datafusion_physical_expr/utils/
mod.rs

1// Licensed to the Apache Software Foundation (ASF) under one
2// or more contributor license agreements.  See the NOTICE file
3// distributed with this work for additional information
4// regarding copyright ownership.  The ASF licenses this file
5// to you under the Apache License, Version 2.0 (the
6// "License"); you may not use this file except in compliance
7// with the License.  You may obtain a copy of the License at
8//
9//   http://www.apache.org/licenses/LICENSE-2.0
10//
11// Unless required by applicable law or agreed to in writing,
12// software distributed under the License is distributed on an
13// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
14// KIND, either express or implied.  See the License for the
15// specific language governing permissions and limitations
16// under the License.
17
18mod guarantee;
19pub use guarantee::{Guarantee, LiteralGuarantee};
20
21use std::borrow::Borrow;
22use std::sync::Arc;
23
24use crate::expressions::{BinaryExpr, Column};
25use crate::tree_node::ExprContext;
26use crate::PhysicalExpr;
27use crate::PhysicalSortExpr;
28
29use arrow::datatypes::Schema;
30use datafusion_common::tree_node::{
31    Transformed, TransformedResult, TreeNode, TreeNodeRecursion,
32};
33use datafusion_common::{HashMap, HashSet, Result};
34use datafusion_expr::Operator;
35
36use petgraph::graph::NodeIndex;
37use petgraph::stable_graph::StableGraph;
38
39/// Assume the predicate is in the form of CNF, split the predicate to a Vec of PhysicalExprs.
40///
41/// For example, split "a1 = a2 AND b1 <= b2 AND c1 != c2" into ["a1 = a2", "b1 <= b2", "c1 != c2"]
42pub fn split_conjunction(
43    predicate: &Arc<dyn PhysicalExpr>,
44) -> Vec<&Arc<dyn PhysicalExpr>> {
45    split_impl(Operator::And, predicate, vec![])
46}
47
48/// Create a conjunction of the given predicates.
49/// If the input is empty, return a literal true.
50/// If the input contains a single predicate, return the predicate.
51/// Otherwise, return a conjunction of the predicates (e.g. `a AND b AND c`).
52pub fn conjunction(
53    predicates: impl IntoIterator<Item = Arc<dyn PhysicalExpr>>,
54) -> Arc<dyn PhysicalExpr> {
55    conjunction_opt(predicates).unwrap_or_else(|| crate::expressions::lit(true))
56}
57
58/// Create a conjunction of the given predicates.
59/// If the input is empty or the return None.
60/// If the input contains a single predicate, return Some(predicate).
61/// Otherwise, return a Some(..) of a conjunction of the predicates (e.g. `Some(a AND b AND c)`).
62pub fn conjunction_opt(
63    predicates: impl IntoIterator<Item = Arc<dyn PhysicalExpr>>,
64) -> Option<Arc<dyn PhysicalExpr>> {
65    predicates
66        .into_iter()
67        .fold(None, |acc, predicate| match acc {
68            None => Some(predicate),
69            Some(acc) => Some(Arc::new(BinaryExpr::new(acc, Operator::And, predicate))),
70        })
71}
72
73/// Assume the predicate is in the form of DNF, split the predicate to a Vec of PhysicalExprs.
74///
75/// For example, split "a1 = a2 OR b1 <= b2 OR c1 != c2" into ["a1 = a2", "b1 <= b2", "c1 != c2"]
76pub fn split_disjunction(
77    predicate: &Arc<dyn PhysicalExpr>,
78) -> Vec<&Arc<dyn PhysicalExpr>> {
79    split_impl(Operator::Or, predicate, vec![])
80}
81
82fn split_impl<'a>(
83    operator: Operator,
84    predicate: &'a Arc<dyn PhysicalExpr>,
85    mut exprs: Vec<&'a Arc<dyn PhysicalExpr>>,
86) -> Vec<&'a Arc<dyn PhysicalExpr>> {
87    match predicate.as_any().downcast_ref::<BinaryExpr>() {
88        Some(binary) if binary.op() == &operator => {
89            let exprs = split_impl(operator, binary.left(), exprs);
90            split_impl(operator, binary.right(), exprs)
91        }
92        Some(_) | None => {
93            exprs.push(predicate);
94            exprs
95        }
96    }
97}
98
99/// This function maps back requirement after ProjectionExec
100/// to the Executor for its input.
