record_batch::RecordBatch;
use arrow::row::{RowConverter, Rows, SortField};
use arrow_array::{Array, ArrayRef};
use arrow_schema::{DataType, SchemaRef};
use arrow_array::ArrayRef;
use arrow_schema::SchemaRef;
use datafusion_common::hash_utils::create_hashes;
use datafusion_common::{DataFusionError, Result};
use datafusion_common::Result;
use datafusion_execution::memory_pool::proxy::{RawTableAllocExt, VecAllocExt};
use datafusion_physical_expr::EmitTo;
use hashbrown::raw::RawTable;

/// A [`GroupValues`] making use of [`Rows`]
pub struct GroupValuesRows {
/// The output schema
schema: SchemaRef,

/// Converter for the group values
row_converter: RowConverter,

Expand Down Expand Up @@ -79,7 +75,6 @@ impl GroupValuesRows {
let map = RawTable::with_capacity(0);

Ok(Self {
schema,
row_converter,
map,
map_size: 0,
Expand Down Expand Up @@ -170,7 +165,7 @@ impl GroupValues for GroupValuesRows {
.take()
.expect("Can not emit from empty rows");

let mut output = match emit_to {
let output = match emit_to {
EmitTo::All => {
let output = self.row_converter.convert_rows(&group_values)?;
group_values.clear();
Expand Down Expand Up @@ -203,20 +198,6 @@ impl GroupValues for GroupValuesRows {
}
};

// TODO: Materialize dictionaries in group keys (#7647)
for (field, array) in self.schema.fields.iter().zip(&mut output) {
let expected = field.data_type();
if let DataType::Dictionary(_, v) = expected {
let actual = array.data_type();
if v.as_ref() != actual {
return Err(DataFusionError::Internal(format!(
"Converted group rows expected dictionary of {v} got {actual}"
)));
}
*array = cast(array.as_ref(), expected)?;
}
}

self.group_values = Some(group_values);
Ok(output)
}
Expand Down
31 changes: 29 additions & 2 deletions datafusion/physical-plan/src/aggregates/mod.rs
Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,7 @@ use crate::{
use arrow::array::ArrayRef;
use arrow::datatypes::{Field, Schema, SchemaRef};
use arrow::record_batch::RecordBatch;
use arrow_schema::DataType;
use datafusion_common::stats::Precision;
use datafusion_common::{not_impl_err, plan_err, DataFusionError, Result};
use datafusion_execution::TaskContext;
Expand Down Expand Up @@ -286,6 +287,9 @@ pub struct AggregateExec {
limit: Option<usize>,
/// Input plan, could be a partial aggregate or the input to the aggregate
pub input: Arc<dyn ExecutionPlan>,
/// Original aggregation schema, could be different from `schema` before dictionary group
/// keys get materialized
original_schema: SchemaRef,
/// Schema after the aggregate is applied
schema: SchemaRef,
/// Input schema before any aggregation is applied. For partial aggregate this will be the
Expand Down Expand Up @@ -469,15 +473,19 @@ impl AggregateExec {
input: Arc<dyn ExecutionPlan>,
input_schema: SchemaRef,
) -> Result<Self> {
let schema = create_schema(
let original_schema = create_schema(
&input.schema(),
&group_by.expr,
&aggr_expr,
group_by.contains_null(),
mode,
)?;

let schema = Arc::new(schema);
let schema = Arc::new(materialize_dict_group_keys(
&original_schema,
group_by.expr.len(),
));
let original_schema = Arc::new(original_schema);
// Reset ordering requirement to `None` if aggregator is not order-sensitive
order_by_expr = aggr_expr
.iter()
Expand Down Expand Up @@ -552,6 +560,7 @@ impl AggregateExec {
filter_expr,
order_by_expr,
input,
original_schema,
schema,
input_schema,
projection_mapping,
Expand Down Expand Up @@ -973,6 +982,24 @@ fn create_schema(
Ok(Schema::new(fields))
}

/// returns schema with dictionary group keys materialized as their value types
/// The actual convertion happens in `RowConverter` and we don't do unnecessary
/// conversion back into dictionaries
fn materialize_dict_group_keys(schema: &Schema, group_count: usize) -> Schema {
let fields = schema
.fields
.iter()
.enumerate()
.map(|(i, field)| match field.data_type() {
DataType::Dictionary(_, value_data_type) if i < group_count => {
Field::new(field.name(), *value_data_type.clone(), field.is_nullable())
}
_ => Field::clone(field),
})
.collect::<Vec<_>>();
Schema::new(fields)
}

fn group_schema(schema: &Schema, group_count: usize) -> SchemaRef {
let group_fields = schema.fields()[0..group_count].to_vec();
Arc::new(Schema::new(group_fields))
Expand Down
4 changes: 3 additions & 1 deletion datafusion/physical-plan/src/aggregates/row_hash.rs
Original file line number Diff line number Diff line change
Expand Up @@ -324,7 +324,9 @@ impl GroupedHashAggregateStream {
.map(create_group_accumulator)
.collect::<Result<_>>()?;

let group_schema = group_schema(&agg_schema, agg_group_by.expr.len());
// we need to use original schema so RowConverter in group_values below
// will do the proper coversion of dictionaries into value types
let group_schema = group_schema(&agg.original_schema, agg_group_by.expr.len());
let spill_expr = group_schema
.fields
.into_iter()
Expand Down

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