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.vector_indices.write().unwrap();
735        indices.insert(name, index);
736        Ok(())
737    }
738
739    /// Create an optimization plan for a query
740    pub fn create_optimization_plan(&self, query: &VectorQuery) -> Result<OptimizationPlan> {
741        let span = span!(Level::DEBUG, "create_optimization_plan");
742        let _enter = span.enter();
743
744        // Generate plan ID
745        let plan_id = format!("plan_{}", uuid::Uuid::new_v4());
746
747        // Analyze query for optimization opportunities
748        let optimization_opportunities = self.analyze_query(query)?;
749
750        // Generate optimization steps
751        let mut steps = Vec::new();
752        for opportunity in optimization_opportunities {
753            let step = self.generate_optimization_step(opportunity, query)?;
754            steps.push(step);
755        }
756
757        // Estimate total cost and time
758        let estimated_cost = steps.iter().map(|s| s.cost).sum();
759        let estimated_time = self.estimate_execution_time(&steps, query)?;
760
761        // Calculate expected quality
762        let expected_quality = self.estimate_quality_score(&steps, query)?;
763
764        let plan = OptimizationPlan {
765            plan_id: plan_id.clone(),
766            steps,
767            estimated_cost,
768            estimated_time,
769            expected_quality,
770            metadata: {
771                let mut metadata = HashMap::new();
772                metadata.insert("created_at".to_string(), chrono::Utc::now().to_rfc3339());
773                metadata.insert("query_type".to_string(), query.query_type.clone());
774                metadata
775            },
776        };
777
778        // Cache the plan
779        {
780            let mut cache = self.optimization_cache.write().unwrap();
781            cache.insert(plan_id, plan.clone());
782        }
783
784        debug!("Created optimization plan with {} steps", plan.steps.len());
785        Ok(plan)
786    }
787
788    /// Analyze query for optimization opportunities
789    fn analyze_query(&self, query: &VectorQuery) -> Result<Vec<OptimizationOpportunity>> {
790        let mut opportunities = Vec::new();
791
792        // Check for vector filter pushdown opportunities
793        if query.has_vector_filters() {
794            opportunities.push(OptimizationOpportunity::FilterPushdown);
795        }
796
797        // Check for join reordering opportunities
798        if query.has_joins() && query.join_count() > 1 {
799            opportunities.push(OptimizationOpportunity::JoinReordering);
800        }
801
802        // Check for index selection opportunities
803        if query.has_vector_operations() {
804            opportunities.push(OptimizationOpportunity::IndexSelection);
805        }
806
807        // Check for batching opportunities
808        if query.has_multiple_similar_operations() {
809            opportunities.push(OptimizationOpportunity::Batching);
810        }
811
812        // Check for caching opportunities
813        if query.has_repeated_subqueries() {
814            opportunities.push(OptimizationOpportunity::Caching);
815        }
816
817        Ok(opportunities)
818    }
819
820    /// Generate optimization step for opportunity
821    fn generate_optimization_step(
822        &self,
823        opportunity: OptimizationOpportunity,
824        query: &VectorQuery,
825    ) -> Result<OptimizationStep> {
826        match opportunity {
827            OptimizationOpportunity::FilterPushdown => Ok(OptimizationStep {
828                step_type: OptimizationStepType::FilterPushdown {
829                    filter_type: FilterType::SimilarityFilter,
830                    pushdown_level: 2,
831                },
832                parameters: HashMap::new(),
833                cost: self.estimate_filter_pushdown_cost(query),
