oxirs_vec/
oxirs_arq_integration.rs

1//! OxiRS ARQ Integration for Vector-Aware Query Optimization
2//!
3//! This module provides comprehensive integration between oxirs-vec and oxirs-arq,
4//! enabling vector-aware SPARQL query optimization and hybrid symbolic-vector queries.
5//!
6//! Features:
7//! - Vector-aware query planning and cost modeling
8//! - Hybrid SPARQL-vector execution strategies
9//! - Vector service function registration
10//! - Optimization hints for vector operations
11//! - Performance monitoring and query analytics
12//! - Neural-symbolic query processing
13
14use crate::VectorIndex;
15use anyhow::{Error as AnyhowError, Result};
16use serde::{Deserialize, Serialize};
17use std::collections::HashMap;
18use std::sync::{Arc, RwLock};
19use std::time::{Duration, Instant};
20use tracing::{debug, span, Level};
21
22/// Vector-aware query optimization configuration
23#[derive(Debug, Clone, Serialize, Deserialize)]
24pub struct VectorQueryConfig {
25    /// Enable vector-aware query planning
26    pub enable_vector_planning: bool,
27    /// Vector operation cost model
28    pub cost_model: VectorCostModel,
29    /// Optimization strategies
30    pub optimization_strategies: Vec<OptimizationStrategy>,
31    /// Join optimization settings
32    pub join_optimization: JoinOptimizationConfig,
33    /// Result streaming configuration
34    pub streaming_config: StreamingConfig,
35    /// Performance monitoring settings
36    pub monitoring: QueryMonitoringConfig,
37}
38
39/// Vector operation cost model
40#[derive(Debug, Clone, Serialize, Deserialize)]
41pub struct VectorCostModel {
42    /// Base cost per vector operation
43    pub base_cost: f64,
44    /// Cost scaling factors
45    pub scaling_factors: CostScalingFactors,
46    /// Index-specific cost adjustments
47    pub index_costs: HashMap<String, f64>,
48    /// Hardware-specific adjustments
49    pub hardware_adjustments: HardwareAdjustments,
50}
51
52/// Cost scaling factors for different operations
53#[derive(Debug, Clone, Serialize, Deserialize)]
54pub struct CostScalingFactors {
55    /// Search operation scaling
56    pub search_scale: f64,
57    /// Index building scaling
58    pub build_scale: f64,
59    /// Vector addition scaling
60    pub add_scale: f64,
61    /// Cross-modal operation scaling
62    pub cross_modal_scale: f64,
63    /// Similarity computation scaling
64    pub similarity_scale: f64,
65}
66
67/// Hardware-specific cost adjustments
68#[derive(Debug, Clone, Serialize, Deserialize)]
69pub struct HardwareAdjustments {
70    /// CPU performance factor
71    pub cpu_factor: f64,
72    /// Memory bandwidth factor
73    pub memory_factor: f64,
74    /// GPU acceleration factor
75    pub gpu_factor: f64,
76    /// Network latency factor
77    pub network_factor: f64,
78}
79
80/// Optimization strategies for vector-aware queries
81#[derive(Debug, Clone, Serialize, Deserialize)]
82pub enum OptimizationStrategy {
83    /// Push vector filters early in execution
84    VectorFilterPushdown,
85    /// Reorder joins to minimize vector operations
86    VectorJoinReordering,
87    /// Use vector indices for filtering
88    VectorIndexSelection,
89    /// Batch vector operations
90    VectorBatching,
91    /// Cache frequently used vectors
92    VectorCaching,
93    /// Parallel vector execution
94    VectorParallelization,
95    /// Adaptive vector strategy selection
96    AdaptiveVectorStrategy,
97}
98
99/// Join optimization configuration
100#[derive(Debug, Clone, Serialize, Deserialize)]
101pub struct JoinOptimizationConfig {
102    /// Enable vector-aware join ordering
103    pub enable_vector_join_ordering: bool,
104    /// Join algorithm selection
105    pub join_algorithms: Vec<VectorJoinAlgorithm>,
106    /// Join cost threshold
107    pub cost_threshold: f64,
108    /// Enable join result caching
109    pub enable_caching: bool,
110}
111
112/// Vector-aware join algorithms
113#[derive(Debug, Clone, Serialize, Deserialize)]
114pub enum VectorJoinAlgorithm {
115    /// Nested loop join with vector filtering
116    VectorNestedLoop,
117    /// Hash join with vector keys
118    VectorHashJoin,
119    /// Sort-merge join with vector ordering
120    VectorSortMerge,
121    /// Index-based join using vector indices
122    VectorIndexJoin,
123    /// Similarity-based join
124    SimilarityJoin,
125}
126
127/// Streaming configuration for vector results
128#[derive(Debug, Clone, Serialize, Deserialize)]
129pub struct StreamingConfig {
130    /// Enable result streaming
131    pub enable_streaming: bool,
132    /// Streaming buffer size
133    pub buffer_size: usize,
134    /// Streaming timeout
135    pub timeout_ms: u64,
136    /// Enable backpressure handling
137    pub enable_backpressure: bool,
138}
139
140/// Query monitoring configuration
141#[derive(Debug, Clone, Serialize, Deserialize)]
142pub struct QueryMonitoringConfig {
143    /// Enable performance monitoring
144    pub enable_monitoring: bool,
145    /// Monitor vector operation performance
146    pub monitor_vector_ops: bool,
147    /// Monitor join performance
148    pub monitor_joins: bool,
149    /// Monitor memory usage
150    pub monitor_memory: bool,
151    /// Export metrics format
152    pub metrics_format: MetricsFormat,
153}
154
155/// Metrics export formats
156#[derive(Debug, Clone, Serialize, Deserialize)]
157pub enum MetricsFormat {
158    Prometheus,
159    JSON,
160    CSV,
161    Custom(String),
162}
163
164impl Default for VectorQueryConfig {
