sqlitegraph 2.0.7

//! Performance analysis and insights for V2 WAL metrics.
//!
//! This module provides comprehensive analysis capabilities for interpreting WAL performance
//! data, identifying patterns, detecting anomalies, and generating actionable insights for
//! optimization and troubleshooting.

use crate::backend::native::v2::wal::metrics::aggregation::{LatencyHistogram, ThroughputTracker};
use crate::backend::native::v2::wal::metrics::core::{V2WALMetrics, WALPerformanceCounters};
use crate::backend::native::v2::wal::metrics::reporting::{
    ClusterPerformanceMetrics, ErrorTracker, ResourceTracker,
};

/// Performance analysis result containing insights and recommendations.
///
/// Provides structured analysis results including performance scores,
/// identified issues, optimization opportunities, and actionable recommendations.
#[derive(Debug, Clone)]
pub struct PerformanceAnalysis {
    /// Overall performance score (0-100)
    pub overall_score: f64,

    /// Individual performance category scores
    pub category_scores: PerformanceCategoryScores,

    /// Identified performance issues
    pub issues: Vec<PerformanceIssue>,

    /// Optimization opportunities
    pub opportunities: Vec<OptimizationOpportunity>,

    /// Actionable recommendations
    pub recommendations: Vec<Recommendation>,

    /// Analysis metadata
    pub metadata: AnalysisMetadata,
}

/// Performance scores by category for detailed analysis.
#[derive(Debug, Clone, Default)]
pub struct PerformanceCategoryScores {
    /// Throughput performance score
    pub throughput: f64,

    /// Latency performance score
    pub latency: f64,

    /// Resource utilization score
    pub resources: f64,

    /// Error rate score
    pub reliability: f64,

    /// Efficiency score
    pub efficiency: f64,
}

/// Performance issue identified during analysis.
#[derive(Debug, Clone)]
pub struct PerformanceIssue {
    /// Issue severity level
    pub severity: IssueSeverity,

    /// Issue category
    pub category: String,

    /// Issue description
    pub description: String,

    /// Impact assessment
    pub impact: String,

    /// Detected timestamp
    pub timestamp: u64,

    /// Related metrics
    pub related_metrics: Vec<String>,
}

/// Optimization opportunity identified.
#[derive(Debug, Clone)]
pub struct OptimizationOpportunity {
    /// Expected improvement magnitude
    pub potential_impact: f64,

    /// Implementation difficulty
    pub difficulty: ImplementationDifficulty,

    /// Opportunity description
    pub description: String,

    /// Specific actions required
    pub actions: Vec<String>,

    /// Expected timeframe
    pub timeframe: String,
}

/// Actionable recommendation for performance improvement.
#[derive(Debug, Clone)]
pub struct Recommendation {
    /// Recommendation priority
    pub priority: RecommendationPriority,

    /// Action category
    pub category: String,

    /// Specific recommendation
    pub action: String,

    /// Expected benefit
    pub benefit: String,

    /// Implementation notes
    pub notes: Vec<String>,
}

/// Analysis metadata for context and tracking.
#[derive(Debug, Clone)]
pub struct AnalysisMetadata {
    /// Analysis timestamp
    pub timestamp: u64,

    /// Data period covered
    pub data_period: (u64, u64),

    /// Analysis version
    pub version: String,

    /// Data quality indicators
    pub data_quality: DataQuality,
}

/// Data quality assessment for analysis reliability.
#[derive(Debug, Clone, Default)]
pub struct DataQuality {
    /// Completeness score (0-1)
    pub completeness: f64,

    /// Freshness score (0-1)
    pub freshness: f64,

    /// Consistency score (0-1)
    pub consistency: f64,
}

/// Issue severity levels for prioritization.
#[derive(Debug, Clone, PartialEq)]
pub enum IssueSeverity {
    Critical,
    High,
    Medium,
    Low,
    Info,
}

impl PartialOrd for IssueSeverity {
    fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
        // Higher severity should be considered "greater" in ordering
        use std::cmp::Ordering;
        match (self, other) {
            (IssueSeverity::Critical, IssueSeverity::Critical) => Some(Ordering::Equal),
            (IssueSeverity::Critical, _) => Some(Ordering::Greater),
            (_, IssueSeverity::Critical) => Some(Ordering::Less),

            (IssueSeverity::High, IssueSeverity::High) => Some(Ordering::Equal),
            (
                IssueSeverity::High,
                IssueSeverity::Medium | IssueSeverity::Low | IssueSeverity::Info,
            ) => Some(Ordering::Greater),
            (
                IssueSeverity::Medium | IssueSeverity::Low | IssueSeverity::Info,
                IssueSeverity::High,
            ) => Some(Ordering::Less),

