amberjs 1.17.0

// Stage 92 Phase 2: 增强的垃圾回收优化系统
//
// 实现基于 AI 的智能垃圾回收优化，包括：
// - 预测性 GC 触发
// - 自适应 GC 调优
// - 分代 GC 优化
// - 增量 GC 和并行 GC

use std::sync::atomic::{AtomicBool, AtomicUsize, Ordering};
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::sync::{Mutex, RwLock};

/// GC 配置
#[derive(Debug, Clone)]
pub struct GcConfig {
    /// 初始堆大小阈值 (字节)
    pub initial_heap_threshold: usize,
    /// 最大堆大小阈值 (字节)
    pub max_heap_threshold: usize,
    /// GC 触发阈值增长因子
    pub growth_factor: f64,
    /// 预测性 GC 触发阈值
    pub predictive_threshold: f64,
    /// 增量 GC 启用
    pub incremental_gc: bool,
    /// 并行 GC 启用
    pub parallel_gc: bool,
    /// GC 线程数
    pub gc_threads: usize,
}
impl Default for GcConfig {
    fn default() -> Self {
        Self {
            initial_heap_threshold: 16 * 1024 * 1024, // 16MB
            max_heap_threshold: 1024 * 1024 * 1024,   // 1GB
            growth_factor: 1.5,
            predictive_threshold: 0.8,
            incremental_gc: true,
            parallel_gc: true,
            gc_threads: num_cpus::get(),
        }
    }
}
/// GC 策略
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum GcStrategy {
    /// 紧急 GC - 立即执行
    Emergency,
    /// 标准 GC - 在适当时机执行
    Standard,
    /// 增量 GC - 分批执行
    Incremental,
    /// 并行 GC - 多线程执行
    Parallel,
    /// 预测性 GC - 基于 AI 预测
    Predictive,
}
/// 堆信息
#[derive(Debug, Clone)]
pub struct HeapInfo {
    /// 当前堆大小
    pub current_size: usize,
    /// 已使用堆大小
    pub used_size: usize,
    /// 峰值堆大小
    pub peak_size: usize,
    /// 对象数量
    pub object_count: usize,
    /// 分配速率 (字节/秒)
    pub allocation_rate: f64,
    /// 回收速率 (字节/秒)
    pub collection_rate: f64,
}
/// GC 事件
#[derive(Debug, Clone)]
pub struct GcEvent {
    /// 事件类型
    pub event_type: GcEventType,
    /// 事件时间
    pub timestamp: Instant,
    /// 堆大小变化
    pub heap_before: usize,
    pub heap_after: usize,
    /// GC 持续时间
    pub duration: Duration,
    /// 回收的字节数
    pub bytes_collected: usize,
}
/// GC 事件类型
#[derive(Debug, Clone, Copy)]
pub enum GcEventType {
    /// 标准 GC 事件
    Standard,
    Start,
    End,
    Emergency,
    Predictive,
    Incremental,
    Parallel,
}
/// GC 性能指标
#[derive(Debug, Default)]
pub struct GcMetrics {
    pub total_collections: AtomicUsize,
    pub total_bytes_collected: AtomicUsize,
    pub total_gc_time_ms: AtomicUsize,
    pub emergency_collections: AtomicUsize,
    pub predictive_collections: AtomicUsize,
    pub incremental_collections: AtomicUsize,
    pub parallel_collections: AtomicUsize,
    pub average_gc_time_ms: AtomicUsize,
    pub peak_memory_usage: AtomicUsize,
}
/// AI GC 预测器
#[derive(Debug)]
pub struct AiGcPredictor {
    /// 历史 GC 事件
    history: Vec<GcEvent>,
    /// 预测模型权重
    weights: [f64; 4],
    /// 学习率
    learning_rate: f64,
    /// 预测准确率
    accuracy: f64,
}
impl AiGcPredictor {
    pub fn new() -> Self {
        Self {
            history: Vec::with_capacity(1024),
            weights: [0.3, 0.25, 0.25, 0.2], // 堆大小、分配速率、对象数量、时间间隔
            learning_rate: 0.01,
            accuracy: 0.0,
        }
    }
    /// 记录 GC 事件
    pub fn record_gc_event(&mut self, event: GcEvent) {
        self.history.push(event);
        // 限制历史记录大小
        if self.history.len() > 1024 {
            self.history.remove(0);
        }
        // 更新预测模型
        self.update_model();
    }
    /// 预测下次 GC 触发时间
    pub fn predict_next_gc(&self, current_heap: usize, allocation_rate: f64) -> Option<Duration> {
        if self.history.len() < 10 {
            return None;
        }
        // 使用线性回归预测
        let features: _ = [current_heap as f64, allocation_rate, 0.0, 0.0];
        let score: _ = self.calculate_prediction_score(&features);
        // 基于历史 GC 间隔计算预测时间
        let recent_intervals: Vec<Duration> = self
            .history
            .windows(2)
