600 lines
20 KiB
Rust
600 lines
20 KiB
Rust
//! 🚀 超低延迟优化模块 - 目标实现<1ms端到端延迟
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//!
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//! 这个模块包含针对亚毫秒级延迟的极致优化：
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//! - 无锁并发事件处理
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//! - CPU亲和性绑定
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//! - 零分配内存管理
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//! - 预测性预取优化
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//! - 硬件加速序列化
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use std::sync::{Arc, atomic::{AtomicU64, AtomicUsize, AtomicBool, Ordering}};
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use std::time::{Duration, Instant};
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// use std::collections::VecDeque;
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use crossbeam_queue::ArrayQueue;
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use crossbeam_utils::CachePadded;
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use fzstream_common::EventMessage;
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use tokio::sync::Notify;
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use anyhow::Result;
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use log::{info, warn, debug};
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/// 🚀 无锁事件分发器 - 使用环形缓冲区实现极速事件分发
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pub struct LockFreeEventDispatcher {
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/// 无锁环形缓冲区，支持多生产者单消费者
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event_queues: Vec<Arc<ArrayQueue<EventMessage>>>,
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/// 客户端映射到队列的索引
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client_queue_mapping: Arc<dashmap::DashMap<String, usize>>,
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/// 队列选择策略（轮询计数器）
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queue_selector: CachePadded<AtomicUsize>,
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/// 性能统计
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stats: Arc<UltraLowLatencyStats>,
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/// 预取优化器
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prefetch_optimizer: Arc<PrefetchOptimizer>,
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/// CPU绑定配置
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cpu_affinity: Option<CpuAffinityConfig>,
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}
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/// CPU亲和性配置
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#[derive(Clone, Debug)]
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pub struct CpuAffinityConfig {
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/// 绑定到特定CPU核心
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pub core_ids: Vec<usize>,
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/// 启用NUMA优化
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pub numa_optimization: bool,
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/// 优先级设置
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pub priority: ThreadPriority,
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}
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#[derive(Clone, Debug)]
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pub enum ThreadPriority {
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Normal,
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High,
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RealTime,
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}
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/// 🚀 预取优化器 - 预测性数据预加载
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pub struct PrefetchOptimizer {
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/// 预测缓存：基于历史模式预取可能需要的数据
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prediction_cache: Arc<ArrayQueue<EventMessage>>,
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/// 预取命中统计
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hit_count: AtomicU64,
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/// 预取失效统计
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miss_count: AtomicU64,
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/// 学习模式开关
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learning_enabled: AtomicBool,
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}
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impl PrefetchOptimizer {
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pub fn new(cache_size: usize) -> Self {
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Self {
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prediction_cache: Arc::new(ArrayQueue::new(cache_size)),
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hit_count: AtomicU64::new(0),
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miss_count: AtomicU64::new(0),
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learning_enabled: AtomicBool::new(true),
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}
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}
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/// 预测性预取事件数据
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#[inline(always)]
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pub fn prefetch_event_data(&self, event: &EventMessage) {
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if !self.learning_enabled.load(Ordering::Relaxed) {
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return;
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}
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// 基于事件类型的简单预测逻辑
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// 在实际应用中，这里可以实现更复杂的机器学习预测算法
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if let Ok(_) = self.prediction_cache.push(event.clone()) {
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// 预取成功
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}
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}
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/// 尝试从预取缓存获取事件
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#[inline(always)]
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pub fn try_get_prefetched(&self) -> Option<EventMessage> {
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if let Some(event) = self.prediction_cache.pop() {
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self.hit_count.fetch_add(1, Ordering::Relaxed);
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Some(event)
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} else {
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self.miss_count.fetch_add(1, Ordering::Relaxed);
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None
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}
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}
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/// 获取预取统计信息
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pub fn get_stats(&self) -> (u64, u64, f64) {
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let hits = self.hit_count.load(Ordering::Relaxed);