            (IssueSeverity::Medium, IssueSeverity::Medium) => Some(Ordering::Equal),
            (IssueSeverity::Medium, IssueSeverity::Low | IssueSeverity::Info) => {
                Some(Ordering::Greater)
            }
            (IssueSeverity::Low | IssueSeverity::Info, IssueSeverity::Medium) => {
                Some(Ordering::Less)
            }

            (IssueSeverity::Low, IssueSeverity::Low) => Some(Ordering::Equal),
            (IssueSeverity::Low, IssueSeverity::Info) => Some(Ordering::Greater),
            (IssueSeverity::Info, IssueSeverity::Low) => Some(Ordering::Less),

            (IssueSeverity::Info, IssueSeverity::Info) => Some(Ordering::Equal),
        }
    }
}

/// Implementation difficulty for opportunities.
#[derive(Debug, Clone, PartialEq, PartialOrd)]
pub enum ImplementationDifficulty {
    Easy,
    Moderate,
    Hard,
    Expert,
}

/// Recommendation priority levels.
#[derive(Debug, Clone, PartialEq)]
pub enum RecommendationPriority {
    Immediate,
    High,
    Medium,
    Low,
    Optional,
}

impl PartialOrd for RecommendationPriority {
    fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
        // Higher priority should be considered "greater" in ordering
        use std::cmp::Ordering;
        match (self, other) {
            (RecommendationPriority::Immediate, RecommendationPriority::Immediate) => {
                Some(Ordering::Equal)
            }
            (RecommendationPriority::Immediate, _) => Some(Ordering::Greater),
            (_, RecommendationPriority::Immediate) => Some(Ordering::Less),

            (RecommendationPriority::High, RecommendationPriority::High) => Some(Ordering::Equal),
            (
                RecommendationPriority::High,
                RecommendationPriority::Medium
                | RecommendationPriority::Low
                | RecommendationPriority::Optional,
            ) => Some(Ordering::Greater),
            (
                RecommendationPriority::Medium
                | RecommendationPriority::Low
                | RecommendationPriority::Optional,
                RecommendationPriority::High,
            ) => Some(Ordering::Less),

            (RecommendationPriority::Medium, RecommendationPriority::Medium) => {
                Some(Ordering::Equal)
            }
            (
                RecommendationPriority::Medium,
                RecommendationPriority::Low | RecommendationPriority::Optional,
            ) => Some(Ordering::Greater),
            (
                RecommendationPriority::Low | RecommendationPriority::Optional,
                RecommendationPriority::Medium,
            ) => Some(Ordering::Less),

            (RecommendationPriority::Low, RecommendationPriority::Low) => Some(Ordering::Equal),
            (RecommendationPriority::Low, RecommendationPriority::Optional) => {
                Some(Ordering::Greater)
            }
            (RecommendationPriority::Optional, RecommendationPriority::Low) => Some(Ordering::Less),

            (RecommendationPriority::Optional, RecommendationPriority::Optional) => {
                Some(Ordering::Equal)
            }
        }
    }
}

/// Performance trend analysis for monitoring changes over time.
#[derive(Debug, Clone)]
pub struct PerformanceTrend {
    /// Trend direction
    pub direction: TrendDirection,

    /// Change magnitude (percentage)
    pub magnitude: f64,

    /// Confidence in trend (0-1)
    pub confidence: f64,

    /// Time period analyzed
    pub period: (u64, u64),

    /// Trend description
    pub description: String,
}

/// Trend direction indicators.
#[derive(Debug, Clone, PartialEq)]
pub enum TrendDirection {
    Improving,
    Degrading,
    Stable,
    Volatile,
}

/// Performance analyzer for comprehensive WAL metrics analysis.
///
/// Provides high-level analysis capabilities that combine metrics from all
/// components to generate insights, detect patterns, and identify optimization opportunities.
pub struct PerformanceAnalyzer {
    /// Analysis configuration
    config: AnalysisConfig,
}

/// Configuration for performance analysis behavior.
#[derive(Debug, Clone)]
pub struct AnalysisConfig {
    /// Sensitivity threshold for anomaly detection
    pub anomaly_threshold: f64,

    /// Minimum data points required for reliable analysis
    pub min_data_points: u64,

    /// Time window for trend analysis (seconds)
    pub trend_window: u64,

    /// Enable predictive analysis
    pub enable_prediction: bool,

    /// Custom thresholds for different metrics
    pub thresholds: MetricThresholds,
}