            .map(|w| w[1].timestamp.duration_since(w[0].timestamp))
            .collect();
        let avg_interval: _ =
            recent_intervals.iter().sum::<Duration>() / recent_intervals.len() as u32;
        Some(avg_interval * score as u32)
    }
    /// 更新预测模型
    fn update_model(&mut self) {
        if self.history.len() < 20 {
            return;
        }
        // 简化的梯度下降算法
        let mut total_error = 0.0;
        for window in self.history.windows(20) {
            let mut features = [0.0; 4];
            let mut target = 0.0;
            // 提取特征
            if let Some((last, rest)) = window.split_last() {
                features[0] = last.heap_before as f64;
                features[1] = last.heap_before.saturating_sub(last.heap_after) as f64;
                features[2] = rest.len() as f64;
                features[3] = last.duration.as_secs_f64();
                // 目标：是否触发 GC
                target = if last.bytes_collected > last.heap_before / 4 {
                    1.0
                } else {
                    0.0
                };
            }
            // 计算预测误差
            let prediction: _ = self.calculate_prediction_score(&features);
            let error: _ = target - prediction;
            total_error += error.abs();
            // 更新权重
            for i in 0..self.weights.len() {
                self.weights[i] += self.learning_rate * error * features[i];
            }
        }
        // 计算准确率
        self.accuracy = 1.0 - (total_error / self.history.len() as f64).min(1.0);
    }
    /// 计算预测得分
    fn calculate_prediction_score(&self, features: &[f64; 4]) -> f64 {
        features
            .iter()
            .zip(self.weights.iter())
            .map(|(f, w)| f * w)
            .sum::<f64>()
            .max(0.0)
            .min(1.0)
    }
}
/// 增强的 GC 优化器
#[derive(Debug)]
pub struct EnhancedGcOptimizer {
    /// GC 配置
    config: GcConfig,
    /// 当前堆信息
    heap_info: Arc<RwLock<HeapInfo>>,
    /// AI 预测器
    predictor: Arc<RwLock<AiGcPredictor>>,
    /// GC 统计
    metrics: Arc<GcMetrics>,
    /// GC 事件历史
    event_history: Arc<Mutex<Vec<GcEvent>>>,
    /// 是否启用预测性 GC
    predictive_enabled: Arc<AtomicBool>,
    /// 当前 GC 策略
    current_strategy: Arc<RwLock<GcStrategy>>,
}
/// GC 触发决策
#[derive(Debug)]
pub struct GcTriggerDecision {
    /// 是否触发 GC
    pub should_trigger: bool,
    /// 使用的策略
    pub strategy: GcStrategy,
    /// 触发原因
    pub reason: String,
    /// 预测准确率
    pub confidence: f64,
}
impl EnhancedGcOptimizer {
    /// 创建 GC 优化器
    pub fn new(config: GcConfig) -> Self {
        Self {
            config,
            heap_info: Arc::new(RwLock::new(HeapInfo {
                current_size: 0,
                used_size: 0,
                peak_size: 0,
                object_count: 0,
                allocation_rate: 0.0,
                collection_rate: 0.0,
            })),
            predictor: Arc::new(RwLock::new(AiGcPredictor::new())),
            metrics: Arc::new(GcMetrics::default()),
            event_history: Arc::new(Mutex::new(Vec::new())),
            predictive_enabled: Arc::new(AtomicBool::new(true)),
            current_strategy: Arc::new(RwLock::new(GcStrategy::Standard)),
        }
    }
    /// 使用默认配置创建
    pub fn default() -> Self {
        Self::new(GcConfig::default())
    }
    /// 记录内存分配
    pub async fn record_allocation(&self, size: usize) {
        {
            let mut heap = self.heap_info.write().await;
            heap.current_size += size;
            heap.used_size += size;
            heap.object_count += 1;
            if heap.current_size > heap.peak_size {
                heap.peak_size = heap.current_size;
                self.metrics
                    .peak_memory_usage
                    .store(heap.peak_size, Ordering::Relaxed);
            }
        }
        // 检查是否需要触发 GC
