datafusion_optimizer/

common_subexpr_eliminate.rs

// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements.  See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership.  The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License.  You may obtain a copy of the License at
//
//   http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied.  See the License for the
// specific language governing permissions and limitations
// under the License.

//! [`CommonSubexprEliminate`] to avoid redundant computation of common sub-expressions

use std::collections::BTreeSet;
use std::fmt::Debug;
use std::sync::Arc;

use crate::{OptimizerConfig, OptimizerRule};

use crate::optimizer::ApplyOrder;
use crate::utils::NamePreserver;
use datafusion_common::alias::AliasGenerator;

use datafusion_common::cse::{CSEController, FoundCommonNodes, CSE};
use datafusion_common::tree_node::{Transformed, TreeNode};
use datafusion_common::{qualified_name, Column, DFSchema, DFSchemaRef, Result};
use datafusion_expr::expr::{Alias, ScalarFunction};
use datafusion_expr::logical_plan::{
    Aggregate, Filter, LogicalPlan, Projection, Sort, Window,
};
use datafusion_expr::tree_node::replace_sort_expressions;
use datafusion_expr::{col, BinaryExpr, Case, Expr, Operator};

const CSE_PREFIX: &str = "__common_expr";

/// Performs Common Sub-expression Elimination optimization.
///
/// This optimization improves query performance by computing expressions that
/// appear more than once and reusing those results rather than re-computing the
/// same value
///
/// Currently only common sub-expressions within a single `LogicalPlan` are
/// eliminated.
///
/// # Example
///
/// Given a projection that computes the same expensive expression
/// multiple times such as parsing as string as a date with `to_date` twice:
///
/// ```text
/// ProjectionExec(expr=[extract (day from to_date(c1)), extract (year from to_date(c1))])
/// ```
///
/// This optimization will rewrite the plan to compute the common expression once
/// using a new `ProjectionExec` and then rewrite the original expressions to
/// refer to that new column.
///
/// ```text
/// ProjectionExec(exprs=[extract (day from new_col), extract (year from new_col)]) <-- reuse here
///   ProjectionExec(exprs=[to_date(c1) as new_col]) <-- compute to_date once
/// ```
#[derive(Debug)]
pub struct CommonSubexprEliminate {}

impl CommonSubexprEliminate {
    pub fn new() -> Self {
        Self {}
    }

    fn try_optimize_proj(
        &self,
        projection: Projection,
        config: &dyn OptimizerConfig,
    ) -> Result<Transformed<LogicalPlan>> {
        let Projection {
            expr,
            input,
            schema,
            ..
        } = projection;
        let input = Arc::unwrap_or_clone(input);
        self.try_unary_plan(expr, input, config)?
            .map_data(|(new_expr, new_input)| {
                Projection::try_new_with_schema(new_expr, Arc::new(new_input), schema)
                    .map(LogicalPlan::Projection)
            })
    }
    fn try_optimize_sort(
        &self,
        sort: Sort,
        config: &dyn OptimizerConfig,
    ) -> Result<Transformed<LogicalPlan>> {
        let Sort { expr, input, fetch } = sort;
        let input = Arc::unwrap_or_clone(input);
        let sort_expressions = expr.iter().map(|sort| sort.expr.clone()).collect();
        let new_sort = self
            .try_unary_plan(sort_expressions, input, config)?
            .update_data(|(new_expr, new_input)| {
                LogicalPlan::Sort(Sort {
                    expr: replace_sort_expressions(expr, new_expr),
                    input: Arc::new(new_input),
                    fetch,
                })
            });
        Ok(new_sort)
    }

    fn try_optimize_filter(
        &self,
        filter: Filter,
        config: &dyn OptimizerConfig,
    ) -> Result<Transformed<LogicalPlan>> {
        let Filter {
            predicate, input, ..
        } = filter;
        let input = Arc::unwrap_or_clone(input);
        let expr = vec![predicate];
        self.try_unary_plan(expr, input, config)?
            .map_data(|(mut new_expr, new_input)| {
                assert_eq!(new_expr.len(), 1); // passed in vec![predicate]
                let new_predicate = new_expr.pop().unwrap();
                Filter::try_new(new_predicate, Arc::new(new_input))
                    .map(LogicalPlan::Filter)
            })
    }

    fn try_optimize_window(
        &self,
        window: Window,
        config: &dyn OptimizerConfig,
    ) -> Result<Transformed<LogicalPlan>> {
        // Collects window expressions from consecutive `LogicalPlan::Window` nodes into
        // a list.
        let (window_expr_list, window_schemas, input) =
            get_consecutive_window_exprs(window);

        // Extract common sub-expressions from the list.

        match CSE::new(ExprCSEController::new(
            config.alias_generator().as_ref(),
            ExprMask::Normal,
        ))
        .extract_common_nodes(window_expr_list)?
        {
            // If there are common sub-expressions, then the insert a projection node
            // with the common expressions between the new window nodes and the
            // original input.
            FoundCommonNodes::Yes {
                common_nodes: common_exprs,
                new_nodes_list: new_exprs_list,
                original_nodes_list: original_exprs_list,
            } => build_common_expr_project_plan(input, common_exprs).map(|new_input| {
                Transformed::yes((new_exprs_list, new_input, Some(original_exprs_list)))
            }),
            FoundCommonNodes::No {
                original_nodes_list: original_exprs_list,
            } => Ok(Transformed::no((original_exprs_list, input, None))),
        }?
        // Recurse into the new input.
        // (This is similar to what a `ApplyOrder::TopDown` optimizer rule would do.)
        .transform_data(|(new_window_expr_list, new_input, window_expr_list)| {
            self.rewrite(new_input, config)?.map_data(|new_input| {
                Ok((new_window_expr_list, new_input, window_expr_list))
            })
        })?
        // Rebuild the consecutive window nodes.
        .map_data(|(new_window_expr_list, new_input, window_expr_list)| {
            // If there were common expressions extracted, then we need to make sure
            // we restore the original column names.
            // TODO: Although `find_common_exprs()` inserts aliases around extracted
            //  common expressions this doesn't mean that the original column names
            //  (schema) are preserved due to the inserted aliases are not always at
            //  the top of the expression.
            //  Let's consider improving `find_common_exprs()` to always keep column
            //  names and get rid of additional name preserving logic here.
            if let Some(window_expr_list) = window_expr_list {
                let name_preserver = NamePreserver::new_for_projection();
                let saved_names = window_expr_list
                    .iter()
                    .map(|exprs| {
                        exprs
                            .iter()
                            .map(|expr| name_preserver.save(expr))
                            .collect::<Vec<_>>()
                    })
                    .collect::<Vec<_>>();
                new_window_expr_list.into_iter().zip(saved_names).try_rfold(
                    new_input,
                    |plan, (new_window_expr, saved_names)| {
                        let new_window_expr = new_window_expr
                            .into_iter()
                            .zip(saved_names)
                            .map(|(new_window_expr, saved_name)| {
                                saved_name.restore(new_window_expr)
                            })
                            .collect::<Vec<_>>();
                        Window::try_new(new_window_expr, Arc::new(plan))
                            .map(LogicalPlan::Window)
                    },
                )
            } else {
                new_window_expr_list
                    .into_iter()
                    .zip(window_schemas)
                    .try_rfold(new_input, |plan, (new_window_expr, schema)| {
                        Window::try_new_with_schema(
                            new_window_expr,
                            Arc::new(plan),
                            schema,
                        )
                        .map(LogicalPlan::Window)
                    })
            }
        })
    }

