feat: 文件夹批量处理引擎（后端）
- BatchWorker 单线程轮询 batch_jobs 表，处理 source=batch 的运行， 与主调度器互不抢占（next_queued_run 排除 batch 来源） - 直接读取用户所选文件夹下的视频逐个执行流水线，不上传到工作目录； 中间态与产物落在视频旁同名文件夹，batch.done.json 完成标记去重 - 支持暂停/继续、失败容错（单视频失败不阻塞后续）、删除任务只清库 - 孤儿清理跳过 source=batch 运行，防止误删用户视频文件夹 - workflow_runs 新增 source 列（upload/batch），旧库自动迁移
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"""文件夹批量处理引擎。
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本地版的核心能力：**不把视频上传到工作目录**，而是直接读取用户所选文件夹
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下的所有视频，逐个调用现有的工作流流水线（复用 WorkflowScheduler 的 DAG
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执行与断点续跑逻辑）。
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数据落盘约定：
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- 每个视频的中间态数据（runs/、chunks/、帧图等）与最终产物都存放在**视频
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所在目录的同名文件夹**里（movie.mp4 → movie/），源视频目录保持干净。
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- 最终产物（SRT/ASS）在任务完成后从节点产物目录复制到同名文件夹根目录，
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同时写入 `batch.done.json` 完成标记；再次批量处理同一文件夹时，已有完成
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标记且产物文件齐全的视频直接跳过（已经处理过的不再处理）。
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暂停/恢复语义（对应前端"暂停/继续"按钮）：
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- 暂停批量任务：把批量任务置为 PAUSED，并暂停当前正在执行的 run（写
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paused.flag，whisper 按分块、OCR 按帧检查后停止），处理中的视频保持
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PAUSED，后续视频不再开始。
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- 继续：批量任务恢复 QUEUED，引擎从断点继续——PAUSED 视频的 run 显式
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resume 后由 execute_run 从产物表断点续跑，已完成节点不重复执行。
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主调度器不会抢占批量 run（next_queued_run 排除 source=batch），批量引擎
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使用 per-video 的 WorkflowScheduler 实例，storage 指向同名文件夹。
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"""
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from __future__ import annotations
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import json
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import shutil
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import threading
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import time
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import uuid
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from datetime import datetime, timezone
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from pathlib import Path
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from wov_app import registry
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from wov_app.config import BATCH_INTERVAL_SECONDS
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from wov_app.db import Database
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from wov_app.logging import get_logger
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from wov_app.scheduler import WorkflowScheduler
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from wov_sdk.models import WorkflowDefinition
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# 批量引擎运行日志：任务进度、视频逐个处理与暂停/续跑等状态变化。
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logger = get_logger("batch")
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# 识别为视频文件的扩展名（大小写不敏感，扫描时统一转小写比较）。
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VIDEO_EXTENSIONS = {
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".mp4", ".mkv", ".avi", ".mov", ".webm", ".flv", ".ts",
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".m4v", ".wmv", ".mpg", ".mpeg", ".3gp",
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}
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# 暂停信号文件名：与节点约定一致，位于 run 根目录（<work_dir>/runs/<run_id>/）。
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PAUSE_FLAG = "paused.flag"
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# 视频完成标记文件名：位于同名文件夹根目录，记录该视频已完成的工作流与最终
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# 产物文件名，跨批量任务去重（已经处理过的不再处理）。
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MARKER_NAME = "batch.done.json"
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def _now_iso() -> str:
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"""返回当前 UTC 时间的 ISO 格式字符串。"""
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return datetime.now(timezone.utc).isoformat()
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def scan_videos(folder: Path, recursive: bool = True) -> list[Path]:
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"""扫描文件夹下的全部视频文件，按路径排序保证处理顺序确定。
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recursive=True 时递归扫描子文件夹；recursive=False 只扫描顶层。
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"""
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if recursive:
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paths = [
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p for p in folder.rglob("*")
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if p.is_file() and p.suffix.lower() in VIDEO_EXTENSIONS
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]
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else:
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paths = [
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p for p in folder.glob("*")
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if p.is_file() and p.suffix.lower() in VIDEO_EXTENSIONS
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]
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return sorted(paths)
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def work_dir_for(video: Path) -> Path:
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"""返回视频的同名文件夹：去掉扩展名，位于视频所在目录。
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例如 movie.mp4 → 旁边的 movie/ 文件夹，中间态与最终产物都放这里。
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"""
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return video.parent / video.stem
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def load_marker(work_dir: Path) -> dict | None:
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"""读取同名文件夹里的完成标记；不存在或损坏时返回 None。"""
