## Coverage Report

#### Created: 2024-10-13 08:39

    /Users/andrewlamb/Software/datafusion/datafusion/physical-expr/src/scalar_function.rs

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    // Licensed to the Apache Software Foundation (ASF) under one
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    // or more contributor license agreements.  See the NOTICE file
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    // distributed with this work for additional information
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    // regarding copyright ownership.  The ASF licenses this file
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    // to you under the Apache License, Version 2.0 (the
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    // "License"); you may not use this file except in compliance
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    // with the License.  You may obtain a copy of the License at
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    //
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    //   http://www.apache.org/licenses/LICENSE-2.0
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    //
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    // Unless required by applicable law or agreed to in writing,
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    // software distributed under the License is distributed on an
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    // "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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    // KIND, either express or implied.  See the License for the
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    // specific language governing permissions and limitations
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    // under the License.
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    //! Declaration of built-in (scalar) functions.
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    //! This module contains built-in functions' enumeration and metadata.
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    //!
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    //! Generally, a function has:
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    //! * a signature
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    //! * a return type, that is a function of the incoming argument's types
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    //! * the computation, that must accept each valid signature
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    //!
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    //! * Signature: see `Signature`
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    //! * Return type: a function `(arg_types) -> return_type`. E.g. for sqrt, ([f32]) -> f32, ([f64]) -> f64.
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    //!
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    //! This module also has a set of coercion rules to improve user experience: if an argument i32 is passed
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    //! to a function that supports f64, it is coerced to f64.
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    use std::any::Any;
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    use std::fmt::{self, Debug, Formatter};
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    use std::hash::{Hash, Hasher};
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    use std::sync::Arc;
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    use crate::physical_expr::{down_cast_any_ref, physical_exprs_equal};
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    use crate::PhysicalExpr;
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    use arrow::datatypes::{DataType, Schema};
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    use arrow::record_batch::RecordBatch;
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    use datafusion_common::{internal_err, DFSchema, Result};
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    use datafusion_expr::interval_arithmetic::Interval;
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    use datafusion_expr::sort_properties::ExprProperties;
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    use datafusion_expr::type_coercion::functions::data_types_with_scalar_udf;
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    use datafusion_expr::{expr_vec_fmt, ColumnarValue, Expr, ScalarUDF};
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    /// Physical expression of a scalar function
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    pub struct ScalarFunctionExpr {
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        fun: Arc<ScalarUDF>,
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        name: String,
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        args: Vec<Arc<dyn PhysicalExpr>>,
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        return_type: DataType,
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        nullable: bool,
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    }
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    impl Debug for ScalarFunctionExpr {
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        fn fmt(&self, f: &mut Formatter) -> fmt::Result {
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            f.debug_struct("ScalarFunctionExpr")
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                .field("fun", &"<FUNC>")
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                .field("name", &self.name)
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                .field("args", &self.args)
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                .field("return_type", &self.return_type)
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                .finish()
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        }
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    }
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    impl ScalarFunctionExpr {
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        /// Create a new Scalar function
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        pub fn new(
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            name: &str,
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            fun: Arc<ScalarUDF>,
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            args: Vec<Arc<dyn PhysicalExpr>>,
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            return_type: DataType,
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        ) -> Self {
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            Self {
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                fun,
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                name: name.to_owned(),
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                args,
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                return_type,
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                nullable: true,
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            }
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        }
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        /// Get the scalar function implementation
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        pub fn fun(&self) -> &ScalarUDF {
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            &self.fun
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        }
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        /// The name for this expression
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        pub fn name(&self) -> &str {
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            &self.name
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        }
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        /// Input arguments
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        pub fn args(&self) -> &[Arc<dyn PhysicalExpr>] {
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            &self.args
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        }
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        /// Data type produced by this expression
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        pub fn return_type(&self) -> &DataType {
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            &self.return_type
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        }
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        pub fn with_nullable(mut self, nullable: bool) -> Self {
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            self.nullable = nullable;
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            self
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        }
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        pub fn nullable(&self) -> bool {
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            self.nullable
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        }
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    }
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    impl fmt::Display for ScalarFunctionExpr {
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        fn fmt(&self, f: &mut Formatter) -> fmt::Result {
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            write!(f, "{}({})", self.name, expr_vec_fmt!(self.args))
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        }
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    }
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    impl PhysicalExpr for ScalarFunctionExpr {
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        /// Return a reference to Any that can be used for downcasting
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        fn as_any(&self) -> &dyn Any {
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            self
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        }
