fix: 限制图片视频处理内存峰值
This commit is contained in:
chenpei
parent
88c98cc081
commit
e2f4911268
@ -100,7 +100,11 @@ export async function POST(request: NextRequest) {
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// 读取图片
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const buffer = await readFile(imagePath)
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const metadata = await sharp(buffer).metadata()
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const metadata = await sharp(buffer, {
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limitInputPixels: 60_000_000,
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sequentialRead: true,
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failOn: 'error',
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}).metadata()
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console.log('[Regenerate Preview] Image size:', metadata.width, 'x', metadata.height)
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// 切片处理
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@ -22,6 +22,10 @@ import {
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} from '@/lib/standard-model-white-background'
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import { normalizeStandardModelWhiteBackgroundSourceType } from '@/lib/standard-model-white-background-metadata'
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import { uploadStandardModelWhiteBackground } from '@/lib/standard-model-white-background-service'
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import {
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STANDARD_MODEL_WHITE_BG_UPLOAD_LIMITS,
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WhiteBackgroundUploadValidationError,
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} from '@/lib/standard-model-white-background-upload-guard'
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export const runtime = 'nodejs'
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@ -180,6 +184,22 @@ export async function POST(request: NextRequest) {
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let idempotencyRecordId: string | null = null
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try {
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const contentLength = Number(request.headers.get('content-length'))
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if (!Number.isFinite(contentLength) || contentLength <= 0) {
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await writeApiLog({ apiKeyId: keyRecord.id, trace, statusCode: 411 })
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return finalizeResponse(
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buildErrorResponse(411, 'CONTENT_LENGTH_REQUIRED', '必须提供有效的请求大小'),
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trace,
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)
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}
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if (contentLength > STANDARD_MODEL_WHITE_BG_UPLOAD_LIMITS.maxRequestBytes) {
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await writeApiLog({ apiKeyId: keyRecord.id, trace, statusCode: 413 })
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return finalizeResponse(
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buildErrorResponse(413, 'REQUEST_TOO_LARGE', '白底图上传请求过大'),
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trace,
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)
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}
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const formData = await request.formData()
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const clientRequestId = String(formData.get('clientRequestId') || '').trim()
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const barcode = String(formData.get('barcode') || '').trim()
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@ -298,6 +318,14 @@ export async function POST(request: NextRequest) {
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return finalizeResponse(buildErrorResponse(error.status, error.code, error.message), trace)
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}
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if (error instanceof WhiteBackgroundUploadValidationError) {
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await writeApiLog({ apiKeyId: keyRecord.id, trace, statusCode: 400 })
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return finalizeResponse(
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buildErrorResponse(400, 'INVALID_IMAGE_UPLOAD', error.message),
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trace,
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)
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}
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const message = error instanceof Error ? error.message : '上传白底图失败'
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console.error('[external/standard-model-white-background/by-barcode] POST error:', error)
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await writeApiLog({ apiKeyId: keyRecord.id, trace, statusCode: 500 })
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@ -24,6 +24,10 @@ import {
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} from '@/lib/standard-model-white-background'
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import { normalizeStandardModelWhiteBackgroundSourceType } from '@/lib/standard-model-white-background-metadata'
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import { uploadStandardModelWhiteBackground } from '@/lib/standard-model-white-background-service'
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import {
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STANDARD_MODEL_WHITE_BG_UPLOAD_LIMITS,
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WhiteBackgroundUploadValidationError,
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} from '@/lib/standard-model-white-background-upload-guard'
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export const runtime = 'nodejs'
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@ -574,6 +578,22 @@ export async function POST(request: NextRequest) {