834                dependencies: Vec::new(),
835            }),
836            OptimizationOpportunity::JoinReordering => {
837                let original_order = query.get_join_order();
838                let optimized_order = self.optimize_join_order(&original_order, query)?;
839
840                Ok(OptimizationStep {
841                    step_type: OptimizationStepType::JoinReordering {
842                        original_order,
843                        optimized_order,
844                    },
845                    parameters: HashMap::new(),
846                    cost: self.estimate_join_reorder_cost(query),
847                    dependencies: Vec::new(),
848                })
849            }
850            OptimizationOpportunity::IndexSelection => {
851                let best_index = self.select_optimal_index(query)?;
852
853                Ok(OptimizationStep {
854                    step_type: OptimizationStepType::IndexSelection {
855                        index_type: best_index,
856                        selection_criteria: SelectionCriteria::Hybrid(vec![0.4, 0.3, 0.3]), // Performance, Memory, Accuracy
857                    },
858                    parameters: HashMap::new(),
859                    cost: self.estimate_index_selection_cost(query),
860                    dependencies: Vec::new(),
861                })
862            }
863            OptimizationOpportunity::Batching => Ok(OptimizationStep {
864                step_type: OptimizationStepType::VectorBatching {
865                    batch_size: self.calculate_optimal_batch_size(query),
866                    batching_strategy: BatchingStrategy::Adaptive,
867                },
868                parameters: HashMap::new(),
869                cost: self.estimate_batching_cost(query),
870                dependencies: Vec::new(),
871            }),
872            OptimizationOpportunity::Caching => Ok(OptimizationStep {
873                step_type: OptimizationStepType::CachingSetup {
874                    cache_type: CacheType::ResultCache,
875                    cache_size: 1000,
876                },
877                parameters: HashMap::new(),
878                cost: self.estimate_caching_cost(query),
879                dependencies: Vec::new(),
880            }),
881        }
882    }
883
884    /// Estimate execution time for optimization steps
885    fn estimate_execution_time(
886        &self,
887        steps: &[OptimizationStep],
888        query: &VectorQuery,
889    ) -> Result<Duration> {
890        let base_time = self.estimate_base_execution_time(query);
891        let optimization_factor = self.calculate_optimization_factor(steps);
892
893        Ok(Duration::from_secs_f64(
894            base_time.as_secs_f64() * optimization_factor,
895        ))
896    }
897
898    /// Estimate quality score for optimization plan
899    fn estimate_quality_score(
900        &self,
901        steps: &[OptimizationStep],
902        _query: &VectorQuery,
903    ) -> Result<f32> {
904        let base_quality = 0.8; // Base quality score
905        let quality_improvement = steps
906            .iter()
907            .map(|step| self.estimate_step_quality_impact(step))
908            .sum::<f32>();
909
910        Ok((base_quality + quality_improvement).min(1.0))
911    }
912
913    /// Helper methods for cost estimation
914    fn estimate_filter_pushdown_cost(&self, _query: &VectorQuery) -> f64 {
915        10.0 // Simplified cost
916    }
917
918    fn estimate_join_reorder_cost(&self, _query: &VectorQuery) -> f64 {
919        20.0
920    }
921
922    fn estimate_index_selection_cost(&self, _query: &VectorQuery) -> f64 {
923        5.0
924    }
925
926    fn estimate_batching_cost(&self, _query: &VectorQuery) -> f64 {
927        15.0
928    }
929
930    fn estimate_caching_cost(&self, _query: &VectorQuery) -> f64 {
931        8.0
932    }
933