/// Thresholds for performance metric evaluation.
#[derive(Debug, Clone)]
pub struct MetricThresholds {
    /// Maximum acceptable write latency (microseconds)
    pub max_write_latency_us: u64,

    /// Maximum acceptable read latency (microseconds)
    pub max_read_latency_us: u64,

    /// Minimum acceptable throughput (records/sec)
    pub min_throughput_rps: f64,

    /// Maximum acceptable error rate (percentage)
    pub max_error_rate_percent: f64,

    /// Maximum acceptable memory usage (percentage)
    pub max_memory_usage_percent: f64,

    /// Minimum acceptable buffer hit rate (percentage)
    pub min_buffer_hit_rate_percent: f64,
}

impl PerformanceAnalysis {
    /// Create a new performance analysis result.
    ///
    /// Initializes an empty analysis structure ready for population
    /// with analysis results and insights.
    ///
    /// # Returns
    ///
    /// A new `PerformanceAnalysis` instance
    pub fn new() -> Self {
        Self {
            overall_score: 0.0,
            category_scores: PerformanceCategoryScores::default(),
            issues: Vec::new(),
            opportunities: Vec::new(),
            recommendations: Vec::new(),
            metadata: AnalysisMetadata {
                timestamp: std::time::SystemTime::now()
                    .duration_since(std::time::UNIX_EPOCH)
                    .unwrap_or_default()
                    .as_secs(),
                data_period: (0, 0),
                version: "1.0.0".to_string(),
                data_quality: DataQuality::default(),
            },
        }
    }

    /// Get analysis summary.
    ///
    /// Returns a formatted summary of the analysis results suitable
    /// for reporting and dashboard display.
    ///
    /// # Returns
    ///
    /// Formatted string with analysis summary
    pub fn get_summary(&self) -> String {
        format!(
            "Performance Score: {:.1}/100, Issues: {}, Opportunities: {}, Recommendations: {}",
            self.overall_score,
            self.issues.len(),
            self.opportunities.len(),
            self.recommendations.len()
        )
    }

    /// Get critical issues only.
    ///
    /// Filters issues by severity to return only critical and high-priority
    /// issues that require immediate attention.
    ///
    /// # Returns
    ///
    /// Vector of critical issues
    pub fn get_critical_issues(&self) -> Vec<&PerformanceIssue> {
        self.issues
            .iter()
            .filter(|issue| {
                matches!(
                    issue.severity,
                    IssueSeverity::Critical | IssueSeverity::High
                )
            })
            .collect()
    }

    /// Get immediate recommendations.
    ///
    /// Returns recommendations with immediate or high priority
    /// that should be addressed first.
    ///
    /// # Returns
    ///
    /// Vector of immediate recommendations
    pub fn get_immediate_recommendations(&self) -> Vec<&Recommendation> {
        self.recommendations
            .iter()
            .filter(|rec| {
                matches!(
                    rec.priority,
                    RecommendationPriority::Immediate | RecommendationPriority::High
                )
            })
            .collect()
    }
}

impl PerformanceAnalyzer {
    /// Create new performance analyzer with default configuration.
    ///
    /// Initializes the analyzer with sensible defaults for typical
    /// V2 WAL workloads and performance expectations.
    ///
    /// # Returns
    ///
    /// A new `PerformanceAnalyzer` instance
    pub fn new() -> Self {
        Self {
            config: AnalysisConfig {
                anomaly_threshold: 2.0, // 2 standard deviations
                min_data_points: 100,
                trend_window: 3600, // 1 hour
                enable_prediction: true,
                thresholds: MetricThresholds {
                    max_write_latency_us: 1000,        // 1ms
                    max_read_latency_us: 500,          // 0.5ms
                    min_throughput_rps: 1000.0,        // 1K records/sec
                    max_error_rate_percent: 1.0,       // 1%
                    max_memory_usage_percent: 80.0,    // 80%
                    min_buffer_hit_rate_percent: 85.0, // 85%
                },
            },
        }
    }

    /// Create performance analyzer with custom configuration.
    ///
    /// Allows customization of analysis behavior for specific
    /// workloads and performance requirements.
    ///
    /// # Arguments
    ///
    /// * `config` - Custom analysis configuration
    ///
    /// # Returns
    ///
    /// A new `PerformanceAnalyzer` instance with custom settings
    pub fn with_config(config: AnalysisConfig) -> Self {
        Self { config }
    }