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let idempotencyRecordId: string | null = null
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try {
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const contentLength = Number(request.headers.get('content-length'))
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if (!Number.isFinite(contentLength) || contentLength <= 0) {
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await writeApiLog({ apiKeyId: keyRecord.id, trace, statusCode: 411 })
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return finalizeResponse(
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buildErrorResponse(411, 'CONTENT_LENGTH_REQUIRED', '必须提供有效的请求大小'),
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trace,
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)
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}
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if (contentLength > STANDARD_MODEL_WHITE_BG_UPLOAD_LIMITS.maxRequestBytes) {
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await writeApiLog({ apiKeyId: keyRecord.id, trace, statusCode: 413 })
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return finalizeResponse(
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buildErrorResponse(413, 'REQUEST_TOO_LARGE', '白底图上传请求过大'),
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trace,
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)
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}
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const formData = await request.formData()
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const clientRequestId = getFormText(formData, 'clientRequestId')
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const materialType = getFormText(formData, 'materialType')
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@ -746,6 +766,14 @@ export async function POST(request: NextRequest) {
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return finalizeResponse(buildErrorResponse(error.status, error.code, error.message), trace)
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}
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if (error instanceof WhiteBackgroundUploadValidationError) {
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await writeApiLog({ apiKeyId: keyRecord.id, trace, statusCode: 400 })
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return finalizeResponse(
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buildErrorResponse(400, 'INVALID_IMAGE_UPLOAD', error.message),
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trace,
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)
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}
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const message = error instanceof Error ? error.message : '上传白底图失败'
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console.error('[external/standard-model-white-background/by-spu] POST error:', error)
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await writeApiLog({ apiKeyId: keyRecord.id, trace, statusCode: 500 })
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@ -426,10 +426,11 @@ export async function POST(request: NextRequest) {
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console.log('[Upload] Preview image uploaded:', previewUrl);
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}
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// 处理其他预览图（作为展示图）- 并行处理以提高速度
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// 处理其他预览图（作为展示图）。图片解码会使用 Sharp 原生内存，
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// 这里必须串行处理，避免一个请求内同时解码多张大图。
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// 详情页/详情模板：其他预览图也作为展示图处理
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if (previewImages.length > 0 && !['模特'].includes(materialType)) {
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console.log(`[Upload] Processing ${previewImages.length} preview images in parallel...`);
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console.log(`[Upload] Processing ${previewImages.length} preview images sequentially...`);
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// 验证所有图片大小
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for (let i = 0; i < previewImages.length; i++) {
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@ -441,8 +442,8 @@ export async function POST(request: NextRequest) {
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}
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}
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// 并行处理所有预览图
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const uploadPromises = previewImages.map(async (image, index) => {
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for (let index = 0; index < previewImages.length; index += 1) {
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const image = previewImages[index]
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console.log(`[Upload] Processing preview image ${index + 2}/${previewImages.length + 1}...`);
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const buffer = Buffer.from(await image.arrayBuffer());
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@ -464,12 +465,8 @@ export async function POST(request: NextRequest) {
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);
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uploadedFileUrls.push(url); // 记录已上传的文件，用于失败时清理
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console.log(`[Upload] Preview image ${index + 2} uploaded:`, url);
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return url;
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});
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// 等待所有预览图上传完成
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const urls = await Promise.all(uploadPromises);
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displayImages.push(...urls);
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displayImages.push(url);
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}
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console.log(`[Upload] All ${previewImages.length} preview images uploaded successfully`);
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}
