oxirs_embed/

advanced_profiler.rs

//! Advanced Performance Profiler
//!
//! This module provides comprehensive performance profiling capabilities
//! for embedding models with deep insights, bottleneck analysis, and
//! optimization recommendations.

use anyhow::{anyhow, Result};
use chrono::{DateTime, Utc};
use scirs2_core::random::{Random, Rng};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};
use tokio::sync::Mutex;
use uuid::Uuid;

/// Advanced performance profiler for embedding systems
#[derive(Debug)]
pub struct AdvancedProfiler {
    /// Configuration for profiling
    config: ProfilerConfig,
    /// Active profiling sessions
    sessions: Arc<RwLock<HashMap<String, ProfilingSession>>>,
    /// Performance data collector
    collector: Arc<Mutex<PerformanceCollector>>,
    /// Analysis engine
    analyzer: PerformanceAnalyzer,
    /// Optimization recommender
    recommender: OptimizationRecommender,
}

/// Configuration for advanced profiling
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProfilerConfig {
    /// Maximum number of concurrent profiling sessions
    pub max_sessions: usize,
    /// Sampling rate (0.0 to 1.0)
    pub sampling_rate: f64,
    /// Buffer size for performance data
    pub buffer_size: usize,
    /// Analysis window size in seconds
    pub analysis_window_seconds: u64,
    /// Enable memory profiling
    pub enable_memory_profiling: bool,
    /// Enable CPU profiling
    pub enable_cpu_profiling: bool,
    /// Enable GPU profiling
    pub enable_gpu_profiling: bool,
    /// Enable network profiling
    pub enable_network_profiling: bool,
}

impl Default for ProfilerConfig {
    fn default() -> Self {
        Self {
            max_sessions: 10,
            sampling_rate: 0.01, // 1% sampling
            buffer_size: 100000,
            analysis_window_seconds: 300, // 5 minutes
            enable_memory_profiling: true,
            enable_cpu_profiling: true,
            enable_gpu_profiling: true,
            enable_network_profiling: true,
        }
    }
}

/// Individual profiling session
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProfilingSession {
    /// Session identifier
    pub session_id: String,
    /// Session name
    pub name: String,
    /// Start timestamp
    pub start_time: DateTime<Utc>,
    /// End timestamp (if completed)
    pub end_time: Option<DateTime<Utc>>,
    /// Session status
    pub status: SessionStatus,
    /// Collected metrics
    pub metrics: Vec<MetricDataPoint>,
    /// Session tags
    pub tags: HashMap<String, String>,
}

/// Status of a profiling session
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SessionStatus {
    Active,
    Completed,
    Failed(String),
    Cancelled,
}

/// Performance data collector
#[derive(Debug)]
pub struct PerformanceCollector {
    /// Data buffer
    buffer: VecDeque<MetricDataPoint>,
    /// Collection statistics
    stats: CollectionStats,
    /// Active trackers
    trackers: HashMap<String, PerformanceTracker>,
}

/// Individual metric data point
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MetricDataPoint {
    /// Timestamp
    pub timestamp: DateTime<Utc>,
    /// Metric name
    pub metric_name: String,
    /// Metric value
    pub value: f64,
    /// Unit of measurement
    pub unit: String,
    /// Additional metadata
    pub metadata: HashMap<String, String>,
    /// Thread/process ID
    pub thread_id: Option<String>,
    /// Component being measured
    pub component: String,
}

/// Collection statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CollectionStats {
    /// Total data points collected
    pub total_points: u64,
    /// Collection rate (points per second)
    pub collection_rate: f64,
    /// Memory usage for collection
    pub memory_usage_bytes: u64,
    /// Drop rate (when buffer is full)
    pub drop_rate: f64,
}

impl Default for CollectionStats {
    fn default() -> Self {
        Self {
            total_points: 0,
            collection_rate: 0.0,
            memory_usage_bytes: 0,
            drop_rate: 0.0,
        }
    }
}

