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Introduce avg_distinct() and sum_distinct() functions to DataFrame API #17536

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Merged
Jefffrey merged 2 commits into from
Sep 22, 2025
Merged

Introduce avg_distinct() and sum_distinct() functions to DataFrame API #17536

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34a2ccb
Introduce `avg_distinct()` and `sum_distinct()` functions to DataFram…
Jefffrey Sep 12, 2025
99386fe
Add to roundtrip proto tests
Jefffrey Sep 14, 2025
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44 changes: 25 additions & 19 deletions datafusion/core/tests/dataframe/mod.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,8 @@ use arrow::util::pretty::pretty_format_batches;
use datafusion::{assert_batches_eq, dataframe};
use datafusion_functions_aggregate::count::{count_all, count_all_window};
use datafusion_functions_aggregate::expr_fn::{
array_agg, avg, count, count_distinct, max, median, min, sum,
array_agg, avg, avg_distinct, count, count_distinct, max, median, min, sum,
sum_distinct,
};
use datafusion_functions_nested::make_array::make_array_udf;
use datafusion_functions_window::expr_fn::{first_value, row_number};
Expand Down Expand Up @@ -496,32 +497,35 @@ async fn drop_with_periods() -> Result<()> {
#[tokio::test]
async fn aggregate() -> Result<()> {
// build plan using DataFrame API
let df = test_table().await?;
// union so some of the distincts have a clearly distinct result
let df = test_table().await?.union(test_table().await?)?;
let group_expr = vec![col("c1")];
let aggr_expr = vec![
min(col("c12")),
max(col("c12")),
avg(col("c12")),
sum(col("c12")),
count(col("c12")),
count_distinct(col("c12")),
min(col("c4")).alias("min(c4)"),
max(col("c4")).alias("max(c4)"),
avg(col("c4")).alias("avg(c4)"),
avg_distinct(col("c4")).alias("avg_distinct(c4)"),
sum(col("c4")).alias("sum(c4)"),
sum_distinct(col("c4")).alias("sum_distinct(c4)"),
count(col("c4")).alias("count(c4)"),
count_distinct(col("c4")).alias("count_distinct(c4)"),
Comment on lines +504 to +511
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@Jefffrey Jefffrey Sep 12, 2025

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I switched to c4 from c12 as c12 had some precision variations for avg_distinct leading to inconsistent test results, and figured it was easier to switch columns than slap round on the outputs

];

let df: Vec<RecordBatch> = df.aggregate(group_expr, aggr_expr)?.collect().await?;

assert_snapshot!(
batches_to_sort_string(&df),
@r###"
+----+-----------------------------+-----------------------------+-----------------------------+-----------------------------+-------------------------------+----------------------------------------+
| c1 | min(aggregate_test_100.c12) | max(aggregate_test_100.c12) | avg(aggregate_test_100.c12) | sum(aggregate_test_100.c12) | count(aggregate_test_100.c12) | count(DISTINCT aggregate_test_100.c12) |
+----+-----------------------------+-----------------------------+-----------------------------+-----------------------------+-------------------------------+----------------------------------------+
| a | 0.02182578039211991 | 0.9800193410444061 | 0.48754517466109415 | 10.238448667882977 | 21 | 21 |
| b | 0.04893135681998029 | 0.9185813970744787 | 0.41040709263815384 | 7.797734760124923 | 19 | 19 |
| c | 0.0494924465469434 | 0.991517828651004 | 0.6600456536439784 | 13.860958726523545 | 21 | 21 |
| d | 0.061029375346466685 | 0.9748360509016578 | 0.48855379387549824 | 8.793968289758968 | 18 | 18 |
| e | 0.01479305307777301 | 0.9965400387585364 | 0.48600669271341534 | 10.206140546981722 | 21 | 21 |
+----+-----------------------------+-----------------------------+-----------------------------+-----------------------------+-------------------------------+----------------------------------------+
"###
@r"
+----+---------+---------+---------------------+---------------------+---------+------------------+-----------+--------------------+
| c1 | min(c4) | max(c4) | avg(c4) | avg_distinct(c4) | sum(c4) | sum_distinct(c4) | count(c4) | count_distinct(c4) |
+----+---------+---------+---------------------+---------------------+---------+------------------+-----------+--------------------+
| a | -28462 | 32064 | 306.04761904761904 | 306.04761904761904 | 12854 | 6427 | 42 | 21 |
| b | -28070 | 25286 | 7732.315789473684 | 7732.315789473684 | 293828 | 146914 | 38 | 19 |
| c | -30508 | 29106 | -1320.5238095238096 | -1320.5238095238096 | -55462 | -27731 | 42 | 21 |
| d | -24558 | 31106 | 10890.111111111111 | 10890.111111111111 | 392044 | 196022 | 36 | 18 |
| e | -31500 | 32514 | -4268.333333333333 | -4268.333333333333 | -179270 | -89635 | 42 | 21 |
+----+---------+---------+---------------------+---------------------+---------+------------------+-----------+--------------------+
"
);

