quantrs2_tytan/advanced_performance_analysis/

core.rs

//! Core advanced performance analyzer implementation

use super::*;

/// Advanced performance analysis system
pub struct AdvancedPerformanceAnalyzer {
    /// Configuration
    pub config: AnalysisConfig,
    /// Performance metrics database
    pub metrics_database: MetricsDatabase,
    /// Real-time monitors
    pub monitors: Vec<Box<dyn PerformanceMonitor>>,
    /// Benchmarking suite
    pub benchmark_suite: BenchmarkingSuite,
    /// Analysis results
    pub analysis_results: AnalysisResults,
    /// Prediction models
    pub prediction_models: Vec<Box<dyn PerformancePredictionModel>>,
}

/// Analysis results
#[derive(Debug)]
pub struct AnalysisResults {
    /// Performance summary
    pub performance_summary: PerformanceSummary,
    /// Bottleneck analysis
    pub bottleneck_analysis: BottleneckAnalysis,
    /// Optimization recommendations
    pub optimization_recommendations: Vec<OptimizationRecommendation>,
    /// Comparative analysis
    pub comparative_analysis: Option<ComparativeAnalysis>,
    /// Report generation
    pub reports: Vec<AnalysisReport>,
}

/// Performance summary
#[derive(Debug, Clone)]
pub struct PerformanceSummary {
    /// Overall performance score
    pub overall_score: f64,
    /// Key performance indicators
    pub kpis: HashMap<String, f64>,
    /// Performance trends
    pub trends: HashMap<String, TrendDirection>,
    /// Critical metrics
    pub critical_metrics: Vec<CriticalMetric>,
    /// Health status
    pub health_status: HealthStatus,
}

/// Critical metric
#[derive(Debug, Clone)]
pub struct CriticalMetric {
    /// Metric name
    pub metric_name: String,
    /// Current value
    pub current_value: f64,
    /// Threshold value
    pub threshold_value: f64,
    /// Severity level
    pub severity: SeverityLevel,
    /// Recommended actions
    pub recommended_actions: Vec<String>,
}

/// Severity levels
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum SeverityLevel {
    Info,
    Warning,
    Critical,
    Emergency,
}

/// Health status
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum HealthStatus {
    Healthy,
    Warning,
    Critical,
    Unknown,
}

/// Optimization recommendation
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    /// Recommendation title
    pub title: String,
    /// Detailed description
    pub description: String,
    /// Priority level
    pub priority: PriorityLevel,
    /// Expected benefit
    pub expected_benefit: f64,
    /// Implementation steps
    pub implementation_steps: Vec<String>,
    /// Prerequisites
    pub prerequisites: Vec<String>,
    /// Risks and mitigation
    pub risks_and_mitigation: Vec<RiskMitigation>,
}

/// Priority levels
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum PriorityLevel {
    Critical,
    High,
    Medium,
    Low,
    Optional,
}

/// Risk and mitigation strategy
#[derive(Debug, Clone)]
pub struct RiskMitigation {
    /// Risk description
    pub risk: String,
    /// Probability
    pub probability: f64,
    /// Impact
    pub impact: f64,
    /// Mitigation strategy
    pub mitigation: String,
}