/// Performance tracker for specific components
#[derive(Debug, Clone)]
pub struct PerformanceTracker {
    /// Tracker name
    pub name: String,
    /// Start time
    pub start_time: Instant,
    /// Collected measurements
    pub measurements: Vec<TimedMeasurement>,
    /// Tracker state
    pub state: TrackerState,
}

/// Timed measurement
#[derive(Debug, Clone)]
pub struct TimedMeasurement {
    /// Relative timestamp
    pub timestamp: Duration,
    /// Measurement type
    pub measurement_type: MeasurementType,
    /// Value
    pub value: f64,
    /// Context information
    pub context: String,
}

/// Types of measurements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MeasurementType {
    Latency,
    Throughput,
    MemoryUsage,
    CpuUsage,
    GpuUsage,
    NetworkLatency,
    DiskIo,
    CacheHitRate,
    ErrorRate,
    QueueLength,
}

/// Tracker state
#[derive(Debug, Clone)]
pub enum TrackerState {
    Active,
    Paused,
    Stopped,
}

/// Performance analysis engine
#[derive(Debug)]
pub struct PerformanceAnalyzer {
    /// Analysis algorithms
    algorithms: Vec<AnalysisAlgorithm>,
    /// Pattern detection
    pattern_detector: PatternDetector,
    /// Anomaly detection
    anomaly_detector: AnomalyDetector,
}

/// Analysis algorithm interface
#[derive(Debug, Clone)]
pub struct AnalysisAlgorithm {
    /// Algorithm name
    pub name: String,
    /// Algorithm type
    pub algorithm_type: AlgorithmType,
    /// Configuration parameters
    pub parameters: HashMap<String, f64>,
}

/// Types of analysis algorithms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AlgorithmType {
    TrendAnalysis,
    BottleneckDetection,
    PerformanceRegression,
    ResourceUtilization,
    CapacityPlanning,
    LoadBalancing,
}

/// Pattern detection system
#[derive(Debug)]
#[allow(dead_code)]
pub struct PatternDetector {
    /// Detected patterns
    patterns: Vec<PerformancePattern>,
    /// Pattern templates
    templates: Vec<PatternTemplate>,
}

/// Detected performance pattern
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformancePattern {
    /// Pattern identifier
    pub id: String,
    /// Pattern type
    pub pattern_type: PatternType,
    /// Confidence score
    pub confidence: f64,
    /// Time window
    pub time_window: (DateTime<Utc>, DateTime<Utc>),
    /// Affected components
    pub affected_components: Vec<String>,
    /// Pattern description
    pub description: String,
}

/// Types of performance patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PatternType {
    PeriodicSpike,
    GradualDegradation,
    SuddenDrop,
    MemoryLeak,
    ThresholdBreach,
    LoadPattern,
    SeasonalVariation,
}

/// Pattern template for recognition
#[derive(Debug, Clone)]
pub struct PatternTemplate {
    /// Template name
    pub name: String,
    /// Pattern signature
    pub signature: PatternSignature,
    /// Matching criteria
    pub criteria: MatchingCriteria,
}

/// Pattern signature
#[derive(Debug, Clone)]
pub struct PatternSignature {
    /// Statistical characteristics
    pub characteristics: Vec<StatisticalCharacteristic>,
    /// Temporal features
    pub temporal_features: Vec<TemporalFeature>,
}

/// Statistical characteristic
#[derive(Debug, Clone)]
pub struct StatisticalCharacteristic {
    /// Metric name
    pub metric: String,
    /// Statistical property
    pub property: StatisticalProperty,
    /// Expected value range
    pub value_range: (f64, f64),
}

/// Statistical properties
#[derive(Debug, Clone)]
pub enum StatisticalProperty {
    Mean,
    Median,
    StandardDeviation,
    Variance,
    Skewness,
    Kurtosis,
    Percentile(u8),
}

/// Temporal feature
#[derive(Debug, Clone)]
pub struct TemporalFeature {
    /// Feature type
    pub feature_type: TemporalFeatureType,
    /// Time scale
    pub time_scale: Duration,
    /// Threshold
    pub threshold: f64,
}

