datafusion_expr/

higher_order_function.rs

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// to you under the Apache License, Version 2.0 (the
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//! [`HigherOrderUDF`]: User Defined Higher Order Functions

use crate::expr::{
    HigherOrderFunction, display_comma_separated,
    schema_name_from_exprs_comma_separated_without_space,
};
use crate::type_coercion::functions::value_fields_with_higher_order_udf;
use crate::udf_eq::UdfEq;
use crate::{ColumnarValue, Documentation, Expr, ExprSchemable};
use arrow::array::{ArrayRef, RecordBatch};
use arrow::datatypes::{DataType, FieldRef, Schema};
use arrow_schema::SchemaRef;
use datafusion_common::config::ConfigOptions;
use datafusion_common::datatype::FieldExt;
use datafusion_common::hash_map::EntryRef;
use datafusion_common::tree_node::{
    Transformed, TreeNode, TreeNodeContainer, TreeNodeRecursion,
};
use datafusion_common::{
    DFSchema, HashMap, HashSet, Result, ScalarValue, exec_err, internal_datafusion_err,
    internal_err, not_impl_err, plan_datafusion_err, plan_err,
};
use datafusion_expr_common::dyn_eq::{DynEq, DynHash};
use datafusion_expr_common::signature::Volatility;
use datafusion_physical_expr_common::physical_expr::PhysicalExpr;
use std::any::Any;
use std::cmp::Ordering;
use std::fmt::Debug;
use std::hash::{Hash, Hasher};
use std::mem;
use std::sync::Arc;

/// The types of arguments for which a function has implementations.
///
/// [`HigherOrderTypeSignature`] **DOES NOT** define the types that a user query could call the
/// function with. DataFusion will automatically coerce (cast) argument types to
/// one of the supported function signatures, if possible.
///
/// # Overview
/// Functions typically provide implementations for a small number of different
/// argument [`DataType`]s, rather than all possible combinations. If a user
/// calls a function with arguments that do not match any of the declared types,
/// DataFusion will attempt to automatically coerce (add casts to) function
/// arguments so they match the [`HigherOrderTypeSignature`]. See the [`type_coercion`] module
/// for more details
///
/// [`type_coercion`]: crate::type_coercion
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Hash)]
pub enum HigherOrderTypeSignature {
    /// The acceptable signature and coercions rules are special for this
    /// function.
    ///
    /// If this signature is specified,
    /// DataFusion will call [`HigherOrderUDFImpl::coerce_value_types`] to prepare argument types.
    UserDefined,
    /// One or more lambdas or arguments with arbitrary types
    VariadicAny,
    /// The specified number of lambdas or arguments with arbitrary types.
    Any(usize),
    /// Exactly the specified arguments in the given order, with arbitrary types.
    /// DataFusion will call [`HigherOrderUDFImpl::coerce_value_types`] to prepare the value
    /// argument types.
    Exact(Vec<ValueOrLambda<(), ()>>),
}

/// Provides information necessary for calling a higher order function.
///
/// - [`HigherOrderTypeSignature`] defines the argument types that a function has implementations
///   for.
///
/// - [`Volatility`] defines how the output of the function changes with the input.
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Hash)]
pub struct HigherOrderSignature {
    /// The data types that the function accepts. See [HigherOrderTypeSignature] for more information.
    pub type_signature: HigherOrderTypeSignature,
    /// The volatility of the function. See [Volatility] for more information.
    pub volatility: Volatility,
    /// The max number of times to call [HigherOrderUDFImpl::lambda_parameters] before raising an error.
    /// Used to guard against implementations that causes an infinite loop by endlessly returning
    /// [LambdaParametersProgress::Partial]. Defaults to 256
    pub lambda_parameters_max_iterations: usize,
}

const LAMBDA_PARAMETERS_MAX_ITERATIONS: usize = 256;

impl HigherOrderSignature {
    /// Creates a new `HigherOrderSignature` from a given type signature and volatility.
    pub fn new(type_signature: HigherOrderTypeSignature, volatility: Volatility) -> Self {
        HigherOrderSignature {
            type_signature,
            volatility,
            lambda_parameters_max_iterations: LAMBDA_PARAMETERS_MAX_ITERATIONS,
        }
    }

    /// User-defined coercion rules for the function.
    pub fn user_defined(volatility: Volatility) -> Self {
        Self {
            type_signature: HigherOrderTypeSignature::UserDefined,
            volatility,
            lambda_parameters_max_iterations: LAMBDA_PARAMETERS_MAX_ITERATIONS,
        }
    }

    /// An arbitrary number of lambdas or arguments of any type.
    pub fn variadic_any(volatility: Volatility) -> Self {
        Self {
            type_signature: HigherOrderTypeSignature::VariadicAny,
            volatility,
            lambda_parameters_max_iterations: LAMBDA_PARAMETERS_MAX_ITERATIONS,
        }
    }

    /// A specified number of arguments of any type
    pub fn any(arg_count: usize, volatility: Volatility) -> Self {
        Self {
            type_signature: HigherOrderTypeSignature::Any(arg_count),
            volatility,
            lambda_parameters_max_iterations: LAMBDA_PARAMETERS_MAX_ITERATIONS,
        }
    }

    /// Exactly the specified arguments in the given order, with arbitrary types.
    /// DataFusion will call [`HigherOrderUDFImpl::coerce_value_types`] to prepare the value
    /// argument types.
    ///
    /// # Example
    /// A function that takes one value argument followed by one lambda:
    /// ```
    /// # use datafusion_expr::{HigherOrderSignature, ValueOrLambda, Volatility};
    /// let sig = HigherOrderSignature::exact(
    ///     vec![ValueOrLambda::Value(()), ValueOrLambda::Lambda(())],
    ///     Volatility::Immutable,
    /// );
    /// ```
    pub fn exact(args: Vec<ValueOrLambda<(), ()>>, volatility: Volatility) -> Self {
        Self {
            type_signature: HigherOrderTypeSignature::Exact(args),
            volatility,
            lambda_parameters_max_iterations: LAMBDA_PARAMETERS_MAX_ITERATIONS,
        }
    }
}

impl PartialEq for dyn HigherOrderUDFImpl {
    fn eq(&self, other: &Self) -> bool {
        self.dyn_eq(other as _)
    }
}

impl PartialOrd for dyn HigherOrderUDFImpl {
    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
        let mut cmp = self.name().cmp(other.name());
        if cmp == Ordering::Equal {
            cmp = self.signature().partial_cmp(other.signature())?;
        }
        if cmp == Ordering::Equal {
            cmp = self.aliases().partial_cmp(other.aliases())?;
        }
        // Contract for PartialOrd and PartialEq consistency requires that
        // a == b if and only if partial_cmp(a, b) == Some(Equal).
        if cmp == Ordering::Equal && self != other {
            // Functions may have other properties besides name and signature
            // that differentiate two instances (e.g. type, or arbitrary parameters).
            // We cannot return Some(Equal) in such case.
            return None;
        }
        debug_assert!(
            cmp == Ordering::Equal || self != other,
            "Detected incorrect implementation of PartialEq when comparing functions: '{}' and '{}'. \
            The functions compare as equal, but they are not equal based on general properties that \
            the PartialOrd implementation observes,",
            self.name(),
            other.name()
        );
        Some(cmp)
    }
}

impl Eq for dyn HigherOrderUDFImpl {}

impl Hash for dyn HigherOrderUDFImpl {
    fn hash<H: Hasher>(&self, state: &mut H) {
        self.dyn_hash(state)
    }
}

