anya-core 1.2.0

//! Performance Optimization Suite for Production Workloads
//! 
//! Comprehensive performance analysis and optimization tools
//! for Layer2 protocols and Bitcoin operations.

use std::collections::HashMap;
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::time::sleep;
use serde::{Deserialize, Serialize};

/// Performance metrics collection and analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    pub throughput_tps: f64,
    pub latency_ms: Vec<u64>,
    pub memory_usage_mb: u64,
    pub cpu_usage_percent: f64,
    pub error_rate_percent: f64,
    pub timestamp: u64,
}

/// Performance optimization recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationRecommendation {
    pub category: String,
    pub priority: String, // "Critical", "High", "Medium", "Low"
    pub description: String,
    pub implementation: String,
    pub expected_improvement: String,
}

/// Performance benchmark configuration
#[derive(Debug, Clone)]
pub struct BenchmarkConfig {
    pub duration_seconds: u64,
    pub concurrent_users: u32,
    pub transaction_rate: u32,
    pub memory_limit_mb: u64,
    pub cpu_limit_percent: f64,
}

impl Default for BenchmarkConfig {
    fn default() -> Self {
        Self {
            duration_seconds: 300, // 5 minutes
            concurrent_users: 100,
            transaction_rate: 1000, // TPS
            memory_limit_mb: 4096,  // 4GB
            cpu_limit_percent: 80.0,
        }
    }
}

/// Performance optimizer for production workloads
pub struct PerformanceOptimizer {
    config: BenchmarkConfig,
    metrics_history: Vec<PerformanceMetrics>,
    recommendations: Vec<OptimizationRecommendation>,
}

impl PerformanceOptimizer {
    pub fn new(config: BenchmarkConfig) -> Self {
        Self {
            config,
            metrics_history: Vec::new(),
            recommendations: Vec::new(),
        }
    }

    /// Run comprehensive performance benchmark
    pub async fn run_performance_benchmark(&mut self) -> Result<PerformanceMetrics, Box<dyn std::error::Error + Send + Sync>> {
        println!("🚀 Starting Performance Benchmark for Production Workloads");
        println!("==========================================================");
        
        let start_time = Instant::now();
        let mut latencies = Vec::new();
        let mut transaction_count = 0u64;
        let mut error_count = 0u64;

        // Simulate high-load scenarios
        let handles = (0..self.config.concurrent_users)
            .map(|user_id| {
                let rate = self.config.transaction_rate;
                let duration = self.config.duration_seconds;
                
                tokio::spawn(async move {
                    let mut user_latencies = Vec::new();
                    let mut user_transactions = 0u64;
                    let mut user_errors = 0u64;
                    
                    let end_time = Instant::now() + Duration::from_secs(duration);
                    
                    while Instant::now() < end_time {
                        let tx_start = Instant::now();
                        
                        // Simulate transaction processing
                        let result = simulate_transaction_processing(user_id).await;
                        
                        let tx_duration = tx_start.elapsed();
                        user_latencies.push(tx_duration.as_millis() as u64);
                        user_transactions += 1;
                        
                        if result.is_err() {
                            user_errors += 1;
                        }
                        
                        // Rate limiting
                        let sleep_duration = Duration::from_millis(1000 / rate as u64);
                        sleep(sleep_duration).await;
                    }
                    
                    (user_latencies, user_transactions, user_errors)
                })
            })
            .collect::<Vec<_>>();

        // Collect results from all concurrent users
        for handle in handles {
            let (user_latencies, user_txs, user_errors) = handle.await?;
            latencies.extend(user_latencies);
            transaction_count += user_txs;
            error_count += user_errors;
        }

        let total_duration = start_time.elapsed();
        let throughput = transaction_count as f64 / total_duration.as_secs_f64();
        let error_rate = (error_count as f64 / transaction_count as f64) * 100.0;
        
        // Calculate memory and CPU usage (simulated)
        let memory_usage = self.estimate_memory_usage();
        let cpu_usage = self.estimate_cpu_usage(&latencies);

        let metrics = PerformanceMetrics {
            throughput_tps: throughput,
            latency_ms: latencies,
            memory_usage_mb: memory_usage,
            cpu_usage_percent: cpu_usage,
            error_rate_percent: error_rate,
            timestamp: std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)?
                .as_secs(),
        };

        self.metrics_history.push(metrics.clone());
        
        println!("📊 Performance Benchmark Results:");
        println!("   Throughput: {:.2} TPS", metrics.throughput_tps);
        println!("   Average Latency: {:.2} ms", 
                 metrics.latency_ms.iter().sum::<u64>() as f64 / metrics.latency_ms.len() as f64);
        println!("   Memory Usage: {} MB", metrics.memory_usage_mb);
        println!("   CPU Usage: {:.2}%", metrics.cpu_usage_percent);
        println!("   Error Rate: {:.2}%", metrics.error_rate_percent);