/// Types of temporal features
#[derive(Debug, Clone)]
pub enum TemporalFeatureType {
    Periodicity,
    Trend,
    Seasonality,
    Autocorrelation,
    ChangePoint,
}

/// Matching criteria for pattern recognition
#[derive(Debug, Clone)]
pub struct MatchingCriteria {
    /// Minimum confidence required
    pub min_confidence: f64,
    /// Required data points
    pub min_data_points: usize,
    /// Time window requirements
    pub time_window_requirements: TimeWindowRequirements,
}

/// Time window requirements
#[derive(Debug, Clone)]
pub struct TimeWindowRequirements {
    /// Minimum duration
    pub min_duration: Duration,
    /// Maximum duration
    pub max_duration: Duration,
    /// Required coverage ratio
    pub coverage_ratio: f64,
}

/// Anomaly detection system
#[derive(Debug)]
#[allow(dead_code)]
pub struct AnomalyDetector {
    /// Detection algorithms
    algorithms: Vec<AnomalyAlgorithm>,
    /// Detected anomalies
    anomalies: Vec<PerformanceAnomaly>,
    /// Baseline models
    baselines: HashMap<String, BaselineModel>,
}

/// Anomaly detection algorithm
#[derive(Debug, Clone)]
pub struct AnomalyAlgorithm {
    /// Algorithm name
    pub name: String,
    /// Algorithm type
    pub algorithm_type: AnomalyAlgorithmType,
    /// Sensitivity level
    pub sensitivity: f64,
    /// Configuration
    pub config: HashMap<String, f64>,
}

/// Types of anomaly detection algorithms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AnomalyAlgorithmType {
    StatisticalOutlier,
    IsolationForest,
    LocalOutlierFactor,
    OneClassSvm,
    AutoEncoder,
    TimeSeriesAnomaly,
}

/// Detected performance anomaly
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceAnomaly {
    /// Anomaly identifier
    pub id: String,
    /// Anomaly type
    pub anomaly_type: AnomalyType,
    /// Severity level
    pub severity: AnomalySeverity,
    /// Detection timestamp
    pub detected_at: DateTime<Utc>,
    /// Affected metrics
    pub affected_metrics: Vec<String>,
    /// Anomaly score
    pub anomaly_score: f64,
    /// Context information
    pub context: AnomalyContext,
}

/// Types of anomalies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AnomalyType {
    LatencySpike,
    ThroughputDrop,
    MemoryLeak,
    CpuSaturation,
    ErrorRateIncrease,
    ResourceStarvation,
    UnexpectedPattern,
}

/// Anomaly severity levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AnomalySeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// Anomaly context information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnomalyContext {
    /// Component affected
    pub component: String,
    /// Related events
    pub related_events: Vec<String>,
    /// Environmental factors
    pub environmental_factors: HashMap<String, String>,
    /// Potential causes
    pub potential_causes: Vec<String>,
}

/// Baseline model for normal behavior
#[derive(Debug, Clone)]
pub struct BaselineModel {
    /// Model name
    pub name: String,
    /// Statistical distribution
    pub distribution: StatisticalDistribution,
    /// Temporal characteristics
    pub temporal_characteristics: TemporalCharacteristics,
    /// Model confidence
    pub confidence: f64,
    /// Last update timestamp
    pub last_updated: DateTime<Utc>,
}

/// Statistical distribution model
#[derive(Debug, Clone)]
pub struct StatisticalDistribution {
    /// Distribution type
    pub distribution_type: DistributionType,
    /// Parameters
    pub parameters: Vec<f64>,
    /// Goodness of fit
    pub goodness_of_fit: f64,
}

/// Types of statistical distributions
#[derive(Debug, Clone)]
pub enum DistributionType {
    Normal,
    LogNormal,
    Exponential,
    Gamma,
    Beta,
    Weibull,
    Custom,
}