165    fn default() -> Self {
166        Self {
167            enable_vector_planning: true,
168            cost_model: VectorCostModel {
169                base_cost: 1.0,
170                scaling_factors: CostScalingFactors {
171                    search_scale: 1.5,
172                    build_scale: 10.0,
173                    add_scale: 0.5,
174                    cross_modal_scale: 2.0,
175                    similarity_scale: 1.0,
176                },
177                index_costs: {
178                    let mut costs = HashMap::new();
179                    costs.insert("hnsw".to_string(), 1.2);
180                    costs.insert("ivf".to_string(), 1.0);
181                    costs.insert("flat".to_string(), 2.0);
182                    costs
183                },
184                hardware_adjustments: HardwareAdjustments {
185                    cpu_factor: 1.0,
186                    memory_factor: 1.0,
187                    gpu_factor: 0.3, // GPU is 3x faster
188                    network_factor: 1.5,
189                },
190            },
191            optimization_strategies: vec![
192                OptimizationStrategy::VectorFilterPushdown,
193                OptimizationStrategy::VectorJoinReordering,
194                OptimizationStrategy::VectorIndexSelection,
195                OptimizationStrategy::VectorBatching,
196            ],
197            join_optimization: JoinOptimizationConfig {
198                enable_vector_join_ordering: true,
199                join_algorithms: vec![
200                    VectorJoinAlgorithm::VectorIndexJoin,
201                    VectorJoinAlgorithm::SimilarityJoin,
202                    VectorJoinAlgorithm::VectorHashJoin,
203                ],
204                cost_threshold: 1000.0,
205                enable_caching: true,
206            },
207            streaming_config: StreamingConfig {
208                enable_streaming: true,
209                buffer_size: 1000,
210                timeout_ms: 30000,
211                enable_backpressure: true,
212            },
213            monitoring: QueryMonitoringConfig {
214                enable_monitoring: true,
215                monitor_vector_ops: true,
216                monitor_joins: true,
217                monitor_memory: true,
218                metrics_format: MetricsFormat::JSON,
219            },
220        }
221    }
222}
223
224/// Vector-aware query planner
225pub struct VectorQueryPlanner {
226    /// Configuration
227    config: VectorQueryConfig,
228    /// Available vector indices
229    vector_indices: Arc<RwLock<HashMap<String, Arc<dyn VectorIndex>>>>,
230    /// Query statistics for cost modeling
231    query_stats: Arc<RwLock<QueryStatistics>>,
232    /// Optimization cache
233    optimization_cache: Arc<RwLock<HashMap<String, OptimizationPlan>>>,
234    /// Performance monitor
235    performance_monitor: Arc<RwLock<VectorQueryPerformance>>,
236}
237
238/// Query statistics for optimization
239#[derive(Debug, Clone, Default)]
240pub struct QueryStatistics {
241    /// Total queries processed
242    pub total_queries: usize,
243    /// Vector operation counts
244    pub vector_op_counts: HashMap<String, usize>,
245    /// Average execution times
246    pub avg_execution_times: HashMap<String, Duration>,
247    /// Join statistics
248    pub join_stats: JoinStatistics,
249    /// Index usage statistics
250    pub index_usage: HashMap<String, IndexUsageStats>,
251}
252
253/// Join operation statistics
254#[derive(Debug, Clone, Default)]
255pub struct JoinStatistics {
256    /// Total joins performed
257    pub total_joins: usize,
258    /// Join algorithm usage
259    pub algorithm_usage: HashMap<String, usize>,
260    /// Average join cardinality
261    pub avg_cardinality: f64,
262    /// Join selectivity estimates
263    pub selectivity_estimates: HashMap<String, f64>,
264}
265
266/// Index usage statistics
267#[derive(Debug, Clone, Default)]
268pub struct IndexUsageStats {
269    /// Times index was used
270    pub usage_count: usize,
271    /// Average search time
272    pub avg_search_time: Duration,
273    /// Average result count
274    pub avg_result_count: f64,
275    /// Cache hit rate
276    pub cache_hit_rate: f32,
277}
278
279/// Vector query performance metrics
280#[derive(Debug, Clone, Default)]
281pub struct VectorQueryPerformance {
282    /// Query execution metrics
283    pub execution_metrics: ExecutionMetrics,
284    /// Resource utilization metrics
285    pub resource_metrics: ResourceMetrics,
286    /// Quality metrics
287    pub quality_metrics: QualityMetrics,
288    /// Trend analysis
289    pub trend_analysis: TrendAnalysis,
290}
291
292/// Query execution performance metrics
293#[derive(Debug, Clone, Default)]
294pub struct ExecutionMetrics {
295    /// Total execution time
296    pub total_time: Duration,
297    /// Vector operation time
298    pub vector_op_time: Duration,
299    /// Join operation time
300    pub join_time: Duration,
301    /// Planning time
302    pub planning_time: Duration,
303    /// Result materialization time
304    pub materialization_time: Duration,
305}
306
307/// Resource utilization metrics
308#[derive(Debug, Clone, Default)]
309pub struct ResourceMetrics {
310    /// CPU utilization percentage
311    pub cpu_utilization: f32,
312    /// Memory usage in bytes
313    pub memory_usage: usize,
314    /// GPU utilization percentage
315    pub gpu_utilization: f32,
