datafusion-physical-expr 39.0.0

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// to you under the Apache License, Version 2.0 (the
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// with the License.  You may obtain a copy of the License at
//
//   http://www.apache.org/licenses/LICENSE-2.0
//
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// under the License.

//! Defines physical expressions that can evaluated at runtime during query execution

use crate::aggregate::utils::down_cast_any_ref;
use crate::expressions::format_state_name;
use crate::{AggregateExpr, PhysicalExpr};
use arrow::array::ArrayRef;
use arrow::datatypes::{DataType, Field};
use arrow_array::Array;
use datafusion_common::cast::as_list_array;
use datafusion_common::utils::array_into_list_array;
use datafusion_common::Result;
use datafusion_common::ScalarValue;
use datafusion_expr::Accumulator;
use std::any::Any;
use std::sync::Arc;

/// ARRAY_AGG aggregate expression
#[derive(Debug)]
pub struct ArrayAgg {
    /// Column name
    name: String,
    /// The DataType for the input expression
    input_data_type: DataType,
    /// The input expression
    expr: Arc<dyn PhysicalExpr>,
    /// If the input expression can have NULLs
    nullable: bool,
}

impl ArrayAgg {
    /// Create a new ArrayAgg aggregate function
    pub fn new(
        expr: Arc<dyn PhysicalExpr>,
        name: impl Into<String>,
        data_type: DataType,
        nullable: bool,
    ) -> Self {
        Self {
            name: name.into(),
            input_data_type: data_type,
            expr,
            nullable,
        }
    }
}

impl AggregateExpr for ArrayAgg {
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn field(&self) -> Result<Field> {
        Ok(Field::new_list(
            &self.name,
            // This should be the same as return type of AggregateFunction::ArrayAgg
            Field::new("item", self.input_data_type.clone(), true),
            self.nullable,
        ))
    }

    fn create_accumulator(&self) -> Result<Box<dyn Accumulator>> {
        Ok(Box::new(ArrayAggAccumulator::try_new(
            &self.input_data_type,
        )?))
    }

    fn state_fields(&self) -> Result<Vec<Field>> {
        Ok(vec![Field::new_list(
            format_state_name(&self.name, "array_agg"),
            Field::new("item", self.input_data_type.clone(), true),
            self.nullable,
        )])
    }

    fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
        vec![self.expr.clone()]
    }

    fn name(&self) -> &str {
        &self.name
    }
}

impl PartialEq<dyn Any> for ArrayAgg {
    fn eq(&self, other: &dyn Any) -> bool {
        down_cast_any_ref(other)
            .downcast_ref::<Self>()
            .map(|x| {
                self.name == x.name
                    && self.input_data_type == x.input_data_type
                    && self.expr.eq(&x.expr)
            })
            .unwrap_or(false)
    }
}

#[derive(Debug)]
pub(crate) struct ArrayAggAccumulator {
    values: Vec<ArrayRef>,
    datatype: DataType,
}

impl ArrayAggAccumulator {
    /// new array_agg accumulator based on given item data type
    pub fn try_new(datatype: &DataType) -> Result<Self> {
        Ok(Self {
            values: vec![],
            datatype: datatype.clone(),
        })
    }
}

impl Accumulator for ArrayAggAccumulator {
    // Append value like Int64Array(1,2,3)
    fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
        if values.is_empty() {
            return Ok(());
        }
        assert!(values.len() == 1, "array_agg can only take 1 param!");
        let val = values[0].clone();
        self.values.push(val);
        Ok(())
    }

    // Append value like ListArray(Int64Array(1,2,3), Int64Array(4,5,6))
    fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
        if states.is_empty() {
            return Ok(());
        }
        assert!(states.len() == 1, "array_agg states must be singleton!");

        let list_arr = as_list_array(&states[0])?;
        for arr in list_arr.iter().flatten() {
            self.values.push(arr);
        }
        Ok(())
    }

    fn state(&mut self) -> Result<Vec<ScalarValue>> {
        Ok(vec![self.evaluate()?])
    }

    fn evaluate(&mut self) -> Result<ScalarValue> {
        // Transform Vec<ListArr> to ListArr

        let element_arrays: Vec<&dyn Array> =
            self.values.iter().map(|a| a.as_ref()).collect();

        if element_arrays.is_empty() {
            let arr = ScalarValue::new_list(&[], &self.datatype);
            return Ok(ScalarValue::List(arr));
        }

        let concated_array = arrow::compute::concat(&element_arrays)?;
        let list_array = array_into_list_array(concated_array);

        Ok(ScalarValue::List(Arc::new(list_array)))
    }

    fn size(&self) -> usize {
        std::mem::size_of_val(self)
            + (std::mem::size_of::<ArrayRef>() * self.values.capacity())
            + self
                .values
                .iter()
                .map(|arr| arr.get_array_memory_size())
                .sum::<usize>()
            + self.datatype.size()
            - std::mem::size_of_val(&self.datatype)
    }
}