datafusion-spark 53.1.0

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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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use arrow::array::{
    Array, AsArray, BooleanArray, BooleanBufferBuilder, GenericListArray, OffsetSizeTrait,
};
use arrow::buffer::{BooleanBuffer, NullBuffer};
use arrow::datatypes::DataType;
use datafusion_common::{Result, exec_err};
use datafusion_expr::{
    ColumnarValue, ScalarFunctionArgs, ScalarUDFImpl, Signature, Volatility,
};
use datafusion_functions_nested::array_has::array_has_udf;
use std::any::Any;
use std::sync::Arc;

/// Spark-compatible `array_contains` function.
///
/// Calls DataFusion's `array_has` and then applies Spark's null semantics:
/// - If the result from `array_has` is `true`, return `true`.
/// - If the result is `false` and the input array row contains any null elements,
///   return `null` (because the element might have been the null).
/// - If the result is `false` and the input array row has no null elements,
///   return `false`.
#[derive(Debug, PartialEq, Eq, Hash)]
pub struct SparkArrayContains {
    signature: Signature,
}

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

impl SparkArrayContains {
    pub fn new() -> Self {
        Self {
            signature: Signature::array_and_element(Volatility::Immutable),
        }
    }
}

impl ScalarUDFImpl for SparkArrayContains {
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn name(&self) -> &str {
        "array_contains"
    }

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

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

    fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> {
        let haystack = args.args[0].clone();
        let array_has_result = array_has_udf().invoke_with_args(args)?;

        let result_array = array_has_result.to_array(1)?;
        let patched = apply_spark_null_semantics(result_array.as_boolean(), &haystack)?;
        Ok(ColumnarValue::Array(Arc::new(patched)))
    }
}

/// For each row where `array_has` returned `false`, set the output to null
/// if that row's input array contains any null elements.
fn apply_spark_null_semantics(
    result: &BooleanArray,
    haystack_arg: &ColumnarValue,
) -> Result<BooleanArray> {
    // happy path
    if result.false_count() == 0 || haystack_arg.data_type() == DataType::Null {
        return Ok(result.clone());
    }

    let haystack = haystack_arg.to_array_of_size(result.len())?;

    let row_has_nulls = compute_row_has_nulls(&haystack)?;

    // A row keeps its validity when result is true OR the row has no nulls.
    let keep_mask = result.values() | &!&row_has_nulls;
    let new_validity = match result.nulls() {
        Some(n) => n.inner() & &keep_mask,
        None => keep_mask,
    };

    Ok(BooleanArray::new(
        result.values().clone(),
        Some(NullBuffer::new(new_validity)),
    ))
}

/// Returns a per-row bitmap where bit i is set if row i's list contains any null element.
fn compute_row_has_nulls(haystack: &dyn Array) -> Result<BooleanBuffer> {
    match haystack.data_type() {
        DataType::List(_) => generic_list_row_has_nulls(haystack.as_list::<i32>()),
        DataType::LargeList(_) => generic_list_row_has_nulls(haystack.as_list::<i64>()),
        DataType::FixedSizeList(_, _) => {
            let list = haystack.as_fixed_size_list();
            let buf = match list.values().nulls() {
                Some(nulls) => {
                    let validity = nulls.inner();
                    let vl = list.value_length() as usize;
                    let mut builder = BooleanBufferBuilder::new(list.len());
                    for i in 0..list.len() {
                        builder.append(validity.slice(i * vl, vl).count_set_bits() < vl);
                    }
                    builder.finish()
                }
                None => BooleanBuffer::new_unset(list.len()),
            };
            Ok(mask_with_list_nulls(buf, list.nulls()))
        }
        dt => exec_err!("compute_row_has_nulls: unsupported data type {dt}"),
    }
}

/// Computes per-row null presence for `List` and `LargeList` arrays.
fn generic_list_row_has_nulls<O: OffsetSizeTrait>(
    list: &GenericListArray<O>,
) -> Result<BooleanBuffer> {
    let buf = match list.values().nulls() {
        Some(nulls) => {
            let validity = nulls.inner();
            let offsets = list.offsets();
            let mut builder = BooleanBufferBuilder::new(list.len());
            for i in 0..list.len() {
                let s = offsets[i].as_usize();
                let len = offsets[i + 1].as_usize() - s;
                builder.append(validity.slice(s, len).count_set_bits() < len);
            }
            builder.finish()
        }
        None => BooleanBuffer::new_unset(list.len()),
    };
    Ok(mask_with_list_nulls(buf, list.nulls()))
}

/// Rows where the list itself is null should not be marked as "has nulls".
fn mask_with_list_nulls(
    buf: BooleanBuffer,
    list_nulls: Option<&NullBuffer>,
) -> BooleanBuffer {
    match list_nulls {
        Some(n) => &buf & n.inner(),
        None => buf,
    }
}