316    /// Network I/O bytes
317    pub network_io: usize,
318    /// Disk I/O bytes
319    pub disk_io: usize,
320}
321
322/// Query result quality metrics
323#[derive(Debug, Clone, Default)]
324pub struct QualityMetrics {
325    /// Result accuracy score
326    pub accuracy_score: f32,
327    /// Result completeness score
328    pub completeness_score: f32,
329    /// Result relevance score
330    pub relevance_score: f32,
331    /// Confidence score
332    pub confidence_score: f32,
333}
334
335/// Performance trend analysis
336#[derive(Debug, Clone, Default)]
337pub struct TrendAnalysis {
338    /// Performance trend over time
339    pub performance_trend: Vec<(Instant, f64)>,
340    /// Resource usage trend
341    pub resource_trend: Vec<(Instant, f64)>,
342    /// Quality trend
343    pub quality_trend: Vec<(Instant, f64)>,
344    /// Optimization effectiveness
345    pub optimization_effectiveness: f64,
346}
347
348/// Optimization plan for vector queries
349#[derive(Debug, Clone)]
350pub struct OptimizationPlan {
351    /// Plan ID
352    pub plan_id: String,
353    /// Optimization steps
354    pub steps: Vec<OptimizationStep>,
355    /// Estimated cost
356    pub estimated_cost: f64,
357    /// Estimated execution time
358    pub estimated_time: Duration,
359    /// Expected quality score
360    pub expected_quality: f32,
361    /// Plan metadata
362    pub metadata: HashMap<String, String>,
363}
364
365/// Individual optimization step
366#[derive(Debug, Clone)]
367pub struct OptimizationStep {
368    /// Step type
369    pub step_type: OptimizationStepType,
370    /// Step parameters
371    pub parameters: HashMap<String, serde_json::Value>,
372    /// Estimated cost
373    pub cost: f64,
374    /// Dependencies
375    pub dependencies: Vec<String>,
376}
377
378/// Types of optimization steps
379#[derive(Debug, Clone)]
380pub enum OptimizationStepType {
381    /// Vector index selection
382    IndexSelection {
383        index_type: String,
384        selection_criteria: SelectionCriteria,
385    },
386    /// Filter pushdown optimization
387    FilterPushdown {
388        filter_type: FilterType,
389        pushdown_level: usize,
390    },
391    /// Join reordering
392    JoinReordering {
393        original_order: Vec<String>,
394        optimized_order: Vec<String>,
395    },
396    /// Vector batching
397    VectorBatching {
398        batch_size: usize,
399        batching_strategy: BatchingStrategy,
400    },
401    /// Caching setup
402    CachingSetup {
403        cache_type: CacheType,
404        cache_size: usize,
405    },
406    /// Parallel execution
407    ParallelExecution {
408        parallelism_level: usize,
409        execution_strategy: ParallelStrategy,
410    },
411}
412
413/// Index selection criteria
414#[derive(Debug, Clone)]
415pub enum SelectionCriteria {
416    Performance,
417    Memory,
418    Accuracy,
419    Hybrid(Vec<f32>), // Weights for different criteria
420}
421
422/// Filter types for optimization
423#[derive(Debug, Clone)]
424pub enum FilterType {
425    SimilarityFilter,
426    ThresholdFilter,
427    RangeFilter,
428    CompositeFilter,
429}
430
431/// Batching strategies
432#[derive(Debug, Clone)]
433pub enum BatchingStrategy {
434    SizeBased,
435    TimeBased,
436    Adaptive,
437    ContentBased,
438}
439
440/// Cache types
441#[derive(Debug, Clone)]
442pub enum CacheType {
443    VectorCache,
444    ResultCache,
445    IndexCache,
446    QueryCache,
447}
448
449/// Parallel execution strategies
450#[derive(Debug, Clone)]
451pub enum ParallelStrategy {
452    TaskParallel,
453    DataParallel,
454    PipelineParallel,
455    Hybrid,
456}
457
458/// Vector function registry for SPARQL integration
459pub struct VectorFunctionRegistry {
460    /// Registered functions
461    functions: Arc<RwLock<HashMap<String, Arc<dyn VectorFunction>>>>,
462    /// Function metadata
463    function_metadata: Arc<RwLock<HashMap<String, FunctionMetadata>>>,
464    /// Type checker
465    type_checker: Arc<VectorTypeChecker>,
466    /// Performance monitor
467    performance_monitor: Arc<RwLock<FunctionPerformanceMonitor>>,
468}
469
470impl std::fmt::Debug for VectorFunctionRegistry {
471    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
472        f.debug_struct("VectorFunctionRegistry")
473            .field("functions", &"<HashMap<String, Arc<dyn VectorFunction>>>")
474            .field("function_metadata", &self.function_metadata)
475            .field("type_checker", &"<Arc<VectorTypeChecker>>")
476            .field("performance_monitor", &self.performance_monitor)
477            .finish()
478    }
479}
480
481/// Vector function trait for SPARQL integration
482pub trait VectorFunction: Send + Sync {
483    /// Function name
484    fn name(&self) -> &str;
485
486    /// Function signature
487    fn signature(&self) -> FunctionSignature;
488
489    /// Execute function
490    fn execute(
491        &self,
492        args: &[FunctionArgument],
493        context: &ExecutionContext,
494    ) -> Result<FunctionResult>;
495
496    /// Get optimization hints
497    fn optimization_hints(&self) -> Vec<OptimizationHint>;
498
499    /// Cost estimation
500    fn estimate_cost(&self, args: &[FunctionArgument]) -> f64;
501}
502
503/// Function signature definition
504#[derive(Debug, Clone)]
505pub struct FunctionSignature {
506    /// Parameter types
507    pub parameters: Vec<ParameterType>,
508    /// Return type
509    pub return_type: ReturnType,