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44
docs/生产图片视频内存事故与处理记录-2026-07-31.md
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44
docs/生产图片视频内存事故与处理记录-2026-07-31.md
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@ -0,0 +1,44 @@
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# 生产图片/视频处理内存事故与处理记录
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日期：2026-07-31
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## 结论
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这次“处理图片后整个服务崩掉”不是单一 Node.js 请求超出 6GB 容器限制，而是生产宿主机发生了全局内存耗尽（`global_oom`）。
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真正的内存来源分两层：
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1. 宿主机的 TrueNAS `middlewared` 长时间占用异常高的内存，历史峰值约 9.54GiB，并设置了 `oom_score_adj=-1000`，内核不会优先杀它。
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2. 应用容器内的 `libvips`（Sharp 的底层图片库）和 `ffmpeg` 在处理大图/视频时产生原生内存尖峰。它们是把整机推过 OOM 阈值的触发器，内核随后杀掉了 `next-server` 或 MinIO。
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因此，单纯把 Node.js 堆内存或应用容器上限调大不能解决问题，反而可能更快吃光宿主机余量。
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## 已核实的生产时间线
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| 时间（北京时间） | 生产证据 | 业务对应 |
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| --- | --- | --- |
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| 7 月 30 日 15:18 | `libvips worker` 触发 `global_oom`；内核杀掉 `next-server`，其匿名 RSS 约 1.405GiB | 同时段任务详情完成接口进入 `sharp.metadata()`、`sliceImage/compressImage`，Nginx 随后出现 502 |
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| 7 月 30 日 16:18 | 同一应用容器同时有两个 `ffmpeg`，RSS 约 642MiB 和 372–408MiB；之后 MinIO 也被杀 | 同时段连续创建淘系视频任务；转码队列未配置时默认并发为 2 |
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| 7 月 30 日 17:39 | `libvips worker` 再次触发 `global_oom`；同时还有一个约 325MiB 的 `ffmpeg`，最终 `next-server` 被杀 | 图片任务详情和抖音视频任务叠加，随后大量 502/504 |
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三次事件都显示：宿主机没有 Swap，且是 `global_oom`，不是应用容器自己的 cgroup OOM。
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## 本次代码处理
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- Sharp 默认并发降为 1，限制 libvips 缓存和输入像素，并增加进程级图片处理门闩。
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- 白底图内部和外部接口统一增加请求大小、单图大小、图片数量、格式、像素和动图门禁；服务层再次校验，防止新调用方绕过路由。
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- 主上传的多张预览图改为串行处理，不再通过 `Promise.all` 同时解码多张图片。
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- 任务详情预览在第一次 `sharp.metadata()` 前增加像素上限；长图限制最大尺寸和切片数。
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- 视频转码队列固定为同一容器最多 1 个任务，FFmpeg 默认 1 线程，并限制进度日志缓存。
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- 同批次关闭上传路径越界、任意文件删除、跨用户白底图替换等高危入口，并移除头像菜单中的网络诊断/缓存刷新入口。
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## 仍需运维处理的风险
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代码修复不能替代宿主机治理。生产运维需要单独完成：
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- 查明 `middlewared` 从正常内存增长到数 GiB 的原因，并设置可用内存告警。
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- 评估在维护窗口增加 Swap 或等效内存缓冲；这不是永久修复，只是降低瞬时 OOM 概率。
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- 评估把 FFmpeg/Sharp 拆到独立 worker，避免媒体任务与 Next.js、MinIO 共用进程/容器。
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- 持续采集容器 RSS、cgroup `memory.events`、宿主机可用内存和 OOM 日志。
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本次排查未重启、杀进程、清理生产数据或修改生产部署脚本。
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@ -82,6 +82,7 @@ describe('external standard-model-white-background by barcode routes', () => {
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headers: new Headers({
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'X-API-Key': 'test-key',
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'user-agent': 'jest',
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'content-length': '1024',
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}),
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formData: jest.fn().mockResolvedValue(formData),
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} as any
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@ -154,6 +155,20 @@ describe('external standard-model-white-background by barcode routes', () => {
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expect(uploadStandardModelWhiteBackground).not.toHaveBeenCalled()
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})
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test('缺少请求大小时在解析 multipart 前拒绝', async () => {
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const request = {
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headers: new Headers({ 'X-API-Key': 'test-key' }),
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formData: jest.fn(),
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} as any
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const response = await uploadByBarcode(request)
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const body = await response.json()
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expect(response.status).toBe(411)
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expect(body.error.code).toBe('CONTENT_LENGTH_REQUIRED')
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expect(request.formData).not.toHaveBeenCalled()
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})
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test('cleanup 只按条码解析到的 SKU 调用 SKU 范围清理', async () => {
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const response = await cleanupByBarcode(
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createCleanupRequest({
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@ -95,6 +95,7 @@ describe('external standard-model-white-background by spu route', () => {
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headers: new Headers({
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'X-API-Key': 'test-key',
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'user-agent': 'jest',
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'content-length': '1024',
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}),
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formData: jest.fn().mockResolvedValue(formData),
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} as any
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46
lib/image-processing-limiter.ts
Normal file