    /// Analyze comprehensive WAL performance.
    ///
    /// Performs a complete analysis of all WAL metrics components,
    /// generating insights, issues, and recommendations.
    ///
    /// # Arguments
    ///
    /// * `metrics` - V2WALMetrics instance to analyze
    ///
    /// # Returns
    ///
    /// Comprehensive performance analysis results
    pub fn analyze(&self, metrics: &V2WALMetrics) -> PerformanceAnalysis {
        let mut analysis = PerformanceAnalysis::new();

        // Gather all metrics data
        let counters = metrics.get_counters();
        let resource_tracker = metrics.get_resource_tracker();
        let cluster_metrics = metrics.get_cluster_metrics();
        let error_tracker = metrics.get_error_tracker();
        let latency_histogram = metrics.get_latency_histogram();
        let throughput_tracker = metrics.get_throughput_tracker();

        // Analyze each performance category
        analysis.category_scores.throughput = self.analyze_throughput(&throughput_tracker);
        analysis.category_scores.latency = self.analyze_latency(&latency_histogram);
        analysis.category_scores.resources = self.analyze_resources(&resource_tracker);
        analysis.category_scores.reliability = self.analyze_reliability(&error_tracker, &counters);
        analysis.category_scores.efficiency = self.analyze_efficiency(&counters, &cluster_metrics);

        // Calculate overall score
        analysis.overall_score = self.calculate_overall_score(&analysis.category_scores);

        // Identify issues and opportunities
        analysis.issues = self.identify_issues(
            &counters,
            &latency_histogram,
            &resource_tracker,
            &error_tracker,
        );
        analysis.opportunities =
            self.identify_opportunities(&counters, &cluster_metrics, &throughput_tracker);

        // Generate recommendations
        analysis.recommendations = self.generate_recommendations(&analysis);

        // Update metadata
        analysis.metadata.data_quality = self.assess_data_quality(&counters);

        analysis
    }

    /// Analyze throughput performance.
    ///
    /// Evaluates throughput metrics against expected performance
    /// thresholds and historical patterns.
    ///
    /// # Arguments
    ///
    /// * `tracker` - ThroughputTracker to analyze
    ///
    /// # Returns
    ///
    /// Throughput performance score (0-100)
    fn analyze_throughput(&self, tracker: &ThroughputTracker) -> f64 {
        let (records_per_sec, _bytes_per_sec, tx_per_sec) = tracker.get_current_throughput();

        // Score based on records per second
        let throughput_score = if records_per_sec >= self.config.thresholds.min_throughput_rps {
            100.0
        } else {
            (records_per_sec / self.config.thresholds.min_throughput_rps) * 100.0
        };

        // Adjust for transaction efficiency
        let tx_efficiency = if records_per_sec > 0.0 {
            (tx_per_sec / records_per_sec) * 100.0
        } else {
            0.0
        };

        // Combine scores
        (throughput_score + tx_efficiency) / 2.0
    }

    /// Analyze latency performance.
    ///
    /// Evaluates latency distribution against acceptable thresholds
    /// and SLA requirements.
    ///
    /// # Arguments
    ///
    /// * `histogram` - LatencyHistogram to analyze
    ///
    /// # Returns
    ///
    /// Latency performance score (0-100)
    fn analyze_latency(&self, histogram: &LatencyHistogram) -> f64 {
        let p95_write = histogram.get_write_percentile(95.0);
        let p95_read = histogram.get_read_percentile(95.0);

        // Score based on P95 latencies
        let write_score = if p95_write <= self.config.thresholds.max_write_latency_us {
            100.0
        } else {
            (self.config.thresholds.max_write_latency_us as f64 / p95_write as f64) * 100.0
        };

        let read_score = if p95_read <= self.config.thresholds.max_read_latency_us {
            100.0
        } else {
            (self.config.thresholds.max_read_latency_us as f64 / p95_read as f64) * 100.0
        };

        // Weight read latency more heavily (typical for databases)
        write_score * 0.4 + read_score * 0.6
    }

    /// Analyze resource utilization.
    ///
    /// Evaluates resource usage patterns and efficiency metrics.
    ///
    /// # Arguments
    ///
    /// * `tracker` - ResourceTracker to analyze
    ///
    /// # Returns
    ///
    /// Resource performance score (0-100)
    fn analyze_resources(&self, tracker: &ResourceTracker) -> f64 {
        let memory_score = if tracker.memory_usage_bytes
            <= self.config.thresholds.max_memory_usage_percent as u64 * 1024 * 1024
        {
            100.0
        } else {
            // Penalize excessive memory usage
            100.0
                - ((tracker.memory_usage_bytes as f64
                    - self.config.thresholds.max_memory_usage_percent as f64 * 1024.0 * 1024.0)
                    / (self.config.thresholds.max_memory_usage_percent as f64 * 1024.0 * 1024.0))
                    * 100.0
        }
        .max(0.0_f64);

        let buffer_score = if tracker.buffer_pool_hit_rate
            >= self.config.thresholds.min_buffer_hit_rate_percent / 100.0
        {
            100.0
        } else {
            (tracker.buffer_pool_hit_rate
                / (self.config.thresholds.min_buffer_hit_rate_percent / 100.0))
                * 100.0
        };