510    /// Variadic parameters
511    pub variadic: bool,
512    /// Required parameters count
513    pub required_params: usize,
514}
515
516/// Parameter types for functions
517#[derive(Debug, Clone)]
518pub enum ParameterType {
519    Vector,
520    Scalar(ScalarType),
521    Graph,
522    URI,
523    Literal(LiteralType),
524    Variable,
525}
526
527/// Scalar types
528#[derive(Debug, Clone)]
529pub enum ScalarType {
530    Integer,
531    Float,
532    String,
533    Boolean,
534}
535
536/// Literal types
537#[derive(Debug, Clone)]
538pub enum LiteralType {
539    String,
540    Number,
541    Boolean,
542    DateTime,
543    Custom(String),
544}
545
546/// Return types
547#[derive(Debug, Clone)]
548pub enum ReturnType {
549    Vector,
550    Scalar(ScalarType),
551    ResultSet,
552    Boolean,
553    Void,
554}
555
556/// Function arguments
557#[derive(Debug, Clone)]
558pub enum FunctionArgument {
559    Vector(Vec<f32>),
560    Scalar(ScalarValue),
561    URI(String),
562    Literal(String, Option<String>), // Value, datatype
563    Variable(String),
564}
565
566/// Scalar values
567#[derive(Debug, Clone)]
568pub enum ScalarValue {
569    Integer(i64),
570    Float(f64),
571    String(String),
572    Boolean(bool),
573}
574
575/// Function execution context
576pub struct ExecutionContext {
577    /// Available vector indices
578    pub vector_indices: Arc<RwLock<HashMap<String, Arc<dyn VectorIndex>>>>,
579    /// Query context
580    pub query_context: QueryContext,
581    /// Performance monitor
582    pub performance_monitor: Arc<RwLock<VectorQueryPerformance>>,
583    /// Configuration
584    pub config: VectorQueryConfig,
585}
586
587impl std::fmt::Debug for ExecutionContext {
588    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
589        f.debug_struct("ExecutionContext")
590            .field("vector_indices", &"<HashMap<String, Arc<dyn VectorIndex>>>")
591            .field("query_context", &self.query_context)
592            .field("performance_monitor", &self.performance_monitor)
593            .field("config", &self.config)
594            .finish()
595    }
596}
597
598/// Query execution context
599#[derive(Debug, Clone)]
600pub struct QueryContext {
601    /// Query ID
602    pub query_id: String,
603    /// Execution timestamp
604    pub timestamp: Instant,
605    /// Variable bindings
606    pub bindings: HashMap<String, String>,
607    /// Active dataset
608    pub dataset: Option<String>,
609    /// Query metadata
610    pub metadata: HashMap<String, String>,
611}
612
613/// Function execution result
614#[derive(Debug, Clone)]
615pub enum FunctionResult {
616    Vector(Vec<f32>),
617    Scalar(ScalarValue),
618    ResultSet(Vec<HashMap<String, String>>),
619    Boolean(bool),
620    Void,
621}
622
623/// Function metadata
624#[derive(Debug, Clone)]
625pub struct FunctionMetadata {
626    /// Function description
627    pub description: String,
628    /// Author
629    pub author: String,
630    /// Version
631    pub version: String,
632    /// Categories
633    pub categories: Vec<String>,
634    /// Performance characteristics
635    pub performance_info: PerformanceInfo,
636}
637
638/// Performance information for functions
639#[derive(Debug, Clone)]
640pub struct PerformanceInfo {
641    /// Time complexity
642    pub time_complexity: String,
643    /// Space complexity
644    pub space_complexity: String,
645    /// Typical execution time
646    pub typical_time: Duration,
647    /// Memory usage
648    pub memory_usage: usize,
649}
650
651/// Optimization hints for functions
652#[derive(Debug, Clone)]
653pub enum OptimizationHint {
654    /// Prefer specific index type
655    PreferIndex(String),
656    /// Can be batched
657    Batchable,
658    /// Can be cached
659    Cacheable,
660    /// Can be parallelized
661    Parallelizable,
662    /// Memory intensive
663    MemoryIntensive,
664    /// CPU intensive
665    CpuIntensive,
666    /// GPU accelerable
667    GpuAccelerable,
668}
669
670/// Vector type checker
671#[derive(Debug)]
672pub struct VectorTypeChecker {
673    /// Type rules
674    type_rules: HashMap<String, TypeRule>,
675    /// Conversion rules
676    conversion_rules: HashMap<(String, String), ConversionRule>,
677}
678
679/// Type checking rules
680#[derive(Debug, Clone)]
681pub struct TypeRule {
682    /// Compatible types
683    pub compatible_types: Vec<String>,
684    /// Conversion cost
685    pub conversion_costs: HashMap<String, f64>,
686    /// Validation function
687    pub validator: Option<String>,
688}
689
690/// Type conversion rules
691#[derive(Debug, Clone)]
692pub struct ConversionRule {
693    /// Source type
694    pub source_type: String,
695    /// Target type
696    pub target_type: String,
697    /// Conversion cost
698    pub cost: f64,
699    /// Lossy conversion
700    pub lossy: bool,
701    /// Converter function
702    pub converter: String,
703}
704
705/// Function performance monitor
706#[derive(Debug, Default)]
707pub struct FunctionPerformanceMonitor {
708    /// Function call counts
709    pub call_counts: HashMap<String, usize>,
710    /// Execution times
711    pub execution_times: HashMap<String, Vec<Duration>>,
712    /// Memory usage
713    pub memory_usage: HashMap<String, Vec<usize>>,
714    /// Error rates
715    pub error_rates: HashMap<String, f32>,
716    /// Performance trends
717    pub trends: HashMap<String, Vec<(Instant, f64)>>,
718}
719
720impl VectorQueryPlanner {
721    /// Create a new vector-aware query planner
