feat(metrics): optimize stats collection and enhance error handling

- Refactored the GetStats method to improve memory management by utilizing an object pool for stats initialization.
- Enhanced error rate calculation and added average response time metrics for better performance insights.
- Streamlined path and referer statistics collection, ensuring only relevant data is processed and displayed.
- Improved sorting and selection logic for top paths and referers, enhancing the clarity of metrics reporting.
- Updated comments for better code readability and understanding of the metrics collection process.

These changes enhance the efficiency and accuracy of the metrics collector, providing more reliable performance data and insights.

internal/metrics/collector.go

@@ -277,13 +277,15 @@ func (c *Collector) GetStats() map[string]interface{} {
 		}
 	}
 
+	// 确保所有字段都被初始化
+	stats := c.statsPool.Get().(map[string]interface{})
+	defer c.statsPool.Put(stats)
+
 	var m runtime.MemStats
 	runtime.ReadMemStats(&m)
 
-	// 确保所有字段都被初始化
-	stats := make(map[string]interface{})
-
-	// 基础指标 - 合并当前会话和持久化的数据
+	// 基础指标
+	uptime := time.Since(c.startTime)
 	currentRequests := atomic.LoadInt64(&c.totalRequests)
 	currentErrors := atomic.LoadInt64(&c.totalErrors)
 	currentBytes := c.totalBytes.Load()
@@ -292,98 +294,97 @@ func (c *Collector) GetStats() map[string]interface{} {
 	totalErrors := currentErrors + c.persistentStats.totalErrors.Load()
 	totalBytes := currentBytes + c.persistentStats.totalBytes.Load()
 
-	// 计算每秒指标
-	uptime := time.Since(c.startTime).Seconds()
-	stats["requests_per_second"] = float64(currentRequests) / Max(uptime, 1)
-	stats["bytes_per_second"] = float64(currentBytes) / Max(uptime, 1)
+	// 计算错误率
+	var errorRate float64
+	if totalRequests > 0 {
+		errorRate = float64(totalErrors) / float64(totalRequests)
+	}
 
+	// 基础指标
+	stats["uptime"] = uptime.String()
 	stats["active_requests"] = atomic.LoadInt64(&c.activeRequests)
 	stats["total_requests"] = totalRequests
 	stats["total_errors"] = totalErrors
+	stats["error_rate"] = errorRate
 	stats["total_bytes"] = totalBytes
+	stats["bytes_per_second"] = float64(currentBytes) / Max(uptime.Seconds(), 1)
+	stats["requests_per_second"] = float64(currentRequests) / Max(uptime.Seconds(), 1)
 
 	// 系统指标
 	stats["num_goroutine"] = runtime.NumGoroutine()
 	stats["memory_usage"] = FormatBytes(m.Alloc)
 
 	// 延迟指标
-	currentTotalRequests := atomic.LoadInt64(&c.totalRequests)
 	latencySum := c.latencySum.Load()
-	if currentTotalRequests <= 0 || latencySum <= 0 {
-		stats["avg_latency"] = int64(0)
+	if currentRequests > 0 {
+		stats["avg_response_time"] = FormatDuration(time.Duration(latencySum / currentRequests))
 	} else {
-		stats["avg_latency"] = latencySum / currentTotalRequests
+		stats["avg_response_time"] = FormatDuration(0)
 	}
 
 	// 状态码统计
 	statusStats := make(map[string]int64)
 	for i := range c.statusStats {
-		if i < len(c.statusStats) {
-			statusStats[fmt.Sprintf("%dxx", i+1)] = c.statusStats[i].Load()
-		}
+		statusStats[fmt.Sprintf("%dxx", i+1)] = c.statusStats[i].Load()
 	}
 	stats["status_code_stats"] = statusStats
 
-	// 获取Top 10路径统计
-	var pathMetrics []models.PathMetrics
-	var allPaths []models.PathMetrics
+	// 延迟百分位数
+	stats["latency_percentiles"] = make([]float64, 0)
 
+	// 路径统计
+	var pathMetrics []models.PathMetrics
 	c.pathStats.Range(func(key, value interface{}) bool {
 		stats := value.(*models.PathStats)
-		if stats.Requests.Load() == 0 {
-			return true
+		if stats.Requests.Load() > 0 {
+			pathMetrics = append(pathMetrics, models.PathMetrics{
+				Path:             key.(string),
+				RequestCount:     stats.Requests.Load(),
+				ErrorCount:       stats.Errors.Load(),
+				AvgLatency:       formatAvgLatency(stats.LatencySum.Load(), stats.Requests.Load()),
+				BytesTransferred: stats.Bytes.Load(),
+			})
 		}
-		allPaths = append(allPaths, models.PathMetrics{
-			Path:             key.(string),
-			RequestCount:     stats.Requests.Load(),
-			ErrorCount:       stats.Errors.Load(),
-			AvgLatency:       formatAvgLatency(stats.LatencySum.Load(), stats.Requests.Load()),
-			BytesTransferred: stats.Bytes.Load(),
-		})
 		return true
 	})
 
-	// 按请求数排序并获取前10个
-	sort.Slice(allPaths, func(i, j int) bool {
-		return allPaths[i].RequestCount > allPaths[j].RequestCount
+	// 按请求数排序
+	sort.Slice(pathMetrics, func(i, j int) bool {
+		return pathMetrics[i].RequestCount > pathMetrics[j].RequestCount
 	})
 
-	if len(allPaths) > 10 {
-		pathMetrics = allPaths[:10]
+	if len(pathMetrics) > 10 {
+		stats["top_paths"] = pathMetrics[:10]
 	} else {
-		pathMetrics = allPaths
+		stats["top_paths"] = pathMetrics
 	}
-	stats["top_paths"] = pathMetrics
 
-	// 获取最近请求
+	// 最近请求
 	stats["recent_requests"] = c.getRecentRequests()
 
-	// 获取Top 10引用来源
+	// 引用来源统计
 	var refererMetrics []models.PathMetrics
-	var allReferers []models.PathMetrics
 	c.refererStats.Range(func(key, value interface{}) bool {
 		stats := value.(*models.PathStats)
-		if stats.Requests.Load() == 0 {
-			return true
+		if stats.Requests.Load() > 0 {
+			refererMetrics = append(refererMetrics, models.PathMetrics{
+				Path:         key.(string),
+				RequestCount: stats.Requests.Load(),
+			})
 		}
-		allReferers = append(allReferers, models.PathMetrics{
-			Path:         key.(string),
-			RequestCount: stats.Requests.Load(),
-		})
 		return true
 	})
 
-	// 按请求数排序并获取前10个
-	sort.Slice(allReferers, func(i, j int) bool {
-		return allReferers[i].RequestCount > allReferers[j].RequestCount
+	// 按请求数排序
+	sort.Slice(refererMetrics, func(i, j int) bool {
+		return refererMetrics[i].RequestCount > refererMetrics[j].RequestCount
 	})
 
-	if len(allReferers) > 10 {
-		refererMetrics = allReferers[:10]
+	if len(refererMetrics) > 10 {
+		stats["top_referers"] = refererMetrics[:10]
 	} else {
-		refererMetrics = allReferers
+		stats["top_referers"] = refererMetrics
 	}
-	stats["top_referers"] = refererMetrics
 
 	// 检查告警
 	c.monitor.CheckMetrics(stats)