impl AdvancedPerformanceAnalyzer {
    /// Create new advanced performance analyzer
    pub fn new(config: AnalysisConfig) -> Self {
        Self {
            config,
            metrics_database: MetricsDatabase {
                time_series: HashMap::new(),
                aggregated_metrics: HashMap::new(),
                historical_data: HistoricalData {
                    daily_summaries: Vec::new(),
                    trends: TrendAnalysis {
                        performance_trends: HashMap::new(),
                        seasonal_patterns: Vec::new(),
                        anomalies: Vec::new(),
                        forecasts: HashMap::new(),
                    },
                    baselines: HashMap::new(),
                    regression_models: Vec::new(),
                },
                metadata: MetricsMetadata {
                    collection_start: Instant::now(),
                    system_info: SystemInfo::collect(),
                    software_versions: HashMap::new(),
                    config_hash: "default".to_string(),
                },
            },
            monitors: Vec::new(),
            benchmark_suite: BenchmarkingSuite {
                benchmarks: Vec::new(),
                results: HashMap::new(),
                baselines: HashMap::new(),
                profiles: Vec::new(),
            },
            analysis_results: AnalysisResults {
                performance_summary: PerformanceSummary {
                    overall_score: 0.0,
                    kpis: HashMap::new(),
                    trends: HashMap::new(),
                    critical_metrics: Vec::new(),
                    health_status: HealthStatus::Unknown,
                },
                bottleneck_analysis: BottleneckAnalysis {
                    bottlenecks: Vec::new(),
                    resource_utilization: ResourceUtilizationAnalysis {
                        cpu_breakdown: CpuUtilizationBreakdown::default(),
                        memory_breakdown: MemoryUtilizationBreakdown::default(),
                        io_breakdown: IoUtilizationBreakdown::default(),
                        network_breakdown: NetworkUtilizationBreakdown::default(),
                    },
                    dependency_analysis: DependencyAnalysis {
                        critical_path: Vec::new(),
                        dependency_graph: DependencyGraph {
                            nodes: Vec::new(),
                            edges: Vec::new(),
                            properties: GraphProperties::default(),
                        },
                        parallelization_opportunities: Vec::new(),
                        serialization_bottlenecks: Vec::new(),
                    },
                    optimization_opportunities: Vec::new(),
                },
                optimization_recommendations: Vec::new(),
                comparative_analysis: None,
                reports: Vec::new(),
            },
            prediction_models: Vec::new(),
        }
    }

    /// Start performance analysis
    pub fn start_analysis(&mut self) -> Result<(), AnalysisError> {
        println!("Starting advanced performance analysis...");

        // Start real-time monitoring
        if self.config.real_time_monitoring {
            self.start_real_time_monitoring()?;
        }

        // Initialize system information
        self.initialize_system_info()?;

        // Set up benchmarks
        self.setup_benchmarks()?;

        // Initialize prediction models
        self.initialize_prediction_models()?;

        println!("Advanced performance analysis started successfully");
        Ok(())
    }

    /// Perform comprehensive analysis
    pub fn perform_comprehensive_analysis(&mut self) -> Result<(), AnalysisError> {
        println!("Performing comprehensive performance analysis...");

        // Collect current metrics
        self.collect_metrics()?;

        // Analyze performance trends
        self.analyze_trends()?;

        // Identify bottlenecks
        self.identify_bottlenecks()?;

        // Generate optimization recommendations
        self.generate_optimization_recommendations()?;

        // Perform comparative analysis
        if self.config.comparative_analysis {
            self.perform_comparative_analysis()?;
        }

        // Generate reports
        self.generate_reports()?;

        println!("Comprehensive analysis completed");
        Ok(())
    }

    /// Start real-time monitoring
    fn start_real_time_monitoring(&mut self) -> Result<(), AnalysisError> {
        // Add various monitors
        self.monitors.push(Box::new(CpuMonitor::new()));
        self.monitors.push(Box::new(MemoryMonitor::new()));
        self.monitors.push(Box::new(IoMonitor::new()));
        self.monitors.push(Box::new(NetworkMonitor::new()));

        // Start all monitors
        for monitor in &mut self.monitors {
            monitor.start_monitoring()?;
        }

        Ok(())
    }

    /// Initialize system information
    fn initialize_system_info(&mut self) -> Result<(), AnalysisError> {
        self.metrics_database.metadata.system_info = SystemInfo::collect();
        Ok(())
    }

    /// Set up benchmarks
    fn setup_benchmarks(&mut self) -> Result<(), AnalysisError> {
        self.benchmark_suite
            .benchmarks
            .push(Box::new(QuboEvaluationBenchmark::new()));
        self.benchmark_suite
            .benchmarks
            .push(Box::new(SamplingBenchmark::new()));
        self.benchmark_suite
            .benchmarks
            .push(Box::new(ConvergenceBenchmark::new()));
        Ok(())
    }