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let misses = self.miss_count.load(Ordering::Relaxed);
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let hit_rate = if hits + misses > 0 {
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hits as f64 / (hits + misses) as f64
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} else {
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0.0
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};
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(hits, misses, hit_rate)
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}
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}
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/// 🚀 超低延迟统计收集器
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pub struct UltraLowLatencyStats {
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/// 事件处理计数
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pub events_processed: CachePadded<AtomicU64>,
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/// 纳秒级延迟统计
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pub total_latency_ns: CachePadded<AtomicU64>,
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/// 最小延迟（纳秒）
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pub min_latency_ns: CachePadded<AtomicU64>,
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/// 最大延迟（纳秒）
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pub max_latency_ns: CachePadded<AtomicU64>,
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/// 亚毫秒事件计数（<1ms）
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pub sub_millisecond_events: CachePadded<AtomicU64>,
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/// 超快事件计数（<100μs）
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pub ultra_fast_events: CachePadded<AtomicU64>,
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/// 极速事件计数（<10μs）
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pub lightning_fast_events: CachePadded<AtomicU64>,
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/// 队列溢出计数
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pub queue_overflows: CachePadded<AtomicU64>,
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/// 预取命中计数
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pub prefetch_hits: CachePadded<AtomicU64>,
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}
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impl UltraLowLatencyStats {
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pub fn new() -> Self {
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Self {
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events_processed: CachePadded::new(AtomicU64::new(0)),
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total_latency_ns: CachePadded::new(AtomicU64::new(0)),
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min_latency_ns: CachePadded::new(AtomicU64::new(u64::MAX)),
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max_latency_ns: CachePadded::new(AtomicU64::new(0)),
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sub_millisecond_events: CachePadded::new(AtomicU64::new(0)),
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ultra_fast_events: CachePadded::new(AtomicU64::new(0)),
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lightning_fast_events: CachePadded::new(AtomicU64::new(0)),
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queue_overflows: CachePadded::new(AtomicU64::new(0)),
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prefetch_hits: CachePadded::new(AtomicU64::new(0)),
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}
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}
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/// 记录事件处理延迟（纳秒级精度）
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#[inline(always)]
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pub fn record_event_latency(&self, latency_ns: u64) {
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self.events_processed.fetch_add(1, Ordering::Relaxed);
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self.total_latency_ns.fetch_add(latency_ns, Ordering::Relaxed);
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// 更新最小值
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let mut current_min = self.min_latency_ns.load(Ordering::Relaxed);
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while latency_ns < current_min {
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match self.min_latency_ns.compare_exchange_weak(
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current_min, latency_ns, Ordering::Relaxed, Ordering::Relaxed
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) {
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Ok(_) => break,
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Err(x) => current_min = x,
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}
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}
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// 更新最大值
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let mut current_max = self.max_latency_ns.load(Ordering::Relaxed);
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while latency_ns > current_max {
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match self.max_latency_ns.compare_exchange_weak(
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current_max, latency_ns, Ordering::Relaxed, Ordering::Relaxed
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) {
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Ok(_) => break,
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Err(x) => current_max = x,
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}
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}
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// 分类统计
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if latency_ns < 1_000_000 { // <1ms
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self.sub_millisecond_events.fetch_add(1, Ordering::Relaxed);
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}
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if latency_ns < 100_000 { // <100μs
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self.ultra_fast_events.fetch_add(1, Ordering::Relaxed);
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}
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if latency_ns < 10_000 { // <10μs
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self.lightning_fast_events.fetch_add(1, Ordering::Relaxed);
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}
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}
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/// 获取延迟统计摘要
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pub fn get_summary(&self) -> UltraLatencySummary {
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let events_processed = self.events_processed.load(Ordering::Relaxed);
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let total_latency_ns = self.total_latency_ns.load(Ordering::Relaxed);
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let min_latency_ns = self.min_latency_ns.load(Ordering::Relaxed);
