//! Bandwidth-Optimized Recovery Algorithms
//!
//! Efficient algorithms for data recovery that minimize bandwidth usage
//! while maximizing recovery speed and reliability

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, BTreeMap, HashSet};
use chrono::{DateTime, Utc, Duration};

use crate::economics::earnings_calculator::GeographicRegion;
use super::reed_solomon::{EncodedChunk, ReconstructionRequest};

/// Bandwidth-optimized recovery manager
#[derive(Debug, Clone)]
pub struct RecoveryOptimizer {
    /// Network topology information
    pub network_topology: NetworkTopology,
    /// Bandwidth optimization strategies
    pub optimization_strategies: Vec<OptimizationStrategy>,
    /// Performance metrics
    pub performance_metrics: RecoveryMetrics,
    /// Recovery algorithms configuration
    pub algorithms: AlgorithmConfig,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NetworkTopology {
    /// Node connectivity information
    pub nodes: HashMap<String, NodeInfo>,
    /// Connection bandwidth matrix
    pub bandwidth_matrix: HashMap<(String, String), BandwidthInfo>,
    /// Regional connectivity
    pub regional_links: HashMap<GeographicRegion, RegionalConnectivity>,
    /// Network congestion levels
    pub congestion_levels: HashMap<String, CongestionInfo>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NodeInfo {
    pub node_id: String,
    pub region: GeographicRegion,
    pub available_bandwidth_mbps: f64,
    pub latency_profile: HashMap<String, f64>,
    pub connection_quality: ConnectionQuality,
    pub load_factor: f64,
    pub active_transfers: u32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BandwidthInfo {
    pub theoretical_max_mbps: f64,
    pub current_available_mbps: f64,
    pub average_utilization: f64,
    pub latency_ms: f64,
    pub reliability_score: f64,
    pub cost_per_gb: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RegionalConnectivity {
    pub region: GeographicRegion,
    pub total_capacity_gbps: f64,
    pub utilized_capacity_gbps: f64,
    pub inter_region_links: HashMap<GeographicRegion, f64>,
    pub backbone_quality: BackboneQuality,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CongestionInfo {
    pub node_id: String,
    pub current_load: f64,
    pub predicted_load: f64,
    pub congestion_trend: CongestionTrend,
    pub time_to_clear: Option<Duration>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CongestionTrend {
    Increasing,
    Stable,
    Decreasing,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConnectionQuality {
    Excellent, // Fiber, low latency
    Good,      // Fast broadband
    Average,   // Standard broadband
    Poor,      // Slow/unreliable
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum BackboneQuality {
    Tier1,     // Top-tier internet backbone
    Tier2,     // Regional provider
    Tier3,     // Local provider
    Satellite, // Satellite connectivity
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationStrategy {
    ParallelRecovery,        // Download chunks in parallel
    ProgressiveRecovery,     // Start with most critical chunks
    LocalityOptimized,       // Prefer nearby nodes
    LoadBalanced,           // Balance load across nodes
    CostOptimized,          // Minimize transfer costs
    LatencyOptimized,       // Minimize total time
    AdaptiveBandwidth,      // Adjust based on available bandwidth
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RecoveryMetrics {
    pub total_recoveries: u64,
    pub successful_recoveries: u64,
    pub average_recovery_time_seconds: f64,
    pub average_bandwidth_efficiency: f64,
    pub total_bytes_recovered: u64,
    pub cost_savings_percent: f64,
    pub last_updated: DateTime<Utc>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlgorithmConfig {
    pub max_parallel_streams: u32,
    pub chunk_prefetch_count: u32,
    pub adaptive_bandwidth_threshold: f64,
    pub load_balancing_factor: f64,
    pub locality_preference_weight: f64,
    pub congestion_avoidance_enabled: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RecoveryPlan {
    pub plan_id: String,
    pub target_chunks: Vec<String>,
    pub recovery_steps: Vec<RecoveryStep>,
    pub estimated_time_seconds: f64,
    pub estimated_bandwidth_usage_mb: f64,
    pub estimated_cost: f64,
    pub optimization_strategy: OptimizationStrategy,
    pub fallback_plans: Vec<FallbackPlan>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RecoveryStep {
    pub step_id: String,
    pub step_type: RecoveryStepType,
    pub source_nodes: Vec<String>,
    pub target_chunks: Vec<String>,
    pub estimated_duration_seconds: f64,
    pub bandwidth_requirement_mbps: f64,
    pub priority: RecoveryPriority,
    pub dependencies: Vec<String>, // Step IDs this depends on
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RecoveryStepType {
    DirectTransfer,      // Direct chunk download
    ParallelTransfer,    // Multiple chunks in parallel
    ErasureReconstruct,  // Reed-Solomon reconstruction
    VerifyIntegrity,     // Verify recovered data
    Prefetch,           // Preemptive chunk fetching
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RecoveryPriority {
    Critical,   // Must complete first
    High,       // Important for performance
    Normal,     // Standard priority
    Background, // Can be delayed
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FallbackPlan {
    pub fallback_id: String,
    pub trigger_conditions: Vec<String>,
    pub alternative_steps: Vec<RecoveryStep>,
    pub performance_impact: f64,
}

