datafusion-functions-nested 55.0.0

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// to you under the Apache License, Version 2.0 (the
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//
//   http://www.apache.org/licenses/LICENSE-2.0
//
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//! [`ScalarUDFImpl`] definitions for array_avg function.

use crate::utils::make_scalar_function;
use arrow::array::{Array, ArrayRef, Float64Array, OffsetSizeTrait};
use arrow::datatypes::{
    DataType,
    DataType::{FixedSizeList, LargeList, List, Null},
    Field,
};
use datafusion_common::cast::{as_float64_array, as_generic_list_array};
use datafusion_common::utils::{ListCoercion, coerced_type_with_base_type_only};
use datafusion_common::{Result, internal_err, plan_err, utils::take_function_args};
use datafusion_expr::{
    ColumnarValue, Documentation, ScalarFunctionArgs, ScalarUDFImpl, Signature,
    Volatility,
};
use datafusion_macros::user_doc;
use std::sync::Arc;

make_udf_expr_and_func!(
    ArrayAvg,
    array_avg,
    array,
    "returns the arithmetic mean of elements in a numeric array.",
    array_avg_udf
);

#[user_doc(
    doc_section(label = "Array Functions"),
    description = "Returns the arithmetic mean (sum divided by count) of the elements of the input array. NULL elements are skipped (per SQL aggregate convention) and excluded from the count. Returns NULL if the input row is NULL, every element is NULL, or the array is empty.",
    syntax_example = "array_avg(array)",
    sql_example = r#"```sql
> select array_avg([1.0, 2.0, 3.0]);
+----------------------------+
| array_avg(List([1.0,2.0,3.0])) |
+----------------------------+
| 2.0                        |
+----------------------------+
```"#,
    argument(
        name = "array",
        description = "Array expression. Can be a constant, column, or function, and any combination of array operators."
    )
)]
#[derive(Debug, PartialEq, Eq, Hash)]
pub struct ArrayAvg {
    signature: Signature,
    aliases: Vec<String>,
}

impl Default for ArrayAvg {
    fn default() -> Self {
        Self::new()
    }
}

impl ArrayAvg {
    pub fn new() -> Self {
        Self {
            signature: Signature::user_defined(Volatility::Immutable),
            aliases: vec!["list_avg".to_string()],
        }
    }
}

impl ScalarUDFImpl for ArrayAvg {
    fn name(&self) -> &str {
        "array_avg"
    }

    fn signature(&self) -> &Signature {
        &self.signature
    }

    fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
        Ok(DataType::Float64)
    }

    fn coerce_types(&self, arg_types: &[DataType]) -> Result<Vec<DataType>> {
        let [arg_type] = take_function_args(self.name(), arg_types)?;
        let coercion = Some(&ListCoercion::FixedSizedListToList);

        if !matches!(arg_type, Null | List(_) | LargeList(_) | FixedSizeList(..)) {
            return plan_err!("{} does not support type {arg_type}", self.name());
        }

        let coerced = if matches!(arg_type, Null) {
            List(Arc::new(Field::new_list_field(DataType::Float64, true)))
        } else {
            coerced_type_with_base_type_only(arg_type, &DataType::Float64, coercion)
        };

        Ok(vec![coerced])
    }

    fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> {
        make_scalar_function(array_avg_inner)(&args.args)
    }

    fn aliases(&self) -> &[String] {
        &self.aliases
    }

    fn documentation(&self) -> Option<&Documentation> {
        self.doc()
    }
}

fn array_avg_inner(args: &[ArrayRef]) -> Result<ArrayRef> {
    let [array] = take_function_args("array_avg", args)?;
    match array.data_type() {
        List(_) => general_array_avg::<i32>(array),
        LargeList(_) => general_array_avg::<i64>(array),
        arg_type => {
            internal_err!("array_avg received unexpected type after coercion: {arg_type}")
        }
    }
}

fn general_array_avg<O: OffsetSizeTrait>(array: &ArrayRef) -> Result<ArrayRef> {
    let list_array = as_generic_list_array::<O>(array)?;
    let values = as_float64_array(list_array.values())?;
    let offsets = list_array.value_offsets();

    let mut builder = Float64Array::builder(list_array.len());

    for row in 0..list_array.len() {
        if list_array.is_null(row) {
            builder.append_null();
            continue;
        }

        let start = offsets[row].as_usize();
        let end = offsets[row + 1].as_usize();

        // Skip NULL elements per SQL aggregate convention (matches PostgreSQL
        // AVG, DuckDB list_avg, Spark aggregate). Empty arrays and all-NULL
        // arrays both yield NULL — same behavior as SQL AVG over an empty
        // set or all-NULL column.
        let mut sum = 0.0_f64;
        let mut count: u64 = 0;
        for i in start..end {
            if values.is_valid(i) {
                sum += values.value(i);
                count += 1;
            }
        }

        if count > 0 {
            builder.append_value(sum / count as f64);
        } else {
            builder.append_null();
        }
    }

    Ok(Arc::new(builder.finish()))
}