oxirs-embed 0.3.1

//! Advanced Profiler Analyzer
//!
//! Trace analysis: flame graph data, hotspot detection, anomaly detection,
//! pattern recognition, regression detection, and optimization recommendations.

use anyhow::{anyhow, Result};
use chrono::Utc;
use scirs2_core::random::{Random, RngExt};
use std::collections::{HashMap, VecDeque};
use std::time::Duration;
use uuid::Uuid;

use super::advanced_profiler_types::{
    AdvancedProfiler, AlgorithmType, AnalysisAlgorithm, AnalysisResult, AnomalyAlgorithm,
    AnomalyAlgorithmType, AnomalyContext, AnomalyDetector, AnomalySeverity, AnomalyType,
    ComparisonOperator, ComplexityLevel, ExpectedImprovement, Finding, FindingSeverity,
    ImpactSeverity, ImpactType, ImplementationEffort, MatchingCriteria, MetricDataPoint,
    OptimizationRecommendation, OptimizationRecommender, PatternDetector, PatternSignature,
    PatternTemplate, PatternType, PerformanceAnalysisReport, PerformanceAnalyzer,
    PerformanceAnomaly, PerformanceCollector, PerformancePattern, PotentialImpact, ProfilerConfig,
    ProfilingSession, RecommendationPriority, RecommendationRule, RecommendationTemplate,
    RecommendationType, RiskAssessment, RiskLevel, SessionStatus, StatisticalCharacteristic,
    StatisticalProperty, TemporalFeature, TemporalFeatureType, TimeWindowRequirements,
    TriggerCondition,
};

// ─── PerformanceAnalyzer ─────────────────────────────────────────────────────

impl Default for PerformanceAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}

impl PerformanceAnalyzer {
    pub fn new() -> Self {
        Self {
            algorithms: Self::default_algorithms(),
            pattern_detector: PatternDetector::new(),
            anomaly_detector: AnomalyDetector::new(),
        }
    }

    fn default_algorithms() -> Vec<AnalysisAlgorithm> {
        vec![
            AnalysisAlgorithm {
                name: "Trend Analysis".to_string(),
                algorithm_type: AlgorithmType::TrendAnalysis,
                parameters: HashMap::from([
                    ("window_size".to_string(), 300.0),
                    ("significance_threshold".to_string(), 0.05),
                ]),
            },
            AnalysisAlgorithm {
                name: "Bottleneck Detection".to_string(),
                algorithm_type: AlgorithmType::BottleneckDetection,
                parameters: HashMap::from([
                    ("threshold_percentile".to_string(), 95.0),
                    ("min_duration".to_string(), 10.0),
                ]),
            },
        ]
    }

    /// Analyze performance data and produce a report
    pub async fn analyze(
        &self,
        session: &ProfilingSession,
        data: &VecDeque<MetricDataPoint>,
    ) -> Result<PerformanceAnalysisReport> {
        let mut report = PerformanceAnalysisReport::new(session.session_id.clone());

        for algorithm in &self.algorithms {
            let result = self.run_algorithm(algorithm, data).await?;
            report.add_analysis_result(result);
        }

        let patterns = self.pattern_detector.detect_patterns(data).await?;
        report.set_detected_patterns(patterns);

        let anomalies = self.anomaly_detector.detect_anomalies(data).await?;
        report.set_detected_anomalies(anomalies);

        Ok(report)
    }

    /// Run a single analysis algorithm over the data
    async fn run_algorithm(
        &self,
        algorithm: &AnalysisAlgorithm,
        _data: &VecDeque<MetricDataPoint>,
    ) -> Result<AnalysisResult> {
        Ok(AnalysisResult {
            algorithm_name: algorithm.name.clone(),
            result_type: algorithm.algorithm_type.clone(),
            findings: vec![Finding {
                title: "Sample Finding".to_string(),
                description: "This is a sample finding for demonstration".to_string(),
                severity: FindingSeverity::Medium,
                confidence: 0.8,
                affected_metrics: vec!["latency".to_string()],
                recommendations: vec!["Consider optimization".to_string()],
            }],
            execution_time: Duration::from_millis(100),
        })
    }
}

