amberjs 1.16.1

// 批量 I/O 操作引擎
// 通过批处理多个 I/O 操作来提高网络吞吐量

use std::collections::VecDeque;
use std::sync::{Arc, Mutex, RwLock};

use super::{NetworkConfig, NetworkStats};
use std::time::{Duration, Instant};
/// 批处理配置
#[derive(Debug, Clone)]
pub struct BatchConfig {
    pub max_batch_size: usize,
    pub batch_timeout_ms: u64,
    pub max_pending_batches: usize,
    pub enable_parallel_processing: bool,
}
impl Default for BatchConfig {
    fn default() -> Self {
        Self {
            max_batch_size: 100,
            batch_timeout_ms: 10,
            max_pending_batches: 1000,
            enable_parallel_processing: true,
        }
    }
}
/// 批处理优先级
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum BatchPriority {
    Low,
    Normal,
    High,
    Critical,
}
/// 批处理操作
#[derive(Debug, Clone)]
pub struct BatchOperation {
    pub id: u64,
    pub priority: BatchPriority,
    pub created_at: Instant,
    pub data: Vec<u8>,
    pub target: String, // 目标地址
}
/// 批处理统计
#[derive(Debug, Clone)]
pub struct BatchStats {
    pub total_batches_processed: u64,
    pub total_operations_batched: u64,
    pub average_batch_size: f64,
    pub batch_processing_time_ns: u64,
    pub throughput_mbps: f64,
}
/// 批量 I/O 引擎
pub struct BatchIoEngine {
    config: NetworkConfig,
    batch_config: BatchConfig,
    stats: Arc<RwLock<BatchStats>>,
    pending_operations: Arc<RwLock<VecDeque<BatchOperation>>>,
    operation_counter: Arc<RwLock<u64>>,
    processor_handle: Option<tokio::task::JoinHandle<()>>,
}
impl BatchIoEngine {
    /// 创建新的批量 I/O 引擎
    pub fn new(config: NetworkConfig) -> Self {
        let batch_config: _ = BatchConfig::default();
        Self {
            processor_handle: None,
            batch_config,
            stats: Arc::new(Mutex::new(BatchStats {
                total_batches_processed: 0,
                total_operations_batched: 0,
                average_batch_size: 0.0,
                batch_processing_time_ns: 0,
                throughput_mbps: 0.0,
            })),
            pending_operations: Arc::new(Mutex::new(VecDeque::new())),
            operation_counter: Arc::new(Mutex::new(0)),
            config,
        }
    }
    /// 启动批处理器
    pub async fn start(&mut self) -> Result<(), Box<dyn std::error::Error>> {
        let pending_operations: _ = Arc::clone(&self.pending_operations);
        let stats: _ = Arc::clone(&self.stats);
        let batch_timeout_ms: _ = self.batch_config.batch_timeout_ms;
        let handle: _ = tokio::spawn(async move {
            let mut interval = tokio::time::interval(
                Duration::from_millis(batch_timeout_ms)
            );
            loop {
                interval.tick().await;
                Self::process_batch(&pending_operations, &stats).await;
            }
        });
        self.processor_handle = Some(handle);
        Ok(())
    }
    /// 停止批处理器
    pub async fn stop(&mut self) -> Result<(), Box<dyn std::error::Error>> {
        if let Some(handle) = self.processor_handle.take() {
            handle.abort();
        }
        Ok(())
    }
    /// 提交批量操作
    pub async fn submit_operation(&self, operation: BatchOperation) -> Result<(), Box<dyn std::error::Error>> {
        let mut pending = self.pending_operations.write().await;
        if pending.len() >= self.batch_config.max_pending_batches {
            return Err("批处理器满".into());
        }
        pending.push_back(operation);
        Ok(())
    }
    /// 处理一批操作
    async fn process_batch(
        pending_operations: &Arc<RwLock<VecDeque<BatchOperation>>>,
        stats: &Arc<RwLock<BatchStats>>,
    ) {
        let start: _ = Instant::now();
        let max_batch_size: _ = BatchConfig::default().max_batch_size;
        // 获取一批操作
        let mut batch = Vec::new();
        {
            let mut pending = pending_operations.write().await;
            // 按优先级排序
            // 简化实现：直接取前 N 个
            while batch.len() < max_batch_size {
                if let Some(op) = pending.pop_front() {
                    batch.push(op);
                } else {
                    break;
                }
            }
        }
        if batch.is_empty() {
            return;
        }
        // 模拟处理过程（实际实现中这里是真实的网络 I/O）
        Self::simulate_batch_processing(&batch).await;
        // 更新统计
        let elapsed: _ = start.elapsed();
        let mut stats_guard = stats.write().await;
        stats_guard.total_batches_processed += 1;
        stats_guard.total_operations_batched += batch.len() as u64;
        stats_guard.batch_processing_time_ns += elapsed.as_nanos() as u64;
        stats_guard.average_batch_size = stats_guard.total_operations_batched as f64
            / stats_guard.total_batches_processed as f64;
    }
    /// 模拟批处理过程
    async fn simulate_batch_processing(batch: &[BatchOperation]) {
        // 实际实现中这里会执行真实的网络 I/O
        // 发送/接收数据
        // 模拟延迟
        tokio::time::sleep(Duration::from_micros(10)).await;
    }
    /// 获取统计信息
    pub async fn get_stats(&self) -> BatchStats {
        self.stats.read().await.clone()
    }
}
impl Drop for BatchIoEngine {
    fn drop(&mut self) {
        if let Some(handle) = &self.processor_handle {
            handle.abort();
        }
    }
}