sklears_compose/circuit_breaker/
failure_detection.rs

1//! Circuit Breaker Failure Detection
2//!
3//! This module provides comprehensive failure detection capabilities for circuit breakers,
4//! including sliding window analysis, statistical pattern detection, machine learning-based
5//! pattern recognition, adaptive threshold management, and optimization engines.
6
7use sklears_core::error::Result as SklResult;
8use std::collections::{HashMap, VecDeque};
9use std::sync::{Arc, Mutex, RwLock};
10use std::time::{Duration, SystemTime};
11
12use crate::fault_core::{FailureDetectionConfig, FaultSeverity, StatisticalMethod};
13
14use super::statistics_tracking::RequestResult;
15
16/// Circuit breaker failure detector with advanced pattern recognition and adaptive thresholds
17#[derive(Debug)]
18pub struct CircuitBreakerFailureDetector {
19    /// Detection configuration
20    config: FailureDetectionConfig,
21    /// Sliding window for request tracking
22    sliding_window: Arc<Mutex<SlidingWindow>>,
23    /// Statistical analyzer for data analysis
24    statistical_analyzer: Arc<StatisticalAnalyzer>,
25    /// Pattern detector for failure patterns
26    pattern_detector: Arc<PatternDetector>,
27    /// Threshold manager for adaptive thresholds
28    threshold_manager: Arc<ThresholdManager>,
29}
30
31/// Sliding window for failure detection
32#[derive(Debug)]
33pub struct SlidingWindow {
34    /// Window entries
35    pub entries: VecDeque<WindowEntry>,
36    /// Window size
37    pub size: usize,
38    /// Window duration
39    pub duration: Duration,
40    /// Success count in window
41    pub success_count: u64,
42    /// Failure count in window
43    pub failure_count: u64,
44}
45
46/// Window entry for tracking individual requests
47#[derive(Debug, Clone)]
48pub struct WindowEntry {
49    /// Entry timestamp
50    pub timestamp: SystemTime,
51    /// Request result
52    pub result: RequestResult,
53    /// Response time
54    pub response_time: Duration,
55    /// Error details
56    pub error_details: Option<String>,
57}
58
59/// Statistical analyzer for failure detection
60#[derive(Debug)]
61pub struct StatisticalAnalyzer {
62    /// Analysis method
63    method: StatisticalMethod,
64    /// Confidence level
65    confidence_level: f64,
66    /// Historical data
67    historical_data: Arc<Mutex<Vec<DataPoint>>>,
68    /// Analysis cache
69    analysis_cache: Arc<Mutex<AnalysisCache>>,
70}
71
72/// Data point for statistical analysis
73#[derive(Debug, Clone)]
74pub struct DataPoint {
75    /// Timestamp
76    pub timestamp: SystemTime,
77    /// Value
78    pub value: f64,
79    /// Data type
80    pub data_type: DataType,
81    /// Metadata
82    pub metadata: HashMap<String, String>,
83}
84
85/// Data type enumeration
86#[derive(Debug, Clone, PartialEq)]
87pub enum DataType {
88    /// ErrorRate
89    ErrorRate,
90    /// ResponseTime
91    ResponseTime,
92    /// Throughput
93    Throughput,
94    /// ResourceUtilization
95    ResourceUtilization,
96    /// Custom
97    Custom(String),
98}
99
100/// Analysis cache for statistical results
101#[derive(Debug)]
102pub struct AnalysisCache {
103    /// Cached results
104    pub results: HashMap<String, AnalysisResult>,
105    /// Cache timestamps
106    pub timestamps: HashMap<String, SystemTime>,
107    /// Cache TTL
108    pub ttl: Duration,
109}
110
111/// Analysis result
112#[derive(Debug, Clone)]
113pub struct AnalysisResult {
114    /// Result identifier
115    pub id: String,
116    /// Analysis type
117    pub analysis_type: String,
118    /// Result value
119    pub value: f64,
120    /// Confidence level
121    pub confidence: f64,
122    /// Analysis timestamp
123    pub timestamp: SystemTime,
124    /// Result metadata
125    pub metadata: HashMap<String, String>,
126}
127
128/// Pattern detector for failure patterns
129#[derive(Debug)]
130pub struct PatternDetector {
131    /// Known patterns
132    patterns: Arc<RwLock<Vec<FailurePattern>>>,
133    /// Pattern matching engine
134    matching_engine: Arc<PatternMatchingEngine>,
135    /// Learning system
136    learning_system: Arc<PatternLearningSystem>,
137}
138
139/// Failure pattern definition
140#[derive(Debug, Clone)]
141pub struct FailurePattern {
