datafusion-comet-spark-expr 0.5.0

DataFusion expressions that emulate Apache Spark's behavior
Documentation
// Licensed to the Apache Software Foundation (ASF) under one
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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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// software distributed under the License is distributed on an
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// specific language governing permissions and limitations
// under the License.

use arrow::array::{ArrayRef, OffsetSizeTrait};
use arrow_array::builder::GenericStringBuilder;
use arrow_array::Array;
use arrow_schema::DataType;
use datafusion::physical_plan::ColumnarValue;
use datafusion_common::{cast::as_generic_string_array, DataFusionError, ScalarValue};
use std::fmt::Write;
use std::sync::Arc;

/// Similar to DataFusion `rpad`, but not to truncate when the string is already longer than length
pub fn spark_read_side_padding(args: &[ColumnarValue]) -> Result<ColumnarValue, DataFusionError> {
    match args {
        [ColumnarValue::Array(array), ColumnarValue::Scalar(ScalarValue::Int32(Some(length)))] => {
            match array.data_type() {
                DataType::Utf8 => spark_read_side_padding_internal::<i32>(array, *length),
                DataType::LargeUtf8 => spark_read_side_padding_internal::<i64>(array, *length),
                // TODO: handle Dictionary types
                other => Err(DataFusionError::Internal(format!(
                    "Unsupported data type {other:?} for function read_side_padding",
                ))),
            }
        }
        other => Err(DataFusionError::Internal(format!(
            "Unsupported arguments {other:?} for function read_side_padding",
        ))),
    }
}

fn spark_read_side_padding_internal<T: OffsetSizeTrait>(
    array: &ArrayRef,
    length: i32,
) -> Result<ColumnarValue, DataFusionError> {
    let string_array = as_generic_string_array::<T>(array)?;
    let length = 0.max(length) as usize;
    let space_string = " ".repeat(length);

    let mut builder =
        GenericStringBuilder::<T>::with_capacity(string_array.len(), string_array.len() * length);

    for string in string_array.iter() {
        match string {
            Some(string) => {
                // It looks Spark's UTF8String is closer to chars rather than graphemes
                // https://stackoverflow.com/a/46290728
                let char_len = string.chars().count();
                if length <= char_len {
                    builder.append_value(string);
                } else {
                    // write_str updates only the value buffer, not null nor offset buffer
                    // This is convenient for concatenating str(s)
                    builder.write_str(string)?;
                    builder.append_value(&space_string[char_len..]);
                }
            }
            _ => builder.append_null(),
        }
    }
    Ok(ColumnarValue::Array(Arc::new(builder.finish())))
}