4e89b57798
- Expand batch upload list area and optimize preview image layout - Improve upload speed tracking and display (server-side & client-side) - Reduce network detection log noise - Optimize Docker build process (caching, env validation) - Add shadow environment deployment support
341 lines
12 KiB
TypeScript
341 lines
12 KiB
TypeScript
import { NextResponse } from 'next/server'
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import os from 'os'
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import { exec } from 'child_process'
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import { promisify } from 'util'
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import { getAllSessions } from '@/lib/upload-sessions-redis'
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import { transcodeQueue } from '@/lib/video-transcode-queue'
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import { prisma } from '@/lib/prisma'
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import { getNetworkSpeed } from '@/lib/network-bandwidth-monitor'
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const execAsync = promisify(exec)
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// 性能监控 - 快速统计接口（用于悬停卡片）
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// GET /api/performance/quick
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export const dynamic = 'force-dynamic'
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export const revalidate = 0
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// 获取CPU使用率（正确的方法：需要两次采样）
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async function getCpuUsage(): Promise<number> {
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try {
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// 方法1：使用 /proc/stat 两次采样计算CPU使用率（最准确）
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const getCpuStats = async () => {
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const { stdout } = await execAsync("cat /proc/stat | grep '^cpu '")
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const parts = stdout.trim().split(/\s+/)
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return {
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user: parseInt(parts[1]) || 0,
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nice: parseInt(parts[2]) || 0,
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system: parseInt(parts[3]) || 0,
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idle: parseInt(parts[4]) || 0,
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iowait: parseInt(parts[5]) || 0,
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irq: parseInt(parts[6]) || 0,
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softirq: parseInt(parts[7]) || 0,
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}
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}
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const stats1 = await getCpuStats()
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await new Promise(resolve => setTimeout(resolve, 1000)) // 等待1秒
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const stats2 = await getCpuStats()
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const total1 = stats1.user + stats1.nice + stats1.system + stats1.idle + stats1.iowait + stats1.irq + stats1.softirq
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const total2 = stats2.user + stats2.nice + stats2.system + stats2.idle + stats2.iowait + stats2.irq + stats2.softirq
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const totalDiff = total2 - total1
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if (totalDiff > 0) {
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const idleDiff = stats2.idle - stats1.idle
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const usedDiff = totalDiff - idleDiff
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const cpuUsage = (usedDiff / totalDiff) * 100
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return Math.min(Math.max(Math.round(cpuUsage), 0), 100)
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}
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} catch (error) {
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console.warn('[PerformanceQuick] Failed to get CPU usage from /proc/stat:', error)
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}
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try {
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// 方法2：尝试使用 top 命令（批处理模式，更准确）
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const { stdout } = await execAsync("top -bn2 -d1 | grep 'Cpu(s)' | tail -1 | awk '{print $2}' | sed 's/%us,//'", { timeout: 3000 })
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const cpuUsage = parseFloat(stdout.trim())
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if (!isNaN(cpuUsage) && cpuUsage >= 0 && cpuUsage <= 100) {
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return Math.round(cpuUsage)
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}
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} catch (error) {
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console.warn('[PerformanceQuick] Failed to get CPU usage from top:', error)
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}
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try {
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// 方法3：使用 vmstat（如果可用）
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const { stdout } = await execAsync("vmstat 1 2 | tail -1 | awk '{print 100-$15}'", { timeout: 3000 })
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const cpuUsage = parseFloat(stdout.trim())
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if (!isNaN(cpuUsage) && cpuUsage >= 0 && cpuUsage <= 100) {
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return Math.round(cpuUsage)
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}
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} catch (error) {
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console.warn('[PerformanceQuick] Failed to get CPU usage from vmstat:', error)
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}
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// 降级方案：使用负载平均值估算（不准确，但总比没有好）
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const loadAvg = os.loadavg()[0]
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const cpuCount = os.cpus().length
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// 负载平均值反映系统负载，但不等同于CPU使用率
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// 负载平均值 = CPU核心数时，表示CPU满载
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// 这里做一个粗略估算：负载平均值 / CPU核心数 * 100，但限制在100%以内
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const estimatedUsage = Math.min(Math.round((loadAvg / cpuCount) * 100), 100)
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console.warn(`[PerformanceQuick] Using loadavg estimation: load=${loadAvg}, cores=${cpuCount}, estimated=${estimatedUsage}%`)
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return estimatedUsage
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}
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export async function GET() {
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try {
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// 1. 系统性能（先用 Node.js os 模块，后续可接 TrueNAS）
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const loadAvg = os.loadavg() // [1min, 5min, 15min]
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const totalMem = os.totalmem()
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const freeMem = os.freemem()
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const usedMem = totalMem - freeMem
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const memPercentage = totalMem > 0 ? Math.round((usedMem / totalMem) * 100) : 0
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// 获取更准确的CPU使用率
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const cpuUsage = await getCpuUsage()
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const cpu = {
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usage: cpuUsage,
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loadAverage: loadAvg,
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}
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const memory = {
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used: Math.round(usedMem / (1024 * 1024)), // MB