101// Specifically, `ProjectionExec` changes index of `Column`s in the schema of its input executor.
102// This function changes requirement given according to ProjectionExec schema to the requirement
103// according to schema of input executor to the ProjectionExec.
104// For instance, Column{"a", 0} would turn to Column{"a", 1}. Please note that this function assumes that
105// name of the Column is unique. If we have a requirement such that Column{"a", 0}, Column{"a", 1}.
106// This function will produce incorrect result (It will only emit single Column as a result).
107pub fn map_columns_before_projection(
108    parent_required: &[Arc<dyn PhysicalExpr>],
109    proj_exprs: &[(Arc<dyn PhysicalExpr>, String)],
110) -> Vec<Arc<dyn PhysicalExpr>> {
111    if parent_required.is_empty() {
112        // No need to build mapping.
113        return vec![];
114    }
115    let column_mapping = proj_exprs
116        .iter()
117        .filter_map(|(expr, name)| {
118            expr.as_any()
119                .downcast_ref::<Column>()
120                .map(|column| (name.clone(), column.clone()))
121        })
122        .collect::<HashMap<_, _>>();
123    parent_required
124        .iter()
125        .filter_map(|r| {
126            r.as_any()
127                .downcast_ref::<Column>()
128                .and_then(|c| column_mapping.get(c.name()))
129        })
130        .map(|e| Arc::new(e.clone()) as _)
131        .collect()
132}
133
134/// This function returns all `Arc<dyn PhysicalExpr>`s inside the given
135/// `PhysicalSortExpr` sequence.
136pub fn convert_to_expr<T: Borrow<PhysicalSortExpr>>(
137    sequence: impl IntoIterator<Item = T>,
138) -> Vec<Arc<dyn PhysicalExpr>> {
139    sequence
140        .into_iter()
141        .map(|elem| Arc::clone(&elem.borrow().expr))
142        .collect()
143}
144
145/// This function finds the indices of `targets` within `items` using strict
146/// equality.
147pub fn get_indices_of_exprs_strict<T: Borrow<Arc<dyn PhysicalExpr>>>(
148    targets: impl IntoIterator<Item = T>,
149    items: &[Arc<dyn PhysicalExpr>],
150) -> Vec<usize> {
151    targets
152        .into_iter()
153        .filter_map(|target| items.iter().position(|e| e.eq(target.borrow())))
154        .collect()
155}
156
157pub type ExprTreeNode<T> = ExprContext<Option<T>>;
158
159/// This struct is used to convert a [`PhysicalExpr`] tree into a DAEG (i.e. an expression
160/// DAG) by collecting identical expressions in one node. Caller specifies the node type
161/// in the DAEG via the `constructor` argument, which constructs nodes in the DAEG from
162/// the [`ExprTreeNode`] ancillary object.
163struct PhysicalExprDAEGBuilder<'a, T, F: Fn(&ExprTreeNode<NodeIndex>) -> Result<T>> {
164    // The resulting DAEG (expression DAG).
165    graph: StableGraph<T, usize>,
166    // A vector of visited expression nodes and their corresponding node indices.
167    visited_plans: Vec<(Arc<dyn PhysicalExpr>, NodeIndex)>,
168    // A function to convert an input expression node to T.
169    constructor: &'a F,
170}
171
172impl<T, F: Fn(&ExprTreeNode<NodeIndex>) -> Result<T>> PhysicalExprDAEGBuilder<'_, T, F> {
173    // This method mutates an expression node by transforming it to a physical expression
174    // and adding it to the graph. The method returns the mutated expression node.
175    fn mutate(
176        &mut self,
177        mut node: ExprTreeNode<NodeIndex>,
178    ) -> Result<Transformed<ExprTreeNode<NodeIndex>>> {
179        // Get the expression associated with the input expression node.
180        let expr = &node.expr;
181
182        // Check if the expression has already been visited.
183        let node_idx = match self.visited_plans.iter().find(|(e, _)| expr.eq(e)) {
184            // If the expression has been visited, return the corresponding node index.
185            Some((_, idx)) => *idx,
186            // If the expression has not been visited, add a new node to the graph and
187            // add edges to its child nodes. Add the visited expression to the vector
188            // of visited expressions and return the newly created node index.