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        fn data_type(&self, _input_schema: &Schema) -> Result<DataType> {
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            Ok(self.return_type.clone())
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        }
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        fn nullable(&self, _input_schema: &Schema) -> Result<bool> {
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            Ok(self.nullable)
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        }
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        fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue> {
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            let inputs = self
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                .args
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                .iter()
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                .map(|e| e.evaluate(batch))
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                .collect::<Result<Vec<_>>>()?;
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            // evaluate the function
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            let output = match self.args.is_empty() {
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                true => self.fun.invoke_no_args(batch.num_rows()),
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                false => self.fun.invoke(&inputs),
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            }?;
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            if let ColumnarValue::Array(array) = &output {
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                if array.len() != batch.num_rows() {
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                    return internal_err!("UDF returned a different number of rows than expected. Expected: {}, Got: {}",
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                            batch.num_rows(), array.len());
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                }
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            }
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            Ok(output)
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        }
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        fn children(&self) -> Vec<&Arc<dyn PhysicalExpr>> {
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            self.args.iter().collect()
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        }
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        fn with_new_children(
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            self: Arc<Self>,
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            children: Vec<Arc<dyn PhysicalExpr>>,
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        ) -> Result<Arc<dyn PhysicalExpr>> {
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            Ok(Arc::new(
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                ScalarFunctionExpr::new(
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                    &self.name,
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                    Arc::clone(&self.fun),
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                    children,
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                    self.return_type().clone(),
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                )
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                .with_nullable(self.nullable),
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            ))
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        }
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        fn evaluate_bounds(&self, children: &[&Interval]) -> Result<Interval> {
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            self.fun.evaluate_bounds(children)
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        }
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        fn propagate_constraints(
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            &self,
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            interval: &Interval,
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            children: &[&Interval],
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        ) -> Result<Option<Vec<Interval>>> {
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            self.fun.propagate_constraints(interval, children)
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        }
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        fn dyn_hash(&self, state: &mut dyn Hasher) {
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            let mut s = state;
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            self.name.hash(&mut s);
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            self.args.hash(&mut s);
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            self.return_type.hash(&mut s);
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            // Add `self.fun` when hash is available
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        }
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        fn get_properties(&self, children: &[ExprProperties]) -> Result<ExprProperties> {
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            let sort_properties = self.fun.output_ordering(children)?;
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            let children_range = children
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                .iter()
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                .map(|props| &props.range)
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                .collect::<Vec<_>>();
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            let range = self.fun().evaluate_bounds(&children_range)?;
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            Ok(ExprProperties {
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                sort_properties,
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                range,
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            })
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        }
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    }
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    impl PartialEq<dyn Any> for ScalarFunctionExpr {
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        /// Comparing name, args and return_type
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        fn eq(&self, other: &dyn Any) -> bool {
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            down_cast_any_ref(other)
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                .downcast_ref::<Self>()
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                .map(|x| {
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                    self.name == x.name
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                        && physical_exprs_equal(&self.args, &x.args)
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                        && self.return_type == x.return_type
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                })
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                .unwrap_or(false)
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        }
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    }
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    /// Create a physical expression for the UDF.
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    pub fn create_physical_expr(
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        fun: &ScalarUDF,
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        input_phy_exprs: &[Arc<dyn PhysicalExpr>],
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        input_schema: &Schema,
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        args: &[Expr],
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        input_dfschema: &DFSchema,
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    ) -> Result<Arc<dyn PhysicalExpr>> {
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        let input_expr_types = input_phy_exprs
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            .iter()
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            .map(|e| e.data_type(input_schema))
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            .collect::<Result<Vec<_>>>()?;
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        // verify that input data types is consistent with function's `TypeSignature`
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        data_types_with_scalar_udf(&input_expr_types, fun)?;
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        // Since we have arg_types, we dont need args and schema.
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        let return_type =
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            fun.return_type_from_exprs(args, input_dfschema, &input_expr_types)?;
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        Ok(Arc::new(
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            ScalarFunctionExpr::new(
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                fun.name(),
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                Arc::new(fun.clone()),
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                input_phy_exprs.to_vec(),
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                return_type,
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            )
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            .with_nullable(fun.is_nullable(args, input_dfschema)),
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        ))
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    }