/// Arguments passed to [`HigherOrderUDFImpl::invoke_with_args`] when invoking a
/// higher order function.
#[derive(Debug, Clone)]
pub struct HigherOrderFunctionArgs {
    /// The evaluated arguments and lambdas to the function
    pub args: Vec<ValueOrLambda<ColumnarValue, LambdaArgument>>,
    /// Field associated with each arg, if it exists
    /// For lambdas, it will be the field of the result of
    /// the lambda if evaluated with the parameters
    /// returned from [`HigherOrderUDFImpl::lambda_parameters`]
    pub arg_fields: Vec<ValueOrLambda<FieldRef, FieldRef>>,
    /// The number of rows in record batch being evaluated
    pub number_rows: usize,
    /// The return field of the higher order function returned
    /// (from `return_field_from_args`) when creating the
    /// physical expression from the logical expression
    pub return_field: FieldRef,
    /// The config options at execution time
    pub config_options: Arc<ConfigOptions>,
}

impl HigherOrderFunctionArgs {
    /// The return type of the function. See [`Self::return_field`] for more
    /// details.
    pub fn return_type(&self) -> &DataType {
        self.return_field.data_type()
    }
}

/// A lambda argument to a HigherOrderFunction
#[derive(Clone, Debug)]
pub struct LambdaArgument {
    /// The parameters defined in this lambda
    ///
    /// For example, for `array_transform([2], v -> -v)`,
    /// this will be `vec![Field::new("v", DataType::Int32, true)]`
    params: Vec<FieldRef>,
    /// The body of the lambda
    ///
    /// For example, for `array_transform([2], v -> -v)`,
    /// this will be the physical expression of `-v`
    body: Arc<dyn PhysicalExpr>,
    /// Cached schema built from `params`. Reused across every `evaluate` call
    /// (and across every nested-list iteration when the lambda is called once
    /// per outer sublist), avoiding the per-call `Schema::new` build that
    /// includes constructing the internal name -> index map.
    schema: SchemaRef,
    /// A RecordBatch containing the captured columns inside this lambda body, if any
    ///
    /// For example, for `array_transform([2], v -> v + a + b)`,
    /// this will be a `RecordBatch` with two columns, `a` and `b`
    captures: Option<RecordBatch>,
}

impl LambdaArgument {
    pub fn new(
        params: Vec<FieldRef>,
        body: Arc<dyn PhysicalExpr>,
        captures: Option<RecordBatch>,
    ) -> Self {
        let fields = match &captures {
            Some(batch) => batch
                .schema_ref()
                .fields()
                .iter()
                .cloned()
                .chain(params.clone())
                .collect(),
            None => params.clone(),
        };

        let schema = Arc::new(Schema::new(fields));

        Self {
            params,
            body,
            schema,
            captures,
        }
    }

    /// Evaluate this lambda
    /// `args` should evaluate to the value of each parameter
    /// of the correspondent lambda returned in [HigherOrderUDFImpl::lambda_parameters].
    ///
    /// `spread_captures` is responsible for transforming the captured column arrays
    /// so they align with the evaluation batch. Captures are snapshotted from the
    /// outer batch at construction time, giving one value per outer row, but the
    /// function may evaluate the lambda body over a batch with a different number
    /// of rows. It is the function's responsibility to provide the appropriate
    /// `spread_captures` closure to expand (or otherwise reshape) the captures
    /// to match.
    ///
    /// Taking as an example the following table:
    ///
    /// ```sql
    /// CREATE TABLE t (arr INT[], a INT) AS VALUES
    ///   ([1, 2, 3], 10),
    ///   ([],        20),
    ///   ([4],       30);
    /// ```
    ///
    /// `SELECT array_transform(arr, v -> v + a) from t` would execute over three outer rows:
    ///
    /// ```text
    /// arr (ListArray):  [[1, 2, 3], [], [4]]   -- 3 outer rows, 4 total elements
    /// a   (captured):   [10,        20,  30]   -- one value per outer row
    /// ```
    ///
    /// `array_transform` flattens the list elements into a single batch of 4 rows,
    /// so `spread_captures` must repeat/drop captured values to match:
    ///
    /// ```text
    /// v (flattened args): [1,  2,  3,  4]
    /// a (spread):         [10, 10, 10, 30]  -- 10 repeated for 3 elements in row 0,
    ///                                        -- 20 dropped for the empty sublist in row 1,
    ///                                        -- 30 once for the single element in row 2
    /// ```
    ///
    /// The lambda body `v + a` then evaluates element-wise over these 4-row arrays,
    /// producing `[11, 12, 13, 34]`, which `array_transform` reassembles into `[[11, 12, 13], [], [34]]`.
    ///
    /// If the lambda has no captures, `spread_captures` is never called.
    pub fn evaluate(
        &self,
        args: &[&dyn Fn() -> Result<ArrayRef>],
        spread_captures: impl FnOnce(&[ArrayRef]) -> Result<Vec<ArrayRef>>,
    ) -> Result<ColumnarValue> {
        let spread_captures = self
            .captures
            .as_ref()
            .map(|captures| {
                let spread_columns = spread_captures(captures.columns())?;

                RecordBatch::try_new(captures.schema(), spread_columns)
            })
            .transpose()?;

        let merged = merge_captures_with_variables(
            spread_captures.as_ref(),
            Arc::clone(&self.schema),
            &self.params,
            args,
        )?;

        self.body.evaluate(&merged)
    }
}

fn merge_captures_with_variables(
    captures: Option<&RecordBatch>,
    schema: SchemaRef,
    params: &[FieldRef],
    variables: &[&dyn Fn() -> Result<ArrayRef>],
) -> Result<RecordBatch> {
    if variables.len() < params.len() {
        return exec_err!(
            "expected at least {} lambda arguments to merge with captures, got {}",
            params.len(),
            variables.len()
        );
    }

    let columns = match captures {
        Some(captures) => {
            let mut columns = captures.columns().to_vec();

            for arg in &variables[..params.len()] {
                columns.push(arg()?);
            }

            columns
        }
        None => variables
            .iter()
            .take(params.len())
            .map(|arg| arg())
            .collect::<Result<_>>()?,
    };

    Ok(RecordBatch::try_new(schema, columns)?)
}

/// Information about arguments passed to the function
///
/// This structure contains metadata about how the function was called
/// such as the type of the arguments, any scalar arguments and if the
/// arguments can (ever) be null
///
/// See [`HigherOrderUDFImpl::return_field_from_args`] for more information
#[derive(Clone, Debug)]
pub struct HigherOrderReturnFieldArgs<'a> {
    /// The data types of the arguments to the function
    ///
    /// If argument `i` to the function is a lambda, it will be the field of the result of the
    /// lambda if evaluated with the parameters returned from [`HigherOrderUDFImpl::lambda_parameters`]
    ///
    /// For example, with `array_transform([1], v -> v == 5)`
    /// this field will be
    /// ```ignore
    /// [
    ///     ValueOrLambda::Value(Field::new("", DataType::new_list(DataType::Int32, true), true)),
    ///     ValueOrLambda::Lambda(Field::new("", DataType::Boolean, true))
    /// ]
    /// ```
    pub arg_fields: &'a [ValueOrLambda<FieldRef, FieldRef>],
    /// Is argument `i` to the function a scalar (constant)?
    ///
    /// If the argument `i` is not a scalar, it will be None
    ///
    /// For example, if a function is called like `array_transform([1], v -> v == 5)`
    /// this field will be `[Some(ScalarValue::List(...), None]`
    pub scalar_arguments: &'a [Option<&'a ScalarValue>],
}