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path = work_dir / MARKER_NAME
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if not path.is_file():
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return None
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try:
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data = json.loads(path.read_text(encoding="utf-8"))
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return data if isinstance(data, dict) else None
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except (json.JSONDecodeError, OSError):
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# 半行写入或权限异常时保守视为未完成，允许重新处理。
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return None
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def ensure_videos(db: Database, job_id: str, videos: list[Path]) -> None:
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"""为扫描到的视频补齐 batch_videos 明细；已存在的记录保持不变。
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同一批量任务反复处理（暂停/续跑）时保留每个视频的状态，已完成的不重置。
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"""
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existing = {item["video_path"] for item in db.list_batch_videos(job_id)}
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now = _now_iso()
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for video in videos:
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path = str(video)
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if path in existing:
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continue
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db.create_batch_video({
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"id": f"bv_{uuid.uuid4().hex[:12]}",
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"job_id": job_id,
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"video_path": path,
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"work_dir": str(work_dir_for(video)),
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"run_id": None,
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"status": "PENDING",
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"error": None,
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"created_at": now,
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"updated_at": now,
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})
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def create_job(
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db: Database,
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folder_path: str,
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workflow_id: str,
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recursive: bool = True,
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) -> str:
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"""创建批量任务：校验文件夹与工作流、扫描视频、落库明细，返回任务 ID。
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校验失败抛出 ValueError（由路由层转为 422 响应）；扫描到的视频全部
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登记为 batch_videos 明细，引擎轮询到该任务后逐个处理。
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"""
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folder = Path(folder_path).expanduser()
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if not folder.is_dir():
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raise ValueError("folder not found")
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workflow = db.get_workflow(workflow_id)
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if workflow is None or not workflow["published"]:
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raise ValueError("published workflow not found")
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if db.get_latest_workflow_version(workflow_id) is None:
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raise ValueError("workflow has no version")
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videos = scan_videos(folder, recursive)
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if not videos:
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raise ValueError("no videos found in folder")
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job_id = f"batch_{uuid.uuid4().hex[:12]}"
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now = _now_iso()
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db.create_batch_job({
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"id": job_id,
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"folder_path": str(folder),
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"workflow_id": workflow_id,
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"recursive": int(recursive),
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"status": "QUEUED",
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"progress": 0,
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"total": 0,
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"done": 0,
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"failed": 0,
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"current_video": None,
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"error": None,
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"created_at": now,
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"updated_at": now,
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})
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ensure_videos(db, job_id, videos)
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logger.info("创建批量任务 %s: 文件夹 %s, 工作流 %s, 视频 %d 个", job_id, folder, workflow_id, len(videos))
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return job_id
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class BatchWorker:
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"""批量处理引擎：单线程轮询 QUEUED 批量任务，逐视频调用现有调度器执行。"""
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def __init__(
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self,
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db: Database,
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interval_seconds: float | None = None,