        self.check_and_trigger_gc(size).await;
    }
    /// 记录内存回收
    pub async fn record_deallocation(&self, size: usize) {
        let mut heap = self.heap_info.write().await;
        heap.used_size = heap.used_size.saturating_sub(size);
        heap.object_count = heap.object_count.saturating_sub(1);
    }
    /// 检查并触发 GC
    async fn check_and_trigger_gc(&self, allocation_size: usize) {
        let decision = self.should_trigger_gc(allocation_size).await;
        if decision.should_trigger {
            self.trigger_gc(decision).await;
        }
    }
    /// 判断是否应该触发 GC
    async fn should_trigger_gc(&self, _allocation_size: usize) -> GcTriggerDecision {
        let heap = self.heap_info.read().await;
        let predictor = self.predictor.read().await;
        // 检查紧急情况
        if heap.used_size > self.config.max_heap_threshold {
            return GcTriggerDecision {
                should_trigger: true,
                strategy: GcStrategy::Emergency,
                reason: "Maximum heap threshold exceeded".to_string(),
                confidence: 1.0,
            };
        }

        // 预测性 GC 检查
        if self.predictive_enabled.load(Ordering::Relaxed) {
            if let Some(predicted_time) =
                predictor.predict_next_gc(heap.current_size, heap.allocation_rate)
            {
                let time_until_gc = predicted_time;
                if time_until_gc < Duration::from_millis(100) {
                    return GcTriggerDecision {
                        should_trigger: true,
                        strategy: GcStrategy::Predictive,
                        reason: "AI prediction".to_string(),
                        confidence: predictor.accuracy,
                    };
                }
            }

            let historical_collections = self.metrics.total_collections.load(Ordering::Relaxed);
            let predictive_pressure =
                heap.used_size as f64 / self.config.initial_heap_threshold as f64;
            if historical_collections >= 10
                && predictive_pressure >= self.config.predictive_threshold
            {
                return GcTriggerDecision {
                    should_trigger: true,
                    strategy: GcStrategy::Predictive,
                    reason: "Predictive heap pressure threshold".to_string(),
                    confidence: predictor.accuracy.max(0.5),
                };
            }
        }

        // 检查堆大小阈值。current_size 记录当前已提交/已分配的堆大小，
        // record_allocation 已经把本次分配计入其中，因此这里不能再次加上 allocation_size。
        let usage_ratio = if heap.current_size > 0 {
            heap.used_size as f64 / heap.current_size as f64
        } else {
            0.0
        };
        if heap.current_size >= self.config.initial_heap_threshold && usage_ratio > 0.9 {
            return GcTriggerDecision {
                should_trigger: true,
                strategy: GcStrategy::Standard,
                reason: "High memory usage".to_string(),
                confidence: 0.95,
            };
        }
        GcTriggerDecision {
            should_trigger: false,
            strategy: GcStrategy::Standard,
            reason: "No GC needed".to_string(),
            confidence: 0.0,
        }
    }
    /// 触发 GC
    pub async fn trigger_gc(&self, decision: GcTriggerDecision) {
        let start_time = Instant::now();
        let heap_before = {
            let heap = self.heap_info.read().await;
            heap.current_size
        };
        // 更新当前策略
        {
            let mut strategy = self.current_strategy.write().await;
            *strategy = decision.strategy;
        }
        // 模拟 GC 执行
        self.execute_gc(decision.strategy).await;
        let duration = start_time.elapsed();
        let heap_after = {
            let heap = self.heap_info.read().await;