Ok(())
Expand All @@ -536,7 +540,9 @@ async fn aggregate_assert_no_empty_batches() -> Result<()> {
min(col("c12")),
max(col("c12")),
avg(col("c12")),
avg_distinct(col("c12")),
sum(col("c12")),
sum_distinct(col("c12")),
count(col("c12")),
count_distinct(col("c12")),
median(col("c12")),
Expand Down
13 changes: 12 additions & 1 deletion datafusion/functions-aggregate/src/average.rs
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Expand Up @@ -36,7 +36,7 @@ use datafusion_expr::type_coercion::aggregates::{avg_return_type, coerce_avg_typ
use datafusion_expr::utils::format_state_name;
use datafusion_expr::Volatility::Immutable;
use datafusion_expr::{
Accumulator, AggregateUDFImpl, Documentation, EmitTo, GroupsAccumulator,
Accumulator, AggregateUDFImpl, Documentation, EmitTo, Expr, GroupsAccumulator,
ReversedUDAF, Signature,
};

Expand All @@ -62,6 +62,17 @@ make_udaf_expr_and_func!(
avg_udaf
);

pub fn avg_distinct(expr: Expr) -> Expr {
Expr::AggregateFunction(datafusion_expr::expr::AggregateFunction::new_udf(
avg_udaf(),
vec![expr],
true,
None,
vec![],
None,
))
}
Comment on lines +65 to +74
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@Jefffrey Jefffrey Sep 12, 2025

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Same as how count handles it:

datafusion/datafusion/functions-aggregate/src/count.rs

Lines 71 to 80 in bfc5067

pub fn count_distinct(expr: Expr) -> Expr {
Expr::AggregateFunction(datafusion_expr::expr::AggregateFunction::new_udf(
count_udaf(),
vec![expr],
true,
None,
vec![],
None,
))
}


#[user_doc(
doc_section(label = "General Functions"),
description = "Returns the average of numeric values in the specified column.",
Expand Down
2 changes: 2 additions & 0 deletions datafusion/functions-aggregate/src/lib.rs
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Expand Up @@ -105,6 +105,7 @@ pub mod expr_fn {
pub use super::approx_percentile_cont_with_weight::approx_percentile_cont_with_weight;
pub use super::array_agg::array_agg;
pub use super::average::avg;
pub use super::average::avg_distinct;
pub use super::bit_and_or_xor::bit_and;
pub use super::bit_and_or_xor::bit_or;
pub use super::bit_and_or_xor::bit_xor;
Expand Down Expand Up @@ -134,6 +135,7 @@ pub mod expr_fn {
pub use super::stddev::stddev;
pub use super::stddev::stddev_pop;
pub use super::sum::sum;
pub use super::sum::sum_distinct;
pub use super::variance::var_pop;
pub use super::variance::var_sample;
}
Expand Down
12 changes: 12 additions & 0 deletions datafusion/functions-aggregate/src/sum.rs
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Expand Up @@ -19,6 +19,7 @@

use ahash::RandomState;
use datafusion_expr::utils::AggregateOrderSensitivity;
use datafusion_expr::Expr;
use std::any::Any;
use std::mem::size_of_val;