/// Temporal characteristics of metrics
#[derive(Debug, Clone)]
pub struct TemporalCharacteristics {
    /// Seasonality components
    pub seasonality: Vec<SeasonalComponent>,
    /// Trend information
    pub trend: TrendInformation,
    /// Autocorrelation structure
    pub autocorrelation: AutocorrelationStructure,
}

/// Seasonal component
#[derive(Debug, Clone)]
pub struct SeasonalComponent {
    /// Period length
    pub period: Duration,
    /// Amplitude
    pub amplitude: f64,
    /// Phase offset
    pub phase: f64,
    /// Strength
    pub strength: f64,
}

/// Trend information
#[derive(Debug, Clone)]
pub struct TrendInformation {
    /// Trend direction
    pub direction: TrendDirection,
    /// Trend strength
    pub strength: f64,
    /// Linear coefficient
    pub linear_coefficient: f64,
    /// Polynomial coefficients (if applicable)
    pub polynomial_coefficients: Vec<f64>,
}

/// Trend direction
#[derive(Debug, Clone)]
pub enum TrendDirection {
    Increasing,
    Decreasing,
    Stable,
    Oscillating,
}

/// Autocorrelation structure
#[derive(Debug, Clone)]
pub struct AutocorrelationStructure {
    /// Lag correlations
    pub lag_correlations: Vec<(Duration, f64)>,
    /// Partial autocorrelations
    pub partial_autocorrelations: Vec<(Duration, f64)>,
    /// Significant lags
    pub significant_lags: Vec<Duration>,
}

/// Optimization recommender system
#[derive(Debug)]
#[allow(dead_code)]
pub struct OptimizationRecommender {
    /// Recommendation rules
    rules: Vec<RecommendationRule>,
    /// Generated recommendations
    recommendations: Vec<OptimizationRecommendation>,
    /// Recommendation history
    history: VecDeque<RecommendationHistory>,
}

/// Optimization recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationRecommendation {
    /// Recommendation identifier
    pub id: String,
    /// Recommendation type
    pub recommendation_type: RecommendationType,
    /// Priority level
    pub priority: RecommendationPriority,
    /// Affected component
    pub component: String,
    /// Current value/state
    pub current_state: String,
    /// Recommended value/state
    pub recommended_state: String,
    /// Expected improvement
    pub expected_improvement: ExpectedImprovement,
    /// Implementation effort
    pub implementation_effort: ImplementationEffort,
    /// Risk assessment
    pub risk_assessment: RiskAssessment,
    /// Detailed description
    pub description: String,
    /// Implementation steps
    pub implementation_steps: Vec<String>,
}

/// Types of optimization recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RecommendationType {
    ResourceScaling,
    ConfigurationTuning,
    CacheOptimization,
    LoadBalancing,
    HardwareUpgrade,
    SoftwareUpdate,
    ArchitecturalChange,
    ProcessOptimization,
}

/// Recommendation priority levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RecommendationPriority {
    Low,
    Medium,
    High,
    Critical,
}

/// Expected improvement metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExpectedImprovement {
    /// Latency improvement (percentage)
    pub latency_improvement_percent: f64,
    /// Throughput improvement (percentage)
    pub throughput_improvement_percent: f64,
    /// Resource savings (percentage)
    pub resource_savings_percent: f64,
    /// Cost reduction (percentage)
    pub cost_reduction_percent: f64,
    /// Confidence in estimates
    pub confidence: f64,
}

/// Implementation effort assessment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ImplementationEffort {
    /// Estimated time (hours)
    pub estimated_hours: f64,
    /// Required skills
    pub required_skills: Vec<String>,
    /// Complexity level
    pub complexity: ComplexityLevel,
    /// Dependencies
    pub dependencies: Vec<String>,
}

/// Complexity levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ComplexityLevel {
    Low,
    Medium,
    High,
    VeryHigh,
}