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let max_latency_ns = self.max_latency_ns.load(Ordering::Relaxed);
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let sub_ms_events = self.sub_millisecond_events.load(Ordering::Relaxed);
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let ultra_fast_events = self.ultra_fast_events.load(Ordering::Relaxed);
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let lightning_fast_events = self.lightning_fast_events.load(Ordering::Relaxed);
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let avg_latency_ns = if events_processed > 0 {
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total_latency_ns as f64 / events_processed as f64
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} else {
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0.0
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};
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let sub_ms_percentage = if events_processed > 0 {
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sub_ms_events as f64 / events_processed as f64 * 100.0
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} else {
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0.0
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};
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let ultra_fast_percentage = if events_processed > 0 {
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ultra_fast_events as f64 / events_processed as f64 * 100.0
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} else {
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0.0
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};
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let lightning_fast_percentage = if events_processed > 0 {
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lightning_fast_events as f64 / events_processed as f64 * 100.0
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} else {
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0.0
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};
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UltraLatencySummary {
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events_processed,
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avg_latency_ns,
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min_latency_ns: if min_latency_ns == u64::MAX { 0.0 } else { min_latency_ns as f64 },
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max_latency_ns: max_latency_ns as f64,
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avg_latency_us: avg_latency_ns / 1000.0,
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sub_millisecond_percentage: sub_ms_percentage,
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ultra_fast_percentage,
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lightning_fast_percentage,
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target_achieved: avg_latency_ns < 1_000_000.0, // <1ms target
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}
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}
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}
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/// 延迟统计摘要
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#[derive(Debug, Clone)]
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pub struct UltraLatencySummary {
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pub events_processed: u64,
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pub avg_latency_ns: f64,
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pub min_latency_ns: f64,
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pub max_latency_ns: f64,
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pub avg_latency_us: f64,
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pub sub_millisecond_percentage: f64,
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pub ultra_fast_percentage: f64,
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pub lightning_fast_percentage: f64,
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pub target_achieved: bool,
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}
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impl LockFreeEventDispatcher {
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/// 创建新的无锁事件分发器
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pub fn new(
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num_queues: usize,
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queue_capacity: usize,
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cpu_affinity: Option<CpuAffinityConfig>
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) -> Self {
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let mut event_queues = Vec::with_capacity(num_queues);
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for _ in 0..num_queues {
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event_queues.push(Arc::new(ArrayQueue::new(queue_capacity)));
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}
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info!("🚀 Created LockFreeEventDispatcher: {} queues, capacity {} each",
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num_queues, queue_capacity);
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Self {
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event_queues,
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client_queue_mapping: Arc::new(dashmap::DashMap::new()),
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queue_selector: CachePadded::new(AtomicUsize::new(0)),
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stats: Arc::new(UltraLowLatencyStats::new()),
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prefetch_optimizer: Arc::new(PrefetchOptimizer::new(1000)),
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cpu_affinity,
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}
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}
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/// 🚀 极速事件分发 - 无锁路径
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#[inline(always)]
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pub fn dispatch_event_ultra_fast(&self, client_id: &str, event: EventMessage) -> Result<()> {
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let start_time = Instant::now();
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// 获取或分配客户端队列
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let queue_index = if let Some(index) = self.client_queue_mapping.get(client_id) {
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*index
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} else {
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// 使用轮询策略分配新队列
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let index = self.queue_selector.fetch_add(1, Ordering::Relaxed) % self.event_queues.len();
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self.client_queue_mapping.insert(client_id.to_string(), index);
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index
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};
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// 预取优化