    fn try_optimize_aggregate(
        &self,
        aggregate: Aggregate,
        config: &dyn OptimizerConfig,
    ) -> Result<Transformed<LogicalPlan>> {
        let Aggregate {
            group_expr,
            aggr_expr,
            input,
            schema,
            ..
        } = aggregate;
        let input = Arc::unwrap_or_clone(input);
        // Extract common sub-expressions from the aggregate and grouping expressions.
        match CSE::new(ExprCSEController::new(
            config.alias_generator().as_ref(),
            ExprMask::Normal,
        ))
        .extract_common_nodes(vec![group_expr, aggr_expr])?
        {
            // If there are common sub-expressions, then insert a projection node
            // with the common expressions between the new aggregate node and the
            // original input.
            FoundCommonNodes::Yes {
                common_nodes: common_exprs,
                new_nodes_list: mut new_exprs_list,
                original_nodes_list: mut original_exprs_list,
            } => {
                let new_aggr_expr = new_exprs_list.pop().unwrap();
                let new_group_expr = new_exprs_list.pop().unwrap();

                build_common_expr_project_plan(input, common_exprs).map(|new_input| {
                    let aggr_expr = original_exprs_list.pop().unwrap();
                    Transformed::yes((
                        new_aggr_expr,
                        new_group_expr,
                        new_input,
                        Some(aggr_expr),
                    ))
                })
            }

            FoundCommonNodes::No {
                original_nodes_list: mut original_exprs_list,
            } => {
                let new_aggr_expr = original_exprs_list.pop().unwrap();
                let new_group_expr = original_exprs_list.pop().unwrap();

                Ok(Transformed::no((
                    new_aggr_expr,
                    new_group_expr,
                    input,
                    None,
                )))
            }
        }?
        // Recurse into the new input.
        // (This is similar to what a `ApplyOrder::TopDown` optimizer rule would do.)
        .transform_data(|(new_aggr_expr, new_group_expr, new_input, aggr_expr)| {
            self.rewrite(new_input, config)?.map_data(|new_input| {
                Ok((
                    new_aggr_expr,
                    new_group_expr,
                    aggr_expr,
                    Arc::new(new_input),
                ))
            })
        })?
        // Try extracting common aggregate expressions and rebuild the aggregate node.
        .transform_data(
            |(new_aggr_expr, new_group_expr, aggr_expr, new_input)| {
                // Extract common aggregate sub-expressions from the aggregate expressions.
                match CSE::new(ExprCSEController::new(
                    config.alias_generator().as_ref(),
                    ExprMask::NormalAndAggregates,
                ))
                .extract_common_nodes(vec![new_aggr_expr])?
                {
                    FoundCommonNodes::Yes {
                        common_nodes: common_exprs,
                        new_nodes_list: mut new_exprs_list,
                        original_nodes_list: mut original_exprs_list,
                    } => {
                        let rewritten_aggr_expr = new_exprs_list.pop().unwrap();
                        let new_aggr_expr = original_exprs_list.pop().unwrap();

                        let mut agg_exprs = common_exprs
                            .into_iter()
                            .map(|(expr, expr_alias)| expr.alias(expr_alias))
                            .collect::<Vec<_>>();

                        let mut proj_exprs = vec![];
                        for expr in &new_group_expr {
                            extract_expressions(expr, &mut proj_exprs)
                        }
                        for (expr_rewritten, expr_orig) in
                            rewritten_aggr_expr.into_iter().zip(new_aggr_expr)
                        {
                            if expr_rewritten == expr_orig {
                                if let Expr::Alias(Alias { expr, name, .. }) =
                                    expr_rewritten
                                {
                                    agg_exprs.push(expr.alias(&name));
                                    proj_exprs
                                        .push(Expr::Column(Column::from_name(name)));
                                } else {
                                    let expr_alias =
                                        config.alias_generator().next(CSE_PREFIX);
                                    let (qualifier, field_name) =
                                        expr_rewritten.qualified_name();
                                    let out_name =
                                        qualified_name(qualifier.as_ref(), &field_name);

                                    agg_exprs.push(expr_rewritten.alias(&expr_alias));
                                    proj_exprs.push(
                                        Expr::Column(Column::from_name(expr_alias))
                                            .alias(out_name),
                                    );
                                }
                            } else {
                                proj_exprs.push(expr_rewritten);
                            }
                        }

                        let agg = LogicalPlan::Aggregate(Aggregate::try_new(
                            new_input,
                            new_group_expr,
                            agg_exprs,
                        )?);
                        Projection::try_new(proj_exprs, Arc::new(agg))
                            .map(|p| Transformed::yes(LogicalPlan::Projection(p)))
                    }

                    // If there aren't any common aggregate sub-expressions, then just
                    // rebuild the aggregate node.
                    FoundCommonNodes::No {
                        original_nodes_list: mut original_exprs_list,
                    } => {
                        let rewritten_aggr_expr = original_exprs_list.pop().unwrap();

                        // If there were common expressions extracted, then we need to
                        // make sure we restore the original column names.
                        // TODO: Although `find_common_exprs()` inserts aliases around
                        //  extracted common expressions this doesn't mean that the
                        //  original column names (schema) are preserved due to the
                        //  inserted aliases are not always at the top of the
                        //  expression.
                        //  Let's consider improving `find_common_exprs()` to always
                        //  keep column names and get rid of additional name
                        //  preserving logic here.
                        if let Some(aggr_expr) = aggr_expr {
                            let name_perserver = NamePreserver::new_for_projection();
                            let saved_names = aggr_expr
                                .iter()
                                .map(|expr| name_perserver.save(expr))
                                .collect::<Vec<_>>();
                            let new_aggr_expr = rewritten_aggr_expr
                                .into_iter()
                                .zip(saved_names)
                                .map(|(new_expr, saved_name)| {
                                    saved_name.restore(new_expr)
                                })
                                .collect::<Vec<Expr>>();