142    /// Pattern identifier
143    pub id: String,
144    /// Pattern name
145    pub name: String,
146    /// Pattern description
147    pub description: String,
148    /// Pattern signature
149    pub signature: PatternSignature,
150    /// Match confidence threshold
151    pub confidence_threshold: f64,
152    /// Pattern severity
153    pub severity: FaultSeverity,
154    /// Pattern metadata
155    pub metadata: HashMap<String, String>,
156}
157
158/// Pattern signature for pattern matching
159#[derive(Debug, Clone)]
160pub struct PatternSignature {
161    /// Error rate threshold
162    pub error_rate_threshold: Option<f64>,
163    /// Response time threshold
164    pub response_time_threshold: Option<Duration>,
165    /// Error message patterns
166    pub error_message_patterns: Vec<String>,
167    /// Temporal patterns
168    pub temporal_patterns: Vec<TemporalPattern>,
169    /// Resource usage patterns
170    pub resource_patterns: Vec<ResourcePattern>,
171}
172
173/// Temporal pattern for time-based analysis
174#[derive(Debug, Clone)]
175pub struct TemporalPattern {
176    /// Pattern type
177    pub pattern_type: TemporalPatternType,
178    /// Time window
179    pub time_window: Duration,
180    /// Frequency threshold
181    pub frequency_threshold: f64,
182    /// Pattern parameters
183    pub parameters: HashMap<String, f64>,
184}
185
186/// Temporal pattern type enumeration
187#[derive(Debug, Clone, PartialEq)]
188pub enum TemporalPatternType {
189    /// Periodic
190    Periodic,
191    /// Burst
192    Burst,
193    /// Gradual
194    Gradual,
195    /// Spike
196    Spike,
197    /// Custom
198    Custom(String),
199}
200
201/// Resource pattern for resource utilization analysis
202#[derive(Debug, Clone)]
203pub struct ResourcePattern {
204    /// Resource type
205    pub resource_type: String,
206    /// Utilization threshold
207    pub utilization_threshold: f64,
208    /// Pattern duration
209    pub duration: Duration,
210    /// Correlation strength
211    pub correlation_strength: f64,
212}
213
214/// Pattern matching engine
215pub struct PatternMatchingEngine {
216    /// Matching algorithms
217    algorithms: HashMap<String, Box<dyn PatternMatchingAlgorithm + Send + Sync>>,
218    /// Matching cache
219    cache: Arc<Mutex<MatchingCache>>,
220    /// Performance metrics
221    metrics: Arc<Mutex<MatchingMetrics>>,
222}
223
224impl std::fmt::Debug for PatternMatchingEngine {
225    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
226        f.debug_struct("PatternMatchingEngine")
227            .field(
228                "algorithms",
229                &format!("<{} algorithms>", self.algorithms.len()),
230            )
231            .field("cache", &"<matching cache>")
232            .field("metrics", &"<matching metrics>")
233            .finish()
234    }
235}
236
237/// Pattern matching algorithm trait
238pub trait PatternMatchingAlgorithm: Send + Sync {
239    /// Match pattern against data
240    fn match_pattern(&self, pattern: &FailurePattern, data: &[DataPoint]) -> PatternMatch;
241
242    /// Get algorithm name
243    fn name(&self) -> &str;
244
245    /// Get algorithm parameters
246    fn parameters(&self) -> HashMap<String, String>;
247}
248
249/// Pattern match result
250#[derive(Debug, Clone)]
251pub struct PatternMatch {
252    /// Pattern identifier
253    pub pattern_id: String,
254    /// Match confidence (0.0 to 1.0)
255    pub confidence: f64,
256    /// Match timestamp
257    pub timestamp: SystemTime,
258    /// Matching data points
259    pub matching_points: Vec<usize>,
260    /// Match metadata
261    pub metadata: HashMap<String, String>,
262}
263
264/// Matching cache for pattern matching performance
265#[derive(Debug, Default)]
266pub struct MatchingCache {
267    /// Cached matches
268    pub matches: HashMap<String, Vec<PatternMatch>>,
269    /// Cache timestamps
270    pub timestamps: HashMap<String, SystemTime>,
271    /// Cache statistics
272    pub statistics: CacheStatistics,
273}
274
275/// Cache statistics
276#[derive(Debug, Default)]
277pub struct CacheStatistics {
278    /// Hit count
279    pub hits: u64,
280    /// Miss count
281    pub misses: u64,
282    /// Eviction count
283    pub evictions: u64,
284    /// Memory usage
