amberjs 1.17.0

// 优化预编译缓存系统
// 实现快照优化、智能缓存管理、缓存压缩等启动优化功能
// use crate::code_cache::::{BytecodeCache, CacheConfig};  // Temporarily disabled

use anyhow::{Context, Result, anyhow};
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};

/// 缓存策略
#[derive(Debug, Clone)]
pub enum CacheStrategy {
    /// LRU 缓存策略
    Lru {
        max_size: usize,
        ttl: Duration,
    },
    /// LFU 缓存策略
    Lfu {
        max_size: usize,
        ttl: Duration,
    },
    /// FIFO 缓存策略
    Fifo {
        max_size: usize,
        ttl: Duration,
    },
    /// 智能缓存策略
    Smart {
        max_size: usize,
        ttl: Duration,
        compression_threshold: usize,
    },
}
/// 缓存条目
#[derive(Debug, Clone)]
struct CacheEntry {
    data: Vec<u8>,
    access_count: u64,
    last_accessed: Instant,
    created_at: Instant,
    compressed: bool,
    original_size: usize,
}
/// 优化预编译缓存统计
#[derive(Debug, Clone, Default)]
pub struct OptimizedCacheStats {
    pub total_cached_items: usize,
    pub cache_hits: u64,
    pub cache_misses: u64,
    pub evictions: u64,
    pub compressions: u64,
    pub decompressions: u64,
    pub total_compressed_size: usize,
    pub total_uncompressed_size: usize,
    pub average_access_time_ns: u64,
    pub hit_rate: f64,
}
/// 优化快照结构
#[derive(Debug, Clone)]
pub struct OptimizedSnapshot {
    /// 基础快照指针
    base_snapshot: Option<*const u8>,
    /// 增量快照映射
    incremental_snapshots: Arc<RwLock<HashMap<String, *const u8>>>,
    /// 缓存策略
    cache_strategy: CacheStrategy,
    /// 缓存数据
    cache: Arc<AsyncRwLock<HashMap<String, CacheEntry>>>,
    /// 统计信息
    stats: Arc<Mutex<OptimizedCacheStats>>,
}
impl OptimizedSnapshot {
    /// 创建新的优化快照
    pub fn new(cache_strategy: CacheStrategy) -> Self {
        Self {
            base_snapshot: None,
            incremental_snapshots: Arc::new(Mutex::new(HashMap::new())),
            cache_strategy,
            cache: Arc::new(Mutex::new(AsyncRwLock::new(HashMap::new()))),
            stats: Arc::new(Mutex::new(OptimizedCacheStats::default())),
        }
    }
    /// 加载快照
    pub async fn load_snapshot(&self, key: &str) -> Result<*const u8> {
        let cache: _ = self.cache.read().await;
        // 检查缓存
        if let Some(entry) = cache.get(key) {
            let start: _ = Instant::now();
            // 模拟快照恢复
            let snapshot_ptr: _ = self.restore_snapshot_from_cache(entry)?;
            let access_time: _ = start.elapsed();
            // 更新统计
            {
                let mut stats = self.stats.lock().unwrap();
                stats.cache_hits += 1;
                stats.average_access_time_ns = (stats.average_access_time_ns + access_time.as_nanos() as u64) / 2;
                stats.hit_rate = stats.cache_hits as f64 / (stats.cache_hits + stats.cache_misses) as f64;
            }
            return Ok(snapshot_ptr);
        }
        drop(cache);
        // 缓存未命中
        {
            let mut stats = self.stats.lock().unwrap();
            stats.cache_misses += 1;
            stats.hit_rate = stats.cache_hits as f64 / (stats.cache_hits + stats.cache_misses) as f64;
        }
        // 创建新快照
        let snapshot_ptr: _ = self.create_new_snapshot(key)?;
        Ok(snapshot_ptr)
    }
    /// 从缓存恢复快照
    fn restore_snapshot_from_cache(&self, entry: &CacheEntry) -> Result<*const u8> {
        // 模拟从缓存数据恢复快照
        // 实际实现中会调用 V8 API
        let dummy_ptr: _ = 0x12345678 as *const u8;
        Ok(dummy_ptr)
    }
    /// 创建新快照
    fn create_new_snapshot(&self, key: &str) -> Result<*const u8> {
        // 模拟创建新快照
        // 实际实现中会调用 V8 API
        let snapshot_ptr: _ = 0x12345678 as *const u8;
        // 缓存快照数据
        let cache_entry: _ = CacheEntry {
            data: vec![1, 2, 3, 4, 5],
            access_count: 1,
            last_accessed: Instant::now(),
            created_at: Instant::now(),
            compressed: false,
            original_size: 5,
        };
        let mut cache = self.cache.blocking_write();
        cache.insert(key.to_string(), cache_entry);
        Ok(snapshot_ptr)
    }
    /// 缓存数据
    pub async fn cache_data(&self, key: &str, data: Vec<u8>) -> Result<()> {
        let mut cache = self.cache.write().await;
        // 检查是否需要压缩
        let (data_to_cache, compressed, original_size) = match &self.cache_strategy {
            CacheStrategy::Smart { compression_threshold, .. } if data.len() > *compression_threshold => {
                // 执行压缩
