datafusion-expr 17.0.0

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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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// specific language governing permissions and limitations
// under the License.

use super::{Between, Expr, Like};
use crate::expr::{
    AggregateFunction, BinaryExpr, Cast, GetIndexedField, Sort, TryCast, WindowFunction,
};
use crate::field_util::get_indexed_field;
use crate::type_coercion::binary::binary_operator_data_type;
use crate::{aggregate_function, function, window_function};
use arrow::compute::can_cast_types;
use arrow::datatypes::DataType;
use datafusion_common::{Column, DFField, DFSchema, DataFusionError, ExprSchema, Result};

/// trait to allow expr to typable with respect to a schema
pub trait ExprSchemable {
    /// given a schema, return the type of the expr
    fn get_type<S: ExprSchema>(&self, schema: &S) -> Result<DataType>;

    /// given a schema, return the nullability of the expr
    fn nullable<S: ExprSchema>(&self, input_schema: &S) -> Result<bool>;

    /// convert to a field with respect to a schema
    fn to_field(&self, input_schema: &DFSchema) -> Result<DFField>;

    /// cast to a type with respect to a schema
    fn cast_to<S: ExprSchema>(self, cast_to_type: &DataType, schema: &S) -> Result<Expr>;
}

impl ExprSchemable for Expr {
    /// Returns the [arrow::datatypes::DataType] of the expression
    /// based on [ExprSchema]
    ///
    /// Note: [DFSchema] implements [ExprSchema].
    ///
    /// # Errors
    ///
    /// This function errors when it is not possible to compute its
    /// [arrow::datatypes::DataType].  This happens when e.g. the
    /// expression refers to a column that does not exist in the
    /// schema, or when the expression is incorrectly typed
    /// (e.g. `[utf8] + [bool]`).
    fn get_type<S: ExprSchema>(&self, schema: &S) -> Result<DataType> {
        match self {
            Expr::Alias(expr, name) => match &**expr {
                Expr::Placeholder { data_type, .. } => match &data_type {
                    None => schema.data_type(&Column::from_name(name)).cloned(),
                    Some(dt) => Ok(dt.clone()),
                },
                _ => expr.get_type(schema),
            },
            Expr::Sort(Sort { expr, .. }) | Expr::Negative(expr) => expr.get_type(schema),
            Expr::Column(c) => Ok(schema.data_type(c)?.clone()),
            Expr::ScalarVariable(ty, _) => Ok(ty.clone()),
            Expr::Literal(l) => Ok(l.get_datatype()),
            Expr::Case(case) => case.when_then_expr[0].1.get_type(schema),
            Expr::Cast(Cast { data_type, .. })
            | Expr::TryCast(TryCast { data_type, .. }) => Ok(data_type.clone()),
            Expr::ScalarUDF { fun, args } => {
                let data_types = args
                    .iter()
                    .map(|e| e.get_type(schema))
                    .collect::<Result<Vec<_>>>()?;
                Ok((fun.return_type)(&data_types)?.as_ref().clone())
            }
            Expr::ScalarFunction { fun, args } => {
                let data_types = args
                    .iter()
                    .map(|e| e.get_type(schema))
                    .collect::<Result<Vec<_>>>()?;
                function::return_type(fun, &data_types)
            }
            Expr::WindowFunction(WindowFunction { fun, args, .. }) => {
                let data_types = args
                    .iter()
                    .map(|e| e.get_type(schema))
                    .collect::<Result<Vec<_>>>()?;
                window_function::return_type(fun, &data_types)
            }
            Expr::AggregateFunction(AggregateFunction { fun, args, .. }) => {
                let data_types = args
                    .iter()
                    .map(|e| e.get_type(schema))
                    .collect::<Result<Vec<_>>>()?;
                aggregate_function::return_type(fun, &data_types)
            }
            Expr::AggregateUDF { fun, args, .. } => {
                let data_types = args
                    .iter()
                    .map(|e| e.get_type(schema))
                    .collect::<Result<Vec<_>>>()?;
                Ok((fun.return_type)(&data_types)?.as_ref().clone())
            }
            Expr::Not(_)
            | Expr::IsNull(_)
            | Expr::Exists { .. }
            | Expr::InSubquery { .. }
            | Expr::Between { .. }
            | Expr::InList { .. }
            | Expr::IsNotNull(_)
            | Expr::IsTrue(_)
            | Expr::IsFalse(_)
            | Expr::IsUnknown(_)
            | Expr::IsNotTrue(_)
            | Expr::IsNotFalse(_)
            | Expr::IsNotUnknown(_) => Ok(DataType::Boolean),
            Expr::ScalarSubquery(subquery) => {
                Ok(subquery.subquery.schema().field(0).data_type().clone())
            }
            Expr::BinaryExpr(BinaryExpr {
                ref left,
                ref right,
                ref op,
            }) => binary_operator_data_type(
                &left.get_type(schema)?,
                op,
                &right.get_type(schema)?,
            ),
            Expr::Like { .. } | Expr::ILike { .. } | Expr::SimilarTo { .. } => {
                Ok(DataType::Boolean)
            }
            Expr::Placeholder { data_type, .. } => data_type.clone().ok_or_else(|| {
                DataFusionError::Plan("Placeholder type could not be resolved".to_owned())
            }),
            Expr::Wildcard => Err(DataFusionError::Internal(
                "Wildcard expressions are not valid in a logical query plan".to_owned(),
            )),
            Expr::QualifiedWildcard { .. } => Err(DataFusionError::Internal(
                "QualifiedWildcard expressions are not valid in a logical query plan"
                    .to_owned(),
            )),
            Expr::GroupingSet(_) => {
                // grouping sets do not really have a type and do not appear in projections
                Ok(DataType::Null)
            }
            Expr::GetIndexedField(GetIndexedField { key, expr }) => {
                let data_type = expr.get_type(schema)?;