46
lib/image-processing-limiter.ts
Normal file
@ -0,0 +1,46 @@
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const DEFAULT_MAX_CONCURRENT_IMAGE_JOBS = 1
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const MAX_ALLOWED_IMAGE_JOBS = 2
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function readMaxConcurrentImageJobs(): number {
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const configured = Number.parseInt(process.env.IMAGE_PROCESSING_MAX_CONCURRENT || '', 10)
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if (!Number.isFinite(configured)) {
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return DEFAULT_MAX_CONCURRENT_IMAGE_JOBS
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}
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return Math.max(1, Math.min(MAX_ALLOWED_IMAGE_JOBS, configured))
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}
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const maxConcurrentImageJobs = readMaxConcurrentImageJobs()
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let activeImageJobs = 0
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const waitingImageJobs: Array<() => void> = []
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async function acquireImageProcessingSlot(): Promise<void> {
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if (activeImageJobs < maxConcurrentImageJobs) {
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activeImageJobs += 1
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return
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}
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await new Promise<void>((resolve) => waitingImageJobs.push(resolve))
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activeImageJobs += 1
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}
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function releaseImageProcessingSlot(): void {
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activeImageJobs = Math.max(0, activeImageJobs - 1)
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const next = waitingImageJobs.shift()
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if (next) {
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next()
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}
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}
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/**
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* 跨请求限制 Sharp 任务数。单个请求内串行并不能阻止多个请求同时解码大图，
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* 所以白底图、预览图和长图处理都必须共用这个进程级门闩。
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*/
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export async function withImageProcessingSlot<T>(task: () => Promise<T>): Promise<T> {
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await acquireImageProcessingSlot()
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try {
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return await task()
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} finally {
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releaseImageProcessingSlot()
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}
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}
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@ -1,4 +1,32 @@
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import sharp from 'sharp'
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import { withImageProcessingSlot } from '@/lib/image-processing-limiter'
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import { configureSharpRuntime } from '@/lib/sharp-runtime'
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configureSharpRuntime()
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const IMAGE_PROCESSING_MAX_PIXELS = 60_000_000
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const IMAGE_PROCESSING_MAX_DIMENSION = 20_000
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const IMAGE_PROCESSING_MAX_SLICES = 60
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function createSharpInput(buffer: Buffer) {
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return sharp(buffer, {
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limitInputPixels: IMAGE_PROCESSING_MAX_PIXELS,
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sequentialRead: true,
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failOn: 'error',
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})
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}
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function assertProcessableMetadata(metadata: sharp.Metadata) {
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if (!metadata.width || !metadata.height) {
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throw new Error('无法获取图片尺寸')
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}
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if (metadata.width > IMAGE_PROCESSING_MAX_DIMENSION || metadata.height > IMAGE_PROCESSING_MAX_DIMENSION) {
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throw new Error('图片尺寸过大')
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}
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if (Number(metadata.pages || 1) > 1) {
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throw new Error('不支持动图或多页图片')
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}
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}
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interface ProcessImageOptions {
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maxSizeKB?: number // 最大文件大小（KB）
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@ -24,63 +52,66 @@ export async function processImage(
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format = 'webp', // 默认webP格式
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} = options
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try {
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// 获取原始图片信息
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const image = sharp(buffer)
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const metadata = await image.metadata()
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return withImageProcessingSlot(async () => {
