xsl
cd0ddb131b
- POST /reports/{id}/export：request_key 幂等 + hd_export 日配额三段式（reserve/commit/refund）+ tier2 熔断场景闸（BudgetGuardDep），仅 succeeded 报告可导
- GET /reports/{id}/export/download：归属校验 + 路径收敛（rendered_asset_key 必须落在 report_export_dir 内），FileResponse 携带 UTF-8 文件名
- celery 任务 meida.report.export（cpu 队列）：渲染→落盘 var/exports→回写 rendered_asset_key；业务性拒绝（报告状态变化）结案退款不重试，基础设施异常重试一次后兜底结案退款
- packages/report/pdf：Jinja2 模板 + WeasyPrint 渲染器（url_fetcher 全拒外部资源防 SSRF；维度中文名按 DIMENSION_BY_KEY 回填；色板维度渲染色卡）
- 单测 10 例（资格/幂等/并发同键/下载校验/渲染）；scripts/seed_pdf_e2e.sql 供本地 E2E 造数
55 lines
1.5 KiB
Python
Executable File
55 lines
1.5 KiB
Python
Executable File
"""Celery application with the four queues from TD-11 / 架构文档 §3.1.
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gpu 换装、发型、抠图 —— 并发上限由 GPU 容量决定，队列即容量 (§7.3)
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cpu 报告视觉分析、渲染
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io 外部 API 调用、推送、上传
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beat 定时任务（单实例，防重）
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Queue isolation is the point: 换装排队不能把报告生成拖慢。
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"""
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from __future__ import annotations
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from celery import Celery
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from kombu import Queue
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from packages.shared.config import get_settings
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from packages.shared.logging import configure_logging
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settings = get_settings()
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configure_logging(
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level=settings.log_level,
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json_output=settings.log_json,
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extra_redact_keys=settings.extra_redact_keys,
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)
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celery_app = Celery(
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"meida",
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broker=settings.celery_broker_url,
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backend=settings.celery_result_backend,
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include=["apps.worker.tasks", "apps.worker.report_tasks", "apps.worker.export_tasks"],
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)
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celery_app.conf.update(
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task_default_queue="cpu",
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task_queues=(
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Queue("gpu"),
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Queue("cpu"),
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Queue("io"),
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Queue("beat"),
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),
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task_routes={
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"meida.tryon.*": {"queue": "gpu"},
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"meida.matting.*": {"queue": "gpu"},
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"meida.report.*": {"queue": "cpu"},
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"meida.notify.*": {"queue": "io"},
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},
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task_acks_late=True,
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task_reject_on_worker_lost=True,
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task_track_started=True,
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worker_prefetch_multiplier=1,
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broker_connection_retry_on_startup=True,
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timezone="Asia/Shanghai",
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result_expires=86400,
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)
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