query/optimizer/
constant_term.rs

1// Copyright 2023 Greptime Team
2//
3// Licensed under the Apache License, Version 2.0 (the "License");
4// you may not use this file except in compliance with the License.
5// You may obtain a copy of the License at
6//
7//     http://www.apache.org/licenses/LICENSE-2.0
8//
9// Unless required by applicable law or agreed to in writing, software
10// distributed under the License is distributed on an "AS IS" BASIS,
11// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12// See the License for the specific language governing permissions and
13// limitations under the License.
14
15use std::fmt;
16use std::fmt::Formatter;
17use std::hash::{Hash, Hasher};
18use std::sync::Arc;
19
20use arrow::array::{AsArray, BooleanArray};
21use common_function::scalars::matches_term::MatchesTermFinder;
22use datafusion::config::ConfigOptions;
23use datafusion::error::Result as DfResult;
24use datafusion::physical_optimizer::PhysicalOptimizerRule;
25use datafusion::physical_plan::ExecutionPlan;
26use datafusion::physical_plan::filter::FilterExec;
27use datafusion_common::ScalarValue;
28use datafusion_common::tree_node::{Transformed, TreeNode};
29use datafusion_expr::ColumnarValue;
30use datafusion_physical_expr::expressions::Literal;
31use datafusion_physical_expr::{PhysicalExpr, ScalarFunctionExpr};
32
33/// A physical expression that uses a pre-compiled term finder for the `matches_term` function.
34///
35/// This expression optimizes the `matches_term` function by pre-compiling the term
36/// when the term is a constant value. This avoids recompiling the term for each row
37/// during execution.
38#[derive(Debug)]
39pub struct PreCompiledMatchesTermExpr {
40    /// The text column expression to search in
41    text: Arc<dyn PhysicalExpr>,
42    /// The constant term to search for
43    term: String,
44    /// The pre-compiled term finder
45    finder: MatchesTermFinder,
46
47    /// No used but show how index tokenizes the term basically.
48    /// Not precise due to column options is unknown but for debugging purpose in most cases it's enough.
49    probes: Vec<String>,
50}
51
52impl fmt::Display for PreCompiledMatchesTermExpr {
53    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
54        write!(
55            f,
56            "MatchesConstTerm({}, term: \"{}\", probes: {:?})",
57            self.text, self.term, self.probes
58        )
59    }
60}
61
62impl Hash for PreCompiledMatchesTermExpr {
63    fn hash<H: Hasher>(&self, state: &mut H) {
64        self.text.hash(state);
65        self.term.hash(state);
66    }
67}
68
69impl PartialEq for PreCompiledMatchesTermExpr {
70    fn eq(&self, other: &Self) -> bool {
71        self.text.eq(&other.text) && self.term.eq(&other.term)
72    }
73}
74
75impl Eq for PreCompiledMatchesTermExpr {}
76
77impl PhysicalExpr for PreCompiledMatchesTermExpr {
78    fn as_any(&self) -> &dyn std::any::Any {
79        self
80    }
81
82    fn data_type(
83        &self,
84        _input_schema: &arrow_schema::Schema,
85    ) -> datafusion::error::Result<arrow_schema::DataType> {
86        Ok(arrow_schema::DataType::Boolean)
87    }
88
89    fn nullable(&self, input_schema: &arrow_schema::Schema) -> datafusion::error::Result<bool> {
90        self.text.nullable(input_schema)
91    }
92
93    fn evaluate(
94        &self,
95        batch: &common_recordbatch::DfRecordBatch,
96    ) -> datafusion::error::Result<ColumnarValue> {
97        let num_rows = batch.num_rows();
98
99        let text_value = self.text.evaluate(batch)?;
100        let array = text_value.into_array(num_rows)?;
101        let str_array = array.as_string::<i32>();
102
103        let mut result = BooleanArray::builder(num_rows);
104        for text in str_array {
105            match text {
106                Some(text) => {
107                    result.append_value(self.finder.find(text));
108                }
109                None => {
110                    result.append_null();
111                }
112            }
113        }
114
115        Ok(ColumnarValue::Array(Arc::new(result.finish())))
116    }
117
118    fn children(&self) -> Vec<&Arc<dyn PhysicalExpr>> {
119        vec![&self.text]
120    }
121
122    fn with_new_children(
123        self: Arc<Self>,
124        children: Vec<Arc<dyn PhysicalExpr>>,
125    ) -> datafusion::error::Result<Arc<dyn PhysicalExpr>> {
126        Ok(Arc::new(PreCompiledMatchesTermExpr {
127            text: children[0].clone(),
128            term: self.term.clone(),
129            finder: self.finder.clone(),
130            probes: self.probes.clone(),
131        }))
132    }
133
134    fn fmt_sql(&self, f: &mut Formatter<'_>) -> fmt::Result {
135        write!(f, "{}", self)
136    }
137}
138
139/// Optimizer rule that pre-compiles constant term in `matches_term` function.