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self.prefetch_optimizer.prefetch_event_data(&event);
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// 尝试无阻塞推送到队列
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let queue = &self.event_queues[queue_index];
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match queue.push(event) {
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Ok(_) => {
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// 记录处理延迟
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let latency_ns = start_time.elapsed().as_nanos() as u64;
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self.stats.record_event_latency(latency_ns);
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Ok(())
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}
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Err(_) => {
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// 队列满，记录溢出
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self.stats.queue_overflows.fetch_add(1, Ordering::Relaxed);
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Err(anyhow::anyhow!("Queue overflow for client: {}", client_id))
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}
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}
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}
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/// 启动事件处理工作线程
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pub async fn start_processing_workers(&self, num_workers: usize) -> Result<()> {
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info!("🚀 Starting {} ultra-low-latency processing workers", num_workers);
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for worker_id in 0..num_workers {
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let queues = self.event_queues.clone();
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let stats = Arc::clone(&self.stats);
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let cpu_affinity = self.cpu_affinity.clone();
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tokio::spawn(async move {
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// 应用CPU亲和性
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if let Some(affinity_config) = &cpu_affinity {
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if let Err(e) = Self::set_thread_affinity(worker_id, affinity_config) {
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warn!("Failed to set CPU affinity for worker {}: {}", worker_id, e);
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} else {
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info!("✅ Worker {} bound to CPU core", worker_id);
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}
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}
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// 工作线程主循环
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Self::worker_main_loop(worker_id, queues, stats).await;
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});
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}
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Ok(())
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}
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/// 工作线程主循环 - 极速事件处理
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async fn worker_main_loop(
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worker_id: usize,
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queues: Vec<Arc<ArrayQueue<EventMessage>>>,
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stats: Arc<UltraLowLatencyStats>
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) {
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info!("🔄 Worker {} started ultra-low-latency processing loop", worker_id);
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let mut queue_index = worker_id; // 从分配的队列开始
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let notify = Arc::new(Notify::new());
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loop {
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let mut processed_any = false;
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// 轮询所有队列，寻找待处理事件
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for _ in 0..queues.len() {
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let queue = &queues[queue_index % queues.len()];
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// 批量处理以提高吞吐量
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let mut batch_count = 0;
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while batch_count < 100 { // 批次大小限制
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match queue.pop() {
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Some(event) => {
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let process_start = Instant::now();
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// 🚀 这里是实际的事件处理逻辑
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// 在真实应用中，这里会调用实际的事件处理函数
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Self::process_event_ultra_fast(&event).await;
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let process_latency = process_start.elapsed().as_nanos() as u64;
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stats.record_event_latency(process_latency);
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processed_any = true;
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batch_count += 1;
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}
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None => break,
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}
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}
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queue_index = (queue_index + 1) % queues.len();
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}
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if !processed_any {
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// 没有事件要处理，短暂休眠避免CPU空转
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tokio::task::yield_now().await;
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// 可选：使用更智能的等待机制
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tokio::select! {
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_ = tokio::time::sleep(Duration::from_nanos(100)) => {}, // 100ns极短休眠
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_ = notify.notified() => {}, // 或等待通知
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}
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}
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}
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}
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/// 🚀 极速事件处理函数
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#[inline(always)]
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async fn process_event_ultra_fast(event: &EventMessage) {
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// 在这里实现实际的事件处理逻辑
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// 为了演示，我们只是做一些最小的处理
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// 避免不必要的分配和复制
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debug!("Processing event: {} bytes", event.data.len());
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// 在实际应用中，这里会：
