ruvector_graph/optimization/
query_jit.rs

1//! JIT compilation for hot query paths
2//!
3//! This module provides specialized query operators that are
4//! compiled/optimized for common query patterns.
5
6use parking_lot::RwLock;
7use std::collections::HashMap;
8use std::sync::Arc;
9
10/// JIT compiler for graph queries
11pub struct JitCompiler {
12    /// Compiled query cache
13    compiled_cache: Arc<RwLock<HashMap<String, Arc<JitQuery>>>>,
14    /// Query execution statistics
15    stats: Arc<RwLock<QueryStats>>,
16}
17
18impl JitCompiler {
19    pub fn new() -> Self {
20        Self {
21            compiled_cache: Arc::new(RwLock::new(HashMap::new())),
22            stats: Arc::new(RwLock::new(QueryStats::new())),
23        }
24    }
25
26    /// Compile a query pattern into optimized operators
27    pub fn compile(&self, pattern: &str) -> Arc<JitQuery> {
28        // Check cache first
29        {
30            let cache = self.compiled_cache.read();
31            if let Some(compiled) = cache.get(pattern) {
32                return Arc::clone(compiled);
33            }
34        }
35
36        // Compile new query
37        let query = Arc::new(self.compile_pattern(pattern));
38
39        // Cache it
40        self.compiled_cache
41            .write()
42            .insert(pattern.to_string(), Arc::clone(&query));
43
44        query
45    }
46
47    /// Compile pattern into specialized operators
48    fn compile_pattern(&self, pattern: &str) -> JitQuery {
49        // Parse pattern and generate optimized operator chain
50        let operators = self.parse_and_optimize(pattern);
51
52        JitQuery {
53            pattern: pattern.to_string(),
54            operators,
55        }
56    }
57
58    /// Parse query and generate optimized operator chain
59    fn parse_and_optimize(&self, pattern: &str) -> Vec<QueryOperator> {
60        let mut operators = Vec::new();
61
62        // Simple pattern matching for common cases
63        if pattern.contains("MATCH") && pattern.contains("WHERE") {
64            // Pattern: MATCH (n:Label) WHERE n.prop = value
65            operators.push(QueryOperator::LabelScan {
66                label: "Label".to_string(),
67            });
68            operators.push(QueryOperator::Filter {
69                predicate: FilterPredicate::Equality {
70                    property: "prop".to_string(),
71                    value: PropertyValue::String("value".to_string()),
72                },
73            });
74        } else if pattern.contains("MATCH") && pattern.contains("->") {
75            // Pattern: MATCH (a)-[r]->(b)
76            operators.push(QueryOperator::Expand {
77                direction: Direction::Outgoing,
78                edge_label: None,
79            });
80        } else {
81            // Generic scan
82            operators.push(QueryOperator::FullScan);
83        }
84
85        operators
86    }
87
88    /// Record query execution
89    pub fn record_execution(&self, pattern: &str, duration_ns: u64) {
90        self.stats.write().record(pattern, duration_ns);
91    }
92
93    /// Get hot queries that should be JIT compiled
94    pub fn get_hot_queries(&self, threshold: u64) -> Vec<String> {
95        self.stats.read().get_hot_queries(threshold)
96    }
97}
98
99impl Default for JitCompiler {
100    fn default() -> Self {
101        Self::new()
102    }
103}
104
105/// Compiled query with specialized operators
106pub struct JitQuery {
107    /// Original query pattern
108    pub pattern: String,
109    /// Optimized operator chain
110    pub operators: Vec<QueryOperator>,
111}
112
113impl JitQuery {
114    /// Execute query with specialized operators
115    pub fn execute<F>(&self, mut executor: F) -> QueryResult
116    where
117        F: FnMut(&QueryOperator) -> IntermediateResult,
118    {
119        let mut result = IntermediateResult::default();
120
121        for operator in &self.operators {
122            result = executor(operator);
123        }
124
125        QueryResult {
126            nodes: result.nodes,
127            edges: result.edges,
128        }
129    }
130}
131
132/// Specialized query operators
133#[derive(Debug, Clone)]
134pub enum QueryOperator {
135    /// Full table scan
136    FullScan,
137
138    /// Label index scan
139    LabelScan { label: String },
140
141    /// Property index scan
142    PropertyScan {
143        property: String,
144        value: PropertyValue,
145    },
146
147    /// Expand edges from nodes
148    Expand {
149        direction: Direction,
150        edge_label: Option<String>,
151    },
152
153    /// Filter nodes/edges
154    Filter { predicate: FilterPredicate },
155
156    /// Project properties
157    Project { properties: Vec<String> },
158
159    /// Aggregate results
160    Aggregate { function: AggregateFunction },
161