140///
141/// This optimizer looks for `matches_term` function calls where the second argument
142/// (the term to match) is a constant value. When found, it replaces the function
143/// call with a specialized `PreCompiledMatchesTermExpr` that uses a pre-compiled
144/// term finder.
145///
146/// Example:
147/// ```sql
148/// -- Before optimization:
149/// matches_term(text_column, 'constant_term')
150///
151/// -- After optimization:
152/// PreCompiledMatchesTermExpr(text_column, 'constant_term')
153/// ```
154///
155/// This optimization improves performance by:
156/// 1. Pre-compiling the term once instead of for each row
157/// 2. Using a specialized expression that avoids function call overhead
158#[derive(Debug)]
159pub struct MatchesConstantTermOptimizer;
160
161impl PhysicalOptimizerRule for MatchesConstantTermOptimizer {
162    fn optimize(
163        &self,
164        plan: Arc<dyn ExecutionPlan>,
165        _config: &ConfigOptions,
166    ) -> DfResult<Arc<dyn ExecutionPlan>> {
167        let res = plan
168            .transform_down(&|plan: Arc<dyn ExecutionPlan>| {
169                if let Some(filter) = plan.as_any().downcast_ref::<FilterExec>() {
170                    let pred = filter.predicate().clone();
171                    let new_pred = pred.transform_down(&|expr: Arc<dyn PhysicalExpr>| {
172                        if let Some(func) = expr.as_any().downcast_ref::<ScalarFunctionExpr>() {
173                            if !func.name().eq_ignore_ascii_case("matches_term") {
174                                return Ok(Transformed::no(expr));
175                            }
176                            let args = func.args();
177                            if args.len() != 2 {
178                                return Ok(Transformed::no(expr));
179                            }
180
181                            if let Some(lit) = args[1].as_any().downcast_ref::<Literal>()
182                                && let ScalarValue::Utf8(Some(term)) = lit.value()
183                            {
184                                let finder = MatchesTermFinder::new(term);
185
186                                // For debugging purpose. Not really precise but enough for most cases.
187                                let probes = term
188                                    .split(|c: char| !c.is_alphanumeric() && c != '_')
189                                    .filter(|s| !s.is_empty())
190                                    .map(|s| s.to_string())
191                                    .collect();
192
193                                let expr = PreCompiledMatchesTermExpr {
194                                    text: args[0].clone(),
195                                    term: term.clone(),
196                                    finder,
197                                    probes,
198                                };
199
200                                return Ok(Transformed::yes(Arc::new(expr)));
201                            }
202                        }
203
204                        Ok(Transformed::no(expr))
205                    })?;
206
207                    if new_pred.transformed {
208                        let exec = FilterExec::try_new(new_pred.data, filter.input().clone())?
209                            .with_default_selectivity(filter.default_selectivity())?
210                            .with_projection(filter.projection().cloned())?;
211                        return Ok(Transformed::yes(Arc::new(exec) as _));
212                    }
213                }
214
215                Ok(Transformed::no(plan))
216            })?