934    fn estimate_base_execution_time(&self, _query: &VectorQuery) -> Duration {
935        Duration::from_millis(100) // Simplified estimation
936    }
937
938    fn calculate_optimization_factor(&self, _steps: &[OptimizationStep]) -> f64 {
939        0.7 // 30% improvement
940    }
941
942    fn estimate_step_quality_impact(&self, _step: &OptimizationStep) -> f32 {
943        0.05 // 5% quality improvement per step
944    }
945
946    fn optimize_join_order(
947        &self,
948        original_order: &[String],
949        _query: &VectorQuery,
950    ) -> Result<Vec<String>> {
951        // Simplified join reordering
952        let mut optimized = original_order.to_vec();
953        optimized.reverse(); // Simple reordering strategy
954        Ok(optimized)
955    }
956
957    fn select_optimal_index(&self, _query: &VectorQuery) -> Result<String> {
958        // Select best index based on query characteristics
959        Ok("hnsw".to_string()) // Default to HNSW
960    }
961
962    fn calculate_optimal_batch_size(&self, _query: &VectorQuery) -> usize {
963        1000 // Default batch size
964    }
965
966    /// Update query statistics after execution
967    pub fn update_statistics(
968        &self,
969        query: &VectorQuery,
970        execution_time: Duration,
971        _result_count: usize,
972    ) -> Result<()> {
973        let mut stats = self.query_stats.write().unwrap();
974
975        stats.total_queries += 1;
976
977        // Update operation counts
978        for op in &query.vector_operations {
979            *stats.vector_op_counts.entry(op.clone()).or_insert(0) += 1;
980        }
981
982        // Update execution times
983        stats
984            .avg_execution_times
985            .insert(query.query_type.clone(), execution_time);
986
987        Ok(())
988    }
989
990    /// Get performance metrics
991    pub fn get_performance_metrics(&self) -> Result<VectorQueryPerformance> {
992        let performance = self.performance_monitor.read().unwrap();
993        Ok(performance.clone())
994    }
995}
996
997impl Default for VectorFunctionRegistry {
998    fn default() -> Self {
999        Self::new()
1000    }
1001}
1002
1003impl VectorFunctionRegistry {
1004    /// Create a new vector function registry
1005    pub fn new() -> Self {
1006        Self {
1007            functions: Arc::new(RwLock::new(HashMap::new())),
1008            function_metadata: Arc::new(RwLock::new(HashMap::new())),
1009            type_checker: Arc::new(VectorTypeChecker::new()),
1010            performance_monitor: Arc::new(RwLock::new(FunctionPerformanceMonitor::default())),
1011        }
1012    }
1013
1014    /// Register a vector function
1015    pub fn register_function(
1016        &self,
1017        function: Arc<dyn VectorFunction>,
1018        metadata: FunctionMetadata,
1019    ) -> Result<()> {
1020        let name = function.name().to_string();
1021
1022        // Validate function signature
1023        self.type_checker
1024            .validate_signature(&function.signature())?;
1025
1026        // Register function
1027        {
1028            let mut functions = self.functions.write().unwrap();
1029            functions.insert(name.clone(), function);
1030        }
1031
1032        // Register metadata
1033        {
1034            let mut meta = self.function_metadata.write().unwrap();
1035            meta.insert(name, metadata);
1036        }
1037
1038        Ok(())
1039    }
1040
1041    /// Execute a registered function
1042    pub fn execute_function(
1043        &self,
1044        name: &str,
1045        args: &[FunctionArgument],
1046        context: &ExecutionContext,
1047    ) -> Result<FunctionResult> {
1048        let function = {
1049            let functions = self.functions.read().unwrap();
1050            functions
1051                .get(name)
1052                .ok_or_else(|| AnyhowError::msg(format!("Function not found: {name}")))?