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try {
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// 获取原始图片信息
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const image = createSharpInput(buffer)
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const metadata = await image.metadata()
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assertProcessableMetadata(metadata)
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// 计算新尺寸（保持比例，同时限制宽度和高度）
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let width = metadata.width || maxWidth
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let height = metadata.height || maxHeight
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// 计算新尺寸（保持比例，同时限制宽度和高度）
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let width = metadata.width || maxWidth
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let height = metadata.height || maxHeight
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// 如果宽度超过限制
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if (width > maxWidth) {
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const ratio = maxWidth / width
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width = maxWidth
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height = Math.round(height * ratio)
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}
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// 如果宽度超过限制
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if (width > maxWidth) {
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const ratio = maxWidth / width
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width = maxWidth
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height = Math.round(height * ratio)
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}
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// 如果高度超过限制，再次调整
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if (height > maxHeight) {
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const ratio = maxHeight / height
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height = maxHeight
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width = Math.round(width * ratio)
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}
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// 如果高度超过限制，再次调整
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if (height > maxHeight) {
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const ratio = maxHeight / height
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height = maxHeight
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width = Math.round(width * ratio)
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}
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// 优化：初始质量从80降到70，在保证视觉质量的前提下减少文件大小
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let quality = 70
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// 优化：初始质量从80降到70，在保证视觉质量的前提下减少文件大小
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let quality = 70
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let processedBuffer: Buffer
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let processedBuffer: Buffer
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|
||||
// 第一次处理
|
||||
processedBuffer = await image
|
||||
.resize(width, height, {
|
||||
fit: 'inside',
|
||||
withoutEnlargement: true,
|
||||
})
|
||||
.webp({ quality })
|
||||
.toBuffer()
|
||||
|
||||
// 如果文件还是太大，逐步降低质量
|
||||
const maxBytes = maxSizeKB * 1024
|
||||
while (processedBuffer.length > maxBytes && quality > 20) {
|
||||
quality -= 10
|
||||
processedBuffer = await sharp(buffer)
|
||||
// 第一次处理
|
||||
processedBuffer = await createSharpInput(buffer)
|
||||
.resize(width, height, {
|
||||
fit: 'inside',
|
||||
withoutEnlargement: true,
|
||||
})
|
||||
.webp({ quality })
|
||||
.toBuffer()
|
||||
|
||||
// 如果文件还是太大，逐步降低质量
|
||||
const maxBytes = maxSizeKB * 1024
|
||||
while (processedBuffer.length > maxBytes && quality > 20) {
|
||||
quality -= 10
|
||||
processedBuffer = await createSharpInput(buffer)
|
||||
.resize(width, height, {
|
||||
fit: 'inside',
|
||||
withoutEnlargement: true,
|
||||
})
|
||||
.webp({ quality })
|
||||
.toBuffer()
|
||||
}
|
||||
|
||||
console.log(`图片处理完成: ${metadata.width}x${metadata.height} -> ${width}x${height}, ${(buffer.length / 1024).toFixed(2)}KB -> ${(processedBuffer.length / 1024).toFixed(2)}KB, 质量: ${quality}`)
|
||||
|
||||
return processedBuffer
|
||||
} catch (error) {
|
||||
console.error('图片处理失败:', error)
|
||||
throw new Error('图片处理失败')
|
||||
}
|
||||
|
||||
console.log(`图片处理完成: ${metadata.width}x${metadata.height} -> ${width}x${height}, ${(buffer.length / 1024).toFixed(2)}KB -> ${(processedBuffer.length / 1024).toFixed(2)}KB, 质量: ${quality}`)
|
||||
|
||||
return processedBuffer
|
||||
} catch (error) {
|
||||
console.error('图片处理失败:', error)
|
||||
throw new Error('图片处理失败')
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
/**
|
||||
@ -103,62 +134,50 @@ export async function sliceImage(
|
||||
maxHeight: number = 1500,
|
||||
maxSizeKB: number = 500
|
||||
): Promise<Buffer[]> {
|
||||
try {
|
||||
const image = sharp(buffer)
|
||||
const metadata = await image.metadata()
|
||||
|
||||
if (!metadata.width || !metadata.height) {
|
||||
throw new Error('无法获取图片尺寸')
|
||||
}
|
||||
return withImageProcessingSlot(async () => {
|
||||
try {
|
||||
const image = createSharpInput(buffer)
|
||||
const metadata = await image.metadata()
|
||||
assertProcessableMetadata(metadata)
|
||||
|
||||
const { width, height } = metadata
|
||||
const slices: Buffer[] = []
|
||||
const maxBytes = maxSizeKB * 1024
|
||||
const { width, height } = metadata
|
||||
const slices: Buffer[] = []
|
||||
const maxBytes = maxSizeKB * 1024
|
||||
|
||||
// 如果图片高度不超过maxHeight，直接返回原图（压缩处理）
|
||||
if (height <= maxHeight) {
|
||||
let quality = 90
|
||||
let processedBuffer = await image
|
||||
.webp({ quality })
|
||||
.toBuffer()
|
||||
|
||||
// 如果文件还是太大，逐步降低质量
|
||||
while (processedBuffer.length > maxBytes && quality > 20) {
|
||||
quality -= 10
|
||||
processedBuffer = await image
|
||||
if (height <= maxHeight) {
|
||||
let quality = 90
|
||||
let processedBuffer = await createSharpInput(buffer)
|
||||
.webp({ quality })
|
||||
.toBuffer()
|
||||
|
||||