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total: Math.round(totalMem / (1024 * 1024)), // MB
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percentage: memPercentage,
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}
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// 获取网络带宽使用情况
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const networkSpeed = await getNetworkSpeed()
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const network = {
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uploadSpeed: networkSpeed?.uploadSpeed || 0, // 字节/秒
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downloadSpeed: networkSpeed?.downloadSpeed || 0, // 字节/秒
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interface: networkSpeed?.interface || 'unknown', // 使用的网卡
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}
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// 2. 上传队列（从 Redis 获取，只显示真正活跃的上传）
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const uploadProcessing: any[] = []
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const uploadWaiting: any[] = []
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const uploadUserIds = new Set<string>()
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const uploadIds = new Set<string>()
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// 从 Redis 获取所有上传会话
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const uploadSessions = await getAllSessions()
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// 只显示真正进行中的上传（进度 < 100% 且最近有活动）
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const now = Date.now()
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// 说明：
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// - lastActivityAt：每个 chunk 上传完成后更新（更能代表“是否真的在上传”）
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// - createdAt：只能代表会话创建时间，不能代表持续活跃
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const ACTIVE_THRESHOLD = 5 * 60 * 1000 // 5分钟内有活动 → 认为“活跃”
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const MAX_SHOW_THRESHOLD = 60 * 60 * 1000 // 超过1小时无活动 → 不再展示（避免长期误导）
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uploadSessions.forEach((session, uploadId) => {
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const completedChunks = session.chunks.size
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const totalChunks = session.totalChunks || 1
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const progress = Math.min(
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100,
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Math.round((completedChunks / totalChunks) * 100)
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)
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const lastActivityAt = (session as any).lastActivityAt || session.createdAt || 0
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const inactiveMs = lastActivityAt > 0 ? now - lastActivityAt : Number.POSITIVE_INFINITY
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const isActive = inactiveMs >= 0 && inactiveMs < ACTIVE_THRESHOLD
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const isTooOld = inactiveMs >= MAX_SHOW_THRESHOLD
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// 只展示：未完成 + 未超过“最大无活动时间”
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// 注意：即使不是“活跃”，也会展示（并返回 inactiveMs），用于解释“队列里有任务但网速很低”的情况
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if (progress < 100) {
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if (!isTooOld) {
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uploadUserIds.add(session.userId)
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uploadIds.add(uploadId)
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uploadProcessing.push({
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uploadId,
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fileName: session.fileName,
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progress,
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userId: session.userId,
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currentSpeed: (session as any).currentSpeed || 0, // 上传速度（字节/秒，估算）
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uploadedBytes: (session as any).uploadedBytes || 0,
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lastActivityAt,
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inactiveMs,
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isActive,
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})
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}
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}
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})
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// 批量查询用户信息
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const userInfoMap = new Map<string, { username: string; realName?: string }>()
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if (uploadUserIds.size > 0) {
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try {
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const users = await prisma.user.findMany({
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where: {
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id: { in: Array.from(uploadUserIds) },
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} as any,
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select: {
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id: true,
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username: true,
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realName: true,
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} as any,
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})
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users.forEach((user: any) => {
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userInfoMap.set(user.id, {
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username: user.username || '未知',
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realName: user.realName || undefined,
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})
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})
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} catch (error) {
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console.error('[PerformanceQuick] Failed to fetch user info:', error)
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}
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}
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// 尝试通过uploadId查询已创建的material记录（获取materialType）
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const uploadMaterialInfoMap = new Map<string, { materialType: string }>()
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if (uploadIds.size > 0) {
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try {
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const materials = await prisma.material.findMany({
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where: {
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uploadId: { in: Array.from(uploadIds) },
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deletedAt: null,
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} as any,
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select: {
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uploadId: true,
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materialType: true,
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} as any,
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})
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materials.forEach((m: any) => {
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if (m.uploadId) {
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uploadMaterialInfoMap.set(m.uploadId, {
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materialType: m.materialType || '未知类型',
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})
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}
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})
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} catch (error) {
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console.error('[PerformanceQuick] Failed to fetch upload material info:', error)
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}
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}