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) -> None:
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"""保存数据库依赖并初始化轮询线程控制字段。"""
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self.db = db
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self.interval_seconds = interval_seconds or BATCH_INTERVAL_SECONDS
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self._thread: threading.Thread | None = None
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self._stopping = False
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def start(self) -> None:
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"""启动批量处理线程；重复调用无副作用。"""
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if self._thread is not None:
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return
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# 独立运行时确保节点已注册；重复注册幂等。
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registry.register_all()
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self._stopping = False
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self._thread = threading.Thread(
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target=self._loop,
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name="wov-batch-worker",
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daemon=True,
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)
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self._thread.start()
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def stop(self) -> None:
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"""请求停止并等待轮询线程退出。"""
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self._stopping = True
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if self._thread is not None:
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self._thread.join(timeout=5)
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self._thread = None
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def _loop(self) -> None:
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"""轮询循环：有排队中的批量任务就处理，否则休眠一个间隔。"""
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while not self._stopping:
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try:
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job = self.db.next_queued_batch_job()
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if job is not None:
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self._process_job(job)
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else:
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time.sleep(self.interval_seconds)
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except Exception: # noqa: BLE001
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# 单次轮询异常不杀死线程，记录后跳过本轮（与主调度器一致）。
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logger.exception("批量引擎轮询异常，跳过本轮")
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time.sleep(self.interval_seconds)
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# ------------------------------------------------------------------
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# 任务执行
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# ------------------------------------------------------------------
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def _process_job(self, job: dict) -> None:
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"""处理一个批量任务：校验、扫描、补齐明细、逐视频执行并复制产物。
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job 以 QUEUED 状态进入，处理期间置 RUNNING；全部视频处理完置
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COMPLETED；被暂停时保持 PAUSED；校验失败置 FAILED。
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"""
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job_id = str(job["id"])
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try:
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self._run_job(job_id)
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except Exception as exc: # noqa: BLE001
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# 任务级兜底：任何未捕获异常都记录到任务而不是卡死在 RUNNING。
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logger.exception("批量任务 %s 处理异常", job_id)
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self.db.update_batch_job(job_id, status="FAILED", error=str(exc), updated_at=_now_iso())
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def _run_job(self, job_id: str) -> None:
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"""批量任务主流程（内部实现，异常由 _process_job 统一处理）。"""
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job = self.db.get_batch_job(job_id)
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if job is None:
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return
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folder = Path(job["folder_path"])
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if not folder.is_dir():
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self.db.update_batch_job(job_id, status="FAILED", error="folder not found", updated_at=_now_iso())
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return
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workflow = self.db.get_workflow(job["workflow_id"])
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if workflow is None or not workflow["published"]:
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self.db.update_batch_job(job_id, status="FAILED", error="workflow not found or unpublished", updated_at=_now_iso())
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return
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version = self.db.get_latest_workflow_version(job["workflow_id"])
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if version is None:
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self.db.update_batch_job(job_id, status="FAILED", error="workflow has no version", updated_at=_now_iso())
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return
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definition = WorkflowDefinition.from_dict(version["definition"])
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definition.validate()
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# 扫描当前文件夹的视频，为新增视频补齐明细（已有明细保留原状态，