                            // Since `group_expr` may have changed, schema may also.
                            // Use `try_new()` method.
                            Aggregate::try_new(new_input, new_group_expr, new_aggr_expr)
                                .map(LogicalPlan::Aggregate)
                                .map(Transformed::no)
                        } else {
                            Aggregate::try_new_with_schema(
                                new_input,
                                new_group_expr,
                                rewritten_aggr_expr,
                                schema,
                            )
                            .map(LogicalPlan::Aggregate)
                            .map(Transformed::no)
                        }
                    }
                }
            },
        )
    }

    /// Rewrites the expr list and input to remove common subexpressions
    ///
    /// # Parameters
    ///
    /// * `exprs`: List of expressions in the node
    /// * `input`: input plan (that produces the columns referred to in `exprs`)
    ///
    /// # Return value
    ///
    ///  Returns `(rewritten_exprs, new_input)`. `new_input` is either:
    ///
    /// 1. The original `input` of no common subexpressions were extracted
    /// 2. A newly added projection on top of the original input
    ///    that computes the common subexpressions
    fn try_unary_plan(
        &self,
        exprs: Vec<Expr>,
        input: LogicalPlan,
        config: &dyn OptimizerConfig,
    ) -> Result<Transformed<(Vec<Expr>, LogicalPlan)>> {
        // Extract common sub-expressions from the expressions.
        match CSE::new(ExprCSEController::new(
            config.alias_generator().as_ref(),
            ExprMask::Normal,
        ))
        .extract_common_nodes(vec![exprs])?
        {
            FoundCommonNodes::Yes {
                common_nodes: common_exprs,
                new_nodes_list: mut new_exprs_list,
                original_nodes_list: _,
            } => {
                let new_exprs = new_exprs_list.pop().unwrap();
                build_common_expr_project_plan(input, common_exprs)
                    .map(|new_input| Transformed::yes((new_exprs, new_input)))
            }
            FoundCommonNodes::No {
                original_nodes_list: mut original_exprs_list,
            } => {
                let new_exprs = original_exprs_list.pop().unwrap();
                Ok(Transformed::no((new_exprs, input)))
            }
        }?
        // Recurse into the new input.
        // (This is similar to what a `ApplyOrder::TopDown` optimizer rule would do.)
        .transform_data(|(new_exprs, new_input)| {
            self.rewrite(new_input, config)?
                .map_data(|new_input| Ok((new_exprs, new_input)))
        })
    }
}

/// Get all window expressions inside the consecutive window operators.
///
/// Returns the window expressions, and the input to the deepest child
/// LogicalPlan.
///
/// For example, if the input window looks like
///
/// ```text
///   LogicalPlan::Window(exprs=[a, b, c])
///     LogicalPlan::Window(exprs=[d])
///       InputPlan
/// ```
///
/// Returns:
/// *  `window_exprs`: `[[a, b, c], [d]]`
/// * InputPlan
///
/// Consecutive window expressions may refer to same complex expression.
///
/// If same complex expression is referred more than once by subsequent
/// `WindowAggr`s, we can cache complex expression by evaluating it with a
/// projection before the first WindowAggr.
///
/// This enables us to cache complex expression "c3+c4" for following plan:
///
/// ```text
/// WindowAggr: windowExpr=[[sum(c9) ORDER BY [c3 + c4] RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW]]
/// --WindowAggr: windowExpr=[[sum(c9) ORDER BY [c3 + c4] RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW]]
/// ```
///
/// where, it is referred once by each `WindowAggr` (total of 2) in the plan.
fn get_consecutive_window_exprs(
    window: Window,
) -> (Vec<Vec<Expr>>, Vec<DFSchemaRef>, LogicalPlan) {
    let mut window_expr_list = vec![];
    let mut window_schemas = vec![];
    let mut plan = LogicalPlan::Window(window);
    while let LogicalPlan::Window(Window {
        input,
        window_expr,
        schema,
    }) = plan
    {
        window_expr_list.push(window_expr);
        window_schemas.push(schema);

        plan = Arc::unwrap_or_clone(input);
    }
    (window_expr_list, window_schemas, plan)
}

impl OptimizerRule for CommonSubexprEliminate {
    fn supports_rewrite(&self) -> bool {
        true
    }

    fn apply_order(&self) -> Option<ApplyOrder> {
        // This rule handles recursion itself in a `ApplyOrder::TopDown` like manner.
        // This is because in some cases adjacent nodes are collected (e.g. `Window`) and
        // CSEd as a group, which can't be done in a simple `ApplyOrder::TopDown` rule.
        None
    }

    fn rewrite(
        &self,
        plan: LogicalPlan,
        config: &dyn OptimizerConfig,
    ) -> Result<Transformed<LogicalPlan>> {
        let original_schema = Arc::clone(plan.schema());

        let optimized_plan = match plan {
            LogicalPlan::Projection(proj) => self.try_optimize_proj(proj, config)?,
            LogicalPlan::Sort(sort) => self.try_optimize_sort(sort, config)?,
            LogicalPlan::Filter(filter) => self.try_optimize_filter(filter, config)?,
            LogicalPlan::Window(window) => self.try_optimize_window(window, config)?,
            LogicalPlan::Aggregate(agg) => self.try_optimize_aggregate(agg, config)?,
            LogicalPlan::Join(_)
            | LogicalPlan::Repartition(_)
            | LogicalPlan::Union(_)
            | LogicalPlan::TableScan(_)
            | LogicalPlan::Values(_)
            | LogicalPlan::EmptyRelation(_)
            | LogicalPlan::Subquery(_)
            | LogicalPlan::SubqueryAlias(_)
            | LogicalPlan::Limit(_)
            | LogicalPlan::Ddl(_)
            | LogicalPlan::Explain(_)
            | LogicalPlan::Analyze(_)
            | LogicalPlan::Statement(_)
            | LogicalPlan::DescribeTable(_)
            | LogicalPlan::Distinct(_)
            | LogicalPlan::Extension(_)
            | LogicalPlan::Dml(_)
            | LogicalPlan::Copy(_)
            | LogicalPlan::Unnest(_)
            | LogicalPlan::RecursiveQuery(_)
            | LogicalPlan::Prepare(_)
            | LogicalPlan::Execute(_) => {
                // This rule handles recursion itself in a `ApplyOrder::TopDown` like
                // manner.
                plan.map_children(|c| self.rewrite(c, config))?
            }
        };