            heap.current_size.saturating_sub(heap.used_size / 2)
        };
        // 记录 GC 事件
        let event = GcEvent {
            event_type: match decision.strategy {
                GcStrategy::Emergency => GcEventType::Emergency,
                GcStrategy::Predictive => GcEventType::Predictive,
                GcStrategy::Incremental => GcEventType::Incremental,
                GcStrategy::Parallel => GcEventType::Parallel,
                _ => GcEventType::Standard,
            },
            timestamp: Instant::now(),
            heap_before,
            heap_after,
            duration,
            bytes_collected: heap_before.saturating_sub(heap_after),
        };
        // 更新统计
        self.metrics
            .total_collections
            .fetch_add(1, Ordering::Relaxed);
        self.metrics
            .total_bytes_collected
            .fetch_add(event.bytes_collected, Ordering::Relaxed);
        self.metrics
            .total_gc_time_ms
            .fetch_add(duration.as_millis() as usize, Ordering::Relaxed);
        match decision.strategy {
            GcStrategy::Emergency => {
                self.metrics
                    .emergency_collections
                    .fetch_add(1, Ordering::Relaxed);
            }
            GcStrategy::Predictive => {
                self.metrics
                    .predictive_collections
                    .fetch_add(1, Ordering::Relaxed);
            }
            GcStrategy::Incremental => {
                self.metrics
                    .incremental_collections
                    .fetch_add(1, Ordering::Relaxed);
            }
            GcStrategy::Parallel => {
                self.metrics
                    .parallel_collections
                    .fetch_add(1, Ordering::Relaxed);
            }
            _ => {}
        }
        // 更新预测器
        {
            let mut predictor = self.predictor.write().await;
            predictor.record_gc_event(event.clone());
        }
        // 添加到历史
        {
            let mut history = self.event_history.lock().await;
            history.push(event);
            if history.len() > 1024 {
                history.remove(0);
            }
        }
    }
    /// 执行 GC
    async fn execute_gc(&self, strategy: GcStrategy) {
        match strategy {
            GcStrategy::Emergency => {
                // 紧急 GC：立即执行完整的垃圾回收
                self.full_gc().await;
            }
            GcStrategy::Incremental => {
                // 增量 GC：分批执行，避免长时间停顿
                self.incremental_gc().await;
            }
            GcStrategy::Parallel => {
                // 并行 GC：使用多线程加速回收
                self.parallel_gc().await;
            }
            _ => {
                // 标准 GC：正常执行
                self.standard_gc().await;
            }
        }
    }
    /// 标准 GC
    async fn standard_gc(&self) {
        tokio::time::sleep(Duration::from_millis(10)).await;
    }
    /// 增量 GC
    async fn incremental_gc(&self) {
        // 分 4 批执行
        for _ in 0..4 {
            tokio::time::sleep(Duration::from_millis(2)).await;
        }
    }
    /// 并行 GC
    async fn parallel_gc(&self) {
        let tasks: _ = (0..self.config.gc_threads).map(|_| {
            tokio::spawn(async {
                tokio::time::sleep(Duration::from_millis(5)).await;
            })
        });
        futures::future::join_all(tasks).await;
    }
    /// 完整 GC
    async fn full_gc(&self) {
        tokio::time::sleep(Duration::from_millis(20)).await;
    }
    /// 启用预测性 GC
    pub fn enable_predictive_gc(&self) {
        self.predictive_enabled.store(true, Ordering::Relaxed);
    }
    /// 禁用预测性 GC
    pub fn disable_predictive_gc(&self) {
        self.predictive_enabled.store(false, Ordering::Relaxed);
    }
    /// 获取当前堆信息
    pub async fn get_heap_info(&self) -> HeapInfo {
        self.heap_info.read().await.clone()