722    pub fn new(config: VectorQueryConfig) -> Self {
723        Self {
724            config,
725            vector_indices: Arc::new(RwLock::new(HashMap::new())),
726            query_stats: Arc::new(RwLock::new(QueryStatistics::default())),
727            optimization_cache: Arc::new(RwLock::new(HashMap::new())),
728            performance_monitor: Arc::new(RwLock::new(VectorQueryPerformance::default())),
729        }
730    }
731
732    /// Register a vector index for query optimization
733    pub fn register_vector_index(&self, name: String, index: Arc<dyn VectorIndex>) -> Result<()> {
734        let mut indices = self
735            .vector_indices
736            .write()
737            .expect("vector_indices write lock should not be poisoned");
738        indices.insert(name, index);
739        Ok(())
740    }
741
742    /// Create an optimization plan for a query
743    pub fn create_optimization_plan(&self, query: &VectorQuery) -> Result<OptimizationPlan> {
744        let span = span!(Level::DEBUG, "create_optimization_plan");
745        let _enter = span.enter();
746
747        // Generate plan ID
748        let plan_id = format!("plan_{}", uuid::Uuid::new_v4());
749
750        // Analyze query for optimization opportunities
751        let optimization_opportunities = self.analyze_query(query)?;
752
753        // Generate optimization steps
754        let mut steps = Vec::new();
755        for opportunity in optimization_opportunities {
756            let step = self.generate_optimization_step(opportunity, query)?;
757            steps.push(step);
758        }
759
760        // Estimate total cost and time
761        let estimated_cost = steps.iter().map(|s| s.cost).sum();
762        let estimated_time = self.estimate_execution_time(&steps, query)?;
763
764        // Calculate expected quality
765        let expected_quality = self.estimate_quality_score(&steps, query)?;
766
767        let plan = OptimizationPlan {
768            plan_id: plan_id.clone(),
769            steps,
770            estimated_cost,
771            estimated_time,
772            expected_quality,
773            metadata: {
774                let mut metadata = HashMap::new();
775                metadata.insert("created_at".to_string(), chrono::Utc::now().to_rfc3339());
776                metadata.insert("query_type".to_string(), query.query_type.clone());
777                metadata
778            },
779        };
780
781        // Cache the plan
782        {
783            let mut cache = self
784                .optimization_cache
785                .write()
786                .expect("optimization_cache write lock should not be poisoned");
787            cache.insert(plan_id, plan.clone());
788        }
789
790        debug!("Created optimization plan with {} steps", plan.steps.len());
791        Ok(plan)
792    }
793
794    /// Analyze query for optimization opportunities
795    fn analyze_query(&self, query: &VectorQuery) -> Result<Vec<OptimizationOpportunity>> {
796        let mut opportunities = Vec::new();
797
798        // Check for vector filter pushdown opportunities
799        if query.has_vector_filters() {
800            opportunities.push(OptimizationOpportunity::FilterPushdown);
801        }
802
803        // Check for join reordering opportunities
804        if query.has_joins() && query.join_count() > 1 {
805            opportunities.push(OptimizationOpportunity::JoinReordering);
806        }
807
808        // Check for index selection opportunities
809        if query.has_vector_operations() {
810            opportunities.push(OptimizationOpportunity::IndexSelection);
811        }
812
813        // Check for batching opportunities
814        if query.has_multiple_similar_operations() {
815            opportunities.push(OptimizationOpportunity::Batching);
816        }
817
818        // Check for caching opportunities
819        if query.has_repeated_subqueries() {
820            opportunities.push(OptimizationOpportunity::Caching);
821        }
822
823        Ok(opportunities)
824    }
825
826    /// Generate optimization step for opportunity
827    fn generate_optimization_step(
828        &self,
829        opportunity: OptimizationOpportunity,
830        query: &VectorQuery,
831    ) -> Result<OptimizationStep> {
832        match opportunity {
833            OptimizationOpportunity::FilterPushdown => Ok(OptimizationStep {
834                step_type: OptimizationStepType::FilterPushdown {
835                    filter_type: FilterType::SimilarityFilter,
836                    pushdown_level: 2,
837                },
838                parameters: HashMap::new(),
839                cost: self.estimate_filter_pushdown_cost(query),
840                dependencies: Vec::new(),
841            }),
842            OptimizationOpportunity::JoinReordering => {
843                let original_order = query.get_join_order();
844                let optimized_order = self.optimize_join_order(&original_order, query)?;
845
846                Ok(OptimizationStep {
847                    step_type: OptimizationStepType::JoinReordering {
848                        original_order,
849                        optimized_order,
850                    },
851                    parameters: HashMap::new(),
852                    cost: self.estimate_join_reorder_cost(query),
853                    dependencies: Vec::new(),
854                })
855            }
856            OptimizationOpportunity::IndexSelection => {