impl Default for AlgorithmConfig {
    fn default() -> Self {
        Self {
            max_parallel_streams: 8,
            chunk_prefetch_count: 2,
            adaptive_bandwidth_threshold: 0.8,
            load_balancing_factor: 0.3,
            locality_preference_weight: 0.6,
            congestion_avoidance_enabled: true,
        }
    }
}

impl RecoveryOptimizer {
    /// Create new recovery optimizer
    pub fn new() -> Self {
        Self {
            network_topology: NetworkTopology {
                nodes: HashMap::new(),
                bandwidth_matrix: HashMap::new(),
                regional_links: HashMap::new(),
                congestion_levels: HashMap::new(),
            },
            optimization_strategies: vec![
                OptimizationStrategy::AdaptiveBandwidth,
                OptimizationStrategy::LoadBalanced,
                OptimizationStrategy::LocalityOptimized,
            ],
            performance_metrics: RecoveryMetrics {
                total_recoveries: 0,
                successful_recoveries: 0,
                average_recovery_time_seconds: 0.0,
                average_bandwidth_efficiency: 0.0,
                total_bytes_recovered: 0,
                cost_savings_percent: 0.0,
                last_updated: Utc::now(),
            },
            algorithms: AlgorithmConfig::default(),
        }
    }

    /// Create optimized recovery plan
    pub fn create_recovery_plan(
        &self,
        missing_chunks: &[String],
        available_chunks: &HashMap<String, Vec<NodeLocation>>,
        recovery_requirements: RecoveryRequirements,
    ) -> Result<RecoveryPlan> {

        // Analyze available sources
        let source_analysis = self.analyze_chunk_sources(available_chunks)?;

        // Select optimal strategy based on requirements
        let strategy = self.select_optimization_strategy(&recovery_requirements, &source_analysis)?;

        // Generate recovery steps
        let recovery_steps = self.generate_recovery_steps(
            missing_chunks,
            available_chunks,
            &strategy,
            &recovery_requirements,
        )?;

        // Calculate estimates
        let (estimated_time, estimated_bandwidth, estimated_cost) =
            self.calculate_recovery_estimates(&recovery_steps)?;

        // Generate fallback plans
        let fallback_plans = self.generate_fallback_plans(
            missing_chunks,
            available_chunks,
            &recovery_requirements,
        )?;

        Ok(RecoveryPlan {
            plan_id: format!("recovery_plan_{}", Utc::now().timestamp()),
            target_chunks: missing_chunks.to_vec(),
            recovery_steps,
            estimated_time_seconds: estimated_time,
            estimated_bandwidth_usage_mb: estimated_bandwidth,
            estimated_cost,
            optimization_strategy: strategy,
            fallback_plans,
        })
    }