    /// Initialize prediction models
    fn initialize_prediction_models(&mut self) -> Result<(), AnalysisError> {
        self.prediction_models
            .push(Box::new(LinearRegressionModel::new()));
        self.prediction_models
            .push(Box::new(RandomForestModel::new()));
        Ok(())
    }

    /// Collect metrics from all monitors
    fn collect_metrics(&mut self) -> Result<(), AnalysisError> {
        let mut all_metrics = Vec::new();
        for monitor in &self.monitors {
            let metrics = monitor.get_current_metrics()?;
            all_metrics.extend(metrics);
        }
        for (metric_name, value) in all_metrics {
            self.add_metric_value(&metric_name, value);
        }
        Ok(())
    }

    /// Add metric value to time series
    fn add_metric_value(&mut self, metric_name: &str, value: f64) {
        let time_series = self
            .metrics_database
            .time_series
            .entry(metric_name.to_string())
            .or_insert_with(|| TimeSeries {
                timestamps: Vec::new(),
                values: Vec::new(),
                metric_name: metric_name.to_string(),
                units: "unknown".to_string(),
                sampling_rate: self.config.monitoring_frequency,
            });

        time_series.timestamps.push(Instant::now());
        time_series.values.push(value);
    }

    /// Analyze performance trends
    fn analyze_trends(&mut self) -> Result<(), AnalysisError> {
        for (metric_name, time_series) in &self.metrics_database.time_series {
            if time_series.values.len() < 10 {
                continue; // Need sufficient data for trend analysis
            }

            let trend = self.calculate_trend(&time_series.values);
            self.analysis_results
                .performance_summary
                .trends
                .insert(metric_name.clone(), trend);
        }
        Ok(())
    }

    /// Calculate trend direction from time series data
    pub fn calculate_trend(&self, values: &[f64]) -> TrendDirection {
        if values.len() < 3 {
            return TrendDirection::Unknown;
        }

        let n = values.len() as f64;
        let x_sum: f64 = (0..values.len()).map(|i| i as f64).sum();
        let y_sum: f64 = values.iter().sum();
        let xy_sum: f64 = values.iter().enumerate().map(|(i, &y)| i as f64 * y).sum();
        let x2_sum: f64 = (0..values.len()).map(|i| (i as f64).powi(2)).sum();

        let slope = n.mul_add(xy_sum, -(x_sum * y_sum)) / x_sum.mul_add(-x_sum, n * x2_sum);

        if slope > 0.01 {
            TrendDirection::Improving
        } else if slope < -0.01 {
            TrendDirection::Degrading
        } else {
            TrendDirection::Stable
        }
    }

    /// Identify performance bottlenecks
    fn identify_bottlenecks(&mut self) -> Result<(), AnalysisError> {
        // Analyze CPU utilization
        if let Some(cpu_time_series) = self.metrics_database.time_series.get("cpu_utilization") {
            if let Some(&max_cpu) = cpu_time_series
                .values
                .iter()
                .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
            {
                if max_cpu > 80.0 {
                    self.analysis_results
                        .bottleneck_analysis
                        .bottlenecks
                        .push(Bottleneck {
                            bottleneck_type: BottleneckType::CPU,
                            location: "CPU cores".to_string(),
                            severity: (max_cpu - 80.0) / 20.0,
                            resource: "CPU".to_string(),
                            mitigation_strategies: vec![
                                "Consider CPU optimization".to_string(),
                                "Implement parallel processing".to_string(),
                                "Profile hot code paths".to_string(),
                            ],
                        });
                }
            }
        }

        // Analyze memory utilization
        if let Some(memory_time_series) =
            self.metrics_database.time_series.get("memory_utilization")
        {
            if let Some(&max_memory) = memory_time_series
                .values
                .iter()
                .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
            {
                if max_memory > 85.0 {
                    self.analysis_results
                        .bottleneck_analysis
                        .bottlenecks
                        .push(Bottleneck {
                            bottleneck_type: BottleneckType::Memory,
                            location: "System memory".to_string(),
                            severity: (max_memory - 85.0) / 15.0,
                            resource: "Memory".to_string(),
                            mitigation_strategies: vec![
                                "Optimize memory usage".to_string(),
                                "Implement memory pooling".to_string(),
                                "Consider data structure optimization".to_string(),
                            ],
                        });
                }
            }
        }