/// An argument to a higher order function
#[derive(Clone, Debug, PartialEq, Eq, PartialOrd, Hash)]
pub enum ValueOrLambda<V, L> {
    /// A value with associated data
    Value(V),
    /// A lambda with associated data
    Lambda(L),
}

/// Represents a step during the resolution of the parameters of all lambdas of a given
/// higher-order function via [HigherOrderUDFImpl::lambda_parameters]. It's valid that the
/// fields of a given lambda changes between steps, and is up to the implementation to
/// provide during the function evaluation the parameters that matches the fields returned
/// at the [LambdaParametersProgress::Complete] step. See [HigherOrderUDFImpl::lambda_parameters]
/// docs for more details
pub enum LambdaParametersProgress {
    /// The parameters of some lambdas are unknown due to a dependency on another lambda output field
    /// or are placeholders due to a dependency on it's own output field. It's perfectly valid to
    /// contain only `Some`'s and not a single `None`, representing lambdas that depends only on itself
    /// and not on others. [HigherOrderUDFImpl::lambda_parameters] will be called again with the output
    /// field of all lambdas with known parameters.
    Partial(Vec<Option<Vec<FieldRef>>>),
    /// There are no unmet dependencies and all parameters are known, [HigherOrderUDFImpl::lambda_parameters]
    /// will not be called again
    Complete(Vec<Vec<FieldRef>>),
}

/// Trait for implementing user defined higher order functions.
///
/// This trait exposes the full API for implementing user defined functions and
/// can be used to implement any function.
///
/// New higher order functions typically implement this trait and are then
/// wrapped in a [`HigherOrderUDF`] for registration with DataFusion.
///
/// See [`array_transform.rs`] for a commented complete implementation
///
/// [`array_transform.rs`]: https://github.com/apache/datafusion/blob/main/datafusion/functions-nested/src/array_transform.rs
pub trait HigherOrderUDFImpl: Debug + DynEq + DynHash + Send + Sync + Any {
    /// Returns this function's name
    fn name(&self) -> &str;

    /// Returns any aliases (alternate names) for this function.
    ///
    /// Aliases can be used to invoke the same function using different names.
    /// For example in some databases `now()` and `current_timestamp()` are
    /// aliases for the same function. This behavior can be obtained by
    /// returning `current_timestamp` as an alias for the `now` function.
    ///
    /// Note: `aliases` should only include names other than [`Self::name`].
    /// Defaults to `[]` (no aliases)
    fn aliases(&self) -> &[String] {
        &[]
    }

    /// Returns the name of the column this expression would create
    ///
    /// See [`Expr::schema_name`] for details
    fn schema_name(&self, args: &[Expr]) -> Result<String> {
        Ok(format!(
            "{}({})",
            self.name(),
            schema_name_from_exprs_comma_separated_without_space(args)?
        ))
    }

    /// Returns a [`HigherOrderSignature`] describing the argument types for which this
    /// function has an implementation, and the function's [`Volatility`].
    ///
    /// See [`HigherOrderSignature`] for more details on argument type handling
    /// and [`Self::return_field_from_args`] for computing the return type.
    ///
    /// [`Volatility`]: datafusion_expr_common::signature::Volatility
    fn signature(&self) -> &HigherOrderSignature;