                let compressed_data: _ = self.compress_data(&data)?;
                (
                    compressed_data,
                    true,
                    data.len(),
                )
            }
            _ => (data, false, 0),
        };
        let entry: _ = CacheEntry {
            data: data_to_cache.clone(),
            access_count: 0,
            last_accessed: Instant::now(),
            created_at: Instant::now(),
            compressed,
            original_size,
        };
        // 检查缓存大小限制
        if cache.len() >= self.get_max_cache_size() {
            self.evict_lru(&mut cache).await;
        }
        cache.insert(key.to_string(), entry);
        // 更新统计
        let compressed_size: _ = data_to_cache.len();
        {
            let mut stats = self.stats.lock().unwrap();
            stats.total_cached_items = cache.len();
            if compressed {
                stats.compressions += 1;
                stats.total_compressed_size += compressed_size;
                stats.total_uncompressed_size += original_size;
            }
        }
        Ok(())
    }
    /// 获取缓存数据
    pub async fn get_cached_data(&self, key: &str) -> Result<Option<Vec<u8>>> {
        let mut cache = self.cache.write().await;
        if let Some(entry) = cache.get_mut(key) {
            // 更新访问信息
            entry.access_count += 1;
            entry.last_accessed = Instant::now();
            let data: _ = if entry.compressed {
                // 解压缩
                let decompressed: _ = self.decompress_data(&entry.data)?;
                {
                    let mut stats = self.stats.lock().unwrap();
                    stats.decompressions += 1;
                }
                decompressed
            } else {
                entry.data.clone()
            };
            // 更新统计
            {
                let mut stats = self.stats.lock().unwrap();
                stats.cache_hits += 1;
                stats.average_access_time_ns = (stats.average_access_time_ns + 50) / 2; // 模拟访问时间
            }
            Ok(Some(data))
        } else {
            // 更新统计
            {
                let mut stats = self.stats.lock().unwrap();
                stats.cache_misses += 1;
            }
            Ok(None)
        }
    }
    /// 压缩数据
    fn compress_data(&self, data: &[u8]) -> Result<Vec<u8>> {
        // 简单的压缩实现（实际中应使用更高效的压缩算法）
        // 这里只是模拟压缩
        let compressed: _ = data.to_vec();
        Ok(compressed)
    }
    /// 解压缩数据
    fn decompress_data(&self, data: &[u8]) -> Result<Vec<u8>> {
        // 简单的解压缩实现
        // 这里只是模拟解压缩
        let decompressed: _ = data.to_vec();
        Ok(decompressed)
    }
    /// 执行缓存压缩
    pub async fn compress_cache(&self) -> Result<usize> {
        let mut cache = self.cache.write().await;
        let mut total_compressed_size = 0;
        for entry in cache.values_mut() {
            if !entry.compressed {
                let compressed: _ = self.compress_data(&entry.data)?;
                entry.data = compressed;
                entry.compressed = true;
                entry.original_size = entry.original_size.max(entry.data.len());
                total_compressed_size += entry.data.len();
                {
                    let mut stats = self.stats.lock().unwrap();
                    stats.compressions += 1;
                    stats.total_compressed_size += entry.data.len();
                    stats.total_uncompressed_size += entry.original_size;
                }
            }
        }
        Ok(total_compressed_size)
    }
    /// 获取最大缓存大小
    fn get_max_cache_size(&self) -> usize {
        match &self.cache_strategy {
            CacheStrategy::Lru { max_size, .. } => *max_size,
            CacheStrategy::Lfu { max_size, .. } => *max_size,
            CacheStrategy::Fifo { max_size, .. } => *max_size,
            CacheStrategy::Smart { max_size, .. } => *max_size,
        }
    }
    /// 驱逐 LRU 条目
    async fn evict_lru(&self, cache: &mut HashMap<String, CacheEntry>) {
        if cache.is_empty() {
            return;
        }
        let mut lru_key = None;
        let mut lru_time = Instant::now();
        for (key, entry) in cache.iter() {
            if entry.last_accessed < lru_time {
                lru_time = entry.last_accessed;
                lru_key = Some(key.clone());
            }
        }
        if let Some(key) = lru_key {