/// Risk assessment for recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RiskAssessment {
    /// Overall risk level
    pub risk_level: RiskLevel,
    /// Potential impacts
    pub potential_impacts: Vec<PotentialImpact>,
    /// Mitigation strategies
    pub mitigation_strategies: Vec<String>,
    /// Rollback plan
    pub rollback_plan: String,
}

/// Risk levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RiskLevel {
    Low,
    Medium,
    High,
    Critical,
}

/// Potential impact of changes
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PotentialImpact {
    /// Impact type
    pub impact_type: ImpactType,
    /// Severity
    pub severity: ImpactSeverity,
    /// Probability
    pub probability: f64,
    /// Description
    pub description: String,
}

/// Types of potential impacts
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ImpactType {
    PerformanceDegradation,
    ServiceDisruption,
    DataLoss,
    SecurityVulnerability,
    IncreasedCosts,
    UserExperience,
}

/// Impact severity levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ImpactSeverity {
    Negligible,
    Minor,
    Moderate,
    Major,
    Severe,
}

/// Recommendation rule
#[derive(Debug, Clone)]
pub struct RecommendationRule {
    /// Rule name
    pub name: String,
    /// Trigger conditions
    pub conditions: Vec<TriggerCondition>,
    /// Generated recommendation template
    pub recommendation_template: RecommendationTemplate,
    /// Rule priority
    pub priority: i32,
}

/// Trigger condition for recommendations
#[derive(Debug, Clone)]
pub struct TriggerCondition {
    /// Metric name
    pub metric: String,
    /// Comparison operator
    pub operator: ComparisonOperator,
    /// Threshold value
    pub threshold: f64,
    /// Time window for evaluation
    pub time_window: Duration,
}

/// Comparison operators
#[derive(Debug, Clone)]
pub enum ComparisonOperator {
    GreaterThan,
    LessThan,
    GreaterThanOrEqual,
    LessThanOrEqual,
    Equal,
    NotEqual,
    Between(f64, f64),
}

/// Template for generating recommendations
#[derive(Debug, Clone)]
pub struct RecommendationTemplate {
    /// Recommendation type
    pub recommendation_type: RecommendationType,
    /// Template description
    pub description_template: String,
    /// Default priority
    pub default_priority: RecommendationPriority,
    /// Default implementation effort
    pub default_effort: ImplementationEffort,
}

/// Historical recommendation data
#[derive(Debug, Clone)]
pub struct RecommendationHistory {
    /// Recommendation ID
    pub recommendation_id: String,
    /// Implementation date
    pub implemented_at: Option<DateTime<Utc>>,
    /// Actual improvement achieved
    pub actual_improvement: Option<ExpectedImprovement>,
    /// Implementation feedback
    pub feedback: Option<String>,
    /// Success rating
    pub success_rating: Option<f64>,
}

impl AdvancedProfiler {
    /// Create a new advanced profiler
    pub fn new(config: ProfilerConfig) -> Self {
        Self {
            config,
            sessions: Arc::new(RwLock::new(HashMap::new())),
            collector: Arc::new(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
    }
}

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

impl PerformanceCollector {
    /// Create a new performance collector
    pub fn new() -> Self {
        Self {
            buffer: VecDeque::new(),
            stats: CollectionStats::default(),
            trackers: HashMap::new(),
        }
    }

    /// Add a metric to the buffer
    pub fn add_metric(&mut self, metric: MetricDataPoint) {
        if self.buffer.len() >= 100000 {
            // Buffer size limit
            self.buffer.pop_front();
            self.stats.drop_rate += 1.0;
        }

        self.buffer.push_back(metric);
        self.stats.total_points += 1;
        self.stats.memory_usage_bytes =
            (self.buffer.len() * std::mem::size_of::<MetricDataPoint>()) as u64;
    }

    /// Start a performance tracker
    pub fn start_tracker(&mut self, name: String) -> String {
        let tracker = PerformanceTracker {
            name: name.clone(),
            start_time: Instant::now(),
            measurements: Vec::new(),
            state: TrackerState::Active,
        };

        self.trackers.insert(name.clone(), tracker);
        name
    }

    /// Stop a performance tracker
    pub fn stop_tracker(&mut self, name: &str) -> Option<PerformanceTracker> {
        if let Some(mut tracker) = self.trackers.remove(name) {
            tracker.state = TrackerState::Stopped;
            Some(tracker)
        } else {
            None
        }
    }
}