        Ok(metrics)
    }

    /// Analyze performance metrics and generate recommendations
    pub fn analyze_and_optimize(&mut self) -> Vec<OptimizationRecommendation> {
        if let Some(latest_metrics) = self.metrics_history.last() {
            self.recommendations.clear();

            // Throughput optimization
            if latest_metrics.throughput_tps < 500.0 {
                self.recommendations.push(OptimizationRecommendation {
                    category: "Throughput".to_string(),
                    priority: "Critical".to_string(),
                    description: "Low transaction throughput detected".to_string(),
                    implementation: "Enable connection pooling, implement batch processing, optimize database queries".to_string(),
                    expected_improvement: "2-3x throughput increase".to_string(),
                });
            }

            // Latency optimization
            let avg_latency = latest_metrics.latency_ms.iter().sum::<u64>() as f64 / latest_metrics.latency_ms.len() as f64;
            if avg_latency > 100.0 {
                self.recommendations.push(OptimizationRecommendation {
                    category: "Latency".to_string(),
                    priority: "High".to_string(),
                    description: "High latency detected".to_string(),
                    implementation: "Implement caching, optimize cryptographic operations, use async processing".to_string(),
                    expected_improvement: "50-70% latency reduction".to_string(),
                });
            }

            // Memory optimization
            if latest_metrics.memory_usage_mb > self.config.memory_limit_mb {
                self.recommendations.push(OptimizationRecommendation {
                    category: "Memory".to_string(),
                    priority: "High".to_string(),
                    description: "Memory usage exceeds configured limits".to_string(),
                    implementation: "Implement memory pooling, optimize data structures, add garbage collection tuning".to_string(),
                    expected_improvement: "30-40% memory reduction".to_string(),
                });
            }

            // CPU optimization
            if latest_metrics.cpu_usage_percent > self.config.cpu_limit_percent {
                self.recommendations.push(OptimizationRecommendation {
                    category: "CPU".to_string(),
                    priority: "Medium".to_string(),
                    description: "High CPU usage detected".to_string(),
                    implementation: "Implement SIMD optimizations, use hardware acceleration, optimize hot paths".to_string(),
                    expected_improvement: "20-30% CPU usage reduction".to_string(),
                });
            }

            // Error rate optimization
            if latest_metrics.error_rate_percent > 1.0 {
                self.recommendations.push(OptimizationRecommendation {
                    category: "Reliability".to_string(),
                    priority: "Critical".to_string(),
                    description: "High error rate detected".to_string(),
                    implementation: "Implement circuit breakers, add retry mechanisms, improve error handling".to_string(),
                    expected_improvement: "90% error reduction".to_string(),
                });
            }
        }

        self.recommendations.clone()
    }

    /// Generate performance optimization report
    pub fn generate_optimization_report(&self) -> String {
        let mut report = String::new();
        
        report.push_str("# Performance Optimization Report\n\n");
        report.push_str(&format!("Generated: {}\n\n", chrono::Utc::now().format("%Y-%m-%d %H:%M:%S UTC")));
        
        if let Some(latest_metrics) = self.metrics_history.last() {
            report.push_str("## Current Performance Metrics\n\n");
            report.push_str(&format!("- **Throughput**: {:.2} TPS\n", latest_metrics.throughput_tps));
            
            let avg_latency = latest_metrics.latency_ms.iter().sum::<u64>() as f64 / latest_metrics.latency_ms.len() as f64;
            report.push_str(&format!("- **Average Latency**: {:.2} ms\n", avg_latency));
            
            let p95_latency = self.calculate_percentile(&latest_metrics.latency_ms, 95.0);
            report.push_str(&format!("- **P95 Latency**: {} ms\n", p95_latency));
            
            report.push_str(&format!("- **Memory Usage**: {} MB\n", latest_metrics.memory_usage_mb));
            report.push_str(&format!("- **CPU Usage**: {:.2}%\n", latest_metrics.cpu_usage_percent));
            report.push_str(&format!("- **Error Rate**: {:.2}%\n\n", latest_metrics.error_rate_percent));
        }
        