        // If we rewrote the plan, ensure the schema stays the same
        if optimized_plan.transformed && optimized_plan.data.schema() != &original_schema
        {
            optimized_plan.map_data(|optimized_plan| {
                build_recover_project_plan(&original_schema, optimized_plan)
            })
        } else {
            Ok(optimized_plan)
        }
    }

    fn name(&self) -> &str {
        "common_sub_expression_eliminate"
    }
}

/// Which type of [expressions](Expr) should be considered for rewriting?
#[derive(Debug, Clone, Copy)]
enum ExprMask {
    /// Ignores:
    ///
    /// - [`Literal`](Expr::Literal)
    /// - [`Columns`](Expr::Column)
    /// - [`ScalarVariable`](Expr::ScalarVariable)
    /// - [`Alias`](Expr::Alias)
    /// - [`Wildcard`](Expr::Wildcard)
    /// - [`AggregateFunction`](Expr::AggregateFunction)
    Normal,

    /// Like [`Normal`](Self::Normal), but includes [`AggregateFunction`](Expr::AggregateFunction).
    NormalAndAggregates,
}

struct ExprCSEController<'a> {
    alias_generator: &'a AliasGenerator,
    mask: ExprMask,

    // how many aliases have we seen so far
    alias_counter: usize,
}

impl<'a> ExprCSEController<'a> {
    fn new(alias_generator: &'a AliasGenerator, mask: ExprMask) -> Self {
        Self {
            alias_generator,
            mask,
            alias_counter: 0,
        }
    }
}

impl CSEController for ExprCSEController<'_> {
    type Node = Expr;

    fn conditional_children(node: &Expr) -> Option<(Vec<&Expr>, Vec<&Expr>)> {
        match node {
            // In case of `ScalarFunction`s we don't know which children are surely
            // executed so start visiting all children conditionally and stop the
            // recursion with `TreeNodeRecursion::Jump`.
            Expr::ScalarFunction(ScalarFunction { func, args })
                if func.short_circuits() =>
            {
                Some((vec![], args.iter().collect()))
            }

            // In case of `And` and `Or` the first child is surely executed, but we
            // account subexpressions as conditional in the second.
            Expr::BinaryExpr(BinaryExpr {
                left,
                op: Operator::And | Operator::Or,
                right,
            }) => Some((vec![left.as_ref()], vec![right.as_ref()])),

            // In case of `Case` the optional base expression and the first when
            // expressions are surely executed, but we account subexpressions as
            // conditional in the others.
            Expr::Case(Case {
                expr,
                when_then_expr,
                else_expr,
            }) => Some((
                expr.iter()
                    .map(|e| e.as_ref())
                    .chain(when_then_expr.iter().take(1).map(|(when, _)| when.as_ref()))
                    .collect(),
                when_then_expr
                    .iter()
                    .take(1)
                    .map(|(_, then)| then.as_ref())
                    .chain(
                        when_then_expr
                            .iter()
                            .skip(1)
                            .flat_map(|(when, then)| [when.as_ref(), then.as_ref()]),
                    )
                    .chain(else_expr.iter().map(|e| e.as_ref()))
                    .collect(),
            )),
            _ => None,
        }
    }

    fn is_valid(node: &Expr) -> bool {
        !node.is_volatile_node()
    }

    fn is_ignored(&self, node: &Expr) -> bool {
        let is_normal_minus_aggregates = matches!(
            node,
            Expr::Literal(..)
                | Expr::Column(..)
                | Expr::ScalarVariable(..)
                | Expr::Alias(..)
                | Expr::Wildcard { .. }
        );

        let is_aggr = matches!(node, Expr::AggregateFunction(..));

        match self.mask {
            ExprMask::Normal => is_normal_minus_aggregates || is_aggr,
            ExprMask::NormalAndAggregates => is_normal_minus_aggregates,
        }
    }

    fn generate_alias(&self) -> String {
        self.alias_generator.next(CSE_PREFIX)
    }

    fn rewrite(&mut self, node: &Self::Node, alias: &str) -> Self::Node {
        // alias the expressions without an `Alias` ancestor node
        if self.alias_counter > 0 {
            col(alias)
        } else {
            self.alias_counter += 1;
            col(alias).alias(node.schema_name().to_string())
        }
    }

    fn rewrite_f_down(&mut self, node: &Expr) {
        if matches!(node, Expr::Alias(_)) {
            self.alias_counter += 1;
        }
    }
    fn rewrite_f_up(&mut self, node: &Expr) {
        if matches!(node, Expr::Alias(_)) {
            self.alias_counter -= 1
        }
    }
}

impl Default for CommonSubexprEliminate {
    fn default() -> Self {
        Self::new()
    }
}

/// Build the "intermediate" projection plan that evaluates the extracted common
/// expressions.
///
/// # Arguments
/// input: the input plan
///
/// common_exprs: which common subexpressions were used (and thus are added to
/// intermediate projection)
///
/// expr_stats: the set of common subexpressions
fn build_common_expr_project_plan(
    input: LogicalPlan,
    common_exprs: Vec<(Expr, String)>,
) -> Result<LogicalPlan> {
    let mut fields_set = BTreeSet::new();
    let mut project_exprs = common_exprs
        .into_iter()
        .map(|(expr, expr_alias)| {
            fields_set.insert(expr_alias.clone());
            Ok(expr.alias(expr_alias))
        })
        .collect::<Result<Vec<_>>>()?;

    for (qualifier, field) in input.schema().iter() {
        if fields_set.insert(qualified_name(qualifier, field.name())) {
            project_exprs.push(Expr::from((qualifier, field)));
        }
    }

    Projection::try_new(project_exprs, Arc::new(input)).map(LogicalPlan::Projection)
}

