87 lines
1.8 KiB
Markdown
87 lines
1.8 KiB
Markdown
# Docker + Prefect
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## 1. 构建镜像
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```bash
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cd /Users/idss/YXT_Project/gmv-analytics
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docker build -t gmv-analytics-prefect:latest .
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```
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## 2. 单次执行
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```bash
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docker run --rm \
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--env-file /Users/idss/YXT_Project/gmv-analytics/.env \
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-e PROJECT_MAPPING_FILE=/app/店铺映射表.xlsx \
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gmv-analytics-prefect:latest \
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python prefect_gmv_scheduler.py
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```
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## 3. 启动每天 09:00 的 Prefect 调度
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这会连接 `.env` 中已经配置的 `PREFECT_API_URL`，并在 Prefect 中启动一个 deployment。
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```bash
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docker run --rm \
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--env-file /Users/idss/YXT_Project/gmv-analytics/.env \
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-e PROJECT_MAPPING_FILE=/app/店铺映射表.xlsx \
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gmv-analytics-prefect:latest
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```
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容器默认执行：
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```bash
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python prefect_gmv_scheduler.py --serve
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```
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也可以直接用 Compose：
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```bash
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cd /Users/idss/YXT_Project/gmv-analytics
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docker compose up -d --build
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```
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停止：
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```bash
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docker compose down
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```
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## 4. 如果你是用 Prefect worker 跑容器
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先保证你的 Prefect worker 已经连到同一个 `PREFECT_API_URL`。
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推荐命令示例：
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```bash
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prefect worker start --pool default-agent-pool --type process
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```
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如果你后面要改成真正的 Docker work pool，再在 Prefect UI 中把 deployment 的基础设施切到 Docker，并指定镜像：
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```text
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gmv-analytics-prefect:latest
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```
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## 5. 常用覆盖参数
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单次 dry-run：
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```bash
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docker run --rm \
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--env-file /Users/idss/YXT_Project/gmv-analytics/.env \
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-e PROJECT_MAPPING_FILE=/app/店铺映射表.xlsx \
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gmv-analytics-prefect:latest \
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python prefect_gmv_scheduler.py --dry-run
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```
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指定时间范围：
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```bash
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docker run --rm \
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--env-file /Users/idss/YXT_Project/gmv-analytics/.env \
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-e PROJECT_MAPPING_FILE=/app/店铺映射表.xlsx \
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gmv-analytics-prefect:latest \
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python prefect_gmv_scheduler.py --start-date 2026-03-01 --end-date 2026-03-12
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```
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