        // CPU and disk are secondary for database workloads
        let cpu_score = 100.0 - tracker.cpu_usage_percent; // Lower CPU usage is better
        let disk_score = if tracker.disk_iops > 0 { 100.0 } else { 50.0 };

        memory_score * 0.3 + buffer_score * 0.3 + cpu_score * 0.2 + disk_score * 0.2
    }

    /// Analyze reliability based on error rates and patterns.
    ///
    /// Evaluates system reliability using error metrics and recovery patterns.
    ///
    /// # Arguments
    ///
    /// * `error_tracker` - ErrorTracker to analyze
    /// * `counters` - Performance counters for context
    ///
    /// # Returns
    ///
    /// Reliability performance score (0-100)
    fn analyze_reliability(
        &self,
        error_tracker: &ErrorTracker,
        counters: &WALPerformanceCounters,
    ) -> f64 {
        let total_operations = counters.records_processed;
        let total_errors: u64 = error_tracker.error_counts.values().sum();

        if total_operations == 0 {
            return 100.0; // No operations means no errors (perfect reliability)
        }

        let error_rate = (total_errors as f64 / total_operations as f64) * 100.0;

        if error_rate <= self.config.thresholds.max_error_rate_percent {
            100.0
        } else {
            (self.config.thresholds.max_error_rate_percent / error_rate) * 100.0
        }
    }

    /// Analyze overall efficiency metrics.
    ///
    /// Evaluates the efficiency of operations including resource utilization
    /// and operational patterns.
    ///
    /// # Arguments
    ///
    /// * `counters` - Performance counters to analyze
    /// * `cluster_metrics` - Cluster performance metrics
    ///
    /// # Returns
    ///
    /// Efficiency performance score (0-100)
    fn analyze_efficiency(
        &self,
        counters: &WALPerformanceCounters,
        cluster_metrics: &ClusterPerformanceMetrics,
    ) -> f64 {
        // Buffer utilization efficiency
        let buffer_efficiency = if counters.buffer_utilization_percent <= 90.0 {
            (counters.buffer_utilization_percent / 90.0) * 100.0
        } else {
            100.0 - ((counters.buffer_utilization_percent - 90.0) / 10.0) * 100.0
        }
        .max(0.0_f64);

        // Cluster efficiency
        let cluster_efficiency = if cluster_metrics.global_metrics.total_clusters > 0 {
            cluster_metrics.global_metrics.utilization_percent
        } else {
            100.0 // No clusters means no inefficiency
        };

        // Operation efficiency (ratio of successful operations)
        let total_ops = counters.edge_operations.total_inserts
            + counters.edge_operations.total_updates
            + counters.node_operations.total_inserts
            + counters.node_operations.total_updates;

        let operation_efficiency = if total_ops > 0 {
            // Assume all recorded operations are successful for this calculation
            100.0
        } else {
            100.0 // No operations means no inefficiency
        };

        buffer_efficiency * 0.4 + cluster_efficiency * 0.3 + operation_efficiency * 0.3
    }

    /// Calculate overall performance score.
    ///
    /// Combines category scores into an overall performance rating.
    ///
    /// # Arguments
    ///
    /// * `category_scores` - Individual category performance scores
    ///
    /// # Returns
    ///
    /// Overall performance score (0-100)
    fn calculate_overall_score(&self, category_scores: &PerformanceCategoryScores) -> f64 {
        // Weight categories by importance for database workloads
        category_scores.throughput * 0.25
            + category_scores.latency * 0.30
            + category_scores.resources * 0.15
            + category_scores.reliability * 0.20
            + category_scores.efficiency * 0.10
    }

    /// Identify performance issues.
    ///
    /// Scans metrics data to identify performance problems and anomalies.
    ///
    /// # Arguments
    ///
    /// * `counters` - Performance counters to examine
    /// * `latency_histogram` - Latency distribution data
    /// * `resource_tracker` - Resource utilization data
    /// * `error_tracker` - Error occurrence data
    ///
    /// # Returns
    ///
    /// Vector of identified performance issues
    fn identify_issues(
        &self,
        _counters: &WALPerformanceCounters,
        latency_histogram: &LatencyHistogram,
        resource_tracker: &ResourceTracker,
        _error_tracker: &ErrorTracker,
    ) -> Vec<PerformanceIssue> {
        let mut issues = Vec::new();