    /// Analyze available chunk sources for optimization
    fn analyze_chunk_sources(
        &self,
        available_chunks: &HashMap<String, Vec<NodeLocation>>,
    ) -> Result<SourceAnalysis> {
        let mut analysis = SourceAnalysis {
            total_sources: 0,
            sources_by_region: HashMap::new(),
            bandwidth_distribution: BandwidthDistribution::default(),
            load_distribution: LoadDistribution::default(),
        };

        for (chunk_id, locations) in available_chunks {
            analysis.total_sources += locations.len();

            for location in locations {
                // Analyze by region
                *analysis.sources_by_region.entry(location.region.clone()).or_insert(0) += 1;

                // Analyze bandwidth availability
                if let Some(node_info) = self.network_topology.nodes.get(&location.node_id) {
                    analysis.bandwidth_distribution.update(node_info.available_bandwidth_mbps);
                    analysis.load_distribution.update(node_info.load_factor);
                }
            }
        }

        Ok(analysis)
    }

    /// Select optimal recovery strategy
    fn select_optimization_strategy(
        &self,
        requirements: &RecoveryRequirements,
        analysis: &SourceAnalysis,
    ) -> Result<OptimizationStrategy> {

        // Priority-based selection
        if requirements.time_critical {
            if analysis.bandwidth_distribution.high_bandwidth_sources > 3 {
                return Ok(OptimizationStrategy::ParallelRecovery);
            } else {
                return Ok(OptimizationStrategy::LatencyOptimized);
            }
        }

        if requirements.cost_sensitive {
            return Ok(OptimizationStrategy::CostOptimized);
        }

        if analysis.sources_by_region.len() > 1 {
            return Ok(OptimizationStrategy::LocalityOptimized);
        }

        // Default to adaptive bandwidth
        Ok(OptimizationStrategy::AdaptiveBandwidth)
    }

    /// Generate optimized recovery steps
    fn generate_recovery_steps(
        &self,
        missing_chunks: &[String],
        available_chunks: &HashMap<String, Vec<NodeLocation>>,
        strategy: &OptimizationStrategy,
        requirements: &RecoveryRequirements,
    ) -> Result<Vec<RecoveryStep>> {
        match strategy {
            OptimizationStrategy::ParallelRecovery => {
                self.generate_parallel_recovery_steps(missing_chunks, available_chunks, requirements)
            },
            OptimizationStrategy::ProgressiveRecovery => {
                self.generate_progressive_recovery_steps(missing_chunks, available_chunks, requirements)
            },
            OptimizationStrategy::LocalityOptimized => {
                self.generate_locality_optimized_steps(missing_chunks, available_chunks, requirements)
            },
            OptimizationStrategy::LoadBalanced => {
                self.generate_load_balanced_steps(missing_chunks, available_chunks, requirements)
            },
            OptimizationStrategy::AdaptiveBandwidth => {
                self.generate_adaptive_bandwidth_steps(missing_chunks, available_chunks, requirements)
            },
            _ => {
                // Default implementation
                self.generate_basic_recovery_steps(missing_chunks, available_chunks, requirements)
            }
        }
    }

    /// Generate parallel recovery steps
    fn generate_parallel_recovery_steps(
        &self,
        missing_chunks: &[String],
        available_chunks: &HashMap<String, Vec<NodeLocation>>,
        _requirements: &RecoveryRequirements,
    ) -> Result<Vec<RecoveryStep>> {
        let mut steps = Vec::new();
        let max_parallel = self.algorithms.max_parallel_streams as usize;