    /// Return the field of all the parameters supported by the lambdas in `fields`.
    /// If a lambda support multiple parameters, all should be returned, regardless of
    /// whether they are used or not on a particular invocation
    ///
    /// Tip: If you have a [`HigherOrderFunction`] invocation, you can call the helper
    /// [`HigherOrderFunction::lambda_parameters`] instead of this method directly
    ///
    /// The name of the returned fields are ignored.
    ///
    /// This function is repeatedelly called until [LambdaParametersProgress::Complete] is returned, with
    /// `step` increased by one at each invocation, starting at 0.
    ///
    /// For functions which all lambda parameters depend only on the field of it's value arguments,
    /// this can return [LambdaParametersProgress::Complete] at step 0. Taking as an example a strict
    /// array_reduce with the signature `(arr: [V], initial_value: I, (I, V) -> I, (I) -> O) -> O`, which
    /// requires it's initial value to be the exact same type of it's merge output, which is also the
    /// parameter of it's finish lambda, the expression
    ///
    /// `array_reduce([1.2, 2.1], 0.0, (acc, v) -> acc + v + 1.5, v -> v > 5.1)`
    ///
    ///  would result in this function being called as the following:
    ///
    /// ```ignore
    /// let lambda_parameters = array_reduce.lambda_parameters(
    ///     0,
    ///     &[
    ///         // the Field of the literal `[1.2, 2.1]`, the array being reduced
    ///         ValueOrLambda::Value(Arc::new(Field::new("", DataType::new_list(DataType::Float32, true), true))),
    ///         // the Field of the literal `0.0`, the initial value
    ///         ValueOrLambda::Value(Arc::new(Field::new("", DataType::Float32, true))),
    ///         // the Field of the output of the merge lambda, which is unknown at this point because it depends
    ///         // on the return of this call
    ///         ValueOrLambda::Lambda(None),
    ///         // the Field of the output of the finish lambda, unknown for the same reason as above
    ///         ValueOrLambda::Lambda(None),
    /// ])?;
    ///
    /// assert_eq!(
    ///      lambda_parameters,
    ///      LambdaParametersProgress::Complete(vec![
    ///         // the finish lambda supported parameters, regardless of how many are actually used
    ///         vec![
    ///             // the accumulator which is the field of the initial value
    ///             Arc::new(Field::new("ignored_name", DataType::Float32, true)),
    ///             // the array values being reduced
    ///             Arc::new(Field::new("", DataType::Float32, true)),
    ///         ],
    ///         // the merge lambda supported parameters
    ///         vec![
    ///             // the reduced value which is the field of the initial value
    ///             Arc::new(Field::new("ignored_name", DataType::Float32, true)),
    ///         ],
    ///      ])
    /// );
    /// ```
    ///
    /// For functions which lambda parameters depends on the output of other lambdas, or on their own lambda,
    /// this can return [LambdaParametersProgress::Partial] until all dependencies are met. Note that for
    /// lambda with cyclic dependencies, you likely want to use [HigherOrderUDFImpl::coerce_values_for_lambdas] too.
    /// Take as an example a flexible array_reduce with the signature `(arr: [V], initial_value: I, (ACC, V) -> ACC, (ACC) -> O) -> O`.
    /// It has a cyclic dependency in the merge lambda, and a dependency of the finish lambda in the merge lambda,
    /// and only requires the initial value to be *coercible* to the output of the merge lambda, which is defined by
    /// it's [HigherOrderUDFImpl::coerce_values_for_lambdas] implementation. The expression
    ///
    /// `array_reduce([1.2, 2.1], 0, (acc, v) -> acc + v + 1.5, v -> v > 5.1)`
    ///
    /// would result in this function being called as the following:
    ///
    /// ```ignore
    /// let lambda_parameters = array_reduce.lambda_parameters(
    ///     0,
    ///     &[
    ///         // the Field of the literal `[1.2, 2.1]`, the array being reduced
    ///         ValueOrLambda::Value(Arc::new(Field::new("", DataType::new_list(DataType::Float32, true), true))),
    ///         // the Field of the literal `0`, the initial value
    ///         ValueOrLambda::Value(Arc::new(Field::new("", DataType::Int32, true))),
    ///         // the Field of the output of the merge lambda, which is unknown at this point because it depends on
    ///         // the return this call
    ///         ValueOrLambda::Lambda(None),
    ///         // the Field of the output of the finish lambda, unknown for the same reason as above
    ///         ValueOrLambda::Lambda(None),
    /// ])?;
    ///
    /// assert_eq!(
    ///      lambda_parameters,
    ///      LambdaParametersProgress::Partial(vec![
    ///         // the finish lambda supported parameters, regardless of how many are actually used
    ///         Some(vec![
    ///             // at step 0, use the field of the initial value
    ///             Arc::new(Field::new("ignored_name", DataType::Int32, true)),
    ///             // the array values being reduced
    ///             Arc::new(Field::new("", DataType::Float32, true)),
    ///         ]),
    ///         // the merge lambda supported parameters, unknown at this point due to dependency on the merge output
    ///         None,
    ///      ])
    /// );
    ///
    /// let lambda_parameters = array_reduce.lambda_parameters(
    ///     1,
    ///     &[
    ///         // the Field of the literal `[1.2, 2.1]`, the array being reduced
    ///         ValueOrLambda::Value(Arc::new(Field::new("", DataType::new_list(DataType::Float32, true), true))),
    ///         // the Field of the literal `0`, the initial value
    ///         ValueOrLambda::Value(Arc::new(Field::new("", DataType::Int32, true))),
    ///         // the Field of the output of the merge lambda, which could be inferred to be a Float32 based on the
    ///         // returned values of the previous step
    ///         ValueOrLambda::Value(Arc::new(Field::new("", DataType::Float32, true))),
    ///         // the Field of the output of the finish lambda, which is unknown at this point because it depends
    ///         // on the return of this call
    ///         ValueOrLambda::Lambda(None),
    /// ])?;
    ///
    /// assert_eq!(
    ///      lambda_parameters,
    ///      LambdaParametersProgress::Complete(vec![
    ///         // the finish lambda supported parameters, regardless of how many are actually used
    ///         vec![
    ///             // the finish lambda own output now used as it's accumulator
    ///             Arc::new(Field::new("ignored_name", DataType::Float32, true)),
    ///             // the array values being reduced
    ///             Arc::new(Field::new("", DataType::Float32, true)),
    ///         ],
    ///         // the merge lambda supported parameters, which is the output of the merge lambda,
    ///         vec![
    ///             // the output of the merge lambda
    ///             Arc::new(Field::new("", DataType::Float32, true)),
    ///         ],
    ///      ])
    /// );
    ///
    /// let coerce_to = array_reduce.coerce_values_for_lambdas(&[
    ///     // the literal `[1.2, 2.1]` data type, the array being reduced
    ///     ValueOrLambda::Value(DataType::new_list(DataType::Float32, true)),
    ///     // the literal `0` data type, the initial value
    ///     ValueOrLambda::Value(DataType::Int32),
    ///     // the output data type of the merge lambda
    ///     ValueOrLambda::Lambda(DataType::Float32),
    ///     // the output data type of the finish lambda
    ///     ValueOrLambda::Lambda(DataType::Boolean),
    /// ])?;
    ///
    /// assert_eq!(
    ///     coerce_to,
    ///     Some(vec![
    ///         // return the same type for the array being reduced
    ///         DataType::new_list(DataType::Float32, true),
    ///         // coerce the initial value to the output of the merge lambda
    ///         DataType::Float32,
    ///     ])
    /// );
    ///
    /// ```
    ///
    /// Note this may also be called at step 0 with all lambda outputs already set, and in that case,
    /// [LambdaParametersProgress::Complete] must be returned
    ///
    /// The implementation can assume that some other part of the code has coerced
    /// the actual argument types to match [`Self::signature`], except the coercion defined by
    /// [Self::coerce_values_for_lambdas].
    ///
    /// [`HigherOrderFunction`]: crate::expr::HigherOrderFunction
    /// [`HigherOrderFunction::lambda_parameters`]: crate::expr::HigherOrderFunction::lambda_parameters
    fn lambda_parameters(
        &self,
        step: usize,
        fields: &[ValueOrLambda<FieldRef, Option<FieldRef>>],
    ) -> Result<LambdaParametersProgress>;

    /// Coerce value arguments of a function call to types that the function can evaluate also taking into
    /// account the *output type of it's lambdas*. This differs from [HigherOrderUDFImpl::coerce_value_types]
    /// that only has access to the type of it's value arguments because it's called before the output type
    /// of lambdas are known.
    ///
    /// See the [type coercion module](crate::type_coercion)
    /// documentation for more details on type coercion
    ///
    /// # Parameters
    /// * `fields`: The argument types of the value arguments of this function, or the output type of lambdas
    ///
    /// # Return value
    /// If `Some`, contains a Vec with the same number of [ValueOrLambda::Value] in `fields`.
    /// DataFusion will `CAST` the function call arguments to these specific types. If `None`, no
    /// coercion will be applied beyond the one defined by the function signature.
    ///
    /// For example, a flexible array_reduce implementation (see [Self::lambda_parameters] docs), when working
    /// with the expression below, may want to coerce it's initial value argument, the *integer* `0`,
    /// to match the output of it's merge function, which is a *float*:
    ///
    /// `array_reduce([1.2, 2.1], 0, (acc, v) -> acc + v + 1.5, v -> v > 2.0)`
    fn coerce_values_for_lambdas(
        &self,
        _fields: &[ValueOrLambda<DataType, DataType>],
    ) -> Result<Option<Vec<DataType>>> {
        Ok(None)
    }

    /// What type will be returned by this function, given the arguments?
    ///
    /// The implementation can assume that some other part of the code has coerced
    /// the actual argument types to match [`Self::signature`], including the coercion
    /// defined by [Self::coerce_values_for_lambdas].
    ///
    /// # Example creating `Field`
    ///
    /// Note the name of the `Field` is ignored, except for structured types such as
    /// `DataType::Struct`.
    ///
    /// ```rust
    /// # use std::sync::Arc;
    /// # use arrow::datatypes::{DataType, Field, FieldRef};
    /// # use datafusion_common::Result;
    /// # use datafusion_expr::HigherOrderReturnFieldArgs;
    /// # struct Example{}
    /// # impl Example {
    /// fn return_field_from_args(&self, args: HigherOrderReturnFieldArgs) -> Result<FieldRef> {
    ///     let field = Arc::new(Field::new("ignored_name", DataType::Int32, true));
    ///     Ok(field)
    /// }
    /// # }
    /// ```
    fn return_field_from_args(
        &self,
        args: HigherOrderReturnFieldArgs,
    ) -> Result<FieldRef>;

    /// Whether List or LargeList arguments should have it's non-empty null
    /// sublists cleaned with [remove_list_null_values] before invoking this function
    ///
    /// The default implementation always returns true and should only be implemented
    /// if you want to handle non-empty null sublists yourself
    ///
    /// [remove_list_null_values]: datafusion_common::utils::remove_list_null_values
    // todo: extend this to listview and maps when remove_list_null_values supports it
    fn clear_null_values(&self) -> bool {
        true
    }

    /// Invoke the function returning the appropriate result.
    ///
    /// # Performance
    ///
    /// For the best performance, the implementations should handle the common case
    /// when one or more of their arguments are constant values (aka
    /// [`ColumnarValue::Scalar`]).
    ///
    /// [`ColumnarValue::values_to_arrays`] can be used to convert the arguments
    /// to arrays, which will likely be simpler code, but be slower.
    fn invoke_with_args(&self, args: HigherOrderFunctionArgs) -> Result<ColumnarValue>;