                get_indexed_field(&data_type, key).map(|x| x.data_type().clone())
            }
        }
    }

    /// Returns the nullability of the expression based on [ExprSchema].
    ///
    /// Note: [DFSchema] implements [ExprSchema].
    ///
    /// # Errors
    ///
    /// This function errors when it is not possible to compute its
    /// nullability.  This happens when the expression refers to a
    /// column that does not exist in the schema.
    fn nullable<S: ExprSchema>(&self, input_schema: &S) -> Result<bool> {
        match self {
            Expr::Alias(expr, _)
            | Expr::Not(expr)
            | Expr::Negative(expr)
            | Expr::Sort(Sort { expr, .. })
            | Expr::InList { expr, .. } => expr.nullable(input_schema),
            Expr::Between(Between { expr, .. }) => expr.nullable(input_schema),
            Expr::Column(c) => input_schema.nullable(c),
            Expr::Literal(value) => Ok(value.is_null()),
            Expr::Case(case) => {
                // this expression is nullable if any of the input expressions are nullable
                let then_nullable = case
                    .when_then_expr
                    .iter()
                    .map(|(_, t)| t.nullable(input_schema))
                    .collect::<Result<Vec<_>>>()?;
                if then_nullable.contains(&true) {
                    Ok(true)
                } else if let Some(e) = &case.else_expr {
                    e.nullable(input_schema)
                } else {
                    // CASE produces NULL if there is no `else` expr
                    // (aka when none of the `when_then_exprs` match)
                    Ok(true)
                }
            }
            Expr::Cast(Cast { expr, .. }) => expr.nullable(input_schema),
            Expr::ScalarVariable(_, _)
            | Expr::TryCast { .. }
            | Expr::ScalarFunction { .. }
            | Expr::ScalarUDF { .. }
            | Expr::WindowFunction { .. }
            | Expr::AggregateFunction { .. }
            | Expr::AggregateUDF { .. } => Ok(true),
            Expr::IsNull(_)
            | Expr::IsNotNull(_)
            | Expr::IsTrue(_)
            | Expr::IsFalse(_)
            | Expr::IsUnknown(_)
            | Expr::IsNotTrue(_)
            | Expr::IsNotFalse(_)
            | Expr::IsNotUnknown(_)
            | Expr::Exists { .. }
            | Expr::Placeholder { .. } => Ok(true),
            Expr::InSubquery { expr, .. } => expr.nullable(input_schema),
            Expr::ScalarSubquery(subquery) => {
                Ok(subquery.subquery.schema().field(0).is_nullable())
            }
            Expr::BinaryExpr(BinaryExpr {
                ref left,
                ref right,
                ..
            }) => Ok(left.nullable(input_schema)? || right.nullable(input_schema)?),
            Expr::Like(Like { expr, .. }) => expr.nullable(input_schema),
            Expr::ILike(Like { expr, .. }) => expr.nullable(input_schema),
            Expr::SimilarTo(Like { expr, .. }) => expr.nullable(input_schema),
            Expr::Wildcard => Err(DataFusionError::Internal(
                "Wildcard expressions are not valid in a logical query plan".to_owned(),
            )),
            Expr::QualifiedWildcard { .. } => Err(DataFusionError::Internal(
                "QualifiedWildcard expressions are not valid in a logical query plan"
                    .to_owned(),
            )),
            Expr::GetIndexedField(GetIndexedField { key, expr }) => {
                let data_type = expr.get_type(input_schema)?;
                get_indexed_field(&data_type, key).map(|x| x.is_nullable())
            }
            Expr::GroupingSet(_) => {
                // grouping sets do not really have the concept of nullable and do not appear
                // in projections
                Ok(true)
            }
        }
    }