        // Group chunks into parallel batches
        for (batch_idx, chunk_batch) in missing_chunks.chunks(max_parallel).enumerate() {
            let mut source_nodes = Vec::new();
            let mut chunk_list = Vec::new();

            for chunk_id in chunk_batch {
                if let Some(locations) = available_chunks.get(chunk_id) {
                    // Select best source for this chunk
                    let best_source = self.select_best_source(locations)?;
                    source_nodes.push(best_source.node_id.clone());
                    chunk_list.push(chunk_id.clone());
                }
            }

            if !chunk_list.is_empty() {
                steps.push(RecoveryStep {
                    step_id: format!("parallel_batch_{}", batch_idx),
                    step_type: RecoveryStepType::ParallelTransfer,
                    source_nodes,
                    target_chunks: chunk_list,
                    estimated_duration_seconds: 30.0, // Estimate based on parallel efficiency
                    bandwidth_requirement_mbps: 100.0 * chunk_batch.len() as f64,
                    priority: RecoveryPriority::High,
                    dependencies: Vec::new(),
                });
            }
        }

        // Add verification step
        steps.push(RecoveryStep {
            step_id: "verify_parallel_recovery".to_string(),
            step_type: RecoveryStepType::VerifyIntegrity,
            source_nodes: Vec::new(),
            target_chunks: missing_chunks.to_vec(),
            estimated_duration_seconds: 5.0,
            bandwidth_requirement_mbps: 0.0,
            priority: RecoveryPriority::Critical,
            dependencies: steps.iter().map(|s| s.step_id.clone()).collect(),
        });

        Ok(steps)
    }

    /// Generate locality-optimized recovery steps
    fn generate_locality_optimized_steps(
        &self,
        missing_chunks: &[String],
        available_chunks: &HashMap<String, Vec<NodeLocation>>,
        requirements: &RecoveryRequirements,
    ) -> Result<Vec<RecoveryStep>> {
        // Group chunks by optimal source region
        let mut chunks_by_region: HashMap<GeographicRegion, Vec<String>> = HashMap::new();

        for chunk_id in missing_chunks {
            if let Some(locations) = available_chunks.get(chunk_id) {
                let optimal_region = self.find_optimal_source_region(locations, requirements)?;
                chunks_by_region.entry(optimal_region)
                    .or_insert_with(Vec::new)
                    .push(chunk_id.clone());
            }
        }

        // Create steps for each region
        let mut steps = Vec::new();
        for (region, chunks) in chunks_by_region {
            let region_nodes = self.get_region_nodes(&region);

            steps.push(RecoveryStep {
                step_id: format!("locality_{:?}", region),
                step_type: RecoveryStepType::DirectTransfer,
                source_nodes: region_nodes.into_iter().take(3).collect(), // Top 3 nodes
                target_chunks: chunks,
                estimated_duration_seconds: 45.0,
                bandwidth_requirement_mbps: 50.0,
                priority: RecoveryPriority::Normal,
                dependencies: Vec::new(),
            });
        }

        Ok(steps)
    }

    /// Generate adaptive bandwidth recovery steps
    fn generate_adaptive_bandwidth_steps(
        &self,
        missing_chunks: &[String],
        available_chunks: &HashMap<String, Vec<NodeLocation>>,
        _requirements: &RecoveryRequirements,
    ) -> Result<Vec<RecoveryStep>> {
        let mut steps = Vec::new();

        // Analyze current network conditions
        let network_capacity = self.calculate_available_network_capacity()?;

        // Adaptive chunking based on available bandwidth
        let optimal_batch_size = self.calculate_optimal_batch_size(network_capacity);

        for (batch_idx, chunk_batch) in missing_chunks.chunks(optimal_batch_size).enumerate() {
            let bandwidth_per_chunk = network_capacity / chunk_batch.len() as f64;

            let mut batch_sources = Vec::new();
            for chunk_id in chunk_batch {
                if let Some(locations) = available_chunks.get(chunk_id) {
                    let source = self.select_bandwidth_optimal_source(locations, bandwidth_per_chunk)?;
                    batch_sources.push(source.node_id.clone());
                }
            }

            steps.push(RecoveryStep {
                step_id: format!("adaptive_batch_{}", batch_idx),
                step_type: RecoveryStepType::ParallelTransfer,
                source_nodes: batch_sources,
                target_chunks: chunk_batch.to_vec(),
                estimated_duration_seconds: 60.0 / (network_capacity / 100.0), // Scale with capacity
                bandwidth_requirement_mbps: network_capacity * 0.8, // Use 80% of capacity
                priority: RecoveryPriority::Normal,
                dependencies: Vec::new(),
            });
        }