        Ok(())
    }

    /// Generate optimization recommendations
    fn generate_optimization_recommendations(&mut self) -> Result<(), AnalysisError> {
        // Generate recommendations based on identified bottlenecks
        for bottleneck in &self.analysis_results.bottleneck_analysis.bottlenecks {
            let recommendation = OptimizationRecommendation {
                title: format!("Optimize {} Performance", bottleneck.resource),
                description: format!(
                    "Address {} bottleneck with severity {:.2}",
                    bottleneck.resource, bottleneck.severity
                ),
                priority: if bottleneck.severity > 0.8 {
                    PriorityLevel::Critical
                } else if bottleneck.severity > 0.5 {
                    PriorityLevel::High
                } else {
                    PriorityLevel::Medium
                },
                expected_benefit: bottleneck.severity * 0.3, // Rough estimate
                implementation_steps: bottleneck.mitigation_strategies.clone(),
                prerequisites: vec!["Performance profiling tools".to_string()],
                risks_and_mitigation: vec![RiskMitigation {
                    risk: "Performance regression during optimization".to_string(),
                    probability: 0.2,
                    impact: 0.3,
                    mitigation: "Implement comprehensive testing".to_string(),
                }],
            };

            self.analysis_results
                .optimization_recommendations
                .push(recommendation);
        }

        Ok(())
    }

    /// Perform comparative analysis
    fn perform_comparative_analysis(&mut self) -> Result<(), AnalysisError> {
        // This would compare current performance with baselines
        let baseline_comparison = BaselineComparison {
            current_performance: HashMap::new(),
            baseline_performance: HashMap::new(),
            performance_changes: HashMap::new(),
            statistical_significance: HashMap::new(),
        };

        self.analysis_results.comparative_analysis = Some(ComparativeAnalysis {
            baseline_comparison,
            algorithm_comparisons: Vec::new(),
            regression_analysis: RegressionAnalysis {
                regression_detected: false,
                regression_severity: 0.0,
                affected_metrics: Vec::new(),
                potential_causes: Vec::new(),
                timeline_analysis: TimelineAnalysis {
                    key_events: Vec::new(),
                    correlations: Vec::new(),
                    change_points: Vec::new(),
                },
            },
            ab_test_results: Vec::new(),
        });

        Ok(())
    }

    /// Generate analysis reports
    fn generate_reports(&mut self) -> Result<(), AnalysisError> {
        // Generate performance summary report
        let summary_report = AnalysisReport {
            report_type: ReportType::PerformanceSummary,
            title: "Performance Analysis Summary".to_string(),
            content: ReportContent {
                executive_summary: "Overall system performance analysis".to_string(),
                key_findings: vec![
                    "System performance is stable".to_string(),
                    "Minor bottlenecks identified".to_string(),
                ],
                sections: Vec::new(),
                visualizations: Vec::new(),
                appendices: Vec::new(),
            },
            timestamp: Instant::now(),
            metadata: ReportMetadata {
                author: "Advanced Performance Analyzer".to_string(),
                version: "1.0.0".to_string(),
                format: ReportFormat::HTML,
                tags: vec!["performance".to_string(), "analysis".to_string()],
                recipients: Vec::new(),
            },
        };

        self.analysis_results.reports.push(summary_report);
        Ok(())
    }
}

/// Create comprehensive performance analyzer
pub fn create_comprehensive_analyzer() -> AdvancedPerformanceAnalyzer {
    let config = create_default_analysis_config();
    AdvancedPerformanceAnalyzer::new(config)
}

/// Create lightweight analyzer for basic monitoring
pub fn create_lightweight_analyzer() -> AdvancedPerformanceAnalyzer {
    let config = create_lightweight_config();
    AdvancedPerformanceAnalyzer::new(config)
}