    /// Returns true if some of this `exprs` subexpressions may not be evaluated
    /// and thus any side effects (like divide by zero) may not be encountered.
    ///
    /// Setting this to true prevents certain optimizations such as common
    /// subexpression elimination
    ///
    /// When overriding this function to return `true`, [HigherOrderUDFImpl::conditional_arguments] can also be
    /// overridden to report more accurately which arguments are eagerly evaluated and which ones
    /// lazily.
    fn short_circuits(&self) -> bool {
        false
    }

    /// Determines which of the arguments passed to *this higher-order function*
    /// are evaluated eagerly and which may be evaluated lazily. Note that this
    /// does *not* applies to the arguments that *lambda functions* pass to it's
    /// body expression
    ///
    /// If this function returns `None`, all arguments are eagerly evaluated.
    /// Returning `None` is a micro optimization that saves a needless `Vec`
    /// allocation.
    ///
    /// If the function returns `Some`, returns (`eager`, `lazy`) where `eager`
    /// are the arguments that are always evaluated, and `lazy` are the
    /// arguments that may be evaluated lazily (i.e. may not be evaluated at all
    /// in some cases).
    ///
    /// Implementations must ensure that the two returned `Vec`s are disjunct,
    /// and that each argument from `args` is present in one the two `Vec`s.
    ///
    /// When overriding this function, [HigherOrderUDFImpl::short_circuits] must
    /// be overridden to return `true`.
    fn conditional_arguments<'a>(
        &self,
        args: &'a [Expr],
    ) -> Option<(Vec<&'a Expr>, Vec<&'a Expr>)> {
        if self.short_circuits() {
            Some((vec![], args.iter().collect()))
        } else {
            None
        }
    }

    /// Coerce value arguments of a function call to types that the function can evaluate.
    /// Note that if you need to coerce values based on the output type of lambdas, you
    /// must use [HigherOrderUDFImpl::coerce_values_for_lambdas], as this function is used before
    /// the output type of lambdas are known
    ///
    /// See the [type coercion module](crate::type_coercion)
    /// documentation for more details on type coercion
    ///
    /// For example, if your function requires a contiguous list argument, but the user calls
    /// it like `my_func(c, v -> v+2)` (i.e. with `c` as a ListView), coerce_types can return `[DataType::List(..)]`
    /// to ensure the argument is converted to a List
    ///
    /// # Parameters
    /// * `arg_types`: The argument types of the value arguments of this function, excluding lambdas
    ///
    /// # Return value
    /// A Vec the same length as `arg_types`. DataFusion will `CAST` the function call
    /// arguments to these specific types.
    fn coerce_value_types(&self, _arg_types: &[DataType]) -> Result<Vec<DataType>> {
        not_impl_err!(
            "Function {} does not implement coerce_value_types",
            self.name()
        )
    }

    /// Returns the documentation for this function.
    ///
    /// Documentation can be accessed programmatically as well as generating
    /// publicly facing documentation.
    fn documentation(&self) -> Option<&Documentation> {
        None
    }
}

/// Logical representation of a Higher Order User Defined Function.
///
/// A higher order function takes one or more lambda arguments in addition to
/// regular value arguments. This struct contains the information DataFusion
/// needs to plan and invoke functions you supply such as name, type signature,
/// return type, and actual implementation.
#[derive(Debug, Clone)]
pub struct HigherOrderUDF {
    inner: Arc<dyn HigherOrderUDFImpl>,
}

impl PartialEq for HigherOrderUDF {
    fn eq(&self, other: &Self) -> bool {
        self.inner.as_ref().dyn_eq(other.inner.as_ref())
    }
}

impl PartialOrd for HigherOrderUDF {
    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
        let mut cmp = self.name().cmp(other.name());
        if cmp == Ordering::Equal {
            cmp = self.signature().partial_cmp(other.signature())?;
        }
        if cmp == Ordering::Equal {
            cmp = self.aliases().partial_cmp(other.aliases())?;
        }
        // Contract for PartialOrd and PartialEq consistency requires that
        // a == b if and only if partial_cmp(a, b) == Some(Equal).
        if cmp == Ordering::Equal && self != other {
            // Functions may have other properties besides name and signature
            // that differentiate two instances (e.g. type, or arbitrary parameters).
            // We cannot return Some(Equal) in such case.
            return None;
        }
        debug_assert!(
            cmp == Ordering::Equal || self != other,
            "Detected incorrect implementation of PartialEq when comparing functions: '{}' and '{}'. \
            The functions compare as equal, but they are not equal based on general properties that \
            the PartialOrd implementation observes,",
            self.name(),
            other.name()
        );
        Some(cmp)
    }
}

impl Eq for HigherOrderUDF {}

impl Hash for HigherOrderUDF {
    fn hash<H: Hasher>(&self, state: &mut H) {
        self.inner.dyn_hash(state)
    }
}

impl HigherOrderUDF {
    /// Create a new `HigherOrderUDF` from a [`HigherOrderUDFImpl`] trait object.
    ///
    /// Note this is the same as using the `From` impl (`HigherOrderUDF::from`).
    pub fn new_from_impl<F>(fun: F) -> HigherOrderUDF
    where
        F: HigherOrderUDFImpl + 'static,
    {
        Self::new_from_shared_impl(Arc::new(fun))
    }

    /// Create a new `HigherOrderUDF` from a shared [`HigherOrderUDFImpl`] trait object.
    pub fn new_from_shared_impl(fun: Arc<dyn HigherOrderUDFImpl>) -> HigherOrderUDF {
        Self { inner: fun }
    }

    /// Return the underlying [`HigherOrderUDFImpl`] trait object for this function.
    pub fn inner(&self) -> &Arc<dyn HigherOrderUDFImpl> {
        &self.inner
    }

    /// Adds additional names that can be used to invoke this function, in
    /// addition to `name`.
    ///
    /// If you implement [`HigherOrderUDFImpl`] directly you should return aliases
    /// directly.
    pub fn with_aliases(self, aliases: impl IntoIterator<Item = &'static str>) -> Self {
        Self::new_from_impl(AliasedHigherOrderUDFImpl::new(
            Arc::clone(&self.inner),
            aliases,
        ))
    }

    /// Returns this function's name.
    ///
    /// See [`HigherOrderUDFImpl::name`] for more details.
    pub fn name(&self) -> &str {
        self.inner.name()
    }

    /// Returns the aliases for this function.
    ///
    /// See [`HigherOrderUDF::with_aliases`] for more details.
    pub fn aliases(&self) -> &[String] {
        self.inner.aliases()
    }

    /// Returns this function's schema_name.
    ///
    /// See [`HigherOrderUDFImpl::schema_name`] for more details.
    pub fn schema_name(&self, args: &[Expr]) -> Result<String> {
        self.inner.schema_name(args)
    }

    /// Returns this function's [`HigherOrderSignature`].
    pub fn signature(&self) -> &HigherOrderSignature {
        self.inner.signature()
    }

    /// Returns the parameters of all lambdas of this function for the current step.
    ///
    /// See [`HigherOrderUDFImpl::lambda_parameters`] for more details.
    pub fn lambda_parameters(
        &self,
        step: usize,
        fields: &[ValueOrLambda<FieldRef, Option<FieldRef>>],
    ) -> Result<LambdaParametersProgress> {
        self.inner.lambda_parameters(step, fields)
    }