// 如果文件还是太大，逐步降低质量
|
||||
while (processedBuffer.length > maxBytes && quality > 20) {
|
||||
quality -= 10
|
||||
processedBuffer = await createSharpInput(buffer)
|
||||
.webp({ quality })
|
||||
.toBuffer()
|
||||
}
|
||||
|
||||
console.log(`图片无需切割: ${width}x${height}, 压缩到 ${(processedBuffer.length / 1024).toFixed(2)}KB, 质量: ${quality}`)
|
||||
return [processedBuffer]
|
||||
}
|
||||
|
||||
console.log(`图片无需切割: ${width}x${height}, 压缩到 ${(processedBuffer.length / 1024).toFixed(2)}KB, 质量: ${quality}`)
|
||||
return [processedBuffer]
|
||||
}
|
||||
|
||||
// 计算需要切割的片数
|
||||
const sliceCount = Math.ceil(height / maxHeight)
|
||||
console.log(`开始切割图片: ${width}x${height} -> ${sliceCount}个片段，每片限制${maxSizeKB}KB`)
|
||||
const sliceCount = Math.ceil(height / maxHeight)
|
||||
if (sliceCount > IMAGE_PROCESSING_MAX_SLICES) {
|
||||
throw new Error(`图片过长，最多支持 ${IMAGE_PROCESSING_MAX_SLICES} 个切片`)
|
||||
}
|
||||
console.log(`开始切割图片: ${width}x${height} -> ${sliceCount}个片段，每片限制${maxSizeKB}KB`)
|
||||
|
||||
// 切割图片
|
||||
for (let i = 0; i < sliceCount; i++) {
|
||||
const top = i * maxHeight
|
||||
const extractHeight = Math.min(maxHeight, height - top)
|
||||
for (let i = 0; i < sliceCount; i++) {
|
||||
const top = i * maxHeight
|
||||
const extractHeight = Math.min(maxHeight, height - top)
|
||||
|
||||
// 初始质量
|
||||
let quality = 90
|
||||
let sliceBuffer = await sharp(buffer)
|
||||
.extract({
|
||||
left: 0,
|
||||
top: top,
|
||||
width: width,
|
||||
height: extractHeight
|
||||
})
|
||||
.webp({ quality })
|
||||
.toBuffer()
|
||||
|
||||
// 如果文件还是太大，逐步降低质量
|
||||
while (sliceBuffer.length > maxBytes && quality > 20) {
|
||||
quality -= 10
|
||||
sliceBuffer = await sharp(buffer)
|
||||
let quality = 90
|
||||
let sliceBuffer = await createSharpInput(buffer)
|
||||
.extract({
|
||||
left: 0,
|
||||
top: top,
|
||||
@ -167,17 +186,31 @@ export async function sliceImage(
|
||||
})
|
||||
.webp({ quality })
|
||||
.toBuffer()
|
||||
|
||||
// 如果文件还是太大，逐步降低质量
|
||||
while (sliceBuffer.length > maxBytes && quality > 20) {
|
||||
quality -= 10
|
||||
sliceBuffer = await createSharpInput(buffer)
|
||||
.extract({
|
||||
left: 0,
|
||||
top: top,
|
||||
width: width,
|
||||
height: extractHeight
|
||||
})
|
||||
.webp({ quality })
|
||||
.toBuffer()
|
||||
}
|
||||
|
||||
slices.push(sliceBuffer)
|
||||
console.log(`切割片段 ${i + 1}/${sliceCount}: 高度 ${extractHeight}px, 大小 ${(sliceBuffer.length / 1024).toFixed(2)}KB, 质量: ${quality}`)
|
||||
}
|
||||
|
||||
slices.push(sliceBuffer)
|
||||
console.log(`切割片段 ${i + 1}/${sliceCount}: 高度 ${extractHeight}px, 大小 ${(sliceBuffer.length / 1024).toFixed(2)}KB, 质量: ${quality}`)
|
||||
return slices
|
||||
} catch (error) {
|
||||
console.error('图片切割失败:', error)
|
||||
throw new Error('图片切割失败')
|
||||
}
|
||||
|
||||
return slices
|
||||
} catch (error) {
|
||||
console.error('图片切割失败:', error)
|
||||
throw new Error('图片切割失败')
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
@ -11,6 +11,9 @@ import { readFile, unlink, writeFile, open } from 'fs/promises'
|
||||
import { tmpdir } from 'os'
|
||||
import path from 'path'
|
||||
import sharp from 'sharp'
|
||||
import { configureSharpRuntime } from '@/lib/sharp-runtime'
|
||||
|
||||
configureSharpRuntime()
|
||||
|
||||
const execFileAsync = promisify(execFile)
|
||||
|
||||
|
||||
@ -1,4 +1,7 @@
|
||||
import sharp from 'sharp'
|
||||
import { configureSharpRuntime } from '@/lib/sharp-runtime'
|
||||
|
||||
configureSharpRuntime()
|
||||
|
||||
export const PERFORMANCE_SURVEY_IMAGE_LIMITS = {
|
||||
maxSourceCount: 60,
|
||||
|
||||
@ -338,7 +338,15 @@ async function generatePreviewsFromAssets(
|
||||
|
||||
buffer = await readAssetBuffer(asset, type)
|
||||
|
||||
const metadata = await sharp.default(buffer).metadata()
|
||||
if (buffer.length > 200 * 1024 * 1024) {
|
||||
throw new Error(`预览源图片 ${asset.fileName} 不能超过 200MB`)
|
||||
}
|
||||
|
||||
const metadata = await sharp.default(buffer, {
|
||||
limitInputPixels: 60_000_000,
|
||||
sequentialRead: true,
|
||||
failOn: 'error',
|
||||
}).metadata()
|
||||
const shouldSlice = type === 'detail' && metadata.height && metadata.height > 1500
|
||||
const buffers = shouldSlice
|
||||
? await sliceImage(buffer, 1500, 200)
|
||||
|
||||
@ -1,6 +1,9 @@
|
||||
import { readPsd, initializeCanvas } from 'ag-psd'
|
||||
import sharp from 'sharp'
|
||||
import fs from 'fs/promises'
|
||||
import { configureSharpRuntime } from '@/lib/sharp-runtime'
|
||||
|
||||
configureSharpRuntime()
|
||||
|
||||
// 尝试初始化 canvas（如果可用）
|
||||
try {
|
||||
|
||||
43
lib/sharp-runtime.ts
Normal file
43
lib/sharp-runtime.ts
Normal file
@ -0,0 +1,43 @@
|
||||
import sharp from 'sharp'
|
||||
|
||||
const DEFAULT_SHARP_CONCURRENCY = 1
|
||||
const MAX_SHARP_CONCURRENCY = 2
|
||||
const SHARP_CACHE_MEMORY_MB = 128
|
||||
const SHARP_CACHE_ITEMS = 64
|
||||
|
||||
function readConcurrency(): number {
|
||||
const configured = Number.parseInt(process.env.SHARP_CONCURRENCY || '', 10)
|
||||
if (!Number.isFinite(configured)) {
|
||||
return DEFAULT_SHARP_CONCURRENCY
|
||||
}
|
||||
|
||||
return Math.max(1, Math.min(MAX_SHARP_CONCURRENCY, configured))
|
||||
}
|
||||
|
||||
let configured = false
|
||||
|
||||
/**
|
||||
* Sharp 的原生解码/编码内存不受 Node old-space 完全约束。
|
||||
* 生产应用与 Next.js 共用一个容器，因此默认只允许一个 Sharp 任务，
|
||||
* 并关闭磁盘缓存、限制内存缓存，避免并发图片请求把宿主机推入 OOM。
|
||||
*/
|
||||
export function configureSharpRuntime(): void {
|
||||
if (configured) {
|
||||
return
|
||||
}
|
||||
|
||||
// 测试或极简运行时可能只提供 sharp() 工厂，没有全局调优方法；
|
||||
// 这时保留原有行为，不让配置本身阻断业务。
|
||||
if (typeof sharp.concurrency !== 'function' || typeof sharp.cache !== 'function') {
|
||||
configured = true
|
||||
return
|
||||
}
|
||||
|
||||
sharp.concurrency(readConcurrency())
|
||||
sharp.cache({
|
||||
memory: SHARP_CACHE_MEMORY_MB,
|
||||
items: SHARP_CACHE_ITEMS,
|
||||
files: 0,
|
||||
})
|
||||
configured = true
|
||||
}
|
||||
@ -37,6 +37,9 @@ import {
|
||||
} from '@/lib/standard-model-white-background'
|
||||
import type { StandardModelWhiteBackgroundSourceType } from '@/lib/standard-model-white-background-metadata'
|
||||
import { buildWhiteBackgroundWebpVariants } from '@/lib/white-background-preview'
|
||||
import {
|
||||
assertWhiteBackgroundUploadFiles,
|
||||
} from '@/lib/standard-model-white-background-upload-guard'
|
||||