/// Build the projection plan to eliminate unnecessary columns produced by
/// the "intermediate" projection plan built in [build_common_expr_project_plan].
///
/// This is required to keep the schema the same for plans that pass the input
/// on to the output, such as `Filter` or `Sort`.
fn build_recover_project_plan(
    schema: &DFSchema,
    input: LogicalPlan,
) -> Result<LogicalPlan> {
    let col_exprs = schema.iter().map(Expr::from).collect();
    Projection::try_new(col_exprs, Arc::new(input)).map(LogicalPlan::Projection)
}

fn extract_expressions(expr: &Expr, result: &mut Vec<Expr>) {
    if let Expr::GroupingSet(groupings) = expr {
        for e in groupings.distinct_expr() {
            let (qualifier, field_name) = e.qualified_name();
            let col = Column::new(qualifier, field_name);
            result.push(Expr::Column(col))
        }
    } else {
        let (qualifier, field_name) = expr.qualified_name();
        let col = Column::new(qualifier, field_name);
        result.push(Expr::Column(col));
    }
}

#[cfg(test)]
mod test {
    use std::any::Any;
    use std::iter;

    use arrow::datatypes::{DataType, Field, Schema};
    use datafusion_expr::logical_plan::{table_scan, JoinType};
    use datafusion_expr::{
        grouping_set, AccumulatorFactoryFunction, AggregateUDF, ColumnarValue, ScalarUDF,
        ScalarUDFImpl, Signature, SimpleAggregateUDF, Volatility,
    };
    use datafusion_expr::{lit, logical_plan::builder::LogicalPlanBuilder};

    use super::*;
    use crate::optimizer::OptimizerContext;
    use crate::test::*;
    use crate::Optimizer;
    use datafusion_expr::test::function_stub::{avg, sum};

    fn assert_optimized_plan_eq(
        expected: &str,
        plan: LogicalPlan,
        config: Option<&dyn OptimizerConfig>,
    ) {
        let optimizer =
            Optimizer::with_rules(vec![Arc::new(CommonSubexprEliminate::new())]);
        let default_config = OptimizerContext::new();
        let config = config.unwrap_or(&default_config);
        let optimized_plan = optimizer.optimize(plan, config, |_, _| ()).unwrap();
        let formatted_plan = format!("{optimized_plan}");
        assert_eq!(expected, formatted_plan);
    }

    #[test]
    fn tpch_q1_simplified() -> Result<()> {
        // SQL:
        //  select
        //      sum(a * (1 - b)),
        //      sum(a * (1 - b) * (1 + c))
        //  from T;
        //
        // The manual assembled logical plan don't contains the outermost `Projection`.

        let table_scan = test_table_scan()?;

        let plan = LogicalPlanBuilder::from(table_scan)
            .aggregate(
                iter::empty::<Expr>(),
                vec![
                    sum(col("a") * (lit(1) - col("b"))),
                    sum((col("a") * (lit(1) - col("b"))) * (lit(1) + col("c"))),
                ],
            )?
            .build()?;

        let expected = "Aggregate: groupBy=[[]], aggr=[[sum(__common_expr_1 AS test.a * Int32(1) - test.b), sum(__common_expr_1 AS test.a * Int32(1) - test.b * (Int32(1) + test.c))]]\
        \n  Projection: test.a * (Int32(1) - test.b) AS __common_expr_1, test.a, test.b, test.c\
        \n    TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        Ok(())
    }

    #[test]
    fn nested_aliases() -> Result<()> {
        let table_scan = test_table_scan()?;

        let plan = LogicalPlanBuilder::from(table_scan)
            .project(vec![
                (col("a") + col("b") - col("c")).alias("alias1") * (col("a") + col("b")),
                col("a") + col("b"),
            ])?
            .build()?;

        let expected = "Projection: __common_expr_1 - test.c AS alias1 * __common_expr_1 AS test.a + test.b, __common_expr_1 AS test.a + test.b\
        \n  Projection: test.a + test.b AS __common_expr_1, test.a, test.b, test.c\
        \n    TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        Ok(())
    }

    #[test]
    fn aggregate() -> Result<()> {
        let table_scan = test_table_scan()?;

        let return_type = DataType::UInt32;
        let accumulator: AccumulatorFactoryFunction = Arc::new(|_| unimplemented!());
        let udf_agg = |inner: Expr| {
            Expr::AggregateFunction(datafusion_expr::expr::AggregateFunction::new_udf(
                Arc::new(AggregateUDF::from(SimpleAggregateUDF::new_with_signature(
                    "my_agg",
                    Signature::exact(vec![DataType::UInt32], Volatility::Stable),
                    return_type.clone(),
                    Arc::clone(&accumulator),
                    vec![Field::new("value", DataType::UInt32, true)],
                ))),
                vec![inner],
                false,
                None,
                None,
                None,
            ))
        };

        // test: common aggregates
        let plan = LogicalPlanBuilder::from(table_scan.clone())
            .aggregate(
                iter::empty::<Expr>(),
                vec![
                    // common: avg(col("a"))
                    avg(col("a")).alias("col1"),
                    avg(col("a")).alias("col2"),
                    // no common
                    avg(col("b")).alias("col3"),
                    avg(col("c")),
                    // common: udf_agg(col("a"))
                    udf_agg(col("a")).alias("col4"),
                    udf_agg(col("a")).alias("col5"),
                    // no common
                    udf_agg(col("b")).alias("col6"),
                    udf_agg(col("c")),
                ],
            )?
            .build()?;

        let expected = "Projection: __common_expr_1 AS col1, __common_expr_1 AS col2, col3, __common_expr_3 AS avg(test.c), __common_expr_2 AS col4, __common_expr_2 AS col5, col6, __common_expr_4 AS my_agg(test.c)\
        \n  Aggregate: groupBy=[[]], aggr=[[avg(test.a) AS __common_expr_1, my_agg(test.a) AS __common_expr_2, avg(test.b) AS col3, avg(test.c) AS __common_expr_3, my_agg(test.b) AS col6, my_agg(test.c) AS __common_expr_4]]\
        \n    TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        // test: trafo after aggregate
        let plan = LogicalPlanBuilder::from(table_scan.clone())
            .aggregate(
                iter::empty::<Expr>(),
                vec![
                    lit(1) + avg(col("a")),
                    lit(1) - avg(col("a")),
                    lit(1) + udf_agg(col("a")),
                    lit(1) - udf_agg(col("a")),
                ],
            )?
            .build()?;