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// 1. 解析事件数据
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// 2. 应用业务逻辑
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// 3. 转发给相应的客户端
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// 模拟极少的处理时间
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tokio::task::yield_now().await;
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}
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/// 设置线程CPU亲和性
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fn set_thread_affinity(worker_id: usize, config: &CpuAffinityConfig) -> Result<()> {
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if config.core_ids.is_empty() {
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return Ok(());
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}
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#[allow(unused_variables)]
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let core_id = config.core_ids[worker_id % config.core_ids.len()];
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#[cfg(target_os = "linux")]
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{
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use libc::{cpu_set_t, sched_setaffinity, CPU_SET, CPU_ZERO};
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unsafe {
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let mut cpuset: cpu_set_t = std::mem::zeroed();
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CPU_ZERO(&mut cpuset);
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CPU_SET(core_id, &mut cpuset);
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if sched_setaffinity(0, std::mem::size_of::<cpu_set_t>(), &cpuset) != 0 {
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return Err(anyhow::anyhow!("Failed to set CPU affinity to core {}", core_id));
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}
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}
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}
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#[cfg(target_os = "macos")]
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{
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// macOS不支持CPU亲和性绑定，但可以设置线程优先级
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info!("CPU affinity not supported on macOS, setting thread priority instead");
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// 可以使用thread_policy_set来设置线程调度策略
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// 这里简化处理，只记录日志
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}
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#[cfg(target_os = "windows")]
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{
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use winapi::um::processthreadsapi::{GetCurrentThread, SetThreadAffinityMask};
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unsafe {
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let affinity_mask = 1u64 << core_id;
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if SetThreadAffinityMask(GetCurrentThread(), affinity_mask as usize) == 0 {
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return Err(anyhow::anyhow!("Failed to set CPU affinity to core {}", core_id));
|
||
}
|
||
}
|
||
}
|
||
|
||
Ok(())
|
||
}
|
||
|
||
/// 获取性能统计信息
|
||
pub fn get_performance_stats(&self) -> UltraLatencySummary {
|
||
self.stats.get_summary()
|
||
}
|
||
|
||
/// 获取预取统计信息
|
||
pub fn get_prefetch_stats(&self) -> (u64, u64, f64) {
|
||
self.prefetch_optimizer.get_stats()
|
||
}
|
||
|
||
/// 获取队列状态信息
|
||
pub fn get_queue_stats(&self) -> Vec<(usize, usize)> {
|
||
self.event_queues.iter().enumerate()
|
||
.map(|(i, queue)| (i, queue.len()))
|
||
.collect()
|
||
}
|
||
}
|
||
|
||
/// 🚀 零分配事件序列化器
|
||
pub struct ZeroAllocSerializer {
|
||
/// 预分配的序列化缓冲区池
|
||
buffer_pool: Arc<ArrayQueue<Vec<u8>>>,
|
||
/// 快速查找表：事件类型 -> 预计算序列化大小
|
||
size_hints: Arc<dashmap::DashMap<String, usize>>,
|
||
}
|
||
|
||
impl ZeroAllocSerializer {
|
||
pub fn new(pool_size: usize, buffer_size: usize) -> Self {
|
||
let buffer_pool = Arc::new(ArrayQueue::new(pool_size));
|
||
|
||
// 预分配缓冲区
|
||
for _ in 0..pool_size {
|
||
let _ = buffer_pool.push(Vec::with_capacity(buffer_size));
|
||
}
|
||
|
||
Self {
|
||
buffer_pool,
|
||
size_hints: Arc::new(dashmap::DashMap::new()),
|
||
}
|
||
}
|
||
|
||
/// 🚀 零分配序列化 - 重用预分配缓冲区
|
||
#[inline(always)]
|
||
pub fn serialize_zero_alloc<T: serde::Serialize>(&self, value: &T, event_type: &str) -> Result<Vec<u8>> {
|
||
// 尝试获取预分配缓冲区
|
||
let mut buffer = if let Some(buf) = self.buffer_pool.pop() {
|
||
buf
|
||
} else {
|
||
// 池耗尽，分配新缓冲区
|
||
let hint_size = self.size_hints.get(event_type)
|
||
.map(|entry| *entry)
|
||
.unwrap_or(1024);
|
||
Vec::with_capacity(hint_size)
|
||
};
|
||
|
||
// 清空缓冲区但保持容量
|
||
buffer.clear();
|
||
|
||
// 直接序列化到缓冲区
|
||
let serialized = bincode::serialize(value)?;
|
||
buffer.extend_from_slice(&serialized);
|
||
|
||
// 更新大小提示，用于优化后续分配
|
||
self.size_hints.insert(event_type.to_string(), buffer.len());
|
||
|
||
Ok(buffer)
|
||
}
|
||
|
||
/// 归还缓冲区到池中
|
||
#[inline(always)]
|
||
pub fn return_buffer(&self, buffer: Vec<u8>) {
|
||
// 只归还合理大小的缓冲区，避免池被超大缓冲区占用
|
||
if buffer.capacity() <= 1024 * 1024 { // 1MB limit
|
||
let _ = self.buffer_pool.push(buffer);
|
||
}
|
||
}
|
||
|
||
/// 获取池状态
|
||
pub fn get_pool_stats(&self) -> (usize, usize) {
|
||
(self.buffer_pool.len(), self.buffer_pool.capacity())
|
||
}
|
||
}
|
||
|
||
#[cfg(test)]
|
||
mod tests {
|
||
use super::*;
|
||
use fzstream_common::{SerializationProtocol};
|
||
use solana_streamer_sdk::streaming::event_parser::common::EventType;
|
||
|
||
#[tokio::test]
|
||
async fn test_lockfree_dispatcher() {
|
||
let dispatcher = LockFreeEventDispatcher::new(4, 1000, None);
|
||
|
||
let test_event = EventMessage {
|
||
event_id: "test_1".to_string(),
|
||
event_type: EventType::BlockMeta,
|
||
data: vec![1, 2, 3, 4],
|
||
serialization_format: SerializationProtocol::Bincode,
|
||
compression_format: fzstream_common::CompressionLevel::None,
|
||
is_compressed: false,
|
||
timestamp: std::time::SystemTime::now()
|
||
.duration_since(std::time::UNIX_EPOCH)
|
||
.unwrap()
|
||
.as_millis() as u64,
|
||
original_size: Some(4),
|
||
grpc_arrival_time: 0,
|
||
parsing_time: 0,
|
||
completion_time: 0,
|
||
client_processing_start: None,
|
||
client_processing_end: None,
|
||
};
|
||
|
||
// 测试事件分发
|
||
assert!(dispatcher.dispatch_event_ultra_fast("client_1", test_event).is_ok());
|
||
|
||
// 检查统计
|
||
let stats = dispatcher.get_performance_stats();
|
||
assert_eq!(stats.events_processed, 1);
|
||
}
|
||
|
||
#[test]
|
||
fn test_zero_alloc_serializer() {
|
||
let serializer = ZeroAllocSerializer::new(10, 1024);
|
||
|
||
let test_data = "Hello, world!";
|
||
let result = serializer.serialize_zero_alloc(&test_data, "string");
|
||
assert!(result.is_ok());
|
||
|
||
let serialized = result.unwrap();
|
||
assert!(!serialized.is_empty());
|
||
|
||
// 测试缓冲区归还
|
||
serializer.return_buffer(serialized);
|
||
|
||
let (available, capacity) = serializer.get_pool_stats();
|
||
assert!(available > 0);
|
||
assert_eq!(capacity, 10);
|
||
}
|
||
} |