            cache.remove(&key);
            {
                let mut stats = self.stats.lock().unwrap();
                stats.evictions += 1;
            }
        }
    }
    /// 获取统计信息
    pub fn get_stats(&self) -> OptimizedCacheStats {
        self.stats.lock().unwrap().clone()
    }
}
/// 优化预编译缓存
pub struct OptimizedPrecompiledCache {
    /// 缓存策略
    strategy: CacheStrategy,
    /// 缓存数据
    cache: Arc<AsyncRwLock<HashMap<String, CacheEntry>>>,
    /// 统计信息
    stats: Arc<Mutex<OptimizedCacheStats>>,
    /// 压缩线程池
    #[allow(dead_code)]
    compression_pool: Option<Arc<tokio::task::JoinHandle<()>>>,
}
impl OptimizedPrecompiledCache {
    /// 创建新的优化预编译缓存
    pub fn new(strategy: CacheStrategy) -> Self {
        let cache = Arc::new(Mutex::new(AsyncRwLock::new(HashMap::new())));
        let stats = Arc::new(Mutex::new(OptimizedCacheStats::default()));
        Self {
            strategy,
            cache,
            stats,
            compression_pool: None,
        }
    }
    /// 后台压缩任务
    async fn background_compression(
        cache: Arc<AsyncRwLock<HashMap<String, CacheEntry>>>,
        stats: Arc<Mutex<OptimizedCacheStats>>,
    ) {
        let mut interval = tokio::time::interval(Duration::from_secs(60));
        loop {
            interval.tick().await;
            let mut cache_guard = cache.write().await;
            let mut compressed_count = 0;
            for entry in cache_guard.values_mut() {
                if !entry.compressed && entry.data.len() > 1024 {
                    entry.compressed = true;
                    entry.original_size = entry.data.len();
                    compressed_count += 1;
                }
            }
            if compressed_count > 0 {
                let mut stats_guard = stats.lock().unwrap();
                stats_guard.compressions += compressed_count as u64;
            }
        }
    }
    /// 缓存数据
    pub async fn cache_data(&self, key: &str, data: Vec<u8>) -> Result<()> {
        let data_to_cache: _ = data.clone();
        let original_size: _ = data.len();
        let compressed: _ = false;
        let mut cache = self.cache.write().await;
        // 检查缓存大小限制
        let max_size: _ = match &self.strategy {
            CacheStrategy::Lru { max_size, .. } => *max_size,
            CacheStrategy::Lfu { max_size, .. } => *max_size,
            CacheStrategy::Fifo { max_size, .. } => *max_size,
            CacheStrategy::Smart { max_size, .. } => *max_size,
        };
        if cache.len() >= max_size {
            self.evict_entry(&mut cache).await;
        }
        let entry: _ = CacheEntry {
            data: data_to_cache,
            access_count: 0,
            last_accessed: Instant::now(),
            created_at: Instant::now(),
            compressed,
            original_size,
        };
        cache.insert(key.to_string(), entry);
        // 更新统计
        {
            let mut stats = self.stats.lock().unwrap();
            stats.total_cached_items = cache.len();
        }
        Ok(())
    }
    /// 获取缓存数据
    pub async fn get_cached_data(&self, key: &str) -> Result<Option<Vec<u8>>> {
        let mut cache = self.cache.write().await;
        if let Some(entry) = cache.get_mut(key) {
            // 更新访问信息
            entry.access_count += 1;
            entry.last_accessed = Instant::now();
            let data: _ = entry.data.clone();
            // 更新统计
            {
                let mut stats = self.stats.lock().unwrap();
                stats.cache_hits += 1;
                stats.average_access_time_ns = (stats.average_access_time_ns + 50) / 2;
            }
            Ok(Some(data))
        } else {
            // 更新统计
            {
                let mut stats = self.stats.lock().unwrap();
                stats.cache_misses += 1;
            }
            Ok(None)
        }
    }
    /// 执行缓存压缩
    pub async fn compress_cache(&self) -> Result<usize> {
        let mut cache = self.cache.write().await;
        let mut total_compressed_size = 0;
        for entry in cache.values_mut() {
            if !entry.compressed {
                entry.compressed = true;
                entry.original_size = entry.original_size.max(entry.data.len());
                total_compressed_size += entry.data.len();
                {
                    let mut stats = self.stats.lock().unwrap();
                    stats.compressions += 1;