189            None => {
190                let node_idx = self.graph.add_node((self.constructor)(&node)?);
191                for expr_node in node.children.iter() {
192                    self.graph.add_edge(node_idx, expr_node.data.unwrap(), 0);
193                }
194                self.visited_plans.push((Arc::clone(expr), node_idx));
195                node_idx
196            }
197        };
198        // Set the data field of the input expression node to the corresponding node index.
199        node.data = Some(node_idx);
200        // Return the mutated expression node.
201        Ok(Transformed::yes(node))
202    }
203}
204
205// A function that builds a directed acyclic graph of physical expression trees.
206pub fn build_dag<T, F>(
207    expr: Arc<dyn PhysicalExpr>,
208    constructor: &F,
209) -> Result<(NodeIndex, StableGraph<T, usize>)>
210where
211    F: Fn(&ExprTreeNode<NodeIndex>) -> Result<T>,
212{
213    // Create a new expression tree node from the input expression.
214    let init = ExprTreeNode::new_default(expr);
215    // Create a new `PhysicalExprDAEGBuilder` instance.
216    let mut builder = PhysicalExprDAEGBuilder {
217        graph: StableGraph::<T, usize>::new(),
218        visited_plans: Vec::<(Arc<dyn PhysicalExpr>, NodeIndex)>::new(),
219        constructor,
220    };
221    // Use the builder to transform the expression tree node into a DAG.
222    let root = init.transform_up(|node| builder.mutate(node)).data()?;
223    // Return a tuple containing the root node index and the DAG.
224    Ok((root.data.unwrap(), builder.graph))
225}
226
227/// Recursively extract referenced [`Column`]s within a [`PhysicalExpr`].
228pub fn collect_columns(expr: &Arc<dyn PhysicalExpr>) -> HashSet<Column> {
229    let mut columns = HashSet::<Column>::new();
230    expr.apply(|expr| {
231        if let Some(column) = expr.as_any().downcast_ref::<Column>() {
232            columns.get_or_insert_owned(column);
233        }
234        Ok(TreeNodeRecursion::Continue)
235    })
236    // pre_visit always returns OK, so this will always too
237    .expect("no way to return error during recursion");
238    columns
239}
240
241/// Re-assign indices of [`Column`]s within the given [`PhysicalExpr`] according to
242/// the provided [`Schema`].
243///
244/// This can be useful when attempting to map an expression onto a different schema.
245///
246/// # Errors
247///
248/// This function will return an error if any column in the expression cannot be found
249/// in the provided schema.
250pub fn reassign_expr_columns(
251    expr: Arc<dyn PhysicalExpr>,
252    schema: &Schema,
253) -> Result<Arc<dyn PhysicalExpr>> {
254    expr.transform_down(|expr| {
255        if let Some(column) = expr.as_any().downcast_ref::<Column>() {
256            let index = schema.index_of(column.name())?;
257
258            return Ok(Transformed::yes(Arc::new(Column::new(
259                column.name(),
260                index,
261            ))));
262        }
263        Ok(Transformed::no(expr))
264    })
265    .data()
266}
267
268#[cfg(test)]
269pub(crate) mod tests {
270    use std::any::Any;
271    use std::fmt::{Display, Formatter};
272
273    use super::*;
274    use crate::expressions::{binary, cast, col, in_list, lit, Literal};
275
276    use arrow::array::{ArrayRef, Float32Array, Float64Array};
277    use arrow::datatypes::{DataType, Field, Schema};
278    use datafusion_common::{exec_err, internal_datafusion_err, ScalarValue};
279    use datafusion_expr::sort_properties::{ExprProperties, SortProperties};
280    use datafusion_expr::{
281        ColumnarValue, ScalarFunctionArgs, ScalarUDFImpl, Signature, Volatility,
282    };
283
284    use petgraph::visit::Bfs;
285
286    #[derive(Debug, PartialEq, Eq, Hash)]
287    pub struct TestScalarUDF {
288        pub(crate) signature: Signature,
289    }
290
291    impl TestScalarUDF {
292        pub fn new() -> Self {
293            use DataType::*;
294            Self {
295                signature: Signature::uniform(
296                    1,
297                    vec![Float64, Float32],
298                    Volatility::Immutable,
299                ),
300            }
301        }
302    }
303
304    impl ScalarUDFImpl for TestScalarUDF {
305        fn as_any(&self) -> &dyn Any {
306            self
307        }
308        fn name(&self) -> &str {
309            "test-scalar-udf"
310        }
311
312        fn signature(&self) -> &Signature {
313            &self.signature
314        }
315
316        fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> {