// ─── PatternDetector ─────────────────────────────────────────────────────────

impl Default for PatternDetector {
    fn default() -> Self {
        Self::new()
    }
}

impl PatternDetector {
    pub fn new() -> Self {
        Self {
            patterns: Vec::new(),
            templates: Self::default_templates(),
        }
    }

    fn default_templates() -> Vec<PatternTemplate> {
        vec![PatternTemplate {
            name: "Memory Leak Pattern".to_string(),
            signature: PatternSignature {
                characteristics: vec![StatisticalCharacteristic {
                    metric: "memory_usage".to_string(),
                    property: StatisticalProperty::Mean,
                    value_range: (0.0, f64::INFINITY),
                }],
                temporal_features: vec![TemporalFeature {
                    feature_type: TemporalFeatureType::Trend,
                    time_scale: Duration::from_secs(3600),
                    threshold: 0.1,
                }],
            },
            criteria: MatchingCriteria {
                min_confidence: 0.7,
                min_data_points: 100,
                time_window_requirements: TimeWindowRequirements {
                    min_duration: Duration::from_secs(300),
                    max_duration: Duration::from_secs(86400),
                    coverage_ratio: 0.8,
                },
            },
        }]
    }

    /// Detect patterns in performance data
    pub async fn detect_patterns(
        &self,
        data: &VecDeque<MetricDataPoint>,
    ) -> Result<Vec<PerformancePattern>> {
        let mut detected = Vec::new();

        for template in &self.templates {
            if let Some(pattern) = self.match_template(template, data).await? {
                detected.push(pattern);
            }
        }

        Ok(detected)
    }

    async fn match_template(
        &self,
        template: &PatternTemplate,
        data: &VecDeque<MetricDataPoint>,
    ) -> Result<Option<PerformancePattern>> {
        if data.len() >= template.criteria.min_data_points {
            Ok(Some(PerformancePattern {
                id: Uuid::new_v4().to_string(),
                pattern_type: PatternType::MemoryLeak,
                confidence: 0.8,
                time_window: (Utc::now() - chrono::Duration::hours(1), Utc::now()),
                affected_components: vec!["embedding_service".to_string()],
                description: "Potential memory leak detected".to_string(),
            }))
        } else {
            Ok(None)
        }
    }
}

// ─── AnomalyDetector ─────────────────────────────────────────────────────────

impl Default for AnomalyDetector {
    fn default() -> Self {
        Self::new()
    }
}

impl AnomalyDetector {
    pub fn new() -> Self {
        Self {
            algorithms: Self::default_algorithms(),
            anomalies: Vec::new(),
            baselines: HashMap::new(),
        }
    }

    fn default_algorithms() -> Vec<AnomalyAlgorithm> {
        vec![
            AnomalyAlgorithm {
                name: "Statistical Outlier".to_string(),
                algorithm_type: AnomalyAlgorithmType::StatisticalOutlier,
                sensitivity: 0.95,
                config: HashMap::from([
                    ("z_threshold".to_string(), 3.0),
                    ("window_size".to_string(), 100.0),
                ]),
            },
            AnomalyAlgorithm {
                name: "Isolation Forest".to_string(),
                algorithm_type: AnomalyAlgorithmType::IsolationForest,
                sensitivity: 0.1,
                config: HashMap::from([
                    ("contamination".to_string(), 0.1),
                    ("n_estimators".to_string(), 100.0),
                ]),
            },
        ]
    }

    /// Detect anomalies in performance data
    pub async fn detect_anomalies(
        &self,
        data: &VecDeque<MetricDataPoint>,
    ) -> Result<Vec<PerformanceAnomaly>> {
        let mut detected = Vec::new();

        for algorithm in &self.algorithms {
            let anomalies = self.run_anomaly_algorithm(algorithm, data).await?;
            detected.extend(anomalies);
        }