285    pub memory_usage: u64,
286}
287
288/// Matching metrics
289#[derive(Debug, Default)]
290pub struct MatchingMetrics {
291    /// Total matches attempted
292    pub total_attempts: u64,
293    /// Successful matches
294    pub successful_matches: u64,
295    /// Average matching time
296    pub avg_matching_time: Duration,
297    /// Pattern accuracy
298    pub pattern_accuracy: HashMap<String, f64>,
299}
300
301/// Pattern learning system for adaptive pattern recognition
302pub struct PatternLearningSystem {
303    /// Learning algorithm
304    algorithm: Box<dyn PatternLearningAlgorithm + Send + Sync>,
305    /// Training data
306    training_data: Arc<Mutex<Vec<TrainingExample>>>,
307    /// Model performance
308    performance: Arc<Mutex<ModelPerformance>>,
309    /// Learning configuration
310    config: LearningConfig,
311}
312
313impl std::fmt::Debug for PatternLearningSystem {
314    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
315        f.debug_struct("PatternLearningSystem")
316            .field("algorithm", &"<learning algorithm>")
317            .field("training_data", &"<training data>")
318            .field("performance", &"<model performance>")
319            .field("config", &self.config)
320            .finish()
321    }
322}
323
324/// Pattern learning algorithm trait
325pub trait PatternLearningAlgorithm: Send + Sync {
326    /// Train on new data
327    fn train(&mut self, examples: &[TrainingExample]) -> SklResult<()>;
328
329    /// Predict pattern for new data
330    fn predict(&self, data: &[DataPoint]) -> SklResult<Vec<PatternPrediction>>;
331
332    /// Get model metadata
333    fn metadata(&self) -> HashMap<String, String>;
334}
335
336/// Training example for pattern learning
337#[derive(Debug, Clone)]
338pub struct TrainingExample {
339    /// Input data
340    pub input: Vec<DataPoint>,
341    /// Expected pattern
342    pub expected_pattern: Option<String>,
343    /// Example weight
344    pub weight: f64,
345    /// Example metadata
346    pub metadata: HashMap<String, String>,
347}
348
349/// Pattern prediction
350#[derive(Debug, Clone)]
351pub struct PatternPrediction {
352    /// Predicted pattern
353    pub pattern_id: String,
354    /// Prediction confidence
355    pub confidence: f64,
356    /// Prediction timestamp
357    pub timestamp: SystemTime,
358    /// Supporting evidence
359    pub evidence: Vec<String>,
360}
361
362/// Model performance metrics
363#[derive(Debug)]
364pub struct ModelPerformance {
365    /// Accuracy
366    pub accuracy: f64,
367    /// Precision
368    pub precision: f64,
369    /// Recall
370    pub recall: f64,
371    /// F1 score
372    pub f1_score: f64,
373    /// Training examples count
374    pub training_examples: u64,
375    /// Last training time
376    pub last_training: SystemTime,
377}
378
379impl Default for ModelPerformance {
380    fn default() -> Self {
381        Self {
382            accuracy: 0.0,
383            precision: 0.0,
384            recall: 0.0,
385            f1_score: 0.0,
386            training_examples: 0,
387            last_training: SystemTime::UNIX_EPOCH,
388        }
389    }
390}
391
392/// Learning configuration
393#[derive(Debug, Clone)]
394pub struct LearningConfig {
395    /// Enable online learning
396    pub online_learning: bool,
397    /// Training batch size
398    pub batch_size: usize,
399    /// Learning rate
400    pub learning_rate: f64,
401    /// Model update frequency
402    pub update_frequency: Duration,
403    /// Performance threshold for retraining
404    pub retrain_threshold: f64,
405}
406
407/// Threshold manager for adaptive threshold management
408#[derive(Debug)]
409pub struct ThresholdManager {
410    /// Static thresholds
411    static_thresholds: HashMap<String, f64>,
412    /// Dynamic thresholds
413    dynamic_thresholds: Arc<RwLock<HashMap<String, DynamicThreshold>>>,
414    /// Adaptive threshold calculator
415    adaptive_calculator: Arc<AdaptiveThresholdCalculator>,
416    /// Threshold optimization engine
417    optimization_engine: Arc<ThresholdOptimizationEngine>,
418}
419
420/// Dynamic threshold for adaptive threshold management
421#[derive(Debug, Clone)]
422pub struct DynamicThreshold {
423    /// Current threshold value