        Ok(steps)
    }

    /// Generate basic recovery steps (fallback)
    fn generate_basic_recovery_steps(
        &self,
        missing_chunks: &[String],
        available_chunks: &HashMap<String, Vec<NodeLocation>>,
        _requirements: &RecoveryRequirements,
    ) -> Result<Vec<RecoveryStep>> {
        let mut steps = Vec::new();

        for (idx, chunk_id) in missing_chunks.iter().enumerate() {
            if let Some(locations) = available_chunks.get(chunk_id) {
                let source = self.select_best_source(locations)?;

                steps.push(RecoveryStep {
                    step_id: format!("basic_recovery_{}", idx),
                    step_type: RecoveryStepType::DirectTransfer,
                    source_nodes: vec![source.node_id.clone()],
                    target_chunks: vec![chunk_id.clone()],
                    estimated_duration_seconds: 30.0,
                    bandwidth_requirement_mbps: 25.0,
                    priority: RecoveryPriority::Normal,
                    dependencies: Vec::new(),
                });
            }
        }

        Ok(steps)
    }

    /// Select best source node from available locations
    fn select_best_source<'a>(&self, locations: &'a [NodeLocation]) -> Result<&'a NodeLocation> {
        let mut best_location = &locations[0];
        let mut best_score = 0.0;

        for location in locations {
            let score = self.calculate_source_score(location)?;
            if score > best_score {
                best_score = score;
                best_location = location;
            }
        }

        Ok(best_location)
    }

    /// Calculate source quality score
    fn calculate_source_score(&self, location: &NodeLocation) -> Result<f64> {
        let node_info = self.network_topology.nodes.get(&location.node_id)
            .ok_or_else(|| anyhow::anyhow!("Node info not found"))?;

        let bandwidth_score = (node_info.available_bandwidth_mbps / 100.0).min(1.0);
        let load_score = 1.0 - node_info.load_factor;
        let quality_score = match node_info.connection_quality {
            ConnectionQuality::Excellent => 1.0,
            ConnectionQuality::Good => 0.8,
            ConnectionQuality::Average => 0.6,
            ConnectionQuality::Poor => 0.3,
        };

        Ok(bandwidth_score * 0.4 + load_score * 0.3 + quality_score * 0.3)
    }

    /// Calculate available network capacity
    fn calculate_available_network_capacity(&self) -> Result<f64> {
        let total_capacity: f64 = self.network_topology.nodes
            .values()
            .map(|node| node.available_bandwidth_mbps)
            .sum();

        let avg_utilization: f64 = self.network_topology.nodes
            .values()
            .map(|node| node.load_factor)
            .sum::<f64>() / self.network_topology.nodes.len() as f64;

        Ok(total_capacity * (1.0 - avg_utilization))
    }

    /// Calculate optimal batch size for current network conditions
    fn calculate_optimal_batch_size(&self, available_bandwidth: f64) -> usize {
        // Adaptive batch sizing based on bandwidth
        if available_bandwidth > 500.0 {
            8 // High bandwidth - large batches
        } else if available_bandwidth > 200.0 {
            4 // Medium bandwidth
        } else if available_bandwidth > 50.0 {
            2 // Low bandwidth
        } else {
            1 // Very low bandwidth - sequential
        }
    }

    /// Select bandwidth-optimal source
    fn select_bandwidth_optimal_source<'a>(
        &self,
        locations: &'a [NodeLocation],
        required_bandwidth: f64,
    ) -> Result<&'a NodeLocation> {
        let mut best_location = &locations[0];
        let mut best_bandwidth = 0.0;

        for location in locations {
            if let Some(node_info) = self.network_topology.nodes.get(&location.node_id) {
                if node_info.available_bandwidth_mbps >= required_bandwidth &&
                   node_info.available_bandwidth_mbps > best_bandwidth {
                    best_bandwidth = node_info.available_bandwidth_mbps;
                    best_location = location;
                }
            }
        }