|
||||
type WhiteBackgroundAssetRecord = WhiteBackgroundAssetLike & {
|
||||
id: string
|
||||
@ -197,6 +200,13 @@ export async function uploadStandardModelWhiteBackground(
|
||||
throw new Error('请至少上传 1 张白底图')
|
||||
}
|
||||
|
||||
// 服务层再次执行门禁，避免外部 API 或未来新增调用方绕过路由校验，
|
||||
// 把超大、高像素或动图直接交给 Sharp。
|
||||
await assertWhiteBackgroundUploadFiles({
|
||||
fixedImages: input.fixedImages,
|
||||
otherImages,
|
||||
})
|
||||
|
||||
const existingAssets = await prisma.materialAsset.findMany({
|
||||
where: {
|
||||
materialId: input.materialId,
|
||||
|
||||
@ -2,6 +2,10 @@ import sharp from 'sharp'
|
||||
import type {
|
||||
StandardModelWhiteBgKey,
|
||||
} from '@/lib/standard-model-white-background'
|
||||
import { withImageProcessingSlot } from '@/lib/image-processing-limiter'
|
||||
import { configureSharpRuntime } from '@/lib/sharp-runtime'
|
||||
|
||||
configureSharpRuntime()
|
||||
|
||||
export const STANDARD_MODEL_WHITE_BG_UPLOAD_LIMITS = {
|
||||
maxOtherImages: 12,
|
||||
@ -10,6 +14,7 @@ export const STANDARD_MODEL_WHITE_BG_UPLOAD_LIMITS = {
|
||||
maxTotalBytes: 100 * 1024 * 1024,
|
||||
maxPixelsPerImage: 40_000_000,
|
||||
maxTotalPixels: 160_000_000,
|
||||
maxRequestBytes: 105 * 1024 * 1024,
|
||||
} as const
|
||||
|
||||
const ALLOWED_IMAGE_MIME_TYPES = new Set([
|
||||
@ -111,47 +116,49 @@ export async function assertWhiteBackgroundUploadFiles(input: {
|
||||
throw new WhiteBackgroundUploadValidationError('单次上传白底图总体积不能超过 100MB')
|
||||
}
|
||||
|
||||
let totalPixels = 0
|
||||
for (let index = 0; index < files.length; index++) {
|
||||
const file = files[index]
|
||||
const label = `图片「${file.name || index + 1}」`
|
||||
await withImageProcessingSlot(async () => {
|
||||
let totalPixels = 0
|
||||
for (let index = 0; index < files.length; index++) {
|
||||
const file = files[index]
|
||||
const label = `图片「${file.name || index + 1}」`
|
||||
|
||||
try {
|
||||
const buffer = Buffer.from(await file.arrayBuffer())
|
||||
const metadata = await sharp(buffer, {
|
||||
limitInputPixels: STANDARD_MODEL_WHITE_BG_UPLOAD_LIMITS.maxPixelsPerImage,
|
||||
sequentialRead: true,
|
||||
failOn: 'error',
|
||||
}).metadata()
|
||||
try {
|
||||
const buffer = Buffer.from(await file.arrayBuffer())
|
||||
const metadata = await sharp(buffer, {
|
||||
limitInputPixels: STANDARD_MODEL_WHITE_BG_UPLOAD_LIMITS.maxPixelsPerImage,
|
||||
sequentialRead: true,
|
||||
failOn: 'error',
|
||||
}).metadata()
|
||||
|
||||
if (
|
||||
!metadata.width ||
|
||||
!metadata.height ||
|
||||
!metadata.format ||
|
||||
!ALLOWED_IMAGE_FORMATS.has(metadata.format)
|
||||
) {
|
||||
throw new WhiteBackgroundUploadValidationError(`${label}不是支持的图片`)
|
||||
}
|
||||
if (Number(metadata.pages || 1) > 1) {
|
||||
throw new WhiteBackgroundUploadValidationError(`${label}不支持动图或多页图片`)
|
||||
}
|
||||
if (
|
||||
!metadata.width ||
|
||||
!metadata.height ||
|
||||
!metadata.format ||
|
||||
!ALLOWED_IMAGE_FORMATS.has(metadata.format)
|
||||
) {
|
||||
throw new WhiteBackgroundUploadValidationError(`${label}不是支持的图片`)
|
||||
}
|
||||
if (Number(metadata.pages || 1) > 1) {
|
||||
throw new WhiteBackgroundUploadValidationError(`${label}不支持动图或多页图片`)
|
||||
}
|
||||
|
||||
const pixels = assertWhiteBackgroundImageDimensions(
|
||||
metadata.width,
|
||||
metadata.height,
|
||||
label,
|
||||
)
|
||||
totalPixels += pixels
|
||||
if (totalPixels > STANDARD_MODEL_WHITE_BG_UPLOAD_LIMITS.maxTotalPixels) {
|
||||
throw new WhiteBackgroundUploadValidationError('单次上传白底图总像素量过大')
|
||||
const pixels = assertWhiteBackgroundImageDimensions(
|
||||
metadata.width,
|
||||
metadata.height,
|
||||
label,
|
||||
)
|
||||
totalPixels += pixels
|
||||
if (totalPixels > STANDARD_MODEL_WHITE_BG_UPLOAD_LIMITS.maxTotalPixels) {
|
||||
throw new WhiteBackgroundUploadValidationError('单次上传白底图总像素量过大')
|
||||
}
|
||||
} catch (error) {
|
||||
if (error instanceof WhiteBackgroundUploadValidationError) {
|
||||
throw error
|
||||
}
|
||||
throw new WhiteBackgroundUploadValidationError(
|
||||
`${label}无法读取或像素总量过大`,
|
||||
)
|
||||
}
|
||||
} catch (error) {
|
||||
if (error instanceof WhiteBackgroundUploadValidationError) {
|
||||
throw error
|
||||
}
|
||||
throw new WhiteBackgroundUploadValidationError(
|
||||
`${label}无法读取或像素总量过大`,
|
||||
)
|
||||
}
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
@ -38,6 +38,8 @@ export const TRANSCODE_CONFIG = {
|
||||
timeout: parseInt(process.env.VIDEO_TRANSCODE_TIMEOUT || '3600000', 10),
|
||||
// 临时文件大小限制（字节，默认 10GB）
|
||||
maxTempFileSize: parseInt(process.env.VIDEO_MAX_TEMP_FILE_SIZE || '10737418240', 10),
|
||||
// 与 Next.js 共用容器时限制 ffmpeg 线程，避免单个转码任务占满 CPU 和原生内存
|
||||
threads: Math.max(1, Math.min(2, parseInt(process.env.VIDEO_TRANSCODE_THREADS || '1', 10) || 1)),
|
||||
}
|
||||
|
||||
interface VideoTranscodeOptions {
|
||||
@ -199,7 +201,7 @@ async function execFFmpegWithTimeout(
|
||||
onProgress?: (progress: number) => void // progress: 0-100
|
||||
): Promise<{ stdout: string; stderr: string }> {
|
||||
return new Promise((resolve, reject) => {
|
||||
const childProcess = exec(command, { maxBuffer: 10 * 1024 * 1024 }, (error, stdout, stderr) => {
|
||||
const childProcess = exec(command, { maxBuffer: 4 * 1024 * 1024 }, (error, stdout, stderr) => {
|
||||
if (error) {
|
||||
// FFmpeg 即使成功也可能返回非零退出码，需要检查 stderr 是否包含错误
|
||||
if (stderr && !stderr.includes('frame=') && !stderr.includes('Duration:')) {
|
||||
@ -217,7 +219,8 @@ async function execFFmpegWithTimeout(
|
||||
|
||||
if (onProgress && childProcess.stderr) {
|
||||
childProcess.stderr.on('data', (chunk: Buffer) => {
|
||||
stderrBuffer += chunk.toString()
|
||||
// 只保留最近一段日志；FFmpeg 长任务会持续输出进度，不能让字符串无限增长。
|
||||
stderrBuffer = `${stderrBuffer}${chunk.toString()}`.slice(-64 * 1024)
|
||||
|
||||
// 提取视频时长
|
||||
const durationMatch = stderrBuffer.match(/Duration:\s*(\d{2}):(\d{2}):(\d{2})\.(\d{2})/)
|
||||
@ -303,7 +306,7 @@ async function transcodeToPreview(
|
||||
* -preset medium: 平衡质量和速度（H.265 转码时间约为 H.264 的 3-5 倍）
|
||||
* -crf 22: 预览视频质量（中等-高质量，适合 4K 源视频缩放到 720P）
|
||||
*/
|
||||
const command = `ffmpeg -i "${inputPath}" -vf "scale=-2:${height}:flags=lanczos" -c:v libx265 -preset medium -crf ${TRANSCODE_CONFIG.previewCrf} -c:a aac -b:a 128k -movflags +faststart -y "${outputPath}"`
|
||||