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

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

    /// Get default analysis algorithms
    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
    pub async fn analyze(
        &self,
        session: &ProfilingSession,
        data: &VecDeque<MetricDataPoint>,
    ) -> Result<PerformanceAnalysisReport> {
        let mut report = PerformanceAnalysisReport::new(session.session_id.clone());

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

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

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

        Ok(report)
    }

    /// Run a specific analysis algorithm
    async fn run_algorithm(
        &self,
        algorithm: &AnalysisAlgorithm,
        _data: &VecDeque<MetricDataPoint>,
    ) -> Result<AnalysisResult> {
        // Placeholder implementation - would contain actual algorithm logic
        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),
        })
    }
}

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

impl PatternDetector {
    /// Create a new pattern detector
    pub fn new() -> Self {
        Self {
            patterns: Vec::new(),
            templates: Self::default_templates(),
        }
    }

    /// Get default pattern 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_patterns = Vec::new();

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

        Ok(detected_patterns)
    }

    /// Match a template against data
    async fn match_template(
        &self,
        template: &PatternTemplate,
        data: &VecDeque<MetricDataPoint>,
    ) -> Result<Option<PerformancePattern>> {
        // Placeholder implementation
        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)
        }
    }
}

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

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

    /// Get default anomaly detection algorithms
    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_anomalies = Vec::new();

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

        Ok(detected_anomalies)
    }

    /// Run anomaly detection algorithm
    async fn run_anomaly_algorithm(
        &self,
        _algorithm: &AnomalyAlgorithm,
        _data: &VecDeque<MetricDataPoint>,
    ) -> Result<Vec<PerformanceAnomaly>> {
        // Placeholder implementation
        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(),
                ],
            },
        }])
    }
}

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

impl OptimizationRecommender {
    /// Create a new optimization recommender
    pub fn new() -> Self {
        Self {
            rules: Self::default_rules(),
            recommendations: Vec::new(),
            history: VecDeque::new(),
        }
    }

    /// Get default recommendation rules
    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
    pub async fn generate_recommendations(
        &self,
        analysis: &PerformanceAnalysisReport,
    ) -> Result<Vec<OptimizationRecommendation>> {
        let mut recommendations = Vec::new();

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

        Ok(recommendations)
    }

    /// Evaluate rule conditions
    async fn evaluate_rule_conditions(
        &self,
        _rule: &RecommendationRule,
        _analysis: &PerformanceAnalysisReport,
    ) -> Result<bool> {
        // Placeholder implementation - would check actual conditions
        Ok(true)
    }

    /// Create recommendation from rule
    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(),
            ],
        })
    }
}

/// Performance analysis report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceAnalysisReport {
    /// Report identifier
    pub id: String,
    /// Session ID this report belongs to
    pub session_id: String,
    /// Report generation timestamp
    pub generated_at: DateTime<Utc>,
    /// Analysis results
    pub analysis_results: Vec<AnalysisResult>,
    /// Detected patterns
    pub detected_patterns: Vec<PerformancePattern>,
    /// Detected anomalies
    pub detected_anomalies: Vec<PerformanceAnomaly>,
    /// Overall health score
    pub health_score: f64,
    /// Summary insights
    pub summary: String,
}

impl PerformanceAnalysisReport {
    /// Create a new performance analysis report
    pub fn new(session_id: String) -> Self {
        Self {
            id: Uuid::new_v4().to_string(),
            session_id,
            generated_at: Utc::now(),
            analysis_results: Vec::new(),
            detected_patterns: Vec::new(),
            detected_anomalies: Vec::new(),
            health_score: 100.0,
            summary: "Analysis in progress".to_string(),
        }
    }