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# 保证暂停/续跑时已完成与进行中的视频不被重置）。
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self._ensure_videos(job_id, scan_videos(folder, bool(job["recursive"])))
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items = self.db.list_batch_videos(job_id)
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total = len(items)
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self.db.update_batch_job(
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job_id, status="RUNNING", total=total, progress=0,
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current_video=None, error=None, updated_at=_now_iso(),
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)
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for index, item in enumerate(items):
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# 暂停检查：批量任务被暂停后停止处理后续视频，等待用户继续。
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current = self.db.get_batch_job(job_id)
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if current is None or current["status"] == "PAUSED":
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logger.info("批量任务 %s 已暂停，停止在视频 %s", job_id, item["video_path"])
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return
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# 已完成/已跳过的视频不再处理。
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if item["status"] in ("COMPLETED", "SKIPPED"):
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continue
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video = Path(item["video_path"])
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if not video.is_file():
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self.db.update_batch_video(item["id"], status="FAILED", error="video file not found", updated_at=_now_iso())
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continue
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work_dir = Path(item["work_dir"])
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# 同名文件夹里已有同工作流的完成标记且产物齐全 → 直接跳过。
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if self._is_done(work_dir, job["workflow_id"]):
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self.db.update_batch_video(item["id"], status="SKIPPED", updated_at=_now_iso())
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continue
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self.db.update_batch_job(
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job_id, current_video=str(video),
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progress=index / total if total else 0,
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updated_at=_now_iso(),
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)
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try:
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self._process_video(job, item, version, definition, work_dir)
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except Exception as exc: # noqa: BLE001
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# 单视频兜底：不中断整个批量任务，记录错误后继续下一个视频。
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logger.exception("批量任务 %s 视频 %s 处理异常", job_id, video)
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self.db.update_batch_video(item["id"], status="FAILED", error=str(exc), updated_at=_now_iso())
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# 重新读取视频明细：_process_video 可能刚创建 run（快照里 run_id
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# 还是 None），必须取最新记录才能拿到 run_id 判断暂停状态。
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item = self.db.get_batch_video(item["id"])
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# 视频处理中被暂停：批量任务整体保持 PAUSED，等待用户继续。
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run = self.db.get_run(item["run_id"]) if item and item.get("run_id") else None
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if run is not None and run["status"] == "PAUSED":
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self.db.update_batch_video(item["id"], status="PAUSED", updated_at=_now_iso())
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self.db.update_batch_job(job_id, status="PAUSED", updated_at=_now_iso())
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return
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# 全部视频处理完成：汇总已处理与失败数量，任务置为 COMPLETED。
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items = self.db.list_batch_videos(job_id)
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done = sum(1 for item in items if item["status"] in ("COMPLETED", "SKIPPED"))
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failed = sum(1 for item in items if item["status"] == "FAILED")
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self.db.update_batch_job(
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job_id, status="COMPLETED", progress=1.0, done=done, failed=failed,
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current_video=None, error=None, updated_at=_now_iso(),
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)
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logger.info(
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"批量任务 %s 完成: 共 %d 个视频, 完成/跳过 %d, 失败 %d",
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job_id, total, done, failed,
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)
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def _process_video(
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self,
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job: dict,
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item: dict,
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version: dict,
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definition: WorkflowDefinition,
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work_dir: Path,
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) -> None:
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"""处理单个视频：建 run（复用现有调度器）并执行，完成后复制最终产物。
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per-video 的 WorkflowScheduler 以同名文件夹为 storage，中间态落在
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<work_dir>/runs/<run_id>/steps/ 下；产物表记录全部节点输出，暂停后