1053                .clone()
1054        };
1055
1056        // Type check arguments
1057        self.type_checker
1058            .check_arguments(&function.signature(), args)?;
1059
1060        // Execute function
1061        let start_time = Instant::now();
1062        let result = function.execute(args, context)?;
1063        let execution_time = start_time.elapsed();
1064
1065        // Update performance metrics
1066        self.update_function_performance(name, execution_time)?;
1067
1068        Ok(result)
1069    }
1070
1071    /// Update function performance metrics
1072    fn update_function_performance(&self, name: &str, execution_time: Duration) -> Result<()> {
1073        let mut monitor = self.performance_monitor.write().unwrap();
1074
1075        // Update call count
1076        *monitor.call_counts.entry(name.to_string()).or_insert(0) += 1;
1077
1078        // Update execution times
1079        monitor
1080            .execution_times
1081            .entry(name.to_string())
1082            .or_default()
1083            .push(execution_time);
1084
1085        // Add performance trend point
1086        monitor
1087            .trends
1088            .entry(name.to_string())
1089            .or_default()
1090            .push((Instant::now(), execution_time.as_secs_f64()));
1091
1092        Ok(())
1093    }
1094
1095    /// Get function performance statistics
1096    pub fn get_function_stats(&self, name: &str) -> Result<FunctionStats> {
1097        let monitor = self.performance_monitor.read().unwrap();
1098
1099        let call_count = monitor.call_counts.get(name).copied().unwrap_or(0);
1100        let execution_times = monitor
1101            .execution_times
1102            .get(name)
1103            .cloned()
1104            .unwrap_or_default();
1105
1106        let avg_time = if !execution_times.is_empty() {
1107            execution_times.iter().sum::<Duration>() / execution_times.len() as u32
1108        } else {
1109            Duration::ZERO
1110        };
1111
1112        Ok(FunctionStats {
1113            name: name.to_string(),
1114            call_count,
1115            avg_execution_time: avg_time,
1116            total_execution_time: execution_times.iter().sum(),
1117            error_rate: monitor.error_rates.get(name).copied().unwrap_or(0.0),
1118        })
1119    }
1120}
1121
1122impl Default for VectorTypeChecker {
1123    fn default() -> Self {
1124        Self::new()
1125    }
1126}
1127
1128impl VectorTypeChecker {
1129    /// Create a new type checker
1130    pub fn new() -> Self {
1131        Self {
1132            type_rules: HashMap::new(),
1133            conversion_rules: HashMap::new(),
1134        }
1135    }
1136
1137    /// Validate function signature
1138    pub fn validate_signature(&self, _signature: &FunctionSignature) -> Result<()> {
1139        // Simplified validation
1140        Ok(())
1141    }
1142
1143    /// Check function arguments against signature
1144    pub fn check_arguments(
1145        &self,
1146        signature: &FunctionSignature,
1147        args: &[FunctionArgument],
1148    ) -> Result<()> {
1149        if args.len() < signature.required_params {
1150            return Err(AnyhowError::msg("Insufficient arguments"));
1151        }
1152
1153        if !signature.variadic && args.len() > signature.parameters.len() {
1154            return Err(AnyhowError::msg("Too many arguments"));
1155        }
1156
1157        // Type check each argument
1158        for (i, arg) in args.iter().enumerate() {
1159            if i < signature.parameters.len() {
1160                self.check_argument_type(arg, &signature.parameters[i])?;
1161            }
1162        }
1163
1164        Ok(())
1165    }
1166
1167    /// Check individual argument type
1168    fn check_argument_type(
1169        &self,
1170        arg: &FunctionArgument,
1171        expected_type: &ParameterType,
1172    ) -> Result<()> {
1173        match (arg, expected_type) {
1174            (FunctionArgument::Vector(_), ParameterType::Vector) => Ok(()),
1175            (FunctionArgument::Scalar(_), ParameterType::Scalar(_)) => Ok(()),
1176            (FunctionArgument::URI(_), ParameterType::URI) => Ok(()),
1177            (FunctionArgument::Literal(_, _), ParameterType::Literal(_)) => Ok(()),