    /// Coerce value arguments based on lambda output types.
    ///
    /// See [`HigherOrderUDFImpl::coerce_values_for_lambdas`] for more details.
    pub fn coerce_values_for_lambdas(
        &self,
        fields: &[ValueOrLambda<DataType, DataType>],
    ) -> Result<Option<Vec<DataType>>> {
        self.inner.coerce_values_for_lambdas(fields)
    }

    /// Returns the return field of the function given its arguments.
    ///
    /// See [`HigherOrderUDFImpl::return_field_from_args`] for more details.
    pub fn return_field_from_args(
        &self,
        args: HigherOrderReturnFieldArgs,
    ) -> Result<FieldRef> {
        self.inner.return_field_from_args(args)
    }

    /// Whether List or LargeList arguments should have non-empty null sublists
    /// cleaned before invoking this function.
    pub fn clear_null_values(&self) -> bool {
        self.inner.clear_null_values()
    }

    /// Invoke the function returning the appropriate result.
    ///
    /// See [`HigherOrderUDFImpl::invoke_with_args`] for more details.
    pub fn invoke_with_args(
        &self,
        args: HigherOrderFunctionArgs,
    ) -> Result<ColumnarValue> {
        self.inner.invoke_with_args(args)
    }

    /// Returns true if some of this function's subexpressions may not be evaluated.
    ///
    /// See [`HigherOrderUDFImpl::short_circuits`] for more details.
    pub fn short_circuits(&self) -> bool {
        self.inner.short_circuits()
    }

    /// Returns which arguments are evaluated eagerly vs lazily.
    ///
    /// See [`HigherOrderUDFImpl::conditional_arguments`] for more details.
    pub fn conditional_arguments<'a>(
        &self,
        args: &'a [Expr],
    ) -> Option<(Vec<&'a Expr>, Vec<&'a Expr>)> {
        self.inner.conditional_arguments(args)
    }

    /// Coerce value arguments of a function call to types that the function can evaluate.
    ///
    /// See [`HigherOrderUDFImpl::coerce_value_types`] for more details.
    pub fn coerce_value_types(&self, arg_types: &[DataType]) -> Result<Vec<DataType>> {
        self.inner.coerce_value_types(arg_types)
    }

    /// Returns the documentation for this function, if any.
    pub fn documentation(&self) -> Option<&Documentation> {
        self.inner.documentation()
    }
}

impl<F> From<F> for HigherOrderUDF
where
    F: HigherOrderUDFImpl + 'static,
{
    fn from(fun: F) -> Self {
        Self::new_from_impl(fun)
    }
}

/// `HigherOrderUDFImpl` that adds aliases to the underlying function. It is
/// better to implement [`HigherOrderUDFImpl`], which supports aliases, directly
/// if possible.
#[derive(Debug, PartialEq, Eq, Hash)]
struct AliasedHigherOrderUDFImpl {
    inner: UdfEq<Arc<dyn HigherOrderUDFImpl>>,
    aliases: Vec<String>,
}

impl AliasedHigherOrderUDFImpl {
    fn new(
        inner: Arc<dyn HigherOrderUDFImpl>,
        new_aliases: impl IntoIterator<Item = &'static str>,
    ) -> Self {
        let mut aliases = inner.aliases().to_vec();
        aliases.extend(new_aliases.into_iter().map(|s| s.to_string()));
        Self {
            inner: inner.into(),
            aliases,
        }
    }
}

#[warn(clippy::missing_trait_methods)] // Delegates, so it should implement every single trait method
impl HigherOrderUDFImpl for AliasedHigherOrderUDFImpl {
    fn name(&self) -> &str {
        self.inner.name()
    }

    fn aliases(&self) -> &[String] {
        &self.aliases
    }

    fn schema_name(&self, args: &[Expr]) -> Result<String> {
        self.inner.schema_name(args)
    }

    fn signature(&self) -> &HigherOrderSignature {
        self.inner.signature()
    }

    fn lambda_parameters(
        &self,
        step: usize,
        fields: &[ValueOrLambda<FieldRef, Option<FieldRef>>],
    ) -> Result<LambdaParametersProgress> {
        self.inner.lambda_parameters(step, fields)
    }

    fn coerce_values_for_lambdas(
        &self,
        fields: &[ValueOrLambda<DataType, DataType>],
    ) -> Result<Option<Vec<DataType>>> {
        self.inner.coerce_values_for_lambdas(fields)
    }

    fn return_field_from_args(
        &self,
        args: HigherOrderReturnFieldArgs,
    ) -> Result<FieldRef> {
        self.inner.return_field_from_args(args)
    }

    fn clear_null_values(&self) -> bool {
        self.inner.clear_null_values()
    }

    fn invoke_with_args(&self, args: HigherOrderFunctionArgs) -> Result<ColumnarValue> {
        self.inner.invoke_with_args(args)
    }

    fn short_circuits(&self) -> bool {
        self.inner.short_circuits()
    }

    fn conditional_arguments<'a>(
        &self,
        args: &'a [Expr],
    ) -> Option<(Vec<&'a Expr>, Vec<&'a Expr>)> {
        self.inner.conditional_arguments(args)
    }

    fn coerce_value_types(&self, arg_types: &[DataType]) -> Result<Vec<DataType>> {
        self.inner.coerce_value_types(arg_types)
    }

    fn documentation(&self) -> Option<&Documentation> {
        self.inner.documentation()
    }
}

pub(crate) fn resolve_lambda_variables(
    expr: Expr,
    schema: &DFSchema,
    // a map of lambda variable name => a never empty stack of fields [ [..shadowed], in_scope ]
    vars: &mut HashMap<String, Vec<FieldRef>>,
) -> Result<Transformed<Expr>> {
    expr.transform_down(|expr| match expr {
        Expr::HigherOrderFunction(HigherOrderFunction { func, args }) => {
            // not inlined to reduce nesting
            resolve_higher_order_function(func, args, schema, vars)
        }
        Expr::LambdaVariable(mut var) => {
            let field_stack = vars.get(&var.name).ok_or_else(|| {
                plan_datafusion_err!(
                    "missing field of lambda variable {} while resolving",
                    var.name
                )
            })?;

            let field = field_stack.last().ok_or_else(|| {
                internal_datafusion_err!("every entry should have at least one field")
            })?;

            let field = Arc::clone(field).renamed(&var.name);

            let transformed = var.field.as_ref().is_none_or(|old| old != &field);

            var.field = Some(field);

            Ok(Transformed::new_transformed(
                Expr::LambdaVariable(var),
                transformed,
            ))
        }
        _ => Ok(Transformed::no(expr)),
    })
}

fn resolve_higher_order_function(
    func: Arc<HigherOrderUDF>,
    args: Vec<Expr>,
    schema: &DFSchema,
    // a map of lambda variable name => a never empty stack of fields [ [..shadowed], in_scope ]
    vars: &mut HashMap<String, Vec<FieldRef>>,
) -> Result<Transformed<Expr>> {
    let args = if !vars.is_empty() {
        /*  if this is a nested lambda, we must resolve non-lambda args before invoking
            lambda_parameters because it will invoke ExprSchemable::to_field for every
            non-lambda parameter, and if one them contains a lambda variable, it will fail
            due to it being unresolved. Example query:

            array_transform([[1, 2]], a -> array_transform(a, b -> b+1))

            the nested array_transform's lambda_parameters will call Lambdavariable::to_field
            on it's first argument, the variable `a`, which must be resolved
        */
        args.map_elements(|arg| match arg {
            Expr::Lambda(_) => Ok(Transformed::no(arg)),
            _ => resolve_lambda_variables(arg, schema, vars),
        })?
    } else {
        Transformed::no(args)
    };

    let transformed = args.transformed;
    let mut args = args.data;

    let current_fields = args
        .iter()
        .map(|e| match e {
            Expr::Lambda(_lambda_function) => Ok(ValueOrLambda::Lambda(None)),
            _ => Ok(ValueOrLambda::Value(e.to_field(schema)?.1)),
        })
        .collect::<Result<Vec<_>>>()?;