const command = `ffmpeg -threads ${TRANSCODE_CONFIG.threads} -i "${inputPath}" -vf "scale=-2:${height}:flags=lanczos" -c:v libx265 -preset medium -crf ${TRANSCODE_CONFIG.previewCrf} -c:a aac -b:a 128k -movflags +faststart -y "${outputPath}"`
|
||||
|
||||
const { stdout, stderr } = await execFFmpegWithTimeout(command, TRANSCODE_CONFIG.timeout, onProgress)
|
||||
// ffmpeg 通常将进度信息输出到 stderr，这是正常的
|
||||
@ -330,7 +333,7 @@ async function generateClip(
|
||||
* -an: 移除音轨（卡片不需要声音，节省文件大小）
|
||||
* 使用 libx265 (H.265) 编码器，相比 libx264 能以更低码率获得更好质量
|
||||
*/
|
||||
const command = `ffmpeg -i "${inputPath}" -t ${duration} -vf "scale=-2:${height}:flags=lanczos" -c:v libx265 -preset ultrafast -crf ${TRANSCODE_CONFIG.clipCrf} -an -movflags +faststart -y "${outputPath}"`
|
||||
const command = `ffmpeg -threads ${TRANSCODE_CONFIG.threads} -i "${inputPath}" -t ${duration} -vf "scale=-2:${height}:flags=lanczos" -c:v libx265 -preset ultrafast -crf ${TRANSCODE_CONFIG.clipCrf} -an -movflags +faststart -y "${outputPath}"`
|
||||
|
||||
const { stdout, stderr } = await execFFmpegWithTimeout(command, TRANSCODE_CONFIG.timeout, onProgress)
|
||||
if (stderr && !stderr.includes('frame=') && !stderr.includes('Duration:')) {
|
||||
@ -349,7 +352,7 @@ async function extractCoverImage(
|
||||
outputPath: string
|
||||
): Promise<void> {
|
||||
const height = TRANSCODE_CONFIG.previewResolution
|
||||
const command = `ffmpeg -i "${videoPath}" -vf "scale=-2:${height}:flags=lanczos" -frames:v 1 -y "${outputPath}"`
|
||||
const command = `ffmpeg -threads ${TRANSCODE_CONFIG.threads} -i "${videoPath}" -vf "scale=-2:${height}:flags=lanczos" -frames:v 1 -y "${outputPath}"`
|
||||
|
||||
const { stdout, stderr } = await execFFmpegWithTimeout(command)
|
||||
if (stderr && !stderr.includes('frame=') && !stderr.includes('Duration:')) {
|
||||
|
||||
@ -4,15 +4,13 @@
|
||||
* 用于控制转码任务的并发数，充分利用多核 CPU
|
||||
*
|
||||
* 优化说明：
|
||||
* - 默认并发数：2（充分利用多核 CPU）
|
||||
* - 默认并发数：1（与 Next.js 共用容器，优先保护内存）
|
||||
* - 可以通过环境变量配置：VIDEO_TRANSCODE_MAX_CONCURRENT
|
||||
* - 支持队列管理，避免系统过载
|
||||
* - 支持部署中断后自动恢复转码任务
|
||||
*
|
||||
* 并发数建议：
|
||||
* - H.265 编码：2 并发（CPU 占用 60-80%）
|
||||
* - H.265 编码：3-4 并发（高性能服务器，CPU 占用 80-100%）
|
||||
* - H.264 编码：3-4 并发（CPU 占用 60-80%）
|
||||
* - 当前生产容器只允许 1 个转码任务，避免 ffmpeg 与 Sharp 争抢宿主机内存。
|
||||
*/
|
||||
|
||||
import { LEGACY_COMPATIBLE_VIDEO_MATERIAL_TYPES } from './video-material-types'
|
||||
@ -55,6 +53,8 @@ interface RecoverInterruptedTasksOptions {
|
||||
videoTypes?: string[]
|
||||
}
|
||||
|
||||
const SAFE_MAX_CONCURRENT_IN_SHARED_CONTAINER = 1
|
||||
|
||||
class VideoTranscodeQueue {
|
||||
private queue: QueuedTask[] = []
|
||||
private processing: Set<string> = new Set()
|
||||
@ -64,21 +64,21 @@ class VideoTranscodeQueue {
|
||||
private prisma: any = null // R1修复：Prisma 客户端延迟初始化
|
||||
|
||||
constructor(maxConcurrent?: number) {
|
||||
// 默认并发数计算逻辑：
|
||||
// 1. 优先使用环境变量配置
|
||||
// 2. 如果没有配置，使用默认值 2（充分利用多核 CPU）
|
||||
// 3. 可以根据实际负载情况调整到 3-4（高性能服务器）
|
||||
// 当前 Next.js、Sharp 和 ffmpeg 共用一个容器；即使环境变量误配为 2，
|
||||
// 也不能让两个 ffmpeg 同时进入同一容器。后续拆出独立 worker 后再放宽。
|
||||
const envConcurrent = process.env.VIDEO_TRANSCODE_MAX_CONCURRENT
|
||||
if (envConcurrent) {
|
||||
this.maxConcurrent = parseInt(envConcurrent, 10) || 2
|
||||
} else if (maxConcurrent !== undefined) {
|
||||
this.maxConcurrent = maxConcurrent
|
||||
} else {
|
||||
// 默认值：2 并发（充分利用多核 CPU，提升 CPU 使用率）
|
||||
// 可以根据实际负载情况调整到 3-4（高性能服务器）
|
||||
// 注意：使用 H.265 (libx265) 时，2 并发可以达到 60-80% CPU 使用率
|
||||
this.maxConcurrent = 2
|
||||
}
|
||||
const requestedConcurrent = envConcurrent
|
||||
? parseInt(envConcurrent, 10)
|
||||
: maxConcurrent
|
||||
this.maxConcurrent = Math.max(
|
||||
1,
|
||||
Math.min(
|
||||
SAFE_MAX_CONCURRENT_IN_SHARED_CONTAINER,
|
||||
typeof requestedConcurrent === 'number' && Number.isFinite(requestedConcurrent)
|
||||
? requestedConcurrent
|
||||
: 1,
|
||||
),
|
||||
)
|
||||
|
||||
console.log(`[TranscodeQueue] Initialized with maxConcurrent: ${this.maxConcurrent}`)
|
||||
}
|
||||
|
||||
@ -6,6 +6,11 @@ import {
|
||||
buildWhiteBackgroundPublicDisplayPathFromAssetPath,
|
||||
} from '@/lib/public-display'
|
||||
import { getWhiteBackgroundPreviewRelativePathFromAssetPath } from '@/lib/standard-model-white-background'
|
||||
import { withImageProcessingSlot } from '@/lib/image-processing-limiter'
|
||||
import { configureSharpRuntime } from '@/lib/sharp-runtime'
|
||||
import { STANDARD_MODEL_WHITE_BG_UPLOAD_LIMITS } from '@/lib/standard-model-white-background-upload-guard'
|
||||
|
||||
configureSharpRuntime()
|
||||
|
||||
export const WHITE_BACKGROUND_PREVIEW_MAX_DIMENSION = 1500
|
||||
|
||||
@ -38,26 +43,34 @@ function resolveAbsoluteLocalPath(fileUrl: string): string {
|
||||
}
|
||||
|
||||
export async function buildWhiteBackgroundWebpVariants(buffer: Buffer) {
|
||||
const original = await sharp(buffer)
|
||||
.rotate()
|
||||
.webp(WHITE_BACKGROUND_ORIGINAL_WEBP_OPTIONS)
|
||||
.toBuffer()
|
||||
return withImageProcessingSlot(async () => {
|
||||
const sharpOptions = {
|
||||
limitInputPixels: STANDARD_MODEL_WHITE_BG_UPLOAD_LIMITS.maxPixelsPerImage,
|
||||
sequentialRead: true,
|
||||
failOn: 'error' as const,
|
||||
}
|
||||
|
||||
const preview = await sharp(buffer)
|
||||
.rotate()
|
||||
.resize({
|
||||
width: WHITE_BACKGROUND_PREVIEW_MAX_DIMENSION,
|
||||
height: WHITE_BACKGROUND_PREVIEW_MAX_DIMENSION,
|
||||
fit: 'inside',
|
||||
withoutEnlargement: true,
|
||||
})
|
||||
.webp(WHITE_BACKGROUND_PREVIEW_WEBP_OPTIONS)
|
||||
.toBuffer()
|
||||
const original = await sharp(buffer, sharpOptions)
|
||||
.rotate()
|
||||
.webp(WHITE_BACKGROUND_ORIGINAL_WEBP_OPTIONS)
|
||||
.toBuffer()
|
||||
|
||||
return {
|
||||
original,
|
||||
preview,
|
||||
}
|
||||
const preview = await sharp(buffer, sharpOptions)
|
||||
.rotate()
|
||||
.resize({
|
||||
width: WHITE_BACKGROUND_PREVIEW_MAX_DIMENSION,
|
||||
height: WHITE_BACKGROUND_PREVIEW_MAX_DIMENSION,
|
||||
fit: 'inside',
|
||||
withoutEnlargement: true,
|
||||
})
|
||||
.webp(WHITE_BACKGROUND_PREVIEW_WEBP_OPTIONS)
|
||||
.toBuffer()
|
||||
|
||||
return {
|
||||
original,
|
||||
preview,
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
export function resolveWhiteBackgroundPreviewFileUrl(fileUrl?: string | null): string | null {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user