162    /// Sort results
163    Sort { property: String, ascending: bool },
164
165    /// Limit results
166    Limit { count: usize },
167}
168
169#[derive(Debug, Clone)]
170pub enum Direction {
171    Incoming,
172    Outgoing,
173    Both,
174}
175
176#[derive(Debug, Clone)]
177pub enum FilterPredicate {
178    Equality {
179        property: String,
180        value: PropertyValue,
181    },
182    Range {
183        property: String,
184        min: PropertyValue,
185        max: PropertyValue,
186    },
187    Regex {
188        property: String,
189        pattern: String,
190    },
191}
192
193#[derive(Debug, Clone)]
194pub enum PropertyValue {
195    String(String),
196    Integer(i64),
197    Float(f64),
198    Boolean(bool),
199}
200
201#[derive(Debug, Clone)]
202pub enum AggregateFunction {
203    Count,
204    Sum { property: String },
205    Avg { property: String },
206    Min { property: String },
207    Max { property: String },
208}
209
210/// Intermediate result during query execution
211#[derive(Default)]
212pub struct IntermediateResult {
213    pub nodes: Vec<u64>,
214    pub edges: Vec<(u64, u64)>,
215}
216
217/// Final query result
218pub struct QueryResult {
219    pub nodes: Vec<u64>,
220    pub edges: Vec<(u64, u64)>,
221}
222
223/// Query execution statistics
224struct QueryStats {
225    /// Execution count per pattern
226    execution_counts: HashMap<String, u64>,
227    /// Total execution time per pattern
228    total_time_ns: HashMap<String, u64>,
229}
230
231impl QueryStats {
232    fn new() -> Self {
233        Self {
234            execution_counts: HashMap::new(),
235            total_time_ns: HashMap::new(),
236        }
237    }
238
239    fn record(&mut self, pattern: &str, duration_ns: u64) {
240        *self
241            .execution_counts
242            .entry(pattern.to_string())
243            .or_insert(0) += 1;
244        *self.total_time_ns.entry(pattern.to_string()).or_insert(0) += duration_ns;
245    }
246
247    fn get_hot_queries(&self, threshold: u64) -> Vec<String> {
248        self.execution_counts
249            .iter()
250            .filter(|(_, &count)| count >= threshold)
251            .map(|(pattern, _)| pattern.clone())
252            .collect()
253    }
254
255    fn avg_time_ns(&self, pattern: &str) -> Option<u64> {
256        let count = self.execution_counts.get(pattern)?;
257        let total = self.total_time_ns.get(pattern)?;
258
259        if *count > 0 {
260            Some(total / count)
261        } else {
262            None
263        }
264    }
265}
266
267/// Specialized operator implementations
268pub mod specialized_ops {
269    use super::*;
270
271    /// Vectorized label scan
272    pub fn vectorized_label_scan(label: &str, nodes: &[u64]) -> Vec<u64> {
273        // In a real implementation, this would use SIMD to check labels in parallel
274        nodes.iter().copied().collect()
275    }
276
277    /// Vectorized property filter
278    pub fn vectorized_property_filter(
279        property: &str,
280        predicate: &FilterPredicate,
281        nodes: &[u64],
282    ) -> Vec<u64> {
283        // In a real implementation, this would use SIMD for comparisons
284        nodes.iter().copied().collect()
285    }
286
287    /// Cache-friendly edge expansion
288    pub fn cache_friendly_expand(nodes: &[u64], direction: Direction) -> Vec<(u64, u64)> {
289        // In a real implementation, this would use prefetching and cache-optimized layout
290        Vec::new()
291    }
292}
293
294#[cfg(test)]
295mod tests {
296    use super::*;
297
298    #[test]
299    fn test_jit_compiler() {
300        let compiler = JitCompiler::new();
301
302        let query = compiler.compile("MATCH (n:Person) WHERE n.age > 18");
303        assert!(!query.operators.is_empty());
304    }
305
306    #[test]
307    fn test_query_stats() {
308        let compiler = JitCompiler::new();
309
310        compiler.record_execution("MATCH (n)", 1000);
311        compiler.record_execution("MATCH (n)", 2000);
312        compiler.record_execution("MATCH (n)", 3000);
313
314        let hot = compiler.get_hot_queries(2);
315        assert_eq!(hot.len(), 1);
316        assert_eq!(hot[0], "MATCH (n)");
317    }
318
319    #[test]
320    fn test_operator_chain() {
321        let operators = vec![
322            QueryOperator::LabelScan {
323                label: "Person".to_string(),
324            },
325            QueryOperator::Filter {
326                predicate: FilterPredicate::Range {
327                    property: "age".to_string(),
328                    min: PropertyValue::Integer(18),
329                    max: PropertyValue::Integer(65),
330                },
331            },
332            QueryOperator::Limit { count: 10 },
333        ];
334
335        assert_eq!(operators.len(), 3);
336    }
337}