ruvector_graph/executor/
parallel.rs

1//! Parallel query execution using rayon
2//!
3//! Implements data parallelism for graph queries
4
5use crate::executor::operators::Operator;
6use crate::executor::pipeline::{ExecutionContext, RowBatch};
7use crate::executor::plan::PhysicalPlan;
8use crate::executor::{ExecutionError, Result};
9use rayon::prelude::*;
10use std::sync::{Arc, Mutex};
11
12/// Parallel execution configuration
13#[derive(Debug, Clone)]
14pub struct ParallelConfig {
15    /// Enable parallel execution
16    pub enabled: bool,
17    /// Number of worker threads (0 = auto-detect)
18    pub num_threads: usize,
19    /// Batch size for parallel processing
20    pub batch_size: usize,
21}
22
23impl ParallelConfig {
24    /// Create new config with defaults
25    pub fn new() -> Self {
26        Self {
27            enabled: true,
28            num_threads: 0, // Auto-detect
29            batch_size: 1024,
30        }
31    }
32
33    /// Disable parallel execution
34    pub fn sequential() -> Self {
35        Self {
36            enabled: false,
37            num_threads: 1,
38            batch_size: 1024,
39        }
40    }
41
42    /// Create with specific thread count
43    pub fn with_threads(num_threads: usize) -> Self {
44        Self {
45            enabled: true,
46            num_threads,
47            batch_size: 1024,
48        }
49    }
50}
51
52impl Default for ParallelConfig {
53    fn default() -> Self {
54        Self::new()
55    }
56}
57
58/// Parallel query executor
59pub struct ParallelExecutor {
60    config: ParallelConfig,
61    thread_pool: rayon::ThreadPool,
62}
63
64impl ParallelExecutor {
65    /// Create a new parallel executor
66    pub fn new(config: ParallelConfig) -> Self {
67        let num_threads = if config.num_threads == 0 {
68            num_cpus::get()
69        } else {
70            config.num_threads
71        };
72
73        let thread_pool = rayon::ThreadPoolBuilder::new()
74            .num_threads(num_threads)
75            .build()
76            .expect("Failed to create thread pool");
77
78        Self {
79            config,
80            thread_pool,
81        }
82    }
83
84    /// Execute a physical plan in parallel
85    pub fn execute(&self, plan: &PhysicalPlan) -> Result<Vec<RowBatch>> {
86        if !self.config.enabled {
87            return self.execute_sequential(plan);
88        }
89
90        // Determine parallelization strategy based on plan structure
91        if plan.pipeline_breakers.is_empty() {
92            // No pipeline breakers - can parallelize entire pipeline
93            self.execute_parallel_scan(plan)
94        } else {
95            // Has pipeline breakers - need to materialize intermediate results
96            self.execute_parallel_staged(plan)
97        }
98    }
99
100    /// Execute plan sequentially (fallback)
101    fn execute_sequential(&self, plan: &PhysicalPlan) -> Result<Vec<RowBatch>> {
102        let mut results = Vec::new();
103        // Simplified sequential execution
104        Ok(results)
105    }
106
107    /// Parallel scan execution (for scan-heavy queries)
108    fn execute_parallel_scan(&self, plan: &PhysicalPlan) -> Result<Vec<RowBatch>> {
109        let results = Arc::new(Mutex::new(Vec::new()));
110        let num_partitions = self.config.num_threads.max(1);
111
112        // Partition the scan and execute in parallel
113        self.thread_pool.scope(|s| {
114            for partition_id in 0..num_partitions {
115                let results = Arc::clone(&results);
116                s.spawn(move |_| {
117                    // Execute partition
118                    let batch = self.execute_partition(plan, partition_id, num_partitions);
119                    if let Ok(Some(b)) = batch {
120                        results.lock().unwrap().push(b);
121                    }
122                });
123            }
124        });
125
126        let final_results = Arc::try_unwrap(results)
127            .map_err(|_| ExecutionError::Internal("Failed to unwrap results".to_string()))?
128            .into_inner()
129            .map_err(|_| ExecutionError::Internal("Failed to acquire lock".to_string()))?;
130
131        Ok(final_results)
132    }
133
134    /// Execute a partition of the data
135    fn execute_partition(
136        &self,
137        plan: &PhysicalPlan,
138        partition_id: usize,
139        num_partitions: usize,
140    ) -> Result<Option<RowBatch>> {
141        // Simplified partition execution
142        Ok(None)
143    }
144
145    /// Staged parallel execution (for complex queries with pipeline breakers)
146    fn execute_parallel_staged(&self, plan: &PhysicalPlan) -> Result<Vec<RowBatch>> {
147        let mut intermediate_results = Vec::new();
148
149        // Execute each stage between pipeline breakers
150        let mut start = 0;
151        for &breaker in &plan.pipeline_breakers {
152            let stage_results = self.execute_stage(plan, start, breaker)?;
153            intermediate_results = stage_results;
154            start = breaker + 1;
155        }
156
157        // Execute final stage
158        let final_results = self.execute_stage(plan, start, plan.operators.len())?;
159        Ok(final_results)
160    }
161
162    /// Execute a stage of operators
163    fn execute_stage(
164        &self,
165        plan: &PhysicalPlan,
166        start: usize,
167        end: usize,
168    ) -> Result<Vec<RowBatch>> {
169        // Simplified stage execution
170        Ok(Vec::new())
171    }
172
173    /// Parallel batch processing
174    pub fn process_batches_parallel<F>(
175        &self,
176        batches: Vec<RowBatch>,
177        processor: F,
178    ) -> Result<Vec<RowBatch>>
179    where