424    pub value: f64,
425    /// Threshold range
426    pub range: ThresholdRange,
427    /// Adjustment factor
428    pub adjustment_factor: f64,
429    /// Last update time
430    pub last_updated: SystemTime,
431    /// Update frequency
432    pub update_frequency: Duration,
433    /// Threshold metadata
434    pub metadata: HashMap<String, String>,
435}
436
437/// Threshold range definition
438#[derive(Debug, Clone)]
439pub struct ThresholdRange {
440    /// Minimum threshold value
441    pub min: f64,
442    /// Maximum threshold value
443    pub max: f64,
444    /// Default threshold value
445    pub default: f64,
446}
447
448/// Adaptive threshold calculator
449pub struct AdaptiveThresholdCalculator {
450    /// Historical performance data
451    performance_data: Arc<Mutex<Vec<PerformanceDataPoint>>>,
452    /// Calculation algorithms
453    algorithms: HashMap<String, Box<dyn ThresholdCalculationAlgorithm + Send + Sync>>,
454    /// Configuration
455    config: AdaptiveThresholdConfig,
456}
457
458impl std::fmt::Debug for AdaptiveThresholdCalculator {
459    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
460        f.debug_struct("AdaptiveThresholdCalculator")
461            .field("performance_data", &"<performance data>")
462            .field(
463                "algorithms",
464                &format!("<{} algorithms>", self.algorithms.len()),
465            )
466            .field("config", &self.config)
467            .finish()
468    }
469}
470
471/// Performance data point for threshold calculation
472#[derive(Debug, Clone)]
473pub struct PerformanceDataPoint {
474    /// Timestamp
475    pub timestamp: SystemTime,
476    /// Metric name
477    pub metric: String,
478    /// Metric value
479    pub value: f64,
480    /// System context
481    pub context: SystemContext,
482}
483
484/// System context for performance data
485#[derive(Debug, Clone)]
486pub struct SystemContext {
487    /// CPU utilization
488    pub cpu_utilization: f64,
489    /// Memory utilization
490    pub memory_utilization: f64,
491    /// Network utilization
492    pub network_utilization: f64,
493    /// Load average
494    pub load_average: f64,
495    /// Custom metrics
496    pub custom_metrics: HashMap<String, f64>,
497}
498
499/// Threshold calculation algorithm trait
500pub trait ThresholdCalculationAlgorithm: Send + Sync {
501    /// Calculate threshold based on historical data
502    fn calculate_threshold(&self, data: &[PerformanceDataPoint]) -> f64;
503
504    /// Get algorithm name
505    fn name(&self) -> &str;
506
507    /// Get algorithm configuration
508    fn config(&self) -> HashMap<String, String>;
509}
510
511/// Adaptive threshold configuration
512#[derive(Debug, Clone)]
513pub struct AdaptiveThresholdConfig {
514    /// Enable adaptive thresholds
515    pub enabled: bool,
516    /// Data retention period
517    pub data_retention: Duration,
518    /// Minimum data points required
519    pub min_data_points: usize,
520    /// Update interval
521    pub update_interval: Duration,
522    /// Sensitivity factor
523    pub sensitivity: f64,
524}
525
526/// Threshold optimization engine
527pub struct ThresholdOptimizationEngine {
528    /// Optimization objectives
529    objectives: Vec<OptimizationObjective>,
530    /// Optimization algorithms
531    algorithms: HashMap<String, Box<dyn OptimizationAlgorithm + Send + Sync>>,
532    /// Optimization history
533    history: Arc<Mutex<Vec<OptimizationRun>>>,
534    /// Configuration
535    config: OptimizationConfig,
536}
537
538impl std::fmt::Debug for ThresholdOptimizationEngine {
539    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
540        f.debug_struct("ThresholdOptimizationEngine")
541            .field("objectives", &self.objectives)
542            .field(
543                "algorithms",
544                &format!("<{} algorithms>", self.algorithms.len()),
545            )
546            .field("history", &"<optimization history>")
547            .field("config", &self.config)
548            .finish()
549    }
550}
551
552/// Optimization objective
553#[derive(Debug, Clone)]
554pub struct OptimizationObjective {
555    /// Objective name
556    pub name: String,
557    /// Objective type