217            .data;
218
219        Ok(res)
220    }
221
222    fn name(&self) -> &str {
223        "MatchesConstantTerm"
224    }
225
226    fn schema_check(&self) -> bool {
227        false
228    }
229}
230
231#[cfg(test)]
232mod tests {
233    use std::sync::Arc;
234
235    use arrow::array::{ArrayRef, StringArray};
236    use arrow::datatypes::{DataType, Field, Schema};
237    use arrow::record_batch::RecordBatch;
238    use catalog::RegisterTableRequest;
239    use catalog::memory::MemoryCatalogManager;
240    use common_catalog::consts::{DEFAULT_CATALOG_NAME, DEFAULT_SCHEMA_NAME};
241    use common_function::scalars::matches_term::MatchesTermFunction;
242    use common_function::scalars::udf::create_udf;
243    use datafusion::datasource::memory::MemorySourceConfig;
244    use datafusion::datasource::source::DataSourceExec;
245    use datafusion::physical_optimizer::PhysicalOptimizerRule;
246    use datafusion::physical_plan::filter::FilterExec;
247    use datafusion::physical_plan::get_plan_string;
248    use datafusion_common::{Column, DFSchema};
249    use datafusion_expr::expr::ScalarFunction;
250    use datafusion_expr::{Expr, Literal, ScalarUDF};
251    use datafusion_physical_expr::{ScalarFunctionExpr, create_physical_expr};
252    use datatypes::prelude::ConcreteDataType;
253    use datatypes::schema::ColumnSchema;
254    use session::context::QueryContext;
255    use table::metadata::{TableInfoBuilder, TableMetaBuilder};
256    use table::test_util::EmptyTable;
257
258    use super::*;
259    use crate::parser::QueryLanguageParser;
260    use crate::{QueryEngineFactory, QueryEngineRef};
261
262    fn create_test_batch() -> RecordBatch {
263        let schema = Schema::new(vec![Field::new("text", DataType::Utf8, true)]);
264
265        let text_array = StringArray::from(vec![
266            Some("hello world"),
267            Some("greeting"),
268            Some("hello there"),
269            None,
270        ]);
271
272        RecordBatch::try_new(Arc::new(schema), vec![Arc::new(text_array) as ArrayRef]).unwrap()
273    }
274
275    fn create_test_engine() -> QueryEngineRef {
276        let table_name = "test".to_string();
277        let columns = vec![
278            ColumnSchema::new(
279                "text".to_string(),
280                ConcreteDataType::string_datatype(),
281                false,
282            ),
283            ColumnSchema::new(
284                "timestamp".to_string(),
285                ConcreteDataType::timestamp_millisecond_datatype(),
286                false,
287            )
288            .with_time_index(true),
289        ];
290
291        let schema = Arc::new(datatypes::schema::Schema::new(columns));
292        let table_meta = TableMetaBuilder::empty()
293            .schema(schema)
294            .primary_key_indices(vec![])
295            .value_indices(vec![0])
296            .next_column_id(2)
297            .build()
298            .unwrap();
299        let table_info = TableInfoBuilder::default()
300            .name(&table_name)
301            .meta(table_meta)
302            .build()
303            .unwrap();
304        let table = EmptyTable::from_table_info(&table_info);
305        let catalog_list = MemoryCatalogManager::with_default_setup();
306        assert!(
307            catalog_list
308                .register_table_sync(RegisterTableRequest {
309                    catalog: DEFAULT_CATALOG_NAME.to_string(),
310                    schema: DEFAULT_SCHEMA_NAME.to_string(),
311                    table_name,
312                    table_id: 1024,
313                    table,
314                })
315                .is_ok()
316        );
317        QueryEngineFactory::new(
318            catalog_list,
319            None,
320            None,
321            None,
322            None,
323            false,
324            Default::default(),
325        )
326        .query_engine()
327    }
328
329    fn matches_term_udf() -> Arc<ScalarUDF> {
330        Arc::new(create_udf(Arc::new(MatchesTermFunction::default())))
331    }
332
333    #[test]
334    fn test_matches_term_optimization() {
335        let batch = create_test_batch();
336
337        // Create a predicate with a constant pattern
338        let predicate = create_physical_expr(
339            &Expr::ScalarFunction(ScalarFunction::new_udf(
340                matches_term_udf(),
341                vec![Expr::Column(Column::from_name("text")), "hello".lit()],
342            )),
343            &DFSchema::try_from(batch.schema().clone()).unwrap(),
344            &Default::default(),
345        )
346        .unwrap();
347
348        let input = DataSourceExec::from_data_source(
349            MemorySourceConfig::try_new(&[vec![batch.clone()]], batch.schema(), None).unwrap(),
350        );
351        let filter = FilterExec::try_new(predicate, input).unwrap();
352
353        // Apply the optimizer
354        let optimizer = MatchesConstantTermOptimizer;