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续跑从产物表重建已完成节点（断点续跑）。
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"""
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video = Path(item["video_path"])
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work_dir.mkdir(parents=True, exist_ok=True)
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run_id = item.get("run_id")
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if run_id is None:
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# 首次处理：创建 source=batch 的运行，input_uri 直接指向本地视频
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# （不再上传副本），调度器按工作流 DAG 动态组装节点执行。
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run_id = f"run_{uuid.uuid4().hex[:12]}"
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now = _now_iso()
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self.db.create_run({
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"id": run_id,
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"workflow_id": job["workflow_id"],
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"workflow_version": int(version["version"]),
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"status": "QUEUED",
|
||||
"progress": 0,
|
||||
"input_uri": str(video),
|
||||
"param_overrides": None,
|
||||
"source": "batch",
|
||||
"created_at": now,
|
||||
"updated_at": now,
|
||||
})
|
||||
self.db.update_batch_video(item["id"], run_id=run_id, updated_at=_now_iso())
|
||||
|
||||
run = self.db.get_run(run_id)
|
||||
# 已完成（例如上次运行刚完成但未来得及标记）：直接复制产物后返回。
|
||||
if run["status"] == "COMPLETED":
|
||||
self._copy_finals(run_id, work_dir, definition, job["workflow_id"])
|
||||
self.db.update_batch_video(item["id"], status="COMPLETED", error=None, updated_at=_now_iso())
|
||||
return
|
||||
# 暂停的 run 显式 resume 回 QUEUED，由 execute_run 从产物表断点续跑。
|
||||
if run["status"] == "PAUSED":
|
||||
self.db.resume_run(run_id, _now_iso())
|
||||
elif run["status"] == "FAILED":
|
||||
# 失败重跑：**保留**已完成节点的产物记录，只恢复 QUEUED——
|
||||
# execute_run 从产物表重建已完成节点并跳过，只重跑失败节点。
|
||||
# 不再 reset_run 清空产物：extract/ocr 等长耗时节点的成果（如
|
||||
# ABP-885 的 22222 帧 OCR）会被白白丢弃重做（run_e2b74e89e232 实测）。
|
||||
self.db.update_run(run_id, status="QUEUED", error=None, updated_at=_now_iso())
|
||||
elif run["status"] == "RUNNING":
|
||||
# 上次进程被杀残留：恢复 QUEUED（保留产物）由 execute_run 续跑。
|
||||
self.db.update_run(run_id, status="QUEUED", updated_at=_now_iso())
|
||||
# 清除可能残留的暂停信号（重启/异常中断后），避免本次执行误暂停。
|
||||
(work_dir / "runs" / run_id / PAUSE_FLAG).unlink(missing_ok=True)
|
||||
|
||||
scheduler = WorkflowScheduler(self.db, work_dir)
|
||||
scheduler.execute_run(run_id)
|
||||
|
||||
run = self.db.get_run(run_id)
|
||||
if run["status"] == "COMPLETED":
|
||||
# 复制最终产物到同名文件夹根目录并写完成标记（已处理过不再处理）。
|
||||
self._copy_finals(run_id, work_dir, definition, job["workflow_id"])
|
||||
self.db.update_batch_video(item["id"], status="COMPLETED", error=None, updated_at=_now_iso())
|
||||
else:
|
||||
# FAILED 或 PAUSED：由调用方根据 run 状态更新视频状态与任务状态。
|
||||
self.db.update_batch_video(item["id"], status=run["status"], error=run.get("error"), updated_at=_now_iso())
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 明细与产物
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _ensure_videos(self, job_id: str, videos: list[Path]) -> None:
|
||||
"""为扫描到的视频补齐 batch_videos 明细；已存在的记录保持不变。"""
|
||||
ensure_videos(self.db, job_id, videos)
|
||||
|
||||
def _is_done(self, work_dir: Path, workflow_id: str) -> bool:
|
||||
"""判断同名文件夹是否已完成当前工作流的处理。
|
||||
|
||||
完成标记记录 workflow_id 与最终产物文件名；只有工作流一致且产物文件
|
||||
全部存在时才视为已处理（不同工作流的产物不互相误判为完成）。
|
||||
"""
|
||||
marker = load_marker(work_dir)
|
||||
if marker is None or marker.get("workflow_id") != workflow_id:
|
||||
return False
|
||||
finals = marker.get("finals") or {}
|
||||
return bool(finals) and all((work_dir / name).is_file() for name in finals.values())
|
||||
|
||||
def _copy_finals(
|
||||
self,
|
||||
run_id: str,
|
||||
work_dir: Path,
|
||||
definition: WorkflowDefinition,
|
||||
workflow_id: str,
|
||||
) -> None:
|
||||
"""把最终产物从节点目录复制到同名文件夹根目录，并写完成标记。
|
||||
|
||||
调度器收尾时已把产物重命名为 上传文件名.标识.时间戳（如
|
||||
movie.zh-CN.20260819120000.srt），这里原样复制，文件名保留辨识度。
|
||||
"""
|
||||
work_dir.mkdir(parents=True, exist_ok=True)
|
||||
finals: dict[str, str] = {}
|
||||
for alias in definition.final_outputs:
|
||||
artifact = self.db.get_artifact(run_id, alias)
|
||||
if artifact is None:
|
||||
continue
|
||||
source = Path(artifact["uri"])
|
||||
if not source.is_file():
|
||||
continue
|
||||
target = work_dir / source.name
|
||||
# 已存在的产物直接复用，避免重复复制。
|
||||
if not target.is_file() or target.stat().st_size != source.stat().st_size:
|
||||
shutil.copy2(source, target)
|
||||
finals[alias] = source.name
|
||||
marker = {
|
||||
"workflow_id": workflow_id,
|
||||
"workflow_version": definition.version,
|
||||
"run_id": run_id,
|
||||
"completed_at": _now_iso(),
|
||||
"finals": finals,
|
||||
}
|
||||
(work_dir / MARKER_NAME).write_text(
|
||||
json.dumps(marker, ensure_ascii=False, indent=2),
|
||||
encoding="utf-8",
|
||||
)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 暂停/继续
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def pause_job(self, job_id: str) -> None:
|
||||
"""暂停批量任务：停止当前 run 与后续视频处理。
|
||||
|
||||
先把任务置为 PAUSED（引擎在视频间检查后停下），再暂停所有排队/运行
|
||||
中的 run 并写 paused.flag（whisper 按分块、OCR 按帧检查后中止）。
|
||||
"""
|
||||
self.db.update_batch_job(job_id, status="PAUSED", updated_at=_now_iso())
|
||||
for item in self.db.list_batch_videos(job_id):
|
||||
if not item.get("run_id"):
|
||||
continue
|
||||
run = self.db.get_run(item["run_id"])
|
||||
if run is None or run["status"] not in ("QUEUED", "RUNNING"):
|
||||
continue
|
||||
self.db.pause_run(item["run_id"], _now_iso())
|
||||
run_dir = Path(item["work_dir"]) / "runs" / item["run_id"]
|
||||
run_dir.mkdir(parents=True, exist_ok=True)
|
||||
(run_dir / PAUSE_FLAG).write_text("", encoding="utf-8")
|
||||
|
||||
def resume_job(self, job_id: str) -> None:
|
||||
"""继续批量任务：置回 QUEUED，引擎从断点续跑（PAUSED 视频逐个 resume）。"""
|
||||
self.db.update_batch_job(job_id, status="QUEUED", updated_at=_now_iso())
|
||||
Reference in New Issue
Block a user