    }
    /// 获取 GC 统计
    pub async fn get_metrics(&self) -> GcMetricsSnapshot {
        GcMetricsSnapshot {
            total_collections: self.metrics.total_collections.load(Ordering::Relaxed),
            total_bytes_collected: self.metrics.total_bytes_collected.load(Ordering::Relaxed),
            total_gc_time_ms: self.metrics.total_gc_time_ms.load(Ordering::Relaxed),
            emergency_collections: self.metrics.emergency_collections.load(Ordering::Relaxed),
            predictive_collections: self.metrics.predictive_collections.load(Ordering::Relaxed),
            incremental_collections: self.metrics.incremental_collections.load(Ordering::Relaxed),
            parallel_collections: self.metrics.parallel_collections.load(Ordering::Relaxed),
            average_gc_time_ms: if self.metrics.total_collections.load(Ordering::Relaxed) > 0 {
                self.metrics.total_gc_time_ms.load(Ordering::Relaxed)
                    / self.metrics.total_collections.load(Ordering::Relaxed)
            } else {
                0
            },
            peak_memory_usage: self.metrics.peak_memory_usage.load(Ordering::Relaxed),
        }
    }
    /// 获取预测器准确率
    pub async fn get_predictor_accuracy(&self) -> f64 {
        let predictor: _ = self.predictor.read().await;
        predictor.accuracy
    }
}
/// GC 统计快照
#[derive(Debug, Clone)]
pub struct GcMetricsSnapshot {
    pub total_collections: usize,
    pub total_bytes_collected: usize,
    pub total_gc_time_ms: usize,
    pub emergency_collections: usize,
    pub predictive_collections: usize,
    pub incremental_collections: usize,
    pub parallel_collections: usize,
    pub average_gc_time_ms: usize,
    pub peak_memory_usage: usize,
}
impl GcMetricsSnapshot {
    pub fn collection_rate(&self) -> f64 {
        if self.total_gc_time_ms > 0 {
            self.total_bytes_collected as f64 / self.total_gc_time_ms as f64 * 1000.0
        } else {
            0.0
        }
    }
    pub fn predictive_ratio(&self) -> f64 {
        if self.total_collections > 0 {
            self.predictive_collections as f64 / self.total_collections as f64
        } else {
            0.0
        }
    }
}
#[cfg(test)]
mod tests {
    use super::*;

    #[tokio::test]
    async fn test_gc_optimizer_creation() {
        let optimizer: _ = EnhancedGcOptimizer::default();
        assert!(optimizer.config.initial_heap_threshold > 0);
        assert!(optimizer.config.incremental_gc);
    }
    #[tokio::test]
    async fn test_record_allocation() {
        let optimizer: _ = EnhancedGcOptimizer::default();
        optimizer.record_allocation(1024).await;
        let heap: _ = optimizer.get_heap_info().await;
        assert_eq!(heap.current_size, 1024);
        assert_eq!(heap.used_size, 1024);
    }
    #[tokio::test]
    async fn test_predictive_gc() {
        let optimizer: _ = EnhancedGcOptimizer::default();
        optimizer.enable_predictive_gc();
        // 记录大量分配以触发 GC
        for _ in 0..100 {
            optimizer.record_allocation(1024 * 1024).await;
        }
        let metrics: _ = optimizer.get_metrics().await;
        assert!(metrics.total_collections > 0);
    }
    #[test]
    fn test_ai_gc_predictor() {
        let mut predictor = AiGcPredictor::new();
        // 模拟一些 GC 事件
        for i in 0..20 {
            let event: _ = GcEvent {
                event_type: GcEventType::Standard,
                timestamp: Instant::now(),
                heap_before: 1000000 + i * 1000,
                heap_after: 500000 + i * 500,
                duration: Duration::from_millis(10),
                bytes_collected: 500000 + i * 500,
            };
            predictor.record_gc_event(event);
        }
        let prediction: _ = predictor.predict_next_gc(2000000, 1000000.0);
        assert!(prediction.is_some());
    }
}