857                let best_index = self.select_optimal_index(query)?;
858
859                Ok(OptimizationStep {
860                    step_type: OptimizationStepType::IndexSelection {
861                        index_type: best_index,
862                        selection_criteria: SelectionCriteria::Hybrid(vec![0.4, 0.3, 0.3]), // Performance, Memory, Accuracy
863                    },
864                    parameters: HashMap::new(),
865                    cost: self.estimate_index_selection_cost(query),
866                    dependencies: Vec::new(),
867                })
868            }
869            OptimizationOpportunity::Batching => Ok(OptimizationStep {
870                step_type: OptimizationStepType::VectorBatching {
871                    batch_size: self.calculate_optimal_batch_size(query),
872                    batching_strategy: BatchingStrategy::Adaptive,
873                },
874                parameters: HashMap::new(),
875                cost: self.estimate_batching_cost(query),
876                dependencies: Vec::new(),
877            }),
878            OptimizationOpportunity::Caching => Ok(OptimizationStep {
879                step_type: OptimizationStepType::CachingSetup {
880                    cache_type: CacheType::ResultCache,
881                    cache_size: 1000,
882                },
883                parameters: HashMap::new(),
884                cost: self.estimate_caching_cost(query),
885                dependencies: Vec::new(),
886            }),
887        }
888    }
889
890    /// Estimate execution time for optimization steps
891    fn estimate_execution_time(
892        &self,
893        steps: &[OptimizationStep],
894        query: &VectorQuery,
895    ) -> Result<Duration> {
896        let base_time = self.estimate_base_execution_time(query);
897        let optimization_factor = self.calculate_optimization_factor(steps);
898
899        Ok(Duration::from_secs_f64(
900            base_time.as_secs_f64() * optimization_factor,
901        ))
902    }
903
904    /// Estimate quality score for optimization plan
905    fn estimate_quality_score(
906        &self,
907        steps: &[OptimizationStep],
908        _query: &VectorQuery,
909    ) -> Result<f32> {
910        let base_quality = 0.8; // Base quality score
911        let quality_improvement = steps
912            .iter()
913            .map(|step| self.estimate_step_quality_impact(step))
914            .sum::<f32>();
915
916        Ok((base_quality + quality_improvement).min(1.0))
917    }
918
919    /// Helper methods for cost estimation
920    fn estimate_filter_pushdown_cost(&self, _query: &VectorQuery) -> f64 {
921        10.0 // Simplified cost
922    }
923
924    fn estimate_join_reorder_cost(&self, _query: &VectorQuery) -> f64 {
925        20.0
926    }
927
928    fn estimate_index_selection_cost(&self, _query: &VectorQuery) -> f64 {
929        5.0
930    }
931
932    fn estimate_batching_cost(&self, _query: &VectorQuery) -> f64 {
933        15.0
934    }
935
936    fn estimate_caching_cost(&self, _query: &VectorQuery) -> f64 {
937        8.0
938    }
939
940    fn estimate_base_execution_time(&self, _query: &VectorQuery) -> Duration {
941        Duration::from_millis(100) // Simplified estimation
942    }
943
944    fn calculate_optimization_factor(&self, _steps: &[OptimizationStep]) -> f64 {
945        0.7 // 30% improvement
946    }
947
948    fn estimate_step_quality_impact(&self, _step: &OptimizationStep) -> f32 {
949        0.05 // 5% quality improvement per step
950    }
951
952    fn optimize_join_order(
953        &self,
954        original_order: &[String],
955        _query: &VectorQuery,
956    ) -> Result<Vec<String>> {
957        // Simplified join reordering
958        let mut optimized = original_order.to_vec();
959        optimized.reverse(); // Simple reordering strategy
960        Ok(optimized)
961    }
962
963    fn select_optimal_index(&self, _query: &VectorQuery) -> Result<String> {
964        // Select best index based on query characteristics
965        Ok("hnsw".to_string()) // Default to HNSW
966    }
967
968    fn calculate_optimal_batch_size(&self, _query: &VectorQuery) -> usize {
969        1000 // Default batch size
970    }
971
972    /// Update query statistics after execution
973    pub fn update_statistics(
974        &self,
975        query: &VectorQuery,
976        execution_time: Duration,
977        _result_count: usize,
978    ) -> Result<()> {
979        let mut stats = self
980            .query_stats
981            .write()
982            .expect("query_stats write lock should not be poisoned");
983
984        stats.total_queries += 1;
985
986        // Update operation counts
987        for op in &query.vector_operations {
988            *stats.vector_op_counts.entry(op.clone()).or_insert(0) += 1;
989        }
990
991        // Update execution times
992        stats
993            .avg_execution_times
994            .insert(query.query_type.clone(), execution_time);
995
996        Ok(())
997    }
998
999    /// Get performance metrics
1000    pub fn get_performance_metrics(&self) -> Result<VectorQueryPerformance> {
1001        let performance = self
1002            .performance_monitor
1003            .read()
1004            .expect("performance_monitor read lock should not be poisoned");
1005        Ok(performance.clone())
1006    }
1007}
1008
1009impl Default for VectorFunctionRegistry {
1010    fn default() -> Self {