    /// Returns a [arrow::datatypes::Field] compatible with this expression.
    fn to_field(&self, input_schema: &DFSchema) -> Result<DFField> {
        match self {
            Expr::Column(c) => Ok(DFField::new(
                c.relation.as_deref(),
                &c.name,
                self.get_type(input_schema)?,
                self.nullable(input_schema)?,
            )),
            _ => Ok(DFField::new(
                None,
                &self.display_name()?,
                self.get_type(input_schema)?,
                self.nullable(input_schema)?,
            )),
        }
    }

    /// Wraps this expression in a cast to a target [arrow::datatypes::DataType].
    ///
    /// # Errors
    ///
    /// This function errors when it is impossible to cast the
    /// expression to the target [arrow::datatypes::DataType].
    fn cast_to<S: ExprSchema>(self, cast_to_type: &DataType, schema: &S) -> Result<Expr> {
        // TODO(kszucs): most of the operations do not validate the type correctness
        // like all of the binary expressions below. Perhaps Expr should track the
        // type of the expression?
        let this_type = self.get_type(schema)?;
        if this_type == *cast_to_type {
            Ok(self)
        } else if can_cast_types(&this_type, cast_to_type) {
            Ok(Expr::Cast(Cast::new(Box::new(self), cast_to_type.clone())))
        } else {
            Err(DataFusionError::Plan(format!(
                "Cannot automatically convert {this_type:?} to {cast_to_type:?}"
            )))
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::{col, lit};
    use arrow::datatypes::DataType;
    use datafusion_common::Column;

    #[test]
    fn expr_schema_nullability() {
        let expr = col("foo").eq(lit(1));
        assert!(!expr.nullable(&MockExprSchema::new()).unwrap());
        assert!(expr
            .nullable(&MockExprSchema::new().with_nullable(true))
            .unwrap());
    }

    #[test]
    fn expr_schema_data_type() {
        let expr = col("foo");
        assert_eq!(
            DataType::Utf8,
            expr.get_type(&MockExprSchema::new().with_data_type(DataType::Utf8))
                .unwrap()
        );
    }

    struct MockExprSchema {
        nullable: bool,
        data_type: DataType,
    }

    impl MockExprSchema {
        fn new() -> Self {
            Self {
                nullable: false,
                data_type: DataType::Null,
            }
        }

        fn with_nullable(mut self, nullable: bool) -> Self {
            self.nullable = nullable;
            self
        }

        fn with_data_type(mut self, data_type: DataType) -> Self {
            self.data_type = data_type;
            self
        }
    }

    impl ExprSchema for MockExprSchema {
        fn nullable(&self, _col: &Column) -> Result<bool> {
            Ok(self.nullable)
        }

        fn data_type(&self, _col: &Column) -> Result<&DataType> {
            Ok(&self.data_type)
        }
    }
}