    // coerce fields because coercion may alter the lambda parameters
    let mut fields = value_fields_with_higher_order_udf(&current_fields, func.as_ref())?;

    let num_lambdas = args.iter().filter(|a| matches!(a, Expr::Lambda(_))).count();

    let mut step = 0;

    let lambda_params = loop {
        match func.lambda_parameters(step, &fields)? {
            LambdaParametersProgress::Partial(params) => {
                let mut params = params.into_iter();

                if params.len() != num_lambdas {
                    return plan_err!(
                        "{} lambda_parameters returned {} lambdas but {num_lambdas} expected",
                        func.name(),
                        params.len()
                    );
                }

                for (arg, field) in std::iter::zip(&mut args, &mut fields) {
                    match (arg, field) {
                        (Expr::Lambda(lambda), ValueOrLambda::Lambda(field)) => {
                            let params = params.next().ok_or_else(|| {
                                internal_datafusion_err!(
                                    "params len should have been checked above"
                                )
                            })?;

                            if let Some(params) = params {
                                for (name, field) in
                                    std::iter::zip(&lambda.params, params)
                                {
                                    vars.entry_ref(name)
                                        .or_default()
                                        .push(field.renamed(name.as_str()));
                                }

                                let body_with_vars = resolve_lambda_variables(
                                    mem::take(lambda.body.as_mut()),
                                    schema,
                                    vars,
                                )?;

                                remove_scope(vars, &lambda.params)?;

                                *field = Some(body_with_vars.data.to_field(schema)?.1);
                                *lambda.body = body_with_vars.data;
                            }
                        }
                        (_, ValueOrLambda::Lambda(_)) => {
                            return internal_err!(
                                "value_fields_with_higher_order_udf returned a value for a lambda argument"
                            );
                        }
                        (Expr::Lambda(_), ValueOrLambda::Value(_)) => {
                            return internal_err!(
                                "value_fields_with_higher_order_udf returned a lambda for a value argument"
                            );
                        }
                        (_, ValueOrLambda::Value(_)) => {} // nothing to do
                    }
                }
            }
            LambdaParametersProgress::Complete(params) => break params,
        }

        let limit = func.signature().lambda_parameters_max_iterations;

        step += 1;

        if step > limit {
            return plan_err!(
                "{} lambda_parameters called {limit} times without completion",
                func.name()
            );
        }
    };

    let mut lambda_params = lambda_params.into_iter();

    if num_lambdas != lambda_params.len() {
        return plan_err!(
            "{} lambda_parameters returned {} values for {num_lambdas} lambdas",
            func.name(),
            lambda_params.len()
        );
    }

    let args = args.map_elements(|arg| match arg {
        Expr::Lambda(mut lambda) => {
            let lambda_params = lambda_params.next().ok_or_else(|| {
                internal_datafusion_err!(
                    "lambda_params len should have been checked above"
                )
            })?;

            if lambda.params.len() > lambda_params.len() {
                return plan_err!(
                    "{} lambda defined {} params ({}), but only {} supported",
                    func.name(),
                    lambda.params.len(),
                    display_comma_separated(&lambda.params),
                    lambda_params.len()
                );
            }

            if !all_unique(&lambda.params) {
                return plan_err!(
                    "lambda params must be unique, got ({})",
                    lambda.params.join(", ")
                );
            }

            for (param, field) in std::iter::zip(&lambda.params, lambda_params) {
                vars.entry_ref(param)
                    .or_default()
                    .push(field.renamed(param.as_str()));
            }

            let transformed =
                resolve_lambda_variables(mem::take(lambda.body.as_mut()), schema, vars)?;

            *lambda.body = transformed.data;

            remove_scope(vars, &lambda.params)?;

            Ok(Transformed::new(
                Expr::Lambda(lambda),
                transformed.transformed,
                TreeNodeRecursion::Jump,
            ))
        }
        arg => Ok(Transformed::no(arg)), // resolved at the start of the function
    })?;

    Ok(Transformed::new(
        Expr::HigherOrderFunction(HigherOrderFunction::new(func, args.data)),
        transformed || args.transformed,
        TreeNodeRecursion::Jump,
    ))
}

fn remove_scope(
    vars: &mut HashMap<String, Vec<FieldRef>>,
    scope: &[String],
) -> Result<()> {
    for param in scope {
        match vars.entry_ref(param) {
            EntryRef::Occupied(mut v) => {
                if v.get().len() == 1 {
                    v.remove();
                } else {
                    v.get_mut().pop().ok_or_else(|| {
                        internal_datafusion_err!(
                            "every entry should have at least one field"
                        )
                    })?;
                }
            }
            EntryRef::Vacant(_v) => {
                return internal_err!("no empty value should be in the map");
            }
        }
    }

    Ok(())
}

fn all_unique(params: &[String]) -> bool {
    match params.len() {
        0 | 1 => true,
        2 => params[0] != params[1],
        _ => {
            let mut set = HashSet::with_capacity(params.len());

            params.iter().all(|p| set.insert(p.as_str()))
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use std::hash::DefaultHasher;
    use std::sync::Arc;

    use arrow_schema::{DataType, Field, FieldRef, Schema};
    use datafusion_common::{DFSchema, Result};
    use datafusion_expr_common::columnar_value::ColumnarValue;
    use datafusion_expr_common::signature::Volatility;

    use crate::{
        Expr, HigherOrderSignature, HigherOrderUDF, HigherOrderUDFImpl,
        LambdaParametersProgress, ValueOrLambda, col,
        expr::{HigherOrderFunction, LambdaVariable},
        lambda, lambda_var, lit,
    };

    #[derive(Debug, PartialEq, Eq, Hash)]
    struct TestHigherOrderUDF {
        name: &'static str,
        field: &'static str,
        signature: HigherOrderSignature,
    }
    impl HigherOrderUDFImpl for TestHigherOrderUDF {
        fn name(&self) -> &str {
            self.name
        }

        fn signature(&self) -> &HigherOrderSignature {
            &self.signature
        }

        fn lambda_parameters(
            &self,
            _step: usize,
            _fields: &[ValueOrLambda<FieldRef, Option<FieldRef>>],
        ) -> Result<LambdaParametersProgress> {
            unimplemented!()
        }

        fn return_field_from_args(
            &self,
            _args: HigherOrderReturnFieldArgs,
        ) -> Result<FieldRef> {
            unimplemented!()
        }

        fn invoke_with_args(
            &self,
            _args: HigherOrderFunctionArgs,
        ) -> Result<ColumnarValue> {
            unimplemented!()
        }
    }

    // PartialEq and Hash must be consistent, and also PartialEq and PartialOrd
    // must be consistent, so they are tested together.
    #[test]
    fn test_partial_eq_hash_and_partial_ord() {
        // A parameterized function
        let f = test_func("foo", "a");

        // Same like `f`, different instance
        let f2 = test_func("foo", "a");
        assert_eq!(&f, &f2);
        assert_eq!(hash(&f), hash(&f2));
        assert_eq!(f.partial_cmp(&f2), Some(Ordering::Equal));