317            let arg_type = &arg_types[0];
318
319            match arg_type {
320                DataType::Float32 => Ok(DataType::Float32),
321                _ => Ok(DataType::Float64),
322            }
323        }
324
325        fn output_ordering(&self, input: &[ExprProperties]) -> Result<SortProperties> {
326            Ok(input[0].sort_properties)
327        }
328
329        fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> {
330            let args = ColumnarValue::values_to_arrays(&args.args)?;
331
332            let arr: ArrayRef = match args[0].data_type() {
333                DataType::Float64 => Arc::new({
334                    let arg = &args[0]
335                        .as_any()
336                        .downcast_ref::<Float64Array>()
337                        .ok_or_else(|| {
338                            internal_datafusion_err!(
339                                "could not cast {} to {}",
340                                self.name(),
341                                std::any::type_name::<Float64Array>()
342                            )
343                        })?;
344
345                    arg.iter()
346                        .map(|a| a.map(f64::floor))
347                        .collect::<Float64Array>()
348                }),
349                DataType::Float32 => Arc::new({
350                    let arg = &args[0]
351                        .as_any()
352                        .downcast_ref::<Float32Array>()
353                        .ok_or_else(|| {
354                            internal_datafusion_err!(
355                                "could not cast {} to {}",
356                                self.name(),
357                                std::any::type_name::<Float32Array>()
358                            )
359                        })?;
360
361                    arg.iter()
362                        .map(|a| a.map(f32::floor))
363                        .collect::<Float32Array>()
364                }),
365                other => {
366                    return exec_err!(
367                        "Unsupported data type {other:?} for function {}",
368                        self.name()
369                    );
370                }
371            };
372            Ok(ColumnarValue::Array(arr))
373        }
374    }
375
376    #[derive(Clone)]
377    struct DummyProperty {
378        expr_type: String,
379    }
380
381    /// This is a dummy node in the DAEG; it stores a reference to the actual
382    /// [PhysicalExpr] as well as a dummy property.
383    #[derive(Clone)]
384    struct PhysicalExprDummyNode {
385        pub expr: Arc<dyn PhysicalExpr>,
386        pub property: DummyProperty,
387    }
388
389    impl Display for PhysicalExprDummyNode {
390        fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
391            write!(f, "{}", self.expr)
392        }
393    }
394
395    fn make_dummy_node(node: &ExprTreeNode<NodeIndex>) -> Result<PhysicalExprDummyNode> {
396        let expr = Arc::clone(&node.expr);
397        let dummy_property = if expr.as_any().is::<BinaryExpr>() {
398            "Binary"
399        } else if expr.as_any().is::<Column>() {
400            "Column"
401        } else if expr.as_any().is::<Literal>() {
402            "Literal"
403        } else {
404            "Other"
405        }
406        .to_owned();
407        Ok(PhysicalExprDummyNode {
408            expr,
409            property: DummyProperty {
410                expr_type: dummy_property,
411            },
412        })
413    }
414
415    #[test]
416    fn test_build_dag() -> Result<()> {
417        let schema = Schema::new(vec![
418            Field::new("0", DataType::Int32, true),
419            Field::new("1", DataType::Int32, true),
420            Field::new("2", DataType::Int32, true),
421        ]);
422        let expr = binary(
423            cast(
424                binary(
425                    col("0", &schema)?,
426                    Operator::Plus,
427                    col("1", &schema)?,
428                    &schema,
429                )?,
430                &schema,
431                DataType::Int64,
432            )?,
433            Operator::Gt,
434            binary(
435                cast(col("2", &schema)?, &schema, DataType::Int64)?,
436                Operator::Plus,
437                lit(ScalarValue::Int64(Some(10))),
438                &schema,
439            )?,
440            &schema,
441        )?;
442        let mut vector_dummy_props = vec![];