1178            (FunctionArgument::Variable(_), ParameterType::Variable) => Ok(()),
1179            _ => Err(AnyhowError::msg("Type mismatch")),
1180        }
1181    }
1182}
1183
1184/// Vector query representation
1185#[derive(Debug, Clone)]
1186pub struct VectorQuery {
1187    /// Query type
1188    pub query_type: String,
1189    /// Vector operations in query
1190    pub vector_operations: Vec<String>,
1191    /// Join operations
1192    pub joins: Vec<String>,
1193    /// Filter conditions
1194    pub filters: Vec<String>,
1195    /// Query metadata
1196    pub metadata: HashMap<String, String>,
1197}
1198
1199impl VectorQuery {
1200    /// Check if query has vector filters
1201    pub fn has_vector_filters(&self) -> bool {
1202        self.filters
1203            .iter()
1204            .any(|f| f.contains("vector") || f.contains("similarity"))
1205    }
1206
1207    /// Check if query has joins
1208    pub fn has_joins(&self) -> bool {
1209        !self.joins.is_empty()
1210    }
1211
1212    /// Get join count
1213    pub fn join_count(&self) -> usize {
1214        self.joins.len()
1215    }
1216
1217    /// Check if query has vector operations
1218    pub fn has_vector_operations(&self) -> bool {
1219        !self.vector_operations.is_empty()
1220    }
1221
1222    /// Check if query has multiple similar operations
1223    pub fn has_multiple_similar_operations(&self) -> bool {
1224        self.vector_operations.len() > 1
1225    }
1226
1227    /// Check if query has repeated subqueries
1228    pub fn has_repeated_subqueries(&self) -> bool {
1229        // Simplified check
1230        self.metadata.contains_key("repeated_patterns")
1231    }
1232
1233    /// Get join order
1234    pub fn get_join_order(&self) -> Vec<String> {
1235        self.joins.clone()
1236    }
1237}
1238
1239/// Optimization opportunities
1240#[derive(Debug, Clone)]
1241pub enum OptimizationOpportunity {
1242    FilterPushdown,
1243    JoinReordering,
1244    IndexSelection,
1245    Batching,
1246    Caching,
1247}
1248
1249/// Function performance statistics
1250#[derive(Debug, Clone)]
1251pub struct FunctionStats {
1252    /// Function name
1253    pub name: String,
1254    /// Total call count
1255    pub call_count: usize,
1256    /// Average execution time
1257    pub avg_execution_time: Duration,
1258    /// Total execution time
1259    pub total_execution_time: Duration,
1260    /// Error rate
1261    pub error_rate: f32,
1262}
1263
1264#[cfg(test)]
1265mod tests {
1266    use super::*;
1267
1268    #[test]
1269    fn test_vector_query_planner_creation() {
1270        let config = VectorQueryConfig::default();
1271        let planner = VectorQueryPlanner::new(config);
1272
1273        assert_eq!(planner.vector_indices.read().unwrap().len(), 0);
1274    }
1275
1276    #[test]
1277    fn test_vector_function_registry() {
1278        let registry = VectorFunctionRegistry::new();
1279
1280        assert_eq!(registry.functions.read().unwrap().len(), 0);
1281    }
1282
1283    #[test]
1284    fn test_optimization_plan_creation() {
1285        let config = VectorQueryConfig::default();
1286        let planner = VectorQueryPlanner::new(config);
1287
1288        let query = VectorQuery {
1289            query_type: "test".to_string(),
1290            vector_operations: vec!["similarity".to_string()],
1291            joins: vec!["inner_join".to_string()],
1292            filters: vec!["vector_filter".to_string()],
1293            metadata: HashMap::new(),
1294        };
1295
1296        let plan = planner.create_optimization_plan(&query).unwrap();
1297        assert!(!plan.steps.is_empty());
1298    }
1299
1300    #[test]
1301    fn test_vector_query_analysis() {
1302        let query = VectorQuery {
1303            query_type: "test".to_string(),
1304            vector_operations: vec!["similarity".to_string()],
1305            joins: vec!["join1".to_string(), "join2".to_string()],
1306            filters: vec!["similarity_filter".to_string()],
1307            metadata: HashMap::new(),
1308        };
1309
1310        assert!(query.has_vector_filters());
1311        assert!(query.has_joins());
1312        assert!(query.has_vector_operations());
1313        assert_eq!(query.join_count(), 2);
1314    }
1315}
oxirs_vec/oxirs_arq_integration.rs

oxirs_vec/
oxirs_arq_integration.rs