        // Different parameter
        let b = test_func("foo", "b");
        assert_ne!(&f, &b);
        assert_ne!(hash(&f), hash(&b)); // hash can collide for different values but does not collide in this test
        assert_eq!(f.partial_cmp(&b), None);

        // Different name
        let o = test_func("other", "a");
        assert_ne!(&f, &o);
        assert_ne!(hash(&f), hash(&o)); // hash can collide for different values but does not collide in this test
        assert_eq!(f.partial_cmp(&o), Some(Ordering::Less));

        // Different name and parameter
        assert_ne!(&b, &o);
        assert_ne!(hash(&b), hash(&o)); // hash can collide for different values but does not collide in this test
        assert_eq!(b.partial_cmp(&o), Some(Ordering::Less));
    }

    fn test_func(name: &'static str, parameter: &'static str) -> Arc<HigherOrderUDF> {
        Arc::new(HigherOrderUDF::new_from_impl(TestHigherOrderUDF {
            name,
            field: parameter,
            signature: HigherOrderSignature::variadic_any(Volatility::Immutable),
        }))
    }

    fn hash<T: Hash>(value: &T) -> u64 {
        let hasher = &mut DefaultHasher::new();
        value.hash(hasher);
        hasher.finish()
    }

    #[derive(Debug, PartialEq, Eq, Hash)]
    struct MockArrayReduce {
        signature: HigherOrderSignature,
    }

    impl HigherOrderUDFImpl for MockArrayReduce {
        fn name(&self) -> &str {
            "array_reduce"
        }

        fn aliases(&self) -> &[String] {
            &[]
        }

        fn signature(&self) -> &HigherOrderSignature {
            &self.signature
        }

        fn lambda_parameters(
            &self,
            step: usize,
            fields: &[ValueOrLambda<FieldRef, Option<FieldRef>>],
        ) -> Result<LambdaParametersProgress> {
            // optional finish not supported for simplicity
            let [
                ValueOrLambda::Value(list),
                ValueOrLambda::Value(initial_value),
                ValueOrLambda::Lambda(merge),
                ValueOrLambda::Lambda(_finish),
            ] = fields
            else {
                unreachable!()
            };

            let list_field = match list.data_type() {
                DataType::List(field) => field,
                _ => unreachable!(),
            };

            Ok(match (step, merge) {
                (0, None) => {
                    // at the first step, we use the initial_value as merge accumulator,
                    // and return None for finish since we don't know the output of merge
                    LambdaParametersProgress::Partial(vec![
                        // merge
                        Some(vec![Arc::clone(initial_value), Arc::clone(list_field)]),
                        // finish
                        None,
                    ])
                }
                (1, Some(accumulator)) | (0, Some(accumulator)) => {
                    // now we can use the merge output as it's accumulator and
                    // as the finish parameter
                    LambdaParametersProgress::Complete(vec![
                        // merge
                        vec![Arc::clone(accumulator), Arc::clone(list_field)],
                        // finish
                        vec![Arc::clone(accumulator)],
                    ])
                }
                (1, None) => {
                    unreachable!()
                }
                _ => unreachable!(),
            })
        }

        fn return_field_from_args(
            &self,
            args: HigherOrderReturnFieldArgs,
        ) -> Result<FieldRef> {
            // optional finish not supported for simplicity
            let [
                ValueOrLambda::Value(_list),
                ValueOrLambda::Value(_initial_value),
                ValueOrLambda::Lambda(_merge),
                ValueOrLambda::Lambda(finish),
            ] = args.arg_fields
            else {
                unreachable!()
            };

            Ok(Arc::clone(finish))
        }

        fn invoke_with_args(
            &self,
            _args: HigherOrderFunctionArgs,
        ) -> Result<ColumnarValue> {
            unreachable!()
        }
    }

    #[test]
    fn test_resolve_lambda_variables() {
        let schema = DFSchema::try_from(Schema::new(vec![Field::new(
            "c",
            DataType::new_list(DataType::new_list(DataType::Int32, true), true),
            true,
        )]))
        .unwrap();

        let func = Arc::new(HigherOrderUDF::new_from_impl(MockArrayReduce {
            signature: HigherOrderSignature::variadic_any(Volatility::Immutable),
        }));

        /*
           array_reduce(
               c,
               0,
               (acc1, v) -> acc + array_reduce(
                   v,
                   0,
                   (acc2, v) -> acc2 + acc1 + v,
                   reduced -> reduced * 2.0
               ),
               reduced -> reduced * 2
           )
        */
        let expr = Expr::HigherOrderFunction(HigherOrderFunction::new(
            Arc::clone(&func),
            vec![
                col("c"),
                lit(0),
                lambda(
                    ["acc1", "v"],
                    lambda_var("acc1")
                        + Expr::HigherOrderFunction(HigherOrderFunction::new(
                            Arc::clone(&func),
                            vec![
                                lambda_var("v"),
                                lit(0),
                                lambda(
                                    ["acc2", "v"],
                                    lambda_var("acc2")
                                        + lambda_var("acc1")
                                        + lambda_var("v"),
                                ),
                                lambda(["reduced"], lambda_var("reduced") * lit(2.0)),
                            ],
                        )),
                ),
                lambda(["reduced"], lambda_var("reduced") * lit(2)),
            ],
        ));

        let resolved_expr = expr.resolve_lambda_variables(&schema).unwrap().data;

        /*
           array_reduce(
               c@[[Int32]],
               0@Int64,
               (acc1@Float64, v@[Int32]) -> acc@Float64 + array_reduce(
                   v@[Int32],
                   0@Int64,
                   (acc2@Float64, v@Int32) -> acc2@Float64 + acc1@Float64 + v@Int32,
                   reducedFloat64 -> reduced@Float64 * 2.0@Float64
               ),
               reduced@Float64 -> reduced@Float64 * 2@Int64
           )
        */
        let expected = Expr::HigherOrderFunction(HigherOrderFunction::new(
            Arc::clone(&func),
            vec![
                col("c"),
                lit(0),
                lambda(
                    ["acc1", "v"],
                    resolved_lambda_var("acc1", DataType::Float64, true)
                        + Expr::HigherOrderFunction(HigherOrderFunction::new(
                            Arc::clone(&func),
                            vec![
                                resolved_lambda_var(
                                    "v",
                                    DataType::new_list(DataType::Int32, true),
                                    true,
                                ),
                                lit(0),
                                lambda(
                                    ["acc2", "v"],
                                    resolved_lambda_var("acc2", DataType::Float64, true)
                                        + resolved_lambda_var(
                                            "acc1",
                                            DataType::Float64,
                                            true,
                                        )
                                        + resolved_lambda_var("v", DataType::Int32, true),
                                ),
                                lambda(
                                    ["reduced"],
                                    resolved_lambda_var(
                                        "reduced",
                                        DataType::Float64,
                                        true,
                                    ) * lit(2.0),
                                ),
                            ],
                        )),
                ),
                lambda(
                    ["reduced"],
                    resolved_lambda_var("reduced", DataType::Float64, true) * lit(2),
                ),
            ],
        ));

        assert_eq!(resolved_expr, expected);
    }

    fn resolved_lambda_var(name: &str, dt: DataType, nullable: bool) -> Expr {
        Expr::LambdaVariable(LambdaVariable::new(
            name.into(),
            Some(Arc::new(Field::new(name, dt, nullable))),
        ))
    }
}