558    pub objective_type: ObjectiveType,
559    /// Target value
560    pub target_value: f64,
561    /// Weight in multi-objective optimization
562    pub weight: f64,
563    /// Objective constraints
564    pub constraints: Vec<OptimizationConstraint>,
565}
566
567/// Objective type enumeration
568#[derive(Debug, Clone, PartialEq)]
569pub enum ObjectiveType {
570    /// Minimize
571    Minimize,
572    /// Maximize
573    Maximize,
574    /// Target
575    Target,
576}
577
578/// Optimization constraint
579#[derive(Debug, Clone)]
580pub struct OptimizationConstraint {
581    /// Constraint name
582    pub name: String,
583    /// Constraint type
584    pub constraint_type: ConstraintType,
585    /// Constraint value
586    pub value: f64,
587    /// Constraint tolerance
588    pub tolerance: f64,
589}
590
591/// Constraint type enumeration
592#[derive(Debug, Clone, PartialEq)]
593pub enum ConstraintType {
594    /// LessThan
595    LessThan,
596    /// GreaterThan
597    GreaterThan,
598    /// Equal
599    Equal,
600    /// Range
601    Range,
602}
603
604/// Optimization algorithm trait
605pub trait OptimizationAlgorithm: Send + Sync {
606    /// Optimize thresholds
607    fn optimize(
608        &self,
609        current_thresholds: &HashMap<String, f64>,
610        objectives: &[OptimizationObjective],
611    ) -> OptimizationResult;
612
613    /// Get algorithm name
614    fn name(&self) -> &str;
615
616    /// Get algorithm parameters
617    fn parameters(&self) -> HashMap<String, String>;
618}
619
620/// Optimization result
621#[derive(Debug, Clone)]
622pub struct OptimizationResult {
623    /// Optimized thresholds
624    pub thresholds: HashMap<String, f64>,
625    /// Objective values achieved
626    pub objective_values: HashMap<String, f64>,
627    /// Optimization score
628    pub score: f64,
629    /// Convergence information
630    pub convergence: ConvergenceInfo,
631    /// Optimization metadata
632    pub metadata: HashMap<String, String>,
633}
634
635/// Convergence information
636#[derive(Debug, Clone)]
637pub struct ConvergenceInfo {
638    /// Converged successfully
639    pub converged: bool,
640    /// Number of iterations
641    pub iterations: u32,
642    /// Final error
643    pub final_error: f64,
644    /// Convergence time
645    pub convergence_time: Duration,
646}
647
648/// Optimization run
649#[derive(Debug, Clone)]
650pub struct OptimizationRun {
651    /// Run identifier
652    pub id: String,
653    /// Run timestamp
654    pub timestamp: SystemTime,
655    /// Algorithm used
656    pub algorithm: String,
657    /// Initial thresholds
658    pub initial_thresholds: HashMap<String, f64>,
659    /// Result
660    pub result: OptimizationResult,
661    /// Run duration
662    pub duration: Duration,
663}
664
665/// Optimization configuration
666#[derive(Debug, Clone)]
667pub struct OptimizationConfig {
668    /// Enable optimization
669    pub enabled: bool,
670    /// Optimization interval
671    pub interval: Duration,
672    /// Maximum optimization time
673    pub max_time: Duration,
674    /// Convergence tolerance
675    pub convergence_tolerance: f64,
676    /// Performance threshold for optimization
677    pub performance_threshold: f64,
678}
679
680impl CircuitBreakerFailureDetector {
681    /// Create a new failure detector
682    #[must_use]
683    pub fn new(config: FailureDetectionConfig) -> Self {
684        Self {
685            config,
686            sliding_window: Arc::new(Mutex::new(SlidingWindow {
687                entries: VecDeque::new(),
688                size: 100,
689                duration: Duration::from_secs(60),
690                success_count: 0,
691                failure_count: 0,
692            })),
693            statistical_analyzer: Arc::new(StatisticalAnalyzer {
694                method: StatisticalMethod::Simple,
695                confidence_level: 0.95,
696                historical_data: Arc::new(Mutex::new(Vec::new())),
697                analysis_cache: Arc::new(Mutex::new(AnalysisCache::default())),
698            }),
699            pattern_detector: Arc::new(PatternDetector {
700                patterns: Arc::new(RwLock::new(Vec::new())),
701                matching_engine: Arc::new(PatternMatchingEngine {
