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feat: Calculate non-distinct sum from column statistics when available #23863

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feat: Calculate non-distinct sum from column statistics when available #23863

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221 changes: 220 additions & 1 deletion datafusion/core/tests/physical_optimizer/aggregate_statistics.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -29,16 +29,17 @@ use datafusion::datasource::memory::MemorySourceConfig;
use datafusion::datasource::physical_plan::ParquetSource;
use datafusion::datasource::source::DataSourceExec;
use datafusion::prelude::{SessionConfig, SessionContext};
use datafusion_common::assert_batches_eq;
use datafusion_common::cast::as_int64_array;
use datafusion_common::config::ConfigOptions;
use datafusion_common::stats::Precision;
use datafusion_common::{ColumnStatistics, Result, Statistics};
use datafusion_common::{ScalarValue, assert_batches_eq};
use datafusion_datasource::file_scan_config::FileScanConfigBuilder;
use datafusion_execution::TaskContext;
use datafusion_execution::object_store::ObjectStoreUrl;
use datafusion_expr::Operator;
use datafusion_functions_aggregate::count::count_udaf;
use datafusion_functions_aggregate::sum::sum_udaf;
use datafusion_physical_expr::aggregate::AggregateExprBuilder;
use datafusion_physical_expr::expressions::{self, cast};
use datafusion_physical_optimizer::PhysicalOptimizerRule;
Expand Down Expand Up @@ -637,3 +638,221 @@ async fn topk_distinct_preserves_nulls() -> Result<()> {

Ok(())
}

#[tokio::test]
async fn test_sum_from_statistics() -> Result<()> {
enum SumArg {
ColumnA,
ColumnB,
CastColumnA(DataType),
Binary,
}

struct TestCase {
name: &'static str,
data_type: DataType,
sum_value_a: Precision<ScalarValue>,
sum_value_b: Precision<ScalarValue>,
sum_arg: SumArg,
is_distinct: bool,
expected_value: Option<ScalarValue>,
}

for case in [
TestCase {
name: "exact statistics",
data_type: DataType::Int64,
sum_value_a: Precision::Exact(ScalarValue::Int64(Some(10))),
sum_value_b: Precision::Absent,
sum_arg: SumArg::ColumnA,
is_distinct: false,
expected_value: Some(ScalarValue::Int64(Some(10))),
},
TestCase {
name: "second column statistics",
data_type: DataType::Int64,
sum_value_a: Precision::Exact(ScalarValue::Int64(Some(10))),
sum_value_b: Precision::Exact(ScalarValue::Int64(Some(42))),
sum_arg: SumArg::ColumnB,
is_distinct: false,
expected_value: Some(ScalarValue::Int64(Some(42))),
},
TestCase {
name: "casted int32 column statistics",
data_type: DataType::Int32,
sum_value_a: Precision::Exact(ScalarValue::Int32(Some(10))),
sum_value_b: Precision::Absent,
sum_arg: SumArg::CastColumnA(DataType::Int64),
is_distinct: false,
expected_value: Some(ScalarValue::Int64(Some(10))),
},
TestCase {
name: "decimal statistics uses aggregate return type",
data_type: DataType::Decimal128(5, 2),
sum_value_a: Precision::Exact(ScalarValue::Decimal128(Some(12345), 5, 2)),
sum_value_b: Precision::Absent,
sum_arg: SumArg::ColumnA,
is_distinct: false,
expected_value: Some(ScalarValue::Decimal128(Some(12345), 15, 2)),
},
TestCase {
name: "inexact statistics",
data_type: DataType::Int64,
sum_value_a: Precision::Inexact(ScalarValue::Int64(Some(10))),
sum_value_b: Precision::Absent,
sum_arg: SumArg::ColumnA,
is_distinct: false,
expected_value: None,
},
TestCase {
name: "absent statistics",
data_type: DataType::Int64,
sum_value_a: Precision::Absent,
sum_value_b: Precision::Absent,
sum_arg: SumArg::ColumnA,
is_distinct: false,
expected_value: None,
},
TestCase {
name: "null statistics",
data_type: DataType::Int64,
sum_value_a: Precision::Exact(ScalarValue::Int64(None)),
sum_value_b: Precision::Absent,
sum_arg: SumArg::ColumnA,
is_distinct: false,
expected_value: None,
},
TestCase {
name: "binary expr",
data_type: DataType::Int64,
sum_value_a: Precision::Exact(ScalarValue::Int64(Some(10))),
sum_value_b: Precision::Exact(ScalarValue::Int64(Some(42))),
sum_arg: SumArg::Binary,
is_distinct: false,
expected_value: None,
},
TestCase {
name: "distinct sum",
data_type: DataType::Int64,
sum_value_a: Precision::Exact(ScalarValue::Int64(Some(10))),
sum_value_b: Precision::Absent,
sum_arg: SumArg::ColumnA,
is_distinct: true,
expected_value: None,
},
] {
let schema = Arc::new(Schema::new(vec![
Field::new("a", case.data_type.clone(), true),
Field::new("b", case.data_type.clone(), true),
]));