        let expected = "Projection: Int32(1) + __common_expr_1 AS avg(test.a), Int32(1) - __common_expr_1 AS avg(test.a), Int32(1) + __common_expr_2 AS my_agg(test.a), Int32(1) - __common_expr_2 AS my_agg(test.a)\
        \n  Aggregate: groupBy=[[]], aggr=[[avg(test.a) AS __common_expr_1, my_agg(test.a) AS __common_expr_2]]\
        \n    TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        // test: transformation before aggregate
        let plan = LogicalPlanBuilder::from(table_scan.clone())
            .aggregate(
                iter::empty::<Expr>(),
                vec![
                    avg(lit(1u32) + col("a")).alias("col1"),
                    udf_agg(lit(1u32) + col("a")).alias("col2"),
                ],
            )?
            .build()?;

        let expected ="Aggregate: groupBy=[[]], aggr=[[avg(__common_expr_1) AS col1, my_agg(__common_expr_1) AS col2]]\
        \n  Projection: UInt32(1) + test.a AS __common_expr_1, test.a, test.b, test.c\
        \n    TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        // test: common between agg and group
        let plan = LogicalPlanBuilder::from(table_scan.clone())
            .aggregate(
                vec![lit(1u32) + col("a")],
                vec![
                    avg(lit(1u32) + col("a")).alias("col1"),
                    udf_agg(lit(1u32) + col("a")).alias("col2"),
                ],
            )?
            .build()?;

        let expected = "Aggregate: groupBy=[[__common_expr_1 AS UInt32(1) + test.a]], aggr=[[avg(__common_expr_1) AS col1, my_agg(__common_expr_1) AS col2]]\
        \n  Projection: UInt32(1) + test.a AS __common_expr_1, test.a, test.b, test.c\
        \n    TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        // test: all mixed
        let plan = LogicalPlanBuilder::from(table_scan)
            .aggregate(
                vec![lit(1u32) + col("a")],
                vec![
                    (lit(1u32) + avg(lit(1u32) + col("a"))).alias("col1"),
                    (lit(1u32) - avg(lit(1u32) + col("a"))).alias("col2"),
                    avg(lit(1u32) + col("a")),
                    (lit(1u32) + udf_agg(lit(1u32) + col("a"))).alias("col3"),
                    (lit(1u32) - udf_agg(lit(1u32) + col("a"))).alias("col4"),
                    udf_agg(lit(1u32) + col("a")),
                ],
            )?
            .build()?;

        let expected = "Projection: UInt32(1) + test.a, UInt32(1) + __common_expr_2 AS col1, UInt32(1) - __common_expr_2 AS col2, __common_expr_4 AS avg(UInt32(1) + test.a), UInt32(1) + __common_expr_3 AS col3, UInt32(1) - __common_expr_3 AS col4, __common_expr_5 AS my_agg(UInt32(1) + test.a)\
        \n  Aggregate: groupBy=[[__common_expr_1 AS UInt32(1) + test.a]], aggr=[[avg(__common_expr_1) AS __common_expr_2, my_agg(__common_expr_1) AS __common_expr_3, avg(__common_expr_1 AS UInt32(1) + test.a) AS __common_expr_4, my_agg(__common_expr_1 AS UInt32(1) + test.a) AS __common_expr_5]]\
        \n    Projection: UInt32(1) + test.a AS __common_expr_1, test.a, test.b, test.c\
        \n      TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        Ok(())
    }

    #[test]
    fn aggregate_with_relations_and_dots() -> Result<()> {
        let schema = Schema::new(vec![Field::new("col.a", DataType::UInt32, false)]);
        let table_scan = table_scan(Some("table.test"), &schema, None)?.build()?;

        let col_a = Expr::Column(Column::new(Some("table.test"), "col.a"));

        let plan = LogicalPlanBuilder::from(table_scan)
            .aggregate(
                vec![col_a.clone()],
                vec![
                    (lit(1u32) + avg(lit(1u32) + col_a.clone())),
                    avg(lit(1u32) + col_a),
                ],
            )?
            .build()?;

        let expected = "Projection: table.test.col.a, UInt32(1) + __common_expr_2 AS avg(UInt32(1) + table.test.col.a), __common_expr_2 AS avg(UInt32(1) + table.test.col.a)\
        \n  Aggregate: groupBy=[[table.test.col.a]], aggr=[[avg(__common_expr_1 AS UInt32(1) + table.test.col.a) AS __common_expr_2]]\
        \n    Projection: UInt32(1) + table.test.col.a AS __common_expr_1, table.test.col.a\
        \n      TableScan: table.test";

        assert_optimized_plan_eq(expected, plan, None);

        Ok(())
    }

    #[test]
    fn subexpr_in_same_order() -> Result<()> {
        let table_scan = test_table_scan()?;

        let plan = LogicalPlanBuilder::from(table_scan)
            .project(vec![
                (lit(1) + col("a")).alias("first"),
                (lit(1) + col("a")).alias("second"),
            ])?
            .build()?;

        let expected = "Projection: __common_expr_1 AS first, __common_expr_1 AS second\
        \n  Projection: Int32(1) + test.a AS __common_expr_1, test.a, test.b, test.c\
        \n    TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        Ok(())
    }

    #[test]
    fn subexpr_in_different_order() -> Result<()> {
        let table_scan = test_table_scan()?;

        let plan = LogicalPlanBuilder::from(table_scan)
            .project(vec![lit(1) + col("a"), col("a") + lit(1)])?
            .build()?;

        let expected = "Projection: Int32(1) + test.a, test.a + Int32(1)\
        \n  TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        Ok(())
    }

    #[test]
    fn cross_plans_subexpr() -> Result<()> {
        let table_scan = test_table_scan()?;

        let plan = LogicalPlanBuilder::from(table_scan)
            .project(vec![lit(1) + col("a"), col("a")])?
            .project(vec![lit(1) + col("a")])?
            .build()?;

        let expected = "Projection: Int32(1) + test.a\
        \n  Projection: Int32(1) + test.a, test.a\
        \n    TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);
        Ok(())
    }

    #[test]
    fn redundant_project_fields() {
        let table_scan = test_table_scan().unwrap();
        let c_plus_a = col("c") + col("a");
        let b_plus_a = col("b") + col("a");
        let common_exprs_1 = vec![
            (c_plus_a, format!("{CSE_PREFIX}_1")),
            (b_plus_a, format!("{CSE_PREFIX}_2")),
        ];
        let c_plus_a_2 = col(format!("{CSE_PREFIX}_1"));
        let b_plus_a_2 = col(format!("{CSE_PREFIX}_2"));
        let common_exprs_2 = vec![
            (c_plus_a_2, format!("{CSE_PREFIX}_3")),
            (b_plus_a_2, format!("{CSE_PREFIX}_4")),
        ];
        let project = build_common_expr_project_plan(table_scan, common_exprs_1).unwrap();
        let project_2 = build_common_expr_project_plan(project, common_exprs_2).unwrap();

        let mut field_set = BTreeSet::new();
        for name in project_2.schema().field_names() {
            assert!(field_set.insert(name));
        }
    }