355        let optimized_plan = optimizer
356            .optimize(Arc::new(filter), &Default::default())
357            .unwrap();
358
359        let optimized_filter = optimized_plan
360            .as_any()
361            .downcast_ref::<FilterExec>()
362            .unwrap();
363        let predicate = optimized_filter.predicate();
364
365        // The predicate should be a PreCompiledMatchesTermExpr
366        assert!(
367            std::any::TypeId::of::<PreCompiledMatchesTermExpr>() == predicate.as_any().type_id()
368        );
369    }
370
371    #[test]
372    fn test_matches_term_no_optimization() {
373        let batch = create_test_batch();
374
375        // Create a predicate with a non-constant pattern
376        let predicate = create_physical_expr(
377            &Expr::ScalarFunction(ScalarFunction::new_udf(
378                matches_term_udf(),
379                vec![
380                    Expr::Column(Column::from_name("text")),
381                    Expr::Column(Column::from_name("text")),
382                ],
383            )),
384            &DFSchema::try_from(batch.schema().clone()).unwrap(),
385            &Default::default(),
386        )
387        .unwrap();
388
389        let input = DataSourceExec::from_data_source(
390            MemorySourceConfig::try_new(&[vec![batch.clone()]], batch.schema(), None).unwrap(),
391        );
392        let filter = FilterExec::try_new(predicate, input).unwrap();
393
394        let optimizer = MatchesConstantTermOptimizer;
395        let optimized_plan = optimizer
396            .optimize(Arc::new(filter), &Default::default())
397            .unwrap();
398
399        let optimized_filter = optimized_plan
400            .as_any()
401            .downcast_ref::<FilterExec>()
402            .unwrap();
403        let predicate = optimized_filter.predicate();
404
405        // The predicate should still be a ScalarFunctionExpr
406        assert!(std::any::TypeId::of::<ScalarFunctionExpr>() == predicate.as_any().type_id());
407    }
408
409    #[tokio::test]
410    async fn test_matches_term_optimization_from_sql() {
411        let sql = "WITH base AS (
412        SELECT text, timestamp FROM test 
413        WHERE MATCHES_TERM(text, 'hello wo_rld') 
414        AND timestamp > '2025-01-01 00:00:00'
415    ),
416    subquery1 AS (
417        SELECT * FROM base 
418        WHERE MATCHES_TERM(text, 'world')
419    ),
420    subquery2 AS (
421        SELECT * FROM test 
422        WHERE MATCHES_TERM(text, 'greeting') 
423        AND timestamp < '2025-01-02 00:00:00'
424    ),
425    union_result AS (
426        SELECT * FROM subquery1 
427        UNION ALL 
428        SELECT * FROM subquery2
429    ),
430    joined_data AS (
431        SELECT a.text, a.timestamp, b.text as other_text 
432        FROM union_result a 
433        JOIN test b ON a.timestamp = b.timestamp 
434        WHERE MATCHES_TERM(a.text, 'there')
435    )
436    SELECT text, other_text 
437    FROM joined_data 
438    WHERE MATCHES_TERM(text, '42') 
439    AND MATCHES_TERM(other_text, 'foo')";
440
441        let query_ctx = QueryContext::arc();
442
443        let stmt = QueryLanguageParser::parse_sql(sql, &query_ctx).unwrap();
444        let engine = create_test_engine();
445        let logical_plan = engine
446            .planner()
447            .plan(&stmt, query_ctx.clone())
448            .await
449            .unwrap();
450
451        let engine_ctx = engine.engine_context(query_ctx);
452        let state = engine_ctx.state();
453
454        let analyzed_plan = state
455            .analyzer()
456            .execute_and_check(logical_plan.clone(), state.config_options(), |_, _| {})
457            .unwrap();
458
459        let optimized_plan = state
460            .optimizer()
461            .optimize(analyzed_plan, state, |_, _| {})
462            .unwrap();
463
464        let physical_plan = state
465            .query_planner()
466            .create_physical_plan(&optimized_plan, state)
467            .await
468            .unwrap();
469
470        let plan_str = get_plan_string(&physical_plan).join("\n");
471        assert!(plan_str.contains("MatchesConstTerm(text@0, term: \"foo\", probes: [\"foo\"]"));
472        assert!(plan_str.contains(
473            "MatchesConstTerm(text@0, term: \"hello wo_rld\", probes: [\"hello\", \"wo_rld\"]"
474        ));
475        assert!(plan_str.contains("MatchesConstTerm(text@0, term: \"world\", probes: [\"world\"]"));
476        assert!(
477            plan_str
478                .contains("MatchesConstTerm(text@0, term: \"greeting\", probes: [\"greeting\"]")
479        );
480        assert!(plan_str.contains("MatchesConstTerm(text@0, term: \"there\", probes: [\"there\"]"));
481        assert!(plan_str.contains("MatchesConstTerm(text@0, term: \"42\", probes: [\"42\"]"));
482        assert!(!plan_str.contains("matches_term"))
483    }
484}
query/optimizer/constant_term.rs

query/optimizer/
constant_term.rs