        report.push_str("## Optimization Recommendations\n\n");
        
        for (i, rec) in self.recommendations.iter().enumerate() {
            report.push_str(&format!("### {}: {} (Priority: {})\n\n", i + 1, rec.category, rec.priority));
            report.push_str(&format!("**Issue**: {}\n\n", rec.description));
            report.push_str(&format!("**Solution**: {}\n\n", rec.implementation));
            report.push_str(&format!("**Expected Improvement**: {}\n\n", rec.expected_improvement));
        }
        
        report.push_str("## Production Deployment Checklist\n\n");
        report.push_str("- [ ] Enable monitoring and alerting\n");
        report.push_str("- [ ] Configure auto-scaling policies\n");
        report.push_str("- [ ] Set up load balancing\n");
        report.push_str("- [ ] Implement circuit breakers\n");
        report.push_str("- [ ] Configure backup and disaster recovery\n");
        report.push_str("- [ ] Security hardening completed\n");
        report.push_str("- [ ] Performance benchmarks validated\n");
        
        report
    }

    fn estimate_memory_usage(&self) -> u64 {
        // Simulate memory usage calculation based on concurrent users and transaction rate
        let base_memory = 512; // MB
        let per_user_memory = 10; // MB
        let transaction_memory = (self.config.transaction_rate / 100) as u64; // MB
        
        base_memory + (self.config.concurrent_users as u64 * per_user_memory) + transaction_memory
    }

    fn estimate_cpu_usage(&self, latencies: &[u64]) -> f64 {
        // Simulate CPU usage based on latency patterns
        let avg_latency = latencies.iter().sum::<u64>() as f64 / latencies.len() as f64;
        let base_cpu = 20.0;
        let latency_factor = (avg_latency / 10.0).min(50.0);
        let concurrency_factor = (self.config.concurrent_users as f64 / 10.0).min(30.0);
        
        (base_cpu + latency_factor + concurrency_factor).min(100.0)
    }

    fn calculate_percentile(&self, values: &[u64], percentile: f64) -> u64 {
        if values.is_empty() {
            return 0;
        }
        
        let mut sorted_values = values.to_vec();
        sorted_values.sort();
        
        let index = (percentile / 100.0 * (sorted_values.len() - 1) as f64).round() as usize;
        sorted_values[index.min(sorted_values.len() - 1)]
    }
}

/// Simulate transaction processing for benchmarking
async fn simulate_transaction_processing(user_id: u32) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
    // Simulate variable processing time
    let processing_time = match user_id % 10 {
        0..=6 => Duration::from_millis(10 + (user_id % 50) as u64), // Normal case
        7..=8 => Duration::from_millis(50 + (user_id % 100) as u64), // Slower case
        _ => Duration::from_millis(150), // Edge case
    };
    
    sleep(processing_time).await;
    
    // Simulate occasional errors
    if user_id % 100 == 0 {
        return Err("Simulated transaction error".into());
    }
    
    Ok(())
}

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

    #[tokio::test]
    async fn test_performance_optimizer() {
        let config = BenchmarkConfig {
            duration_seconds: 5, // Short test
            concurrent_users: 10,
            transaction_rate: 100,
            memory_limit_mb: 1024,
            cpu_limit_percent: 70.0,
        };
        
        let mut optimizer = PerformanceOptimizer::new(config);
        let metrics = optimizer.run_performance_benchmark().await.unwrap();
        
        assert!(metrics.throughput_tps > 0.0);
        assert!(!metrics.latency_ms.is_empty());
        assert!(metrics.memory_usage_mb > 0);
        
        let recommendations = optimizer.analyze_and_optimize();
        println!("Generated {} recommendations", recommendations.len());
        
        let report = optimizer.generate_optimization_report();
        assert!(report.contains("Performance Optimization Report"));
    }

    #[test]
    fn test_percentile_calculation() {
        let optimizer = PerformanceOptimizer::new(BenchmarkConfig::default());
        let values = vec![10, 20, 30, 40, 50, 60, 70, 80, 90, 100];
        
        assert_eq!(optimizer.calculate_percentile(&values, 50.0), 50);
        assert_eq!(optimizer.calculate_percentile(&values, 95.0), 100);
        assert_eq!(optimizer.calculate_percentile(&values, 0.0), 10);
    }
}