    #[test]
    fn redundant_project_fields_join_input() {
        let table_scan_1 = test_table_scan_with_name("test1").unwrap();
        let table_scan_2 = test_table_scan_with_name("test2").unwrap();
        let join = LogicalPlanBuilder::from(table_scan_1)
            .join(table_scan_2, JoinType::Inner, (vec!["a"], vec!["a"]), None)
            .unwrap()
            .build()
            .unwrap();
        let c_plus_a = col("test1.c") + col("test1.a");
        let b_plus_a = col("test1.b") + col("test1.a");
        let common_exprs_1 = vec![
            (c_plus_a, format!("{CSE_PREFIX}_1")),
            (b_plus_a, format!("{CSE_PREFIX}_2")),
        ];
        let c_plus_a_2 = col(format!("{CSE_PREFIX}_1"));
        let b_plus_a_2 = col(format!("{CSE_PREFIX}_2"));
        let common_exprs_2 = vec![
            (c_plus_a_2, format!("{CSE_PREFIX}_3")),
            (b_plus_a_2, format!("{CSE_PREFIX}_4")),
        ];
        let project = build_common_expr_project_plan(join, common_exprs_1).unwrap();
        let project_2 = build_common_expr_project_plan(project, common_exprs_2).unwrap();

        let mut field_set = BTreeSet::new();
        for name in project_2.schema().field_names() {
            assert!(field_set.insert(name));
        }
    }

    #[test]
    fn eliminated_subexpr_datatype() {
        use datafusion_expr::cast;

        let schema = Schema::new(vec![
            Field::new("a", DataType::UInt64, false),
            Field::new("b", DataType::UInt64, false),
            Field::new("c", DataType::UInt64, false),
        ]);

        let plan = table_scan(Some("table"), &schema, None)
            .unwrap()
            .filter(
                cast(col("a"), DataType::Int64)
                    .lt(lit(1_i64))
                    .and(cast(col("a"), DataType::Int64).not_eq(lit(1_i64))),
            )
            .unwrap()
            .build()
            .unwrap();
        let rule = CommonSubexprEliminate::new();
        let optimized_plan = rule.rewrite(plan, &OptimizerContext::new()).unwrap();
        assert!(optimized_plan.transformed);
        let optimized_plan = optimized_plan.data;

        let schema = optimized_plan.schema();
        let fields_with_datatypes: Vec<_> = schema
            .fields()
            .iter()
            .map(|field| (field.name(), field.data_type()))
            .collect();
        let formatted_fields_with_datatype = format!("{fields_with_datatypes:#?}");
        let expected = r#"[
    (
        "a",
        UInt64,
    ),
    (
        "b",
        UInt64,
    ),
    (
        "c",
        UInt64,
    ),
]"#;
        assert_eq!(expected, formatted_fields_with_datatype);
    }

    #[test]
    fn filter_schema_changed() -> Result<()> {
        let table_scan = test_table_scan()?;

        let plan = LogicalPlanBuilder::from(table_scan)
            .filter((lit(1) + col("a") - lit(10)).gt(lit(1) + col("a")))?
            .build()?;

        let expected = "Projection: test.a, test.b, test.c\
        \n  Filter: __common_expr_1 - Int32(10) > __common_expr_1\
        \n    Projection: Int32(1) + test.a AS __common_expr_1, test.a, test.b, test.c\
        \n      TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        Ok(())
    }

    #[test]
    fn test_extract_expressions_from_grouping_set() -> Result<()> {
        let mut result = Vec::with_capacity(3);
        let grouping = grouping_set(vec![vec![col("a"), col("b")], vec![col("c")]]);
        extract_expressions(&grouping, &mut result);

        assert!(result.len() == 3);
        Ok(())
    }

    #[test]
    fn test_extract_expressions_from_grouping_set_with_identical_expr() -> Result<()> {
        let mut result = Vec::with_capacity(2);
        let grouping = grouping_set(vec![vec![col("a"), col("b")], vec![col("a")]]);
        extract_expressions(&grouping, &mut result);
        assert!(result.len() == 2);
        Ok(())
    }

    #[test]
    fn test_alias_collision() -> Result<()> {
        let table_scan = test_table_scan()?;

        let config = &OptimizerContext::new();
        let common_expr_1 = config.alias_generator().next(CSE_PREFIX);
        let plan = LogicalPlanBuilder::from(table_scan.clone())
            .project(vec![
                (col("a") + col("b")).alias(common_expr_1.clone()),
                col("c"),
            ])?
            .project(vec![
                col(common_expr_1.clone()).alias("c1"),
                col(common_expr_1).alias("c2"),
                (col("c") + lit(2)).alias("c3"),
                (col("c") + lit(2)).alias("c4"),
            ])?
            .build()?;

        let expected = "Projection: __common_expr_1 AS c1, __common_expr_1 AS c2, __common_expr_2 AS c3, __common_expr_2 AS c4\
        \n  Projection: test.c + Int32(2) AS __common_expr_2, __common_expr_1, test.c\
        \n    Projection: test.a + test.b AS __common_expr_1, test.c\
        \n      TableScan: test";

        assert_optimized_plan_eq(expected, plan, Some(config));

        let config = &OptimizerContext::new();
        let _common_expr_1 = config.alias_generator().next(CSE_PREFIX);
        let common_expr_2 = config.alias_generator().next(CSE_PREFIX);
        let plan = LogicalPlanBuilder::from(table_scan)
            .project(vec![
                (col("a") + col("b")).alias(common_expr_2.clone()),
                col("c"),
            ])?
            .project(vec![
                col(common_expr_2.clone()).alias("c1"),
                col(common_expr_2).alias("c2"),
                (col("c") + lit(2)).alias("c3"),
                (col("c") + lit(2)).alias("c4"),
            ])?
            .build()?;

        let expected = "Projection: __common_expr_2 AS c1, __common_expr_2 AS c2, __common_expr_3 AS c3, __common_expr_3 AS c4\
        \n  Projection: test.c + Int32(2) AS __common_expr_3, __common_expr_2, test.c\
        \n    Projection: test.a + test.b AS __common_expr_2, test.c\
        \n      TableScan: test";

        assert_optimized_plan_eq(expected, plan, Some(config));