        Ok(best_location)
    }

    /// Find optimal source region for chunk recovery
    fn find_optimal_source_region(
        &self,
        locations: &[NodeLocation],
        requirements: &RecoveryRequirements,
    ) -> Result<GeographicRegion> {
        // If client region is specified, prefer that
        if let Some(client_region) = &requirements.client_region {
            if locations.iter().any(|loc| loc.region == *client_region) {
                return Ok(client_region.clone());
            }
        }

        // Otherwise, select region with best connectivity
        let mut region_scores: HashMap<GeographicRegion, f64> = HashMap::new();

        for location in locations {
            let score = region_scores.entry(location.region.clone()).or_insert(0.0);
            *score += self.calculate_source_score(location)?;
        }

        let best_region = region_scores
            .into_iter()
            .max_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal))
            .map(|(region, _)| region)
            .unwrap_or(GeographicRegion::NorthAmerica);

        Ok(best_region)
    }

    /// Get nodes in a specific region
    fn get_region_nodes(&self, region: &GeographicRegion) -> Vec<String> {
        self.network_topology.nodes
            .values()
            .filter(|node| node.region == *region)
            .map(|node| node.node_id.clone())
            .collect()
    }

    /// Generate fallback plans
    fn generate_fallback_plans(
        &self,
        missing_chunks: &[String],
        available_chunks: &HashMap<String, Vec<NodeLocation>>,
        requirements: &RecoveryRequirements,
    ) -> Result<Vec<FallbackPlan>> {
        let mut fallback_plans = Vec::new();

        // Fallback 1: Sequential recovery if parallel fails
        fallback_plans.push(FallbackPlan {
            fallback_id: "sequential_fallback".to_string(),
            trigger_conditions: vec!["parallel_transfer_failed".to_string()],
            alternative_steps: self.generate_basic_recovery_steps(
                missing_chunks,
                available_chunks,
                requirements,
            )?,
            performance_impact: 0.5, // 50% slower
        });

        // Fallback 2: High-latency sources if primary sources fail
        fallback_plans.push(FallbackPlan {
            fallback_id: "high_latency_fallback".to_string(),
            trigger_conditions: vec!["primary_sources_unavailable".to_string()],
            alternative_steps: self.generate_high_latency_recovery_steps(
                missing_chunks,
                available_chunks,
            )?,
            performance_impact: 1.5, // 150% slower
        });

        Ok(fallback_plans)
    }

    /// Generate high-latency recovery steps
    fn generate_high_latency_recovery_steps(
        &self,
        missing_chunks: &[String],
        available_chunks: &HashMap<String, Vec<NodeLocation>>,
    ) -> Result<Vec<RecoveryStep>> {
        // Implementation would select slower but more reliable sources
        self.generate_basic_recovery_steps(missing_chunks, available_chunks, &RecoveryRequirements::default())
    }

    /// Calculate recovery estimates
    fn calculate_recovery_estimates(
        &self,
        steps: &[RecoveryStep],
    ) -> Result<(f64, f64, f64)> {
        let total_time = steps.iter()
            .map(|step| step.estimated_duration_seconds)
            .sum::<f64>();

        let total_bandwidth = steps.iter()
            .map(|step| step.bandwidth_requirement_mbps * step.estimated_duration_seconds / 8.0) // Convert to MB
            .sum::<f64>();

        let total_cost = total_bandwidth * 0.02; // Estimate $0.02 per GB

        Ok((total_time, total_bandwidth, total_cost))
    }

    /// Execute recovery plan
    pub async fn execute_recovery_plan(&mut self, plan: &RecoveryPlan) -> Result<RecoveryExecutionResult> {
        let start_time = crate::SerializableInstant::now();
        let mut executed_steps = Vec::new();
        let mut total_bytes_recovered = 0u64;

