92 lines
2.9 KiB
YAML
92 lines
2.9 KiB
YAML
kind: pipeline
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type: docker
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name: ai-flow-backend-prod-deploy
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steps:
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# 步骤1: 将代码同步到远程服务器
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- name: sync-code
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image: appleboy/drone-scp
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settings:
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host:
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from_secret: PROD_HOST
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username:
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from_secret: PROD_USER
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key:
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from_secret: PROD_SSH_KEY
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port: 22
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target: /data/ai_flow_backend-prod/source
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source:
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- .
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rm_target: true
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strip_components: 0
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# 步骤2: SSH到远程服务器执行构建和单实例部署
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- name: build-and-deploy
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image: appleboy/drone-ssh
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settings:
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host:
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from_secret: PROD_HOST
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username:
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from_secret: PROD_USER
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key:
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from_secret: PROD_SSH_KEY
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port: 22
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command_timeout: 20m
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envs:
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- DRONE_TAG
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script:