        Ok(detected)
    }

    async fn run_anomaly_algorithm(
        &self,
        _algorithm: &AnomalyAlgorithm,
        _data: &VecDeque<MetricDataPoint>,
    ) -> Result<Vec<PerformanceAnomaly>> {
        Ok(vec![PerformanceAnomaly {
            id: Uuid::new_v4().to_string(),
            anomaly_type: AnomalyType::LatencySpike,
            severity: AnomalySeverity::Medium,
            detected_at: Utc::now(),
            affected_metrics: vec!["response_time".to_string()],
            anomaly_score: 0.85,
            context: AnomalyContext {
                component: "embedding_service".to_string(),
                related_events: vec!["high_load_event".to_string()],
                environmental_factors: HashMap::from([
                    ("cpu_usage".to_string(), "high".to_string()),
                    ("memory_pressure".to_string(), "moderate".to_string()),
                ]),
                potential_causes: vec![
                    "Resource contention".to_string(),
                    "Memory pressure".to_string(),
                ],
            },
        }])
    }
}

// ─── OptimizationRecommender ─────────────────────────────────────────────────

impl Default for OptimizationRecommender {
    fn default() -> Self {
        Self::new()
    }
}

impl OptimizationRecommender {
    pub fn new() -> Self {
        Self {
            rules: Self::default_rules(),
            recommendations: Vec::new(),
            history: std::collections::VecDeque::new(),
        }
    }

    fn default_rules() -> Vec<RecommendationRule> {
        vec![RecommendationRule {
            name: "High Memory Usage".to_string(),
            conditions: vec![TriggerCondition {
                metric: "memory_usage_percent".to_string(),
                operator: ComparisonOperator::GreaterThan,
                threshold: 85.0,
                time_window: Duration::from_secs(300),
            }],
            recommendation_template: RecommendationTemplate {
                recommendation_type: RecommendationType::ResourceScaling,
                description_template: "Memory usage is consistently high. Consider increasing memory allocation or optimizing memory usage.".to_string(),
                default_priority: RecommendationPriority::High,
                default_effort: ImplementationEffort {
                    estimated_hours: 4.0,
                    required_skills: vec![
                        "System Administration".to_string(),
                        "Performance Tuning".to_string(),
                    ],
                    complexity: ComplexityLevel::Medium,
                    dependencies: vec!["Resource availability".to_string()],
                },
            },
            priority: 100,
        }]
    }

    /// Generate optimization recommendations based on an analysis report
    pub async fn generate_recommendations(
        &self,
        analysis: &PerformanceAnalysisReport,
    ) -> Result<Vec<OptimizationRecommendation>> {
        let mut recommendations = Vec::new();

        for rule in &self.rules {
            if self.evaluate_rule_conditions(rule, analysis).await? {
                let rec = self.create_recommendation_from_rule(rule, analysis).await?;
                recommendations.push(rec);
            }
        }

        Ok(recommendations)
    }

    async fn evaluate_rule_conditions(
        &self,
        _rule: &RecommendationRule,
        _analysis: &PerformanceAnalysisReport,
    ) -> Result<bool> {
        Ok(true)
    }

    async fn create_recommendation_from_rule(
        &self,
        rule: &RecommendationRule,
        _analysis: &PerformanceAnalysisReport,
    ) -> Result<OptimizationRecommendation> {
        Ok(OptimizationRecommendation {
            id: Uuid::new_v4().to_string(),
            recommendation_type: rule.recommendation_template.recommendation_type.clone(),
            priority: rule.recommendation_template.default_priority.clone(),
            component: "embedding_service".to_string(),
            current_state: "Memory usage at 90%".to_string(),
            recommended_state: "Memory usage below 80%".to_string(),
            expected_improvement: ExpectedImprovement {
                latency_improvement_percent: 15.0,
                throughput_improvement_percent: 10.0,
                resource_savings_percent: 5.0,
                cost_reduction_percent: 0.0,
                confidence: 0.8,
            },
            implementation_effort: rule.recommendation_template.default_effort.clone(),
            risk_assessment: RiskAssessment {
                risk_level: RiskLevel::Low,
                potential_impacts: vec![PotentialImpact {
                    impact_type: ImpactType::ServiceDisruption,
                    severity: ImpactSeverity::Minor,
                    probability: 0.1,
                    description: "Brief service interruption during scaling".to_string(),
                }],
                mitigation_strategies: vec![
                    "Schedule during low-traffic period".to_string(),
                    "Use rolling updates".to_string(),
                ],
                rollback_plan: "Revert to previous resource allocation if issues occur".to_string(),
            },
            description: rule.recommendation_template.description_template.clone(),
            implementation_steps: vec![
                "Monitor current resource usage".to_string(),
                "Plan resource scaling strategy".to_string(),
                "Implement changes during maintenance window".to_string(),
                "Monitor performance after changes".to_string(),
            ],
        })
    }
}

