native/spark-expr/src/array_funcs/array_repeat.rs

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// distributed with this work for additional information
// regarding copyright ownership.  The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License.  You may obtain a copy of the License at
//
//   http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
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// KIND, either express or implied.  See the License for the
// specific language governing permissions and limitations
// under the License.

use arrow::array::{
    new_null_array, Array, ArrayRef, Capacities, GenericListArray, ListArray, MutableArrayData,
    NullBufferBuilder, OffsetSizeTrait, UInt64Array,
};
use arrow::buffer::OffsetBuffer;
use arrow::compute;
use arrow::compute::cast;
use arrow::datatypes::DataType::{LargeList, List};
use arrow::datatypes::{DataType, Field};
use datafusion::common::cast::{as_large_list_array, as_list_array, as_uint64_array};
use datafusion::common::{exec_err, DataFusionError, ScalarValue};
use datafusion::logical_expr::ColumnarValue;
use std::sync::Arc;

pub fn make_scalar_function<F>(
    inner: F,
) -> impl Fn(&[ColumnarValue]) -> Result<ColumnarValue, DataFusionError>
where
    F: Fn(&[ArrayRef]) -> Result<ArrayRef, DataFusionError>,
{
    move |args: &[ColumnarValue]| {
        // first, identify if any of the arguments is an Array. If yes, store its `len`,
        // as any scalar will need to be converted to an array of len `len`.
        let len = args
            .iter()
            .fold(Option::<usize>::None, |acc, arg| match arg {
                ColumnarValue::Scalar(_) => acc,
                ColumnarValue::Array(a) => Some(a.len()),
            });

        let is_scalar = len.is_none();

        let args = ColumnarValue::values_to_arrays(args)?;

        let result = (inner)(&args);

        if is_scalar {
            // If all inputs are scalar, keeps output as scalar
            let result = result.and_then(|arr| ScalarValue::try_from_array(&arr, 0));
            result.map(ColumnarValue::Scalar)
        } else {
            result.map(ColumnarValue::Array)
        }
    }
}

pub fn spark_array_repeat(args: &[ColumnarValue]) -> Result<ColumnarValue, DataFusionError> {
    make_scalar_function(spark_array_repeat_inner)(args)
}

/// Array_repeat SQL function
fn spark_array_repeat_inner(args: &[ArrayRef]) -> datafusion::common::Result<ArrayRef> {
    let element = &args[0];
    let count_array = &args[1];

    let count_array = match count_array.data_type() {
        DataType::Int64 => &cast(count_array, &DataType::UInt64)?,
        DataType::UInt64 => count_array,
        _ => return exec_err!("count must be an integer type"),
    };

    let count_array = as_uint64_array(count_array)?;

    match element.data_type() {
        List(_) => {
            let list_array = as_list_array(element)?;
            general_list_repeat::<i32>(list_array, count_array)
        }
        LargeList(_) => {
            let list_array = as_large_list_array(element)?;
            general_list_repeat::<i64>(list_array, count_array)
        }
        _ => general_repeat::<i32>(element, count_array),
    }
}

/// For each element of `array[i]` repeat `count_array[i]` times.
///
/// Assumption for the input:
///     1. `count[i] >= 0`
///     2. `array.len() == count_array.len()`
///
/// For example,
/// ```text
/// array_repeat(
///     [1, 2, 3], [2, 0, 1] => [[1, 1], [], [3]]
/// )
/// ```
fn general_repeat<O: OffsetSizeTrait>(
    array: &ArrayRef,
    count_array: &UInt64Array,
) -> datafusion::common::Result<ArrayRef> {
    let data_type = array.data_type();
    let mut new_values = vec![];

    let count_vec = count_array
        .values()
        .to_vec()
        .iter()
        .map(|x| *x as usize)
        .collect::<Vec<_>>();

    let mut nulls = NullBufferBuilder::new(count_array.len());

    for (row_index, &count) in count_vec.iter().enumerate() {
        nulls.append(!count_array.is_null(row_index));
        let repeated_array = if array.is_null(row_index) {
            new_null_array(data_type, count)
        } else {
            let original_data = array.to_data();
            let capacity = Capacities::Array(count);
            let mut mutable =
                MutableArrayData::with_capacities(vec![&original_data], false, capacity);

            for _ in 0..count {
                mutable.extend(0, row_index, row_index + 1);
            }

            let data = mutable.freeze();
            arrow::array::make_array(data)
        };
        new_values.push(repeated_array);
    }

    let new_values: Vec<_> = new_values.iter().map(|a| a.as_ref()).collect();
    let values = compute::concat(&new_values)?;

    Ok(Arc::new(GenericListArray::<O>::try_new(
        Arc::new(Field::new_list_field(data_type.to_owned(), true)),
        OffsetBuffer::from_lengths(count_vec),
        values,
        nulls.finish(),
    )?))
}

/// Handle List version of `general_repeat`
///
/// For each element of `list_array[i]` repeat `count_array[i]` times.
///
/// For example,
/// ```text
/// array_repeat(
///     [[1, 2, 3], [4, 5], [6]], [2, 0, 1] => [[[1, 2, 3], [1, 2, 3]], [], [[6]]]
/// )
/// ```
fn general_list_repeat<O: OffsetSizeTrait>(
    list_array: &GenericListArray<O>,
    count_array: &UInt64Array,
) -> datafusion::common::Result<ArrayRef> {
    let data_type = list_array.data_type();
    let value_type = list_array.value_type();
    let mut new_values = vec![];

    let count_vec = count_array
        .values()
        .to_vec()
        .iter()
        .map(|x| *x as usize)
        .collect::<Vec<_>>();

    for (list_array_row, &count) in list_array.iter().zip(count_vec.iter()) {
        let list_arr = match list_array_row {
            Some(list_array_row) => {
                let original_data = list_array_row.to_data();
                let capacity = Capacities::Array(original_data.len() * count);
                let mut mutable =
                    MutableArrayData::with_capacities(vec![&original_data], false, capacity);

                for _ in 0..count {
                    mutable.extend(0, 0, original_data.len());
                }

                let data = mutable.freeze();
                let repeated_array = arrow::array::make_array(data);

                let list_arr = GenericListArray::<O>::try_new(
                    Arc::new(Field::new_list_field(value_type.clone(), true)),
                    OffsetBuffer::<O>::from_lengths(vec![original_data.len(); count]),
                    repeated_array,
                    None,
                )?;
                Arc::new(list_arr) as ArrayRef
            }
            None => new_null_array(data_type, count),
        };
        new_values.push(list_arr);
    }

    let lengths = new_values.iter().map(|a| a.len()).collect::<Vec<_>>();
    let new_values: Vec<_> = new_values.iter().map(|a| a.as_ref()).collect();
    let values = compute::concat(&new_values)?;

    Ok(Arc::new(ListArray::try_new(
        Arc::new(Field::new_list_field(data_type.to_owned(), true)),
        OffsetBuffer::<i32>::from_lengths(lengths),
        values,
        None,
    )?))
}