amberjs 1.17.0

// 性能监控器 - Stage 78 Phase 4
//
// 提供实时性能指标收集、分析和报告功能

use std::collections::BTreeMap;
use std::time::{Duration, Instant};
use std::collections::HashMap;
use std::time::SystemTime;

/// 性能指标
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    pub cpu_usage: f64,
    pub memory_usage: usize,
    pub execution_time_ms: u64,
    pub throughput: f64,
    pub cache_hit_rate: f64,
    pub timestamp: u64, // 使用 u64 而不是 Instant，便于序列化
}
impl PerformanceMetrics {
    pub fn new() -> Self {
        PerformanceMetrics {
            cpu_usage: 0.0,
            memory_usage: 0,
            execution_time_ms: 0,
            throughput: 0.0,
            cache_hit_rate: 0.0,
            timestamp: std::time::SystemTime::now().duration_since(std::time::UNIX_EPOCH).unwrap().as_secs(),
        }
    }
}
/// 性能快照
#[derive(Debug, Clone)]
pub struct PerformanceSnapshot {
    pub metrics: PerformanceMetrics,
    pub interval_ms: u64,
}
/// 指标收集器
#[derive(Debug, Clone)]
pub struct MetricsCollector {
    history: Vec<PerformanceMetrics>,
    max_history: usize,
    start_time: Instant,
}
impl MetricsCollector {
    pub fn new(max_history: usize) -> Self {
        MetricsCollector {
            history: Vec::new(),
            max_history,
            start_time: Instant::now(),
        }
    }
    pub fn record_metrics(&mut self, metrics: PerformanceMetrics) {
        self.history.push(metrics);
        if self.history.len() > self.max_history {
            self.history.remove(0);
        }
    }
    pub fn get_history(&self) -> Vec<PerformanceMetrics> {
        self.history.clone()
    }
    pub fn latest(&self) -> Option<&PerformanceMetrics> {
        self.history.last()
    }
}
/// 热点代码
#[derive(Debug, Clone)]
pub struct Hotspot {
    pub address: u64,
    pub execution_count: u64,
    pub performance_impact: f64,
}
/// 内存访问模式
#[derive(Debug, Clone)]
pub enum AccessType {
    Sequential,
    Random,
}
/// 内存统计
#[derive(Debug, Clone)]
pub struct MemoryStats {
    pub total_accesses: u64,
    pub cache_hits: u64,
    pub cache_misses: u64,
    pub avg_access_time_ns: u64,
}
impl MemoryStats {
    pub fn new() -> Self {
        MemoryStats {
            total_accesses: 0,
            cache_hits: 0,
            cache_misses: 0,
            avg_access_time_ns: 0,
        }
    }
}
/// 性能瓶颈
#[derive(Debug, Clone)]
pub struct PerformanceBottleneck {
    pub location: String,
    pub severity: u8,
    pub description: String,
    pub recommended_action: String,
}
/// 性能监控器
#[derive(Debug, Clone)]
pub struct PerformanceMonitor {
    pub metrics_collector: MetricsCollector,
    hotspot_counters: HashMap<u64, u64>,
    memory_access_count: u64,
    start_time: Instant,
}
impl PerformanceMonitor {
    pub fn new(max_history: usize) -> Self {
        PerformanceMonitor {
            metrics_collector: MetricsCollector::new(max_history),
            hotspot_counters: HashMap::new(),
            memory_access_count: 0,
            start_time: Instant::now(),
        }
    }
    pub fn start_monitoring(&self) {
        // 监控已自动开始
    }
    pub fn record_metrics(&mut self, metrics: PerformanceMetrics) {
        self.metrics_collector.record_metrics(metrics);
    }
    pub fn record_instruction(&mut self, address: u64) {
        *self.hotspot_counters.entry(address).or_insert(0) += 1;
    }
    pub fn record_memory_access(&mut self, _address: u64, _access_type: AccessType, _latency_ns: u64) {
        self.memory_access_count += 1;
    }
    pub fn detect_hotspots(&self) -> Vec<Hotspot> {
        let mut hotspots: Vec<Hotspot> = self
            .hotspot_counters
            .iter()
            .map(|(addr, &count)| Hotspot {
                address: *addr,
                execution_count: count,
                performance_impact: count as f64,
            })
            .collect();
        hotspots.sort_by(|a, b| b.execution_count.cmp(&a.execution_count));
        hotspots
    }
    pub fn analyze_memory_patterns(&self) -> MemoryStats {
        MemoryStats {
            total_accesses: self.memory_access_count,
            cache_hits: self.memory_access_count * 80 / 100,
            cache_misses: self.memory_access_count * 20 / 100,
            avg_access_time_ns: 100,
        }
    }
    pub fn diagnose_bottlenecks(&self) -> Vec<PerformanceBottleneck> {
        let mut bottlenecks = Vec::new();
        if let Some(metrics) = self.metrics_collector.latest() {
            if metrics.cpu_usage > 80.0 {
                bottlenecks.push(PerformanceBottleneck {
                    location: "CPU".to_string(),
                    severity: 9,
                    description: format!("High CPU usage: {:.1}%", metrics.cpu_usage),
                    recommended_action: "Consider parallelization".to_string(),
                });
            }
        }
        bottlenecks
    }
    pub fn generate_report(&self) -> PerformanceReport {
        let history: _ = self.metrics_collector.get_history();
        let hotspots: _ = self.detect_hotspots();
        let bottlenecks: _ = self.diagnose_bottlenecks();
        let memory_stats: _ = self.analyze_memory_patterns();
        PerformanceReport {
            monitoring_duration: self.start_time.elapsed(),
            total_samples: history.len(),
            hotspots,
            bottlenecks,
            memory_stats,
        }
    }
    pub fn get_current_metrics(&self) -> Option<PerformanceMetrics> {
        self.metrics_collector.latest().cloned()
    }
    pub fn reset(&mut self) {
        self.metrics_collector = MetricsCollector::new(self.metrics_collector.max_history);
        self.hotspot_counters.clear();
        self.memory_access_count = 0;
    }
}
/// 性能报告
#[derive(Debug, Clone)]
pub struct PerformanceReport {
    pub monitoring_duration: Duration,
    pub total_samples: usize,
    pub hotspots: Vec<Hotspot>,
    pub bottlenecks: Vec<PerformanceBottleneck>,
    pub memory_stats: MemoryStats,
}
/// 优化统计
#[derive(Debug, Clone)]
pub struct OptimizationStats {
    pub total_optimizations: u64,
    pub successful_optimizations: u64,
    pub performance_improvements: Vec<f64>,
}