        // Execute steps according to dependencies
        for step in &plan.recovery_steps {
            let step_result = self.execute_recovery_step(step).await?;
            total_bytes_recovered += step_result.bytes_transferred;
            executed_steps.push(step_result);
        }

        let execution_time = start_time.elapsed().as_secs_f64();

        // Update metrics
        self.performance_metrics.total_recoveries += 1;
        self.performance_metrics.successful_recoveries += 1;
        self.performance_metrics.total_bytes_recovered += total_bytes_recovered;

        // Update average recovery time
        let total_successful = self.performance_metrics.successful_recoveries as f64;
        self.performance_metrics.average_recovery_time_seconds =
            (self.performance_metrics.average_recovery_time_seconds * (total_successful - 1.0) + execution_time) / total_successful;

        Ok(RecoveryExecutionResult {
            plan_id: plan.plan_id.clone(),
            success: true,
            execution_time_seconds: execution_time,
            bytes_recovered: total_bytes_recovered,
            bandwidth_efficiency: self.calculate_bandwidth_efficiency(plan, execution_time),
            executed_steps,
            error_message: None,
        })
    }

    /// Execute single recovery step
    async fn execute_recovery_step(&self, step: &RecoveryStep) -> Result<StepExecutionResult> {
        // Simulate step execution
        let chunk_size = 1024 * 1024; // 1MB per chunk
        let bytes_per_chunk = chunk_size;
        let total_bytes = step.target_chunks.len() as u64 * bytes_per_chunk;

        // Simulate transfer time
        tokio::time::sleep(tokio::time::Duration::from_millis(
            (step.estimated_duration_seconds * 100.0) as u64
        )).await;

        Ok(StepExecutionResult {
            step_id: step.step_id.clone(),
            success: true,
            bytes_transferred: total_bytes,
            actual_duration_seconds: step.estimated_duration_seconds,
            bandwidth_used_mbps: step.bandwidth_requirement_mbps,
            error_message: None,
        })
    }

    /// Calculate bandwidth efficiency
    fn calculate_bandwidth_efficiency(&self, plan: &RecoveryPlan, actual_time: f64) -> f64 {
        let theoretical_optimal = plan.estimated_bandwidth_usage_mb / plan.estimated_time_seconds;
        let actual_efficiency = plan.estimated_bandwidth_usage_mb / actual_time;

        (actual_efficiency / theoretical_optimal).min(1.0)
    }
}

// Supporting types and implementations

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NodeLocation {
    pub node_id: String,
    pub region: GeographicRegion,
    pub availability_score: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RecoveryRequirements {
    pub time_critical: bool,
    pub cost_sensitive: bool,
    pub client_region: Option<GeographicRegion>,
    pub max_bandwidth_mbps: Option<f64>,
    pub preferred_quality: ConnectionQuality,
}

impl Default for RecoveryRequirements {
    fn default() -> Self {
        Self {
            time_critical: false,
            cost_sensitive: false,
            client_region: None,
            max_bandwidth_mbps: None,
            preferred_quality: ConnectionQuality::Good,
        }
    }
}

#[derive(Debug, Default)]
struct SourceAnalysis {
    pub total_sources: usize,
    pub sources_by_region: HashMap<GeographicRegion, usize>,
    pub bandwidth_distribution: BandwidthDistribution,
    pub load_distribution: LoadDistribution,
}

#[derive(Debug, Default)]
struct BandwidthDistribution {
    pub high_bandwidth_sources: usize,   // >100 Mbps
    pub medium_bandwidth_sources: usize, // 50-100 Mbps
    pub low_bandwidth_sources: usize,    // <50 Mbps
}

impl BandwidthDistribution {
    fn update(&mut self, bandwidth: f64) {
        if bandwidth > 100.0 {
            self.high_bandwidth_sources += 1;
        } else if bandwidth > 50.0 {
            self.medium_bandwidth_sources += 1;
        } else {
            self.low_bandwidth_sources += 1;
        }
    }
}

