datafusion_physical_expr/expressions/

is_not_null.rs

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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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//! IS NOT NULL expression

use std::hash::{Hash, Hasher};
use std::{any::Any, sync::Arc};

use crate::physical_expr::down_cast_any_ref;
use crate::PhysicalExpr;
use arrow::{
    datatypes::{DataType, Schema},
    record_batch::RecordBatch,
};
use datafusion_common::Result;
use datafusion_common::ScalarValue;
use datafusion_expr::ColumnarValue;

/// IS NOT NULL expression
#[derive(Debug, Hash)]
pub struct IsNotNullExpr {
    /// The input expression
    arg: Arc<dyn PhysicalExpr>,
}

impl IsNotNullExpr {
    /// Create new not expression
    pub fn new(arg: Arc<dyn PhysicalExpr>) -> Self {
        Self { arg }
    }

    /// Get the input expression
    pub fn arg(&self) -> &Arc<dyn PhysicalExpr> {
        &self.arg
    }
}

impl std::fmt::Display for IsNotNullExpr {
    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
        write!(f, "{} IS NOT NULL", self.arg)
    }
}

impl PhysicalExpr for IsNotNullExpr {
    /// Return a reference to Any that can be used for downcasting
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn data_type(&self, _input_schema: &Schema) -> Result<DataType> {
        Ok(DataType::Boolean)
    }

    fn nullable(&self, _input_schema: &Schema) -> Result<bool> {
        Ok(false)
    }

    fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue> {
        let arg = self.arg.evaluate(batch)?;
        match arg {
            ColumnarValue::Array(array) => {
                let is_not_null = arrow::compute::is_not_null(&array)?;
                Ok(ColumnarValue::Array(Arc::new(is_not_null)))
            }
            ColumnarValue::Scalar(scalar) => Ok(ColumnarValue::Scalar(
                ScalarValue::Boolean(Some(!scalar.is_null())),
            )),
        }
    }

    fn children(&self) -> Vec<&Arc<dyn PhysicalExpr>> {
        vec![&self.arg]
    }

    fn with_new_children(
        self: Arc<Self>,
        children: Vec<Arc<dyn PhysicalExpr>>,
    ) -> Result<Arc<dyn PhysicalExpr>> {
        Ok(Arc::new(IsNotNullExpr::new(Arc::clone(&children[0]))))
    }

    fn dyn_hash(&self, state: &mut dyn Hasher) {
        let mut s = state;
        self.hash(&mut s);
    }
}

impl PartialEq<dyn Any> for IsNotNullExpr {
    fn eq(&self, other: &dyn Any) -> bool {
        down_cast_any_ref(other)
            .downcast_ref::<Self>()
            .map(|x| self.arg.eq(&x.arg))
            .unwrap_or(false)
    }
}
/// Create an IS NOT NULL expression
pub fn is_not_null(arg: Arc<dyn PhysicalExpr>) -> Result<Arc<dyn PhysicalExpr>> {
    Ok(Arc::new(IsNotNullExpr::new(arg)))
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::expressions::col;
    use arrow::{
        array::{BooleanArray, StringArray},
        datatypes::*,
    };
    use arrow_array::{Array, Float64Array, Int32Array, UnionArray};
    use arrow_buffer::ScalarBuffer;
    use datafusion_common::cast::as_boolean_array;

    #[test]
    fn is_not_null_op() -> Result<()> {
        let schema = Schema::new(vec![Field::new("a", DataType::Utf8, true)]);
        let a = StringArray::from(vec![Some("foo"), None]);
        let expr = is_not_null(col("a", &schema)?).unwrap();
        let batch = RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a)])?;

        // expression: "a is not null"
        let result = expr
            .evaluate(&batch)?
            .into_array(batch.num_rows())
            .expect("Failed to convert to array");
        let result =
            as_boolean_array(&result).expect("failed to downcast to BooleanArray");

        let expected = &BooleanArray::from(vec![true, false]);

        assert_eq!(expected, result);

        Ok(())
    }

    #[test]
    fn union_is_not_null_op() {
        // union of [{A=1}, {A=}, {B=1.1}, {B=1.2}, {B=}]
        let int_array = Int32Array::from(vec![Some(1), None, None, None, None]);
        let float_array =
            Float64Array::from(vec![None, None, Some(1.1), Some(1.2), None]);
        let type_ids = [0, 0, 1, 1, 1].into_iter().collect::<ScalarBuffer<i8>>();

        let children = vec![Arc::new(int_array) as Arc<dyn Array>, Arc::new(float_array)];

        let union_fields: UnionFields = [
            (0, Arc::new(Field::new("A", DataType::Int32, true))),
            (1, Arc::new(Field::new("B", DataType::Float64, true))),
        ]
        .into_iter()
        .collect();

        let array =
            UnionArray::try_new(union_fields.clone(), type_ids, None, children).unwrap();

        let field = Field::new(
            "my_union",
            DataType::Union(union_fields, UnionMode::Sparse),
            true,
        );

        let schema = Schema::new(vec![field]);
        let expr = is_not_null(col("my_union", &schema).unwrap()).unwrap();
        let batch =
            RecordBatch::try_new(Arc::new(schema), vec![Arc::new(array)]).unwrap();

        // expression: "a is not null"
        let actual = expr
            .evaluate(&batch)
            .unwrap()
            .into_array(batch.num_rows())
            .expect("Failed to convert to array");
        let actual = as_boolean_array(&actual).unwrap();

        let expected = &BooleanArray::from(vec![true, false, true, true, false]);

        assert_eq!(expected, actual);
    }
}