702                    algorithms: HashMap::new(),
703                    cache: Arc::new(Mutex::new(MatchingCache::default())),
704                    metrics: Arc::new(Mutex::new(MatchingMetrics::default())),
705                }),
706                learning_system: Arc::new(PatternLearningSystem {
707                    algorithm: Box::new(DummyLearningAlgorithm),
708                    training_data: Arc::new(Mutex::new(Vec::new())),
709                    performance: Arc::new(Mutex::new(ModelPerformance::default())),
710                    config: LearningConfig {
711                        online_learning: false,
712                        batch_size: 100,
713                        learning_rate: 0.01,
714                        update_frequency: Duration::from_secs(3600),
715                        retrain_threshold: 0.8,
716                    },
717                }),
718            }),
719            threshold_manager: Arc::new(ThresholdManager {
720                static_thresholds: HashMap::new(),
721                dynamic_thresholds: Arc::new(RwLock::new(HashMap::new())),
722                adaptive_calculator: Arc::new(AdaptiveThresholdCalculator {
723                    performance_data: Arc::new(Mutex::new(Vec::new())),
724                    algorithms: HashMap::new(),
725                    config: AdaptiveThresholdConfig {
726                        enabled: true,
727                        data_retention: Duration::from_secs(86400),
728                        min_data_points: 50,
729                        update_interval: Duration::from_secs(300),
730                        sensitivity: 0.5,
731                    },
732                }),
733                optimization_engine: Arc::new(ThresholdOptimizationEngine {
734                    objectives: Vec::new(),
735                    algorithms: HashMap::new(),
736                    history: Arc::new(Mutex::new(Vec::new())),
737                    config: OptimizationConfig {
738                        enabled: false,
739                        interval: Duration::from_secs(3600),
740                        max_time: Duration::from_secs(300),
741                        convergence_tolerance: 0.001,
742                        performance_threshold: 0.8,
743                    },
744                }),
745            }),
746        }
747    }
748
749    /// Check if circuit should trip based on failure detection
750    #[must_use]
751    pub fn should_trip(&self) -> bool {
752        // Simplified failure detection logic
753        let window = self.sliding_window.lock().unwrap();
754        window.failure_count > 5 // Simple threshold
755    }
756
757    /// Record a request result for analysis
758    pub fn record_request(
759        &self,
760        result: RequestResult,
761        response_time: Duration,
762        error_details: Option<String>,
763    ) {
764        let mut window = self.sliding_window.lock().unwrap();
765
766        let entry = WindowEntry {
767            timestamp: SystemTime::now(),
768            result: result.clone(),
769            response_time,
770            error_details,
771        };
772
773        window.entries.push_back(entry);
774
775        // Update counters
776        match result {
777            RequestResult::Success => {
778                window.success_count += 1;
779            }
780            RequestResult::Failure | RequestResult::Timeout => {
781                window.failure_count += 1;
782            }
783            _ => {}
784        }
785
786        // Maintain window size
787        if window.entries.len() > window.size {
788            if let Some(removed) = window.entries.pop_front() {
789                match removed.result {
790                    RequestResult::Success => {
791                        window.success_count = window.success_count.saturating_sub(1);
792                    }
793                    RequestResult::Failure | RequestResult::Timeout => {
794                        window.failure_count = window.failure_count.saturating_sub(1);
795                    }
796                    _ => {}
797                }
798            }
799        }
800    }
801
802    /// Get current failure rate
803    #[must_use]