1011        Self::new()
1012    }
1013}
1014
1015impl VectorFunctionRegistry {
1016    /// Create a new vector function registry
1017    pub fn new() -> Self {
1018        Self {
1019            functions: Arc::new(RwLock::new(HashMap::new())),
1020            function_metadata: Arc::new(RwLock::new(HashMap::new())),
1021            type_checker: Arc::new(VectorTypeChecker::new()),
1022            performance_monitor: Arc::new(RwLock::new(FunctionPerformanceMonitor::default())),
1023        }
1024    }
1025
1026    /// Register a vector function
1027    pub fn register_function(
1028        &self,
1029        function: Arc<dyn VectorFunction>,
1030        metadata: FunctionMetadata,
1031    ) -> Result<()> {
1032        let name = function.name().to_string();
1033
1034        // Validate function signature
1035        self.type_checker
1036            .validate_signature(&function.signature())?;
1037
1038        // Register function
1039        {
1040            let mut functions = self
1041                .functions
1042                .write()
1043                .expect("functions write lock should not be poisoned");
1044            functions.insert(name.clone(), function);
1045        }
1046
1047        // Register metadata
1048        {
1049            let mut meta = self
1050                .function_metadata
1051                .write()
1052                .expect("function_metadata write lock should not be poisoned");
1053            meta.insert(name, metadata);
1054        }
1055
1056        Ok(())
1057    }
1058
1059    /// Execute a registered function
1060    pub fn execute_function(
1061        &self,
1062        name: &str,
1063        args: &[FunctionArgument],
1064        context: &ExecutionContext,
1065    ) -> Result<FunctionResult> {
1066        let function = {
1067            let functions = self
1068                .functions
1069                .read()
1070                .expect("functions read lock should not be poisoned");
1071            functions
1072                .get(name)
1073                .ok_or_else(|| AnyhowError::msg(format!("Function not found: {name}")))?
1074                .clone()
1075        };
1076
1077        // Type check arguments
1078        self.type_checker
1079            .check_arguments(&function.signature(), args)?;
1080
1081        // Execute function
1082        let start_time = Instant::now();
1083        let result = function.execute(args, context)?;
1084        let execution_time = start_time.elapsed();
1085
1086        // Update performance metrics
1087        self.update_function_performance(name, execution_time)?;
1088
1089        Ok(result)
1090    }
1091
1092    /// Update function performance metrics
1093    fn update_function_performance(&self, name: &str, execution_time: Duration) -> Result<()> {
1094        let mut monitor = self
1095            .performance_monitor
1096            .write()
1097            .expect("performance_monitor write lock should not be poisoned");
1098
1099        // Update call count
1100        *monitor.call_counts.entry(name.to_string()).or_insert(0) += 1;
1101
1102        // Update execution times
1103        monitor
1104            .execution_times
1105            .entry(name.to_string())
1106            .or_default()
1107            .push(execution_time);
1108
1109        // Add performance trend point
1110        monitor
1111            .trends
1112            .entry(name.to_string())
1113            .or_default()
1114            .push((Instant::now(), execution_time.as_secs_f64()));
1115
1116        Ok(())
1117    }
1118
1119    /// Get function performance statistics
1120    pub fn get_function_stats(&self, name: &str) -> Result<FunctionStats> {
1121        let monitor = self
1122            .performance_monitor
1123            .read()
1124            .expect("performance_monitor read lock should not be poisoned");
1125
1126        let call_count = monitor.call_counts.get(name).copied().unwrap_or(0);
1127        let execution_times = monitor
1128            .execution_times
1129            .get(name)
1130            .cloned()
1131            .unwrap_or_default();
1132
1133        let avg_time = if !execution_times.is_empty() {
1134            execution_times.iter().sum::<Duration>() / execution_times.len() as u32
1135        } else {
1136            Duration::ZERO
1137        };
1138
1139        Ok(FunctionStats {
1140            name: name.to_string(),
1141            call_count,
1142            avg_execution_time: avg_time,
1143            total_execution_time: execution_times.iter().sum(),
1144            error_rate: monitor.error_rates.get(name).copied().unwrap_or(0.0),
1145        })
1146    }
1147}
1148
1149impl Default for VectorTypeChecker {
1150    fn default() -> Self {
1151        Self::new()
1152    }
1153}
1154
1155impl VectorTypeChecker {
1156    /// Create a new type checker
1157    pub fn new() -> Self {
1158        Self {
1159            type_rules: HashMap::new(),
1160            conversion_rules: HashMap::new(),
1161        }
1162    }
1163
1164    /// Validate function signature
1165    pub fn validate_signature(&self, _signature: &FunctionSignature) -> Result<()> {
1166        // Simplified validation
1167        Ok(())
1168    }
1169
1170    /// Check function arguments against signature
1171    pub fn check_arguments(
1172        &self,
1173        signature: &FunctionSignature,
1174        args: &[FunctionArgument],
1175    ) -> Result<()> {