443        let (root, graph) = build_dag(expr, &make_dummy_node)?;
444        let mut bfs = Bfs::new(&graph, root);
445        while let Some(node_index) = bfs.next(&graph) {
446            let node = &graph[node_index];
447            vector_dummy_props.push(node.property.clone());
448        }
449
450        assert_eq!(
451            vector_dummy_props
452                .iter()
453                .filter(|property| property.expr_type == "Binary")
454                .count(),
455            3
456        );
457        assert_eq!(
458            vector_dummy_props
459                .iter()
460                .filter(|property| property.expr_type == "Column")
461                .count(),
462            3
463        );
464        assert_eq!(
465            vector_dummy_props
466                .iter()
467                .filter(|property| property.expr_type == "Literal")
468                .count(),
469            1
470        );
471        assert_eq!(
472            vector_dummy_props
473                .iter()
474                .filter(|property| property.expr_type == "Other")
475                .count(),
476            2
477        );
478        Ok(())
479    }
480
481    #[test]
482    fn test_convert_to_expr() -> Result<()> {
483        let schema = Schema::new(vec![Field::new("a", DataType::UInt64, false)]);
484        let sort_expr = vec![PhysicalSortExpr {
485            expr: col("a", &schema)?,
486            options: Default::default(),
487        }];
488        assert!(convert_to_expr(&sort_expr)[0].eq(&sort_expr[0].expr));
489        Ok(())
490    }
491
492    #[test]
493    fn test_get_indices_of_exprs_strict() {
494        let list1: Vec<Arc<dyn PhysicalExpr>> = vec![
495            Arc::new(Column::new("a", 0)),
496            Arc::new(Column::new("b", 1)),
497            Arc::new(Column::new("c", 2)),
498            Arc::new(Column::new("d", 3)),
499        ];
500        let list2: Vec<Arc<dyn PhysicalExpr>> = vec![
501            Arc::new(Column::new("b", 1)),
502            Arc::new(Column::new("c", 2)),
503            Arc::new(Column::new("a", 0)),
504        ];
505        assert_eq!(get_indices_of_exprs_strict(&list1, &list2), vec![2, 0, 1]);
506        assert_eq!(get_indices_of_exprs_strict(&list2, &list1), vec![1, 2, 0]);
507    }
508
509    #[test]
510    fn test_reassign_expr_columns_in_list() {
511        let int_field = Field::new("should_not_matter", DataType::Int64, true);
512        let dict_field = Field::new(
513            "id",
514            DataType::Dictionary(Box::new(DataType::Int32), Box::new(DataType::Utf8)),
515            true,
516        );
517        let schema_small = Arc::new(Schema::new(vec![dict_field.clone()]));
518        let schema_big = Arc::new(Schema::new(vec![int_field, dict_field]));
519        let pred = in_list(
520            Arc::new(Column::new_with_schema("id", &schema_big).unwrap()),
521            vec![lit(ScalarValue::Dictionary(
522                Box::new(DataType::Int32),
523                Box::new(ScalarValue::from("2")),
524            ))],
525            &false,
526            &schema_big,
527        )
528        .unwrap();
529
530        let actual = reassign_expr_columns(pred, &schema_small).unwrap();
531
532        let expected = in_list(
533            Arc::new(Column::new_with_schema("id", &schema_small).unwrap()),
534            vec![lit(ScalarValue::Dictionary(
535                Box::new(DataType::Int32),
536                Box::new(ScalarValue::from("2")),
537            ))],
538            &false,
539            &schema_small,
540        )
541        .unwrap();
542
543        assert_eq!(actual.as_ref(), expected.as_ref());
544    }
545
546    #[test]
547    fn test_collect_columns() -> Result<()> {
548        let expr1 = Arc::new(Column::new("col1", 2)) as _;
549        let mut expected = HashSet::new();
550        expected.insert(Column::new("col1", 2));
551        assert_eq!(collect_columns(&expr1), expected);
552
553        let expr2 = Arc::new(Column::new("col2", 5)) as _;
554        let mut expected = HashSet::new();
555        expected.insert(Column::new("col2", 5));
556        assert_eq!(collect_columns(&expr2), expected);
557
558        let expr3 = Arc::new(BinaryExpr::new(expr1, Operator::Plus, expr2)) as _;
559        let mut expected = HashSet::new();
560        expected.insert(Column::new("col1", 2));
561        expected.insert(Column::new("col2", 5));
562        assert_eq!(collect_columns(&expr3), expected);
563        Ok(())
564    }
565}
datafusion_physical_expr/utils/mod.rs

datafusion_physical_expr/utils/
mod.rs