        // Check for high latency
        let p99_write = latency_histogram.get_write_percentile(99.0);
        if p99_write > self.config.thresholds.max_write_latency_us * 3 {
            issues.push(PerformanceIssue {
                severity: IssueSeverity::High,
                category: "Latency".to_string(),
                description: "Write latency outliers detected".to_string(),
                impact: "High write latency may cause transaction delays".to_string(),
                timestamp: std::time::SystemTime::now()
                    .duration_since(std::time::UNIX_EPOCH)
                    .unwrap_or_default()
                    .as_secs(),
                related_metrics: vec!["write_latency_p99".to_string()],
            });
        }

        // Check for memory pressure
        let memory_mb = resource_tracker.memory_usage_bytes / (1024 * 1024);
        if memory_mb > 1024 {
            // > 1GB
            issues.push(PerformanceIssue {
                severity: IssueSeverity::Medium,
                category: "Memory".to_string(),
                description: "High memory usage detected".to_string(),
                impact: "May lead to increased GC pressure and reduced performance".to_string(),
                timestamp: std::time::SystemTime::now()
                    .duration_since(std::time::UNIX_EPOCH)
                    .unwrap_or_default()
                    .as_secs(),
                related_metrics: vec!["memory_usage_bytes".to_string()],
            });
        }

        // Check for buffer pool efficiency
        if resource_tracker.buffer_pool_hit_rate < 0.8 {
            issues.push(PerformanceIssue {
                severity: IssueSeverity::Medium,
                category: "Cache".to_string(),
                description: "Low buffer pool hit rate".to_string(),
                impact: "Increased disk I/O and reduced query performance".to_string(),
                timestamp: std::time::SystemTime::now()
                    .duration_since(std::time::UNIX_EPOCH)
                    .unwrap_or_default()
                    .as_secs(),
                related_metrics: vec!["buffer_pool_hit_rate".to_string()],
            });
        }

        issues
    }

    /// Identify optimization opportunities.
    ///
    /// Scans metrics data to identify areas for performance improvement.
    ///
    /// # Arguments
    ///
    /// * `counters` - Performance counters to examine
    /// * `cluster_metrics` - Cluster performance data
    /// * `throughput_tracker` - Throughput data
    ///
    /// # Returns
    ///
    /// Vector of optimization opportunities
    fn identify_opportunities(
        &self,
        counters: &WALPerformanceCounters,
        cluster_metrics: &ClusterPerformanceMetrics,
        _throughput_tracker: &ThroughputTracker,
    ) -> Vec<OptimizationOpportunity> {
        let mut opportunities = Vec::new();

        // Check cluster utilization
        if cluster_metrics.global_metrics.utilization_percent < 50.0 {
            opportunities.push(OptimizationOpportunity {
                potential_impact: 20.0,
                difficulty: ImplementationDifficulty::Moderate,
                description: "Low cluster utilization detected".to_string(),
                actions: vec![
                    "Implement cluster consolidation".to_string(),
                    "Optimize cluster distribution".to_string(),
                ],
                timeframe: "2-4 weeks".to_string(),
            });
        }

        // Check for edge operation patterns
        if counters.edge_operations.total_inserts > counters.edge_operations.total_updates * 10 {
            opportunities.push(OptimizationOpportunity {
                potential_impact: 15.0,
                difficulty: ImplementationDifficulty::Easy,
                description: "High insert-to-update ratio".to_string(),
                actions: vec![
                    "Optimize batch insert operations".to_string(),
                    "Consider write-ahead log tuning".to_string(),
                ],
                timeframe: "1-2 weeks".to_string(),
            });
        }

        opportunities
    }

    /// Generate actionable recommendations.
    ///
    /// Creates specific recommendations based on identified issues
    /// and optimization opportunities.
    ///
    /// # Arguments
    ///
    /// * `analysis` - Current analysis results
    ///
    /// # Returns
    ///
    /// Vector of actionable recommendations
    fn generate_recommendations(&self, analysis: &PerformanceAnalysis) -> Vec<Recommendation> {
        let mut recommendations = Vec::new();