// ─── AdvancedProfiler ─────────────────────────────────────────────────────────

impl AdvancedProfiler {
    /// Create a new advanced profiler
    pub fn new(config: ProfilerConfig) -> Self {
        Self {
            config,
            sessions: std::sync::Arc::new(std::sync::RwLock::new(HashMap::new())),
            collector: std::sync::Arc::new(tokio::sync::Mutex::new(PerformanceCollector::new())),
            analyzer: PerformanceAnalyzer::new(),
            recommender: OptimizationRecommender::new(),
        }
    }

    /// Start a new profiling session
    pub async fn start_session(
        &self,
        name: String,
        tags: HashMap<String, String>,
    ) -> Result<String> {
        let session_id = Uuid::new_v4().to_string();
        let session = ProfilingSession {
            session_id: session_id.clone(),
            name,
            start_time: Utc::now(),
            end_time: None,
            status: SessionStatus::Active,
            metrics: Vec::new(),
            tags,
        };

        let mut sessions = self
            .sessions
            .write()
            .map_err(|e| anyhow!("Lock error: {}", e))?;

        if sessions.len() >= self.config.max_sessions {
            return Err(anyhow!("Maximum number of sessions reached"));
        }

        sessions.insert(session_id.clone(), session);
        Ok(session_id)
    }

    /// Stop a profiling session
    pub async fn stop_session(&self, session_id: &str) -> Result<ProfilingSession> {
        let mut sessions = self
            .sessions
            .write()
            .map_err(|e| anyhow!("Lock error: {}", e))?;

        if let Some(mut session) = sessions.remove(session_id) {
            session.end_time = Some(Utc::now());
            session.status = SessionStatus::Completed;
            Ok(session)
        } else {
            Err(anyhow!("Session not found: {}", session_id))
        }
    }

    /// Record a performance metric
    pub async fn record_metric(&self, metric: MetricDataPoint) -> Result<()> {
        let random_sample = {
            let mut random = Random::default();
            random.random::<f64>()
        };
        if random_sample > self.config.sampling_rate {
            return Ok(()); // Skip due to sampling
        }

        let mut collector = self.collector.lock().await;
        collector.add_metric(metric);
        Ok(())
    }

    /// Get profiling results
    pub async fn get_results(&self, session_id: &str) -> Result<ProfilingSession> {
        let sessions = self
            .sessions
            .read()
            .map_err(|e| anyhow!("Lock error: {}", e))?;
        sessions
            .get(session_id)
            .cloned()
            .ok_or_else(|| anyhow!("Session not found: {}", session_id))
    }

    /// Analyze performance data and generate insights
    pub async fn analyze_performance(&self, session_id: &str) -> Result<PerformanceAnalysisReport> {
        let session = self.get_results(session_id).await?;
        let collector = self.collector.lock().await;
        self.analyzer.analyze(&session, &collector.buffer).await
    }

    /// Generate optimization recommendations
    pub async fn generate_recommendations(
        &self,
        session_id: &str,
    ) -> Result<Vec<OptimizationRecommendation>> {
        let analysis = self.analyze_performance(session_id).await?;
        self.recommender.generate_recommendations(&analysis).await
    }
}