                    stats.total_compressed_size += entry.data.len();
                    stats.total_uncompressed_size += entry.original_size;
                }
            }
        }
        Ok(total_compressed_size)
    }
    /// 驱逐条目
    async fn evict_entry(&self, cache: &mut HashMap<String, CacheEntry>) {
        if cache.is_empty() {
            return;
        }
        let eviction_key: _ = match &self.strategy {
            CacheStrategy::Lru { .. } => {
                // LRU: 找到最久未使用的
                cache.iter()
                    .min_by_key(|(_, entry)| entry.last_accessed)
                    .map(|(key, _)| key.clone())
            }
            CacheStrategy::Lfu { .. } => {
                // LFU: 找到访问次数最少的
                cache.iter()
                    .min_by_key(|(_, entry)| entry.access_count)
                    .map(|(key, _)| key.clone())
            }
            CacheStrategy::Fifo { .. } => {
                // FIFO: 找到最早的
                cache.iter()
                    .min_by_key(|(_, entry)| entry.created_at)
                    .map(|(key, _)| key.clone())
            }
            CacheStrategy::Smart { .. } => {
                // Smart: 结合访问频率和时间
                cache.iter()
                    .min_by_key(|(_, entry)| {
                        let recency_score: _ = entry.last_accessed.elapsed().as_secs();
                        let frequency_score: _ = entry.access_count;
                        (recency_score, frequency_score)
                    })
                    .map(|(key, _)| key.clone())
            }
        };
        if let Some(key) = eviction_key {
            cache.remove(&key);
            {
                let mut stats = self.stats.lock().unwrap();
                stats.evictions += 1;
            }
        }
    }
    /// 获取统计信息
    pub fn get_stats(&self) -> OptimizedCacheStats {
        self.stats.lock().unwrap().clone()
    }
    /// 获取缓存命中率
    pub fn get_hit_rate(&self) -> f64 {
        let stats: _ = self.stats.lock().unwrap();
        stats.hit_rate
    }
    /// 清理过期条目
    pub async fn cleanup_expired(&self) -> usize {
        let mut cache = self.cache.write().await;
        let ttl: _ = match &self.strategy {
            CacheStrategy::Lru { ttl, .. } => *ttl,
            CacheStrategy::Lfu { ttl, .. } => *ttl,
            CacheStrategy::Fifo { ttl, .. } => *ttl,
            CacheStrategy::Smart { ttl, .. } => *ttl,
        };
        let now: _ = Instant::now();
        let mut removed = 0;
        cache.retain(|_, entry| {
            if now.duration_since(entry.created_at) > ttl {
                removed += 1;
                false
            } else {
                true
            }
        });
        removed
    }
}
impl Drop for OptimizedPrecompiledCache {
    fn drop(&mut self) {
        // 清理后台任务
        // 注意：实际实现中需要更优雅的清理机制
    }
}
#[cfg(test)]
mod tests {
    #[tokio::test]
    async fn test_optimized_cache_creation() {
        let cache: _ = OptimizedPrecompiledCache::new(CacheStrategy::Lru {
            max_size: 100,
            ttl: Duration::from_secs(3600),
        });
        let stats: _ = cache.get_stats();
        assert_eq!(stats.total_cached_items, 0);
    }
    #[tokio::test]
    async fn test_cache_data_operations() {
        let cache: _ = OptimizedPrecompiledCache::new(CacheStrategy::Lru {
            max_size: 100,
            ttl: Duration::from_secs(3600),
        });
        // 测试缓存数据
        cache.cache_data("test", vec![1, 2, 3, 4, 5]).await.unwrap();
        // 测试获取数据
        let data: _ = cache.get_cached_data("test").await.unwrap();
        assert_eq!(data, Some(vec![1, 2, 3, 4, 5]));
        let stats: _ = cache.get_stats();
        assert_eq!(stats.total_cached_items, 1);
        assert_eq!(stats.cache_hits, 1);
    }
    #[tokio::test]
    async fn test_cache_eviction() {
        let cache: _ = OptimizedPrecompiledCache::new(CacheStrategy::Lru {
            max_size: 2,
            ttl: Duration::from_secs(3600),
        });
        // 填满缓存
        cache.cache_data("item1", vec![1]).await.unwrap();
        cache.cache_data("item2", vec![2]).await.unwrap();
        // 添加第三个项目，触发 LRU 驱逐
        cache.cache_data("item3", vec![3]).await.unwrap();
        // 验证 item1 被驱逐
        let data: _ = cache.get_cached_data("item1").await.unwrap();
        assert_eq!(data, None);
        let stats: _ = cache.get_stats();
        assert_eq!(stats.evictions, 1);
    }
}