        for issue in &analysis.issues {
            match issue.category.as_str() {
                "Latency" => {
                    recommendations.push(Recommendation {
                        priority: RecommendationPriority::High,
                        category: "Performance".to_string(),
                        action: "Optimize write patterns and batch operations".to_string(),
                        benefit: "Reduced write latency and improved throughput".to_string(),
                        notes: vec![
                            "Consider increasing batch sizes".to_string(),
                            "Review disk I/O patterns".to_string(),
                        ],
                    });
                }
                "Memory" => {
                    recommendations.push(Recommendation {
                        priority: RecommendationPriority::Medium,
                        category: "Resources".to_string(),
                        action: "Implement memory optimization strategies".to_string(),
                        benefit: "Reduced memory footprint and improved stability".to_string(),
                        notes: vec![
                            "Review memory allocation patterns".to_string(),
                            "Consider memory pool implementation".to_string(),
                        ],
                    });
                }
                _ => {}
            }
        }

        recommendations
    }

    /// Assess data quality for analysis reliability.
    ///
    /// Evaluates the quality and completeness of metrics data
    /// to ensure reliable analysis results.
    ///
    /// # Arguments
    ///
    /// * `counters` - Performance counters to evaluate
    ///
    /// # Returns
    ///
    /// Data quality assessment
    fn assess_data_quality(&self, counters: &WALPerformanceCounters) -> DataQuality {
        let completeness = if counters.records_processed > 0 {
            1.0
        } else {
            0.0
        };
        let freshness = 1.0; // Assume data is fresh (would check timestamps in production)
        let consistency = 1.0; // Assume data is consistent (would validate in production)

        DataQuality {
            completeness,
            freshness,
            consistency,
        }
    }
}

impl Default for AnalysisConfig {
    fn default() -> Self {
        AnalysisConfig {
            anomaly_threshold: 2.0,
            min_data_points: 100,
            trend_window: 3600,
            enable_prediction: true,
            thresholds: MetricThresholds {
                max_write_latency_us: 1000,
                max_read_latency_us: 500,
                min_throughput_rps: 1000.0,
                max_error_rate_percent: 1.0,
                max_memory_usage_percent: 80.0,
                min_buffer_hit_rate_percent: 85.0,
            },
        }
    }
}

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

#[cfg(test)]
mod tests {
    use super::*;
    use crate::backend::native::v2::wal::metrics::core::V2WALMetrics;

    #[test]
    fn test_performance_analysis_new() {
        let analysis = PerformanceAnalysis::new();
        assert_eq!(analysis.overall_score, 0.0);
        assert!(analysis.issues.is_empty());
        assert!(analysis.opportunities.is_empty());
        assert!(analysis.recommendations.is_empty());
    }

    #[test]
    fn test_performance_analysis_summary() {
        let mut analysis = PerformanceAnalysis::new();
        analysis.overall_score = 85.5;
        analysis.issues.push(PerformanceIssue {
            severity: IssueSeverity::Medium,
            category: "Test".to_string(),
            description: "Test issue".to_string(),
            impact: "Test impact".to_string(),
            timestamp: 1234567890,
            related_metrics: vec!["test_metric".to_string()],
        });

        let summary = analysis.get_summary();
        assert!(summary.contains("85.5"));
        assert!(summary.contains("Issues: 1"));
    }

    #[test]
    fn test_performance_analyzer_new() {
        let analyzer = PerformanceAnalyzer::new();
        assert_eq!(analyzer.config.anomaly_threshold, 2.0);
        assert_eq!(analyzer.config.min_data_points, 100);
        assert_eq!(analyzer.config.trend_window, 3600);
    }

    #[test]
    fn test_performance_analyzer_analyze() {
        let analyzer = PerformanceAnalyzer::new();
        let metrics = V2WALMetrics::new();

        let analysis = analyzer.analyze(&metrics);
        assert!(analysis.overall_score >= 0.0 && analysis.overall_score <= 100.0);
        assert!(analysis.category_scores.throughput >= 0.0);
        assert!(analysis.category_scores.latency >= 0.0);
    }

    #[test]
    fn test_issue_severity_ordering() {
        assert!(IssueSeverity::Critical > IssueSeverity::High);
        assert!(IssueSeverity::High > IssueSeverity::Medium);
        assert!(IssueSeverity::Medium > IssueSeverity::Low);
        assert!(IssueSeverity::Low > IssueSeverity::Info);
    }

    #[test]
    fn test_recommendation_priority_ordering() {
        assert!(RecommendationPriority::Immediate > RecommendationPriority::High);
        assert!(RecommendationPriority::High > RecommendationPriority::Medium);
        assert!(RecommendationPriority::Medium > RecommendationPriority::Low);
        assert!(RecommendationPriority::Low > RecommendationPriority::Optional);
    }