804    pub fn get_failure_rate(&self) -> f64 {
805        let window = self.sliding_window.lock().unwrap();
806        let total = window.success_count + window.failure_count;
807        if total > 0 {
808            window.failure_count as f64 / total as f64
809        } else {
810            0.0
811        }
812    }
813
814    /// Analyze patterns in failure data
815    #[must_use]
816    pub fn analyze_patterns(&self) -> Vec<PatternMatch> {
817        // Simplified pattern analysis
818        Vec::new()
819    }
820
821    /// Update adaptive thresholds
822    pub fn update_thresholds(&self, performance_data: Vec<PerformanceDataPoint>) {
823        // Simplified threshold update
824    }
825
826    /// Get current configuration
827    #[must_use]
828    pub fn get_config(&self) -> &FailureDetectionConfig {
829        &self.config
830    }
831
832    /// Reset failure detector state
833    pub fn reset(&self) {
834        let mut window = self.sliding_window.lock().unwrap();
835        window.entries.clear();
836        window.success_count = 0;
837        window.failure_count = 0;
838    }
839}
840
841impl SlidingWindow {
842    /// Create a new sliding window
843    #[must_use]
844    pub fn new(size: usize, duration: Duration) -> Self {
845        Self {
846            entries: VecDeque::new(),
847            size,
848            duration,
849            success_count: 0,
850            failure_count: 0,
851        }
852    }
853
854    /// Get window statistics
855    #[must_use]
856    pub fn get_stats(&self) -> WindowStats {
857        /// WindowStats
858        WindowStats {
859            total_requests: self.entries.len() as u64,
860            success_count: self.success_count,
861            failure_count: self.failure_count,
862            failure_rate: if self.success_count + self.failure_count > 0 {
863                self.failure_count as f64 / (self.success_count + self.failure_count) as f64
864            } else {
865                0.0
866            },
867        }
868    }
869}
870
871/// Window statistics
872#[derive(Debug, Clone)]
873pub struct WindowStats {
874    /// Total requests in window
875    pub total_requests: u64,
876    /// Success count
877    pub success_count: u64,
878    /// Failure count
879    pub failure_count: u64,
880    /// Failure rate
881    pub failure_rate: f64,
882}
883
884/// Dummy learning algorithm for compilation
885struct DummyLearningAlgorithm;
886
887impl PatternLearningAlgorithm for DummyLearningAlgorithm {
888    fn train(&mut self, _examples: &[TrainingExample]) -> SklResult<()> {
889        Ok(())
890    }
891
892    fn predict(&self, _data: &[DataPoint]) -> SklResult<Vec<PatternPrediction>> {
893        Ok(Vec::new())
894    }
895
896    fn metadata(&self) -> HashMap<String, String> {
897        HashMap::new()
898    }
899}
900
901impl Default for AnalysisCache {
902    fn default() -> Self {
903        Self {
904            results: HashMap::new(),
905            timestamps: HashMap::new(),
906            ttl: Duration::from_secs(300), // 5 minutes default TTL
907        }
908    }
909}
910
911impl Default for AdaptiveThresholdConfig {
912    fn default() -> Self {
913        Self {
914            enabled: true,
915            data_retention: Duration::from_secs(86400), // 24 hours
916            min_data_points: 50,
917            update_interval: Duration::from_secs(300), // 5 minutes
918            sensitivity: 0.5,
919        }
920    }
921}
922
923impl Default for OptimizationConfig {
924    fn default() -> Self {
925        Self {
926            enabled: false,
927            interval: Duration::from_secs(3600), // 1 hour
928            max_time: Duration::from_secs(300),  // 5 minutes
929            convergence_tolerance: 0.001,
930            performance_threshold: 0.8,
931        }
932    }
933}
934
935impl Default for LearningConfig {
936    fn default() -> Self {
937        Self {
938            online_learning: false,
939            batch_size: 100,
940            learning_rate: 0.01,
941            update_frequency: Duration::from_secs(3600),
942            retrain_threshold: 0.8,
943        }
944    }
945}
sklears_compose/circuit_breaker/failure_detection.rs

sklears_compose/circuit_breaker/
failure_detection.rs