Expand Down Expand Up @@ -53,6 +54,17 @@ make_udaf_expr_and_func!(
sum_udaf
);

pub fn sum_distinct(expr: Expr) -> Expr {
Expr::AggregateFunction(datafusion_expr::expr::AggregateFunction::new_udf(
sum_udaf(),
vec![expr],
true,
None,
vec![],
None,
))
}

/// Sum only supports a subset of numeric types, instead relying on type coercion
///
/// This macro is similar to [downcast_primitive](arrow::array::downcast_primitive)
Expand Down
7 changes: 5 additions & 2 deletions datafusion/proto/tests/cases/roundtrip_logical_plan.rs
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Expand Up @@ -32,6 +32,7 @@ use datafusion::execution::options::ArrowReadOptions;
use datafusion::optimizer::eliminate_nested_union::EliminateNestedUnion;
use datafusion::optimizer::Optimizer;
use datafusion_common::parsers::CompressionTypeVariant;
use datafusion_functions_aggregate::sum::sum_distinct;
use prost::Message;
use std::any::Any;
use std::collections::HashMap;
Expand Down Expand Up @@ -82,8 +83,8 @@ use datafusion_expr::{
};
use datafusion_functions_aggregate::average::avg_udaf;
use datafusion_functions_aggregate::expr_fn::{
approx_distinct, array_agg, avg, bit_and, bit_or, bit_xor, bool_and, bool_or, corr,
nth_value,
approx_distinct, array_agg, avg, avg_distinct, bit_and, bit_or, bit_xor, bool_and,
bool_or, corr, nth_value,
};
use datafusion_functions_aggregate::string_agg::string_agg;
use datafusion_functions_window_common::field::WindowUDFFieldArgs;
Expand Down Expand Up @@ -967,10 +968,12 @@ async fn roundtrip_expr_api() -> Result<()> {
functions_window::nth_value::last_value(lit(1)),
functions_window::nth_value::nth_value(lit(1), 1),
avg(lit(1.5)),
avg_distinct(lit(1.5)),
covar_samp(lit(1.5), lit(2.2)),
covar_pop(lit(1.5), lit(2.2)),
corr(lit(1.5), lit(2.2)),
sum(lit(1)),
sum_distinct(lit(1)),
max(lit(1)),
median(lit(2)),
min(lit(2)),
Expand Down
4 changes: 3 additions & 1 deletion docs/source/user-guide/expressions.md
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Expand Up @@ -288,6 +288,7 @@ select log(-1), log(0), sqrt(-1);
| Syntax | Description |
| ------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| avg(expr) | Сalculates the average value for `expr`. |
| avg_distinct(expr) | Creates an expression to represent the avg(distinct) aggregate function |
| approx_distinct(expr) | Calculates an approximate count of the number of distinct values for `expr`. |
| approx_median(expr) | Calculates an approximation of the median for `expr`. |
| approx_percentile_cont(expr, percentile [, centroids]) | Calculates an approximation of the specified `percentile` for `expr`. Optional `centroids` parameter controls accuracy (default: 100). |
Expand All @@ -298,14 +299,15 @@ select log(-1), log(0), sqrt(-1);
| bool_and(expr) | Returns true if all non-null input values (`expr`) are true, otherwise false. |
| bool_or(expr) | Returns true if any non-null input value (`expr`) is true, otherwise false. |
| count(expr) | Returns the number of rows for `expr`. |
| count_distinct | Creates an expression to represent the count(distinct) aggregate function |
| count_distinct(expr) | Creates an expression to represent the count(distinct) aggregate function |
| cube(exprs) | Creates a grouping set for all combination of `exprs` |
| grouping_set(exprs) | Create a grouping set. |
| max(expr) | Finds the maximum value of `expr`. |
| median(expr) | Сalculates the median of `expr`. |
| min(expr) | Finds the minimum value of `expr`. |
| rollup(exprs) | Creates a grouping set for rollup sets. |
| sum(expr) | Сalculates the sum of `expr`. |
| sum_distinct(expr) | Creates an expression to represent the sum(distinct) aggregate function |

## Aggregate Function Builder

Expand Down