    #[test]
    fn test_implementation_difficulty_ordering() {
        assert!(ImplementationDifficulty::Easy < ImplementationDifficulty::Moderate);
        assert!(ImplementationDifficulty::Moderate < ImplementationDifficulty::Hard);
        assert!(ImplementationDifficulty::Hard < ImplementationDifficulty::Expert);
    }

    #[test]
    fn test_analysis_metadata() {
        let metadata = AnalysisMetadata {
            timestamp: 1234567890,
            data_period: (1234567890, 1234567990),
            version: "1.0.0".to_string(),
            data_quality: DataQuality {
                completeness: 1.0,
                freshness: 0.9,
                consistency: 0.95,
            },
        };

        assert_eq!(metadata.timestamp, 1234567890);
        assert_eq!(metadata.data_period.0, 1234567890);
        assert_eq!(metadata.data_period.1, 1234567990);
        assert_eq!(metadata.version, "1.0.0");
        assert_eq!(metadata.data_quality.completeness, 1.0);
    }

    #[test]
    fn test_performance_trend() {
        let trend = PerformanceTrend {
            direction: TrendDirection::Improving,
            magnitude: 15.5,
            confidence: 0.85,
            period: (1234567890, 1234567990),
            description: "Throughput improving steadily".to_string(),
        };

        assert_eq!(trend.direction, TrendDirection::Improving);
        assert_eq!(trend.magnitude, 15.5);
        assert_eq!(trend.confidence, 0.85);
        assert!(trend.description.contains("improving"));
    }

    #[test]
    fn test_metric_thresholds() {
        let thresholds = MetricThresholds {
            max_write_latency_us: 1000,
            max_read_latency_us: 500,
            min_throughput_rps: 1000.0,
            max_error_rate_percent: 1.0,
            max_memory_usage_percent: 80.0,
            min_buffer_hit_rate_percent: 85.0,
        };

        assert_eq!(thresholds.max_write_latency_us, 1000);
        assert_eq!(thresholds.max_read_latency_us, 500);
        assert_eq!(thresholds.min_throughput_rps, 1000.0);
    }

    #[test]
    fn test_performance_analysis_with_data() {
        let analyzer = PerformanceAnalyzer::new();
        let metrics = V2WALMetrics::new();

        // Add some test data
        metrics.record_write_operation(100, 50, Some(42), "edge_insert");
        metrics.record_write_operation(150, 75, Some(43), "node_insert");

        let analysis = analyzer.analyze(&metrics);
        assert!(analysis.overall_score > 0.0);

        let summary = analysis.get_summary();
        assert!(summary.contains("Performance Score:"));
    }

    #[test]
    fn test_get_critical_issues() {
        let mut analysis = PerformanceAnalysis::new();

        // Add issues of different severities
        analysis.issues.push(PerformanceIssue {
            severity: IssueSeverity::Critical,
            category: "Critical".to_string(),
            description: "Critical issue".to_string(),
            impact: "Critical impact".to_string(),
            timestamp: 1234567890,
            related_metrics: vec!["critical_metric".to_string()],
        });

        analysis.issues.push(PerformanceIssue {
            severity: IssueSeverity::Low,
            category: "Low".to_string(),
            description: "Low issue".to_string(),
            impact: "Low impact".to_string(),
            timestamp: 1234567890,
            related_metrics: vec!["low_metric".to_string()],
        });

        let critical_issues = analysis.get_critical_issues();
        assert_eq!(critical_issues.len(), 1);
        assert_eq!(critical_issues[0].severity, IssueSeverity::Critical);
    }

    #[test]
    fn test_get_immediate_recommendations() {
        let mut analysis = PerformanceAnalysis::new();

        // Add recommendations of different priorities
        analysis.recommendations.push(Recommendation {
            priority: RecommendationPriority::Immediate,
            category: "Immediate".to_string(),
            action: "Immediate action".to_string(),
            benefit: "Immediate benefit".to_string(),
            notes: vec!["Immediate note".to_string()],
        });

        analysis.recommendations.push(Recommendation {
            priority: RecommendationPriority::Low,
            category: "Low".to_string(),
            action: "Low action".to_string(),
            benefit: "Low benefit".to_string(),
            notes: vec!["Low note".to_string()],
        });

        let immediate_recs = analysis.get_immediate_recommendations();
        assert_eq!(immediate_recs.len(), 1);
        assert_eq!(
            immediate_recs[0].priority,
            RecommendationPriority::Immediate
        );
    }
}