        Ok(())
    }

    #[test]
    fn test_extract_expressions_from_col() -> Result<()> {
        let mut result = Vec::with_capacity(1);
        extract_expressions(&col("a"), &mut result);
        assert!(result.len() == 1);
        Ok(())
    }

    #[test]
    fn test_short_circuits() -> Result<()> {
        let table_scan = test_table_scan()?;

        let extracted_short_circuit = col("a").eq(lit(0)).or(col("b").eq(lit(0)));
        let extracted_short_circuit_leg_1 = (col("a") + col("b")).eq(lit(0));
        let not_extracted_short_circuit_leg_2 = (col("a") - col("b")).eq(lit(0));
        let extracted_short_circuit_leg_3 = (col("a") * col("b")).eq(lit(0));
        let plan = LogicalPlanBuilder::from(table_scan)
            .project(vec![
                extracted_short_circuit.clone().alias("c1"),
                extracted_short_circuit.alias("c2"),
                extracted_short_circuit_leg_1
                    .clone()
                    .or(not_extracted_short_circuit_leg_2.clone())
                    .alias("c3"),
                extracted_short_circuit_leg_1
                    .and(not_extracted_short_circuit_leg_2)
                    .alias("c4"),
                extracted_short_circuit_leg_3
                    .clone()
                    .or(extracted_short_circuit_leg_3)
                    .alias("c5"),
            ])?
            .build()?;

        let expected = "Projection: __common_expr_1 AS c1, __common_expr_1 AS c2, __common_expr_2 OR test.a - test.b = Int32(0) AS c3, __common_expr_2 AND test.a - test.b = Int32(0) AS c4, __common_expr_3 OR __common_expr_3 AS c5\
        \n  Projection: test.a = Int32(0) OR test.b = Int32(0) AS __common_expr_1, test.a + test.b = Int32(0) AS __common_expr_2, test.a * test.b = Int32(0) AS __common_expr_3, test.a, test.b, test.c\
        \n    TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        Ok(())
    }

    #[test]
    fn test_volatile() -> Result<()> {
        let table_scan = test_table_scan()?;

        let extracted_child = col("a") + col("b");
        let rand = rand_func().call(vec![]);
        let not_extracted_volatile = extracted_child + rand;
        let plan = LogicalPlanBuilder::from(table_scan)
            .project(vec![
                not_extracted_volatile.clone().alias("c1"),
                not_extracted_volatile.alias("c2"),
            ])?
            .build()?;

        let expected = "Projection: __common_expr_1 + random() AS c1, __common_expr_1 + random() AS c2\
        \n  Projection: test.a + test.b AS __common_expr_1, test.a, test.b, test.c\
        \n    TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        Ok(())
    }

    #[test]
    fn test_volatile_short_circuits() -> Result<()> {
        let table_scan = test_table_scan()?;

        let rand = rand_func().call(vec![]);
        let extracted_short_circuit_leg_1 = col("a").eq(lit(0));
        let not_extracted_volatile_short_circuit_1 =
            extracted_short_circuit_leg_1.or(rand.clone().eq(lit(0)));
        let not_extracted_short_circuit_leg_2 = col("b").eq(lit(0));
        let not_extracted_volatile_short_circuit_2 =
            rand.eq(lit(0)).or(not_extracted_short_circuit_leg_2);
        let plan = LogicalPlanBuilder::from(table_scan)
            .project(vec![
                not_extracted_volatile_short_circuit_1.clone().alias("c1"),
                not_extracted_volatile_short_circuit_1.alias("c2"),
                not_extracted_volatile_short_circuit_2.clone().alias("c3"),
                not_extracted_volatile_short_circuit_2.alias("c4"),
            ])?
            .build()?;

        let expected = "Projection: __common_expr_1 OR random() = Int32(0) AS c1, __common_expr_1 OR random() = Int32(0) AS c2, random() = Int32(0) OR test.b = Int32(0) AS c3, random() = Int32(0) OR test.b = Int32(0) AS c4\
        \n  Projection: test.a = Int32(0) AS __common_expr_1, test.a, test.b, test.c\
        \n    TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        Ok(())
    }

    #[test]
    fn test_non_top_level_common_expression() -> Result<()> {
        let table_scan = test_table_scan()?;

        let common_expr = col("a") + col("b");
        let plan = LogicalPlanBuilder::from(table_scan)
            .project(vec![
                common_expr.clone().alias("c1"),
                common_expr.alias("c2"),
            ])?
            .project(vec![col("c1"), col("c2")])?
            .build()?;

        let expected = "Projection: c1, c2\
        \n  Projection: __common_expr_1 AS c1, __common_expr_1 AS c2\
        \n    Projection: test.a + test.b AS __common_expr_1, test.a, test.b, test.c\
        \n      TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        Ok(())
    }

    #[test]
    fn test_nested_common_expression() -> Result<()> {
        let table_scan = test_table_scan()?;

        let nested_common_expr = col("a") + col("b");
        let common_expr = nested_common_expr.clone() * nested_common_expr;
        let plan = LogicalPlanBuilder::from(table_scan)
            .project(vec![
                common_expr.clone().alias("c1"),
                common_expr.alias("c2"),
            ])?
            .build()?;

        let expected = "Projection: __common_expr_1 AS c1, __common_expr_1 AS c2\
        \n  Projection: __common_expr_2 * __common_expr_2 AS __common_expr_1, test.a, test.b, test.c\
        \n    Projection: test.a + test.b AS __common_expr_2, test.a, test.b, test.c\
        \n      TableScan: test";

        assert_optimized_plan_eq(expected, plan, None);

        Ok(())
    }

    /// returns a "random" function that is marked volatile (aka each invocation
    /// returns a different value)
    ///
    /// Does not use datafusion_functions::rand to avoid introducing a
    /// dependency on that crate.
    fn rand_func() -> ScalarUDF {
        ScalarUDF::new_from_impl(RandomStub::new())
    }

    #[derive(Debug)]
    struct RandomStub {
        signature: Signature,
    }

    impl RandomStub {
        fn new() -> Self {
            Self {
                signature: Signature::exact(vec![], Volatility::Volatile),
            }
        }
    }
    impl ScalarUDFImpl for RandomStub {
        fn as_any(&self) -> &dyn Any {
            self
        }

        fn name(&self) -> &str {
            "random"
        }

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

        fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
            Ok(DataType::Float64)
        }

        fn invoke(&self, _args: &[ColumnarValue]) -> Result<ColumnarValue> {
            unimplemented!()
        }
    }
}