let statistics = Statistics {
num_rows: Precision::Absent,
total_byte_size: Precision::Absent,
column_statistics: vec![
ColumnStatistics {
sum_value: case.sum_value_a,
..Default::default()
},
ColumnStatistics {
sum_value: case.sum_value_b,
..Default::default()
},
],
};

let config = FileScanConfigBuilder::new(
ObjectStoreUrl::parse("test:///").unwrap(),
Arc::new(ParquetSource::new(Arc::clone(&schema))),
)
.with_file(PartitionedFile::new("x".to_string(), 100))
.with_statistics(statistics)
.build();

let source: Arc<dyn ExecutionPlan> = DataSourceExec::from_data_source(config);
let schema = source.schema();

let (agg_args, alias): (Vec<Arc<dyn datafusion_physical_expr::PhysicalExpr>>, _) =
match case.sum_arg {
SumArg::ColumnA => (vec![expressions::col("a", &schema)?], "SUM(a)"),
SumArg::ColumnB => (vec![expressions::col("b", &schema)?], "SUM(b)"),
SumArg::CastColumnA(cast_type) => (
vec![cast(expressions::col("a", &schema)?, &schema, cast_type)?],
"SUM(CAST(a))",
),
SumArg::Binary => (
vec![expressions::binary(
expressions::col("a", &schema)?,
Operator::Plus,
expressions::col("b", &schema)?,
&schema,
)?],
"SUM(a + b)",
),
};

let sum_expr_builder = AggregateExprBuilder::new(sum_udaf(), agg_args)
.schema(Arc::clone(&schema))
.alias(alias);
let sum_expr_builder = if case.is_distinct {
sum_expr_builder.distinct()
} else {
sum_expr_builder
};
let sum_expr = sum_expr_builder.build()?;

let partial_agg = AggregateExec::try_new(
AggregateMode::Partial,
PhysicalGroupBy::default(),
vec![Arc::new(sum_expr.clone())],
vec![None],
source,
Arc::clone(&schema),
)?;

let final_agg = AggregateExec::try_new(
AggregateMode::Final,
PhysicalGroupBy::default(),
vec![Arc::new(sum_expr)],
vec![None],
Arc::new(partial_agg),
Arc::clone(&schema),
)?;

let conf = ConfigOptions::new();
let optimized =
AggregateStatistics::new().optimize(Arc::new(final_agg), &conf)?;

if let Some(expected_value) = case.expected_value {
assert!(
optimized.is::<ProjectionExec>(),
"'{}': expected ProjectionExec",
case.name
);

let task_ctx = Arc::new(TaskContext::default());
let result = common::collect(optimized.execute(0, task_ctx)?).await?;
assert_eq!(result.len(), 1, "'{}': expected 1 batch", case.name);
assert_eq!(
result[0].schema().field(0).data_type(),
&expected_value.data_type(),
"'{}': unexpected data type",
case.name
);
assert_eq!(
ScalarValue::try_from_array(result[0].column(0), 0)?,
expected_value,
"'{}': unexpected value",
case.name
);
} else {
assert!(
optimized.is::<AggregateExec>(),
"'{}': expected AggregateExec (not optimized)",
case.name
);
}
}

Ok(())
}
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