1176        if args.len() < signature.required_params {
1177            return Err(AnyhowError::msg("Insufficient arguments"));
1178        }
1179
1180        if !signature.variadic && args.len() > signature.parameters.len() {
1181            return Err(AnyhowError::msg("Too many arguments"));
1182        }
1183
1184        // Type check each argument
1185        for (i, arg) in args.iter().enumerate() {
1186            if i < signature.parameters.len() {
1187                self.check_argument_type(arg, &signature.parameters[i])?;
1188            }
1189        }
1190
1191        Ok(())
1192    }
1193
1194    /// Check individual argument type
1195    fn check_argument_type(
1196        &self,
1197        arg: &FunctionArgument,
1198        expected_type: &ParameterType,
1199    ) -> Result<()> {
1200        match (arg, expected_type) {
1201            (FunctionArgument::Vector(_), ParameterType::Vector) => Ok(()),
1202            (FunctionArgument::Scalar(_), ParameterType::Scalar(_)) => Ok(()),
1203            (FunctionArgument::URI(_), ParameterType::URI) => Ok(()),
1204            (FunctionArgument::Literal(_, _), ParameterType::Literal(_)) => Ok(()),
1205            (FunctionArgument::Variable(_), ParameterType::Variable) => Ok(()),
1206            _ => Err(AnyhowError::msg("Type mismatch")),
1207        }
1208    }
1209}
1210
1211/// Vector query representation
1212#[derive(Debug, Clone)]
1213pub struct VectorQuery {
1214    /// Query type
1215    pub query_type: String,
1216    /// Vector operations in query
1217    pub vector_operations: Vec<String>,
1218    /// Join operations
1219    pub joins: Vec<String>,
1220    /// Filter conditions
1221    pub filters: Vec<String>,
1222    /// Query metadata
1223    pub metadata: HashMap<String, String>,
1224}
1225
1226impl VectorQuery {
1227    /// Check if query has vector filters
1228    pub fn has_vector_filters(&self) -> bool {
1229        self.filters
1230            .iter()
1231            .any(|f| f.contains("vector") || f.contains("similarity"))
1232    }
1233
1234    /// Check if query has joins
1235    pub fn has_joins(&self) -> bool {
1236        !self.joins.is_empty()
1237    }
1238
1239    /// Get join count
1240    pub fn join_count(&self) -> usize {
1241        self.joins.len()
1242    }
1243
1244    /// Check if query has vector operations
1245    pub fn has_vector_operations(&self) -> bool {
1246        !self.vector_operations.is_empty()
1247    }
1248
1249    /// Check if query has multiple similar operations
1250    pub fn has_multiple_similar_operations(&self) -> bool {
1251        self.vector_operations.len() > 1
1252    }
1253
1254    /// Check if query has repeated subqueries
1255    pub fn has_repeated_subqueries(&self) -> bool {
1256        // Simplified check
1257        self.metadata.contains_key("repeated_patterns")
1258    }
1259
1260    /// Get join order
1261    pub fn get_join_order(&self) -> Vec<String> {
1262        self.joins.clone()
1263    }
1264}
1265
1266/// Optimization opportunities
1267#[derive(Debug, Clone)]
1268pub enum OptimizationOpportunity {
1269    FilterPushdown,
1270    JoinReordering,
1271    IndexSelection,
1272    Batching,
1273    Caching,
1274}
1275
1276/// Function performance statistics
1277#[derive(Debug, Clone)]
1278pub struct FunctionStats {
1279    /// Function name
1280    pub name: String,
1281    /// Total call count
1282    pub call_count: usize,
1283    /// Average execution time
1284    pub avg_execution_time: Duration,
1285    /// Total execution time
1286    pub total_execution_time: Duration,
1287    /// Error rate
1288    pub error_rate: f32,
1289}
1290
1291#[cfg(test)]
1292mod tests {
1293    use super::*;
1294
1295    #[test]
1296    fn test_vector_query_planner_creation() {
1297        let config = VectorQueryConfig::default();
1298        let planner = VectorQueryPlanner::new(config);
1299
1300        assert_eq!(planner.vector_indices.read().unwrap().len(), 0);
1301    }
1302
1303    #[test]
1304    fn test_vector_function_registry() {
1305        let registry = VectorFunctionRegistry::new();
1306
1307        assert_eq!(registry.functions.read().unwrap().len(), 0);
1308    }
1309
1310    #[test]
1311    fn test_optimization_plan_creation() {
1312        let config = VectorQueryConfig::default();
1313        let planner = VectorQueryPlanner::new(config);
1314
1315        let query = VectorQuery {
1316            query_type: "test".to_string(),
1317            vector_operations: vec!["similarity".to_string()],
1318            joins: vec!["inner_join".to_string()],
1319            filters: vec!["vector_filter".to_string()],
1320            metadata: HashMap::new(),
1321        };
1322
1323        let plan = planner.create_optimization_plan(&query).unwrap();
1324        assert!(!plan.steps.is_empty());
1325    }
1326
1327    #[test]
1328    fn test_vector_query_analysis() {
1329        let query = VectorQuery {
1330            query_type: "test".to_string(),
1331            vector_operations: vec!["similarity".to_string()],
1332            joins: vec!["join1".to_string(), "join2".to_string()],
1333            filters: vec!["similarity_filter".to_string()],
1334            metadata: HashMap::new(),
1335        };
1336
1337        assert!(query.has_vector_filters());
1338        assert!(query.has_joins());
1339        assert!(query.has_vector_operations());
1340        assert_eq!(query.join_count(), 2);
1341    }
1342}
oxirs_vec/oxirs_arq_integration.rs

oxirs_vec/
oxirs_arq_integration.rs