180        F: Fn(RowBatch) -> Result<RowBatch> + Send + Sync,
181    {
182        let results: Vec<_> = self.thread_pool.install(|| {
183            batches
184                .into_par_iter()
185                .map(|batch| processor(batch))
186                .collect()
187        });
188
189        // Collect results and check for errors
190        results.into_iter().collect()
191    }
192
193    /// Parallel aggregation
194    pub fn aggregate_parallel<K, V, F, G>(
195        &self,
196        batches: Vec<RowBatch>,
197        key_fn: F,
198        agg_fn: G,
199    ) -> Result<Vec<(K, V)>>
200    where
201        K: Send + Sync + Eq + std::hash::Hash,
202        V: Send + Sync,
203        F: Fn(&RowBatch) -> K + Send + Sync,
204        G: Fn(Vec<RowBatch>) -> V + Send + Sync,
205    {
206        use std::collections::HashMap;
207
208        // Group batches by key
209        let mut groups: HashMap<K, Vec<RowBatch>> = HashMap::new();
210        for batch in batches {
211            let key = key_fn(&batch);
212            groups.entry(key).or_insert_with(Vec::new).push(batch);
213        }
214
215        // Aggregate each group in parallel
216        let results: Vec<_> = self.thread_pool.install(|| {
217            groups
218                .into_par_iter()
219                .map(|(key, batches)| (key, agg_fn(batches)))
220                .collect()
221        });
222
223        Ok(results)
224    }
225
226    /// Get number of worker threads
227    pub fn num_threads(&self) -> usize {
228        self.thread_pool.current_num_threads()
229    }
230}
231
232/// Parallel scan partitioner
233pub struct ScanPartitioner {
234    total_rows: usize,
235    num_partitions: usize,
236}
237
238impl ScanPartitioner {
239    /// Create a new partitioner
240    pub fn new(total_rows: usize, num_partitions: usize) -> Self {
241        Self {
242            total_rows,
243            num_partitions,
244        }
245    }
246
247    /// Get partition range for a given partition ID
248    pub fn partition_range(&self, partition_id: usize) -> (usize, usize) {
249        let rows_per_partition = (self.total_rows + self.num_partitions - 1) / self.num_partitions;
250        let start = partition_id * rows_per_partition;
251        let end = (start + rows_per_partition).min(self.total_rows);
252        (start, end)
253    }
254
255    /// Check if partition is valid
256    pub fn is_valid_partition(&self, partition_id: usize) -> bool {
257        partition_id < self.num_partitions
258    }
259}
260
261/// Parallel join strategies
262pub enum ParallelJoinStrategy {
263    /// Broadcast small table to all workers
264    Broadcast,
265    /// Partition both tables by join key
266    PartitionedHash,
267    /// Sort-merge join with parallel sort
268    SortMerge,
269}
270
271/// Parallel join executor
272pub struct ParallelJoin {
273    strategy: ParallelJoinStrategy,
274    executor: Arc<ParallelExecutor>,
275}
276
277impl ParallelJoin {
278    /// Create new parallel join
279    pub fn new(strategy: ParallelJoinStrategy, executor: Arc<ParallelExecutor>) -> Self {
280        Self { strategy, executor }
281    }
282
283    /// Execute parallel join
284    pub fn execute(&self, left: Vec<RowBatch>, right: Vec<RowBatch>) -> Result<Vec<RowBatch>> {
285        match self.strategy {
286            ParallelJoinStrategy::Broadcast => self.broadcast_join(left, right),
287            ParallelJoinStrategy::PartitionedHash => self.partitioned_hash_join(left, right),
288            ParallelJoinStrategy::SortMerge => self.sort_merge_join(left, right),
289        }
290    }
291
292    fn broadcast_join(&self, left: Vec<RowBatch>, right: Vec<RowBatch>) -> Result<Vec<RowBatch>> {
293        // Broadcast smaller side to all workers
294        let (build_side, probe_side) = if left.len() < right.len() {
295            (left, right)
296        } else {
297            (right, left)
298        };
299
300        // Simplified implementation
301        Ok(Vec::new())
302    }
303
304    fn partitioned_hash_join(
305        &self,
306        left: Vec<RowBatch>,
307        right: Vec<RowBatch>,
308    ) -> Result<Vec<RowBatch>> {
309        // Partition both sides by join key
310        // Each partition is processed independently
311        Ok(Vec::new())
312    }
313
314    fn sort_merge_join(&self, left: Vec<RowBatch>, right: Vec<RowBatch>) -> Result<Vec<RowBatch>> {
315        // Sort both sides in parallel, then merge
316        Ok(Vec::new())
317    }
318}
319
320#[cfg(test)]
321mod tests {
322    use super::*;
323
324    #[test]
325    fn test_parallel_config() {
326        let config = ParallelConfig::new();
327        assert!(config.enabled);
328        assert_eq!(config.num_threads, 0);
329
330        let seq_config = ParallelConfig::sequential();
331        assert!(!seq_config.enabled);
332    }
333
334    #[test]
335    fn test_parallel_executor_creation() {
336        let config = ParallelConfig::with_threads(4);
337        let executor = ParallelExecutor::new(config);
338        assert_eq!(executor.num_threads(), 4);
339    }
340
341    #[test]
342    fn test_scan_partitioner() {
343        let partitioner = ScanPartitioner::new(100, 4);
344
345        let (start, end) = partitioner.partition_range(0);
346        assert_eq!(start, 0);
347        assert_eq!(end, 25);
348
349        let (start, end) = partitioner.partition_range(3);
350        assert_eq!(start, 75);
351        assert_eq!(end, 100);
352    }
353
354    #[test]
355    fn test_partition_validity() {
356        let partitioner = ScanPartitioner::new(100, 4);
357        assert!(partitioner.is_valid_partition(0));
358        assert!(partitioner.is_valid_partition(3));
359        assert!(!partitioner.is_valid_partition(4));
360    }
361}