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// 添加用户名和素材类型到上传队列
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const uploadProcessingWithUsers = uploadProcessing.map((task) => {
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const userInfo = userInfoMap.get(task.userId) || { username: '未知用户' }
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const materialInfo = uploadMaterialInfoMap.get(task.uploadId) || { materialType: '未知类型' }
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return {
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...task,
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username: userInfo.realName || userInfo.username || '未知用户',
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materialType: materialInfo.materialType,
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}
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})
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const uploadQueue = {
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processing: uploadProcessingWithUsers,
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waiting: uploadWaiting,
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total: uploadProcessingWithUsers.length + uploadWaiting.length,
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}
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// 3. 转码队列（基于 transcodeQueue）
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const status = transcodeQueue.getStatus()
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const progressObj: Record<string, number> = {}
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const stageObj: Record<string, string> = {}
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// Map 无法直接序列化，转成普通对象
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status.progress.forEach((value, key) => {
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progressObj[key] = value
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})
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status.stages.forEach((value, key) => {
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stageObj[key] = value
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})
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// 查询转码队列中的素材信息（文件名、类型、上传人等）
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const processingMaterialIds = status.processingIds
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const waitingMaterialIds = status.waiting
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const allMaterialIds = [...processingMaterialIds, ...waitingMaterialIds]
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const materialInfoMap = new Map<string, any>()
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if (allMaterialIds.length > 0) {
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try {
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const materials = await prisma.material.findMany({
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where: {
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id: { in: allMaterialIds },
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deletedAt: null,
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} as any,
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select: {
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id: true,
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zipFileName: true,
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materialType: true,
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authorName: true,
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user: {
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select: {
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username: true,
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},
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},
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} as any,
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})
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materials.forEach((m: any) => {
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materialInfoMap.set(m.id, {
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fileName: m.zipFileName || '未知文件',
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materialType: m.materialType || '未知类型',
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authorName: m.authorName || m.user?.username || '未知用户',
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})
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})
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} catch (error) {
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console.error('[PerformanceQuick] Failed to fetch material info:', error)
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}
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}
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const transcodeQueueData = {
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processing: processingMaterialIds.map((id) => {
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const info = materialInfoMap.get(id) || {
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fileName: '未知文件',
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materialType: '未知类型',
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authorName: '未知用户',
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}
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return {
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materialId: id,
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fileName: info.fileName,
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materialType: info.materialType,
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authorName: info.authorName,
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progress: progressObj[id] ?? null,
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stage: stageObj[id] ?? null,
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}
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}),
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waiting: waitingMaterialIds.map((id, index) => {
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const info = materialInfoMap.get(id) || {
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fileName: '未知文件',
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materialType: '未知类型',
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authorName: '未知用户',
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}
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return {
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materialId: id,
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fileName: info.fileName,
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materialType: info.materialType,
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authorName: info.authorName,
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queuePosition: index + 1,
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}
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}),
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total: status.queueLength,
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maxConcurrent: status.maxConcurrent,
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}
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return NextResponse.json({
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cpu,
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memory,
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network,
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uploadQueue,
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transcodeQueue: transcodeQueueData,
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})
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} catch (error) {
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console.error('[PerformanceQuick] 获取性能数据失败:', error)
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return NextResponse.json(
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{ error: '获取性能数据失败' },
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{ status: 500 }
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)
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}
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}
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