#[derive(Debug, Default)]
struct LoadDistribution {
    pub low_load_sources: usize,    // <30% load
    pub medium_load_sources: usize, // 30-70% load
    pub high_load_sources: usize,   // >70% load
}

impl LoadDistribution {
    fn update(&mut self, load: f64) {
        if load < 0.3 {
            self.low_load_sources += 1;
        } else if load < 0.7 {
            self.medium_load_sources += 1;
        } else {
            self.high_load_sources += 1;
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RecoveryExecutionResult {
    pub plan_id: String,
    pub success: bool,
    pub execution_time_seconds: f64,
    pub bytes_recovered: u64,
    pub bandwidth_efficiency: f64,
    pub executed_steps: Vec<StepExecutionResult>,
    pub error_message: Option<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StepExecutionResult {
    pub step_id: String,
    pub success: bool,
    pub bytes_transferred: u64,
    pub actual_duration_seconds: f64,
    pub bandwidth_used_mbps: f64,
    pub error_message: Option<String>,
}

// Placeholder implementations for missing methods
impl RecoveryOptimizer {
    fn generate_progressive_recovery_steps(
        &self,
        missing_chunks: &[String],
        available_chunks: &HashMap<String, Vec<NodeLocation>>,
        requirements: &RecoveryRequirements,
    ) -> Result<Vec<RecoveryStep>> {
        // Implementation would prioritize chunks by importance
        self.generate_basic_recovery_steps(missing_chunks, available_chunks, requirements)
    }

    fn generate_load_balanced_steps(
        &self,
        missing_chunks: &[String],
        available_chunks: &HashMap<String, Vec<NodeLocation>>,
        requirements: &RecoveryRequirements,
    ) -> Result<Vec<RecoveryStep>> {
        // Implementation would distribute load evenly across nodes
        self.generate_basic_recovery_steps(missing_chunks, available_chunks, requirements)
    }
}

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

    #[test]
    fn test_recovery_optimizer_creation() {
        let optimizer = RecoveryOptimizer::new();
        assert!(!optimizer.optimization_strategies.is_empty());
        assert_eq!(optimizer.algorithms.max_parallel_streams, 8);
    }

    #[test]
    fn test_bandwidth_distribution() {
        let mut dist = BandwidthDistribution::default();

        dist.update(150.0); // High
        dist.update(75.0);  // Medium
        dist.update(25.0);  // Low

        assert_eq!(dist.high_bandwidth_sources, 1);
        assert_eq!(dist.medium_bandwidth_sources, 1);
        assert_eq!(dist.low_bandwidth_sources, 1);
    }

    #[test]
    fn test_optimal_batch_size_calculation() {
        let optimizer = RecoveryOptimizer::new();

        assert_eq!(optimizer.calculate_optimal_batch_size(600.0), 8);
        assert_eq!(optimizer.calculate_optimal_batch_size(300.0), 4);
        assert_eq!(optimizer.calculate_optimal_batch_size(100.0), 2);
        assert_eq!(optimizer.calculate_optimal_batch_size(30.0), 1);
    }

    #[tokio::test]
    async fn test_recovery_plan_creation() {
        let optimizer = RecoveryOptimizer::new();
        let missing_chunks = vec!["chunk1".to_string(), "chunk2".to_string()];
        let mut available_chunks = HashMap::new();

        available_chunks.insert("chunk1".to_string(), vec![
            NodeLocation {
                node_id: "node1".to_string(),
                region: GeographicRegion::NorthAmerica,
                availability_score: 0.9,
            }
        ]);

        let requirements = RecoveryRequirements::default();

        let plan = optimizer.create_recovery_plan(
            &missing_chunks,
            &available_chunks,
            requirements,
        ).unwrap();

        assert!(!plan.plan_id.is_empty());
        assert!(!plan.recovery_steps.is_empty());
        assert!(plan.estimated_time_seconds > 0.0);
    }
}