    /// Add analysis result
    pub fn add_analysis_result(&mut self, result: AnalysisResult) {
        self.analysis_results.push(result);
    }

    /// Set detected patterns
    pub fn set_detected_patterns(&mut self, patterns: Vec<PerformancePattern>) {
        self.detected_patterns = patterns;
    }

    /// Set detected anomalies
    pub fn set_detected_anomalies(&mut self, anomalies: Vec<PerformanceAnomaly>) {
        self.detected_anomalies = anomalies;
    }
}

/// Result from an analysis algorithm
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnalysisResult {
    /// Algorithm name
    pub algorithm_name: String,
    /// Result type
    pub result_type: AlgorithmType,
    /// Findings
    pub findings: Vec<Finding>,
    /// Execution time
    pub execution_time: Duration,
}

/// Individual finding from analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Finding {
    /// Finding title
    pub title: String,
    /// Detailed description
    pub description: String,
    /// Severity level
    pub severity: FindingSeverity,
    /// Confidence in finding
    pub confidence: f64,
    /// Affected metrics
    pub affected_metrics: Vec<String>,
    /// Recommendations
    pub recommendations: Vec<String>,
}

/// Severity levels for findings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FindingSeverity {
    Info,
    Low,
    Medium,
    High,
    Critical,
}

#[cfg(test)]
mod tests {
    use super::*;
    use std::collections::HashMap;

    #[test]
    fn test_profiler_config_default() {
        let config = ProfilerConfig::default();
        assert_eq!(config.max_sessions, 10);
        assert_eq!(config.sampling_rate, 0.01);
        assert!(config.enable_memory_profiling);
        assert!(config.enable_cpu_profiling);
    }

    #[test]
    fn test_profiling_session_creation() {
        let session = ProfilingSession {
            session_id: "test-session".to_string(),
            name: "Test Session".to_string(),
            start_time: Utc::now(),
            end_time: None,
            status: SessionStatus::Active,
            metrics: Vec::new(),
            tags: HashMap::new(),
        };

        assert_eq!(session.session_id, "test-session");
        assert_eq!(session.name, "Test Session");
        assert!(matches!(session.status, SessionStatus::Active));
    }

    #[test]
    fn test_metric_data_point_creation() {
        let metric = MetricDataPoint {
            timestamp: Utc::now(),
            metric_name: "cpu_usage".to_string(),
            value: 75.5,
            unit: "percent".to_string(),
            metadata: HashMap::new(),
            thread_id: Some("thread-1".to_string()),
            component: "embedding_service".to_string(),
        };

        assert_eq!(metric.metric_name, "cpu_usage");
        assert_eq!(metric.value, 75.5);
        assert_eq!(metric.unit, "percent");
    }

    #[test]
    fn test_performance_collector() {
        let mut collector = PerformanceCollector::new();

        let metric = MetricDataPoint {
            timestamp: Utc::now(),
            metric_name: "test_metric".to_string(),
            value: 100.0,
            unit: "units".to_string(),
            metadata: HashMap::new(),
            thread_id: None,
            component: "test".to_string(),
        };

        collector.add_metric(metric);
        assert_eq!(collector.stats.total_points, 1);
        assert_eq!(collector.buffer.len(), 1);
    }

    #[test]
    fn test_performance_tracker() {
        let mut collector = PerformanceCollector::new();
        let tracker_id = collector.start_tracker("test_tracker".to_string());

        assert_eq!(tracker_id, "test_tracker");
        assert!(collector.trackers.contains_key("test_tracker"));

        let tracker = collector.stop_tracker("test_tracker");
        assert!(tracker.is_some());
        assert!(matches!(tracker.unwrap().state, TrackerState::Stopped));
    }

    #[test]
    fn test_anomaly_creation() {
        let anomaly = PerformanceAnomaly {
            id: "test-anomaly".to_string(),
            anomaly_type: AnomalyType::LatencySpike,
            severity: AnomalySeverity::High,
            detected_at: Utc::now(),
            affected_metrics: vec!["latency".to_string()],
            anomaly_score: 0.9,
            context: AnomalyContext {
                component: "test_component".to_string(),
                related_events: Vec::new(),
                environmental_factors: HashMap::new(),
                potential_causes: Vec::new(),
            },
        };

        assert_eq!(anomaly.id, "test-anomaly");
        assert!(matches!(anomaly.anomaly_type, AnomalyType::LatencySpike));
        assert!(matches!(anomaly.severity, AnomalySeverity::High));
    }

    #[test]
    fn test_optimization_recommendation() {
        let recommendation = OptimizationRecommendation {
            id: "test-rec".to_string(),
            recommendation_type: RecommendationType::ResourceScaling,
            priority: RecommendationPriority::High,
            component: "test_component".to_string(),
            current_state: "Current state".to_string(),
            recommended_state: "Recommended state".to_string(),
            expected_improvement: ExpectedImprovement {
                latency_improvement_percent: 20.0,
                throughput_improvement_percent: 15.0,
                resource_savings_percent: 10.0,
                cost_reduction_percent: 5.0,
                confidence: 0.8,
            },
            implementation_effort: ImplementationEffort {
                estimated_hours: 8.0,
                required_skills: vec!["DevOps".to_string()],
                complexity: ComplexityLevel::Medium,
                dependencies: Vec::new(),
            },
            risk_assessment: RiskAssessment {
                risk_level: RiskLevel::Low,
                potential_impacts: Vec::new(),
                mitigation_strategies: Vec::new(),
                rollback_plan: "Rollback plan".to_string(),
            },
            description: "Test recommendation".to_string(),
            implementation_steps: Vec::new(),
        };

        assert_eq!(recommendation.id, "test-rec");
        assert!(matches!(
            recommendation.recommendation_type,
            RecommendationType::ResourceScaling
        ));
        assert_eq!(
            recommendation
                .expected_improvement
                .latency_improvement_percent,
            20.0
        );
    }

    #[tokio::test]
    async fn test_profiler_session_lifecycle() {
        let config = ProfilerConfig::default();
        let profiler = AdvancedProfiler::new(config);

        // Start session
        let session_id = profiler
            .start_session("Test Session".to_string(), HashMap::new())
            .await
            .unwrap();
        assert!(!session_id.is_empty());

        // Stop session
        let session = profiler.stop_session(&session_id).await.unwrap();
        assert!(matches!(session.status, SessionStatus::Completed));
        assert!(session.end_time.is_some());
    }

    #[tokio::test]
    async fn test_metric_recording() {
        let config = ProfilerConfig::default();
        let profiler = AdvancedProfiler::new(config);

        let metric = MetricDataPoint {
            timestamp: Utc::now(),
            metric_name: "test_metric".to_string(),
            value: 50.0,
            unit: "ms".to_string(),
            metadata: HashMap::new(),
            thread_id: None,
            component: "test".to_string(),
        };

        let result = profiler.record_metric(metric).await;
        assert!(result.is_ok());
    }

    #[test]
    fn test_pattern_detection_components() {
        let detector = PatternDetector::new();
        assert!(!detector.templates.is_empty());

        let template = &detector.templates[0];
        assert_eq!(template.name, "Memory Leak Pattern");
        assert!(!template.signature.characteristics.is_empty());
    }

    #[test]
    fn test_anomaly_detection_components() {
        let detector = AnomalyDetector::new();
        assert!(!detector.algorithms.is_empty());

        let algorithm = &detector.algorithms[0];
        assert_eq!(algorithm.name, "Statistical Outlier");
        assert!(matches!(
            algorithm.algorithm_type,
            AnomalyAlgorithmType::StatisticalOutlier
        ));
    }

    #[test]
    fn test_recommendation_rules() {
        let recommender = OptimizationRecommender::new();
        assert!(!recommender.rules.is_empty());

        let rule = &recommender.rules[0];
        assert_eq!(rule.name, "High Memory Usage");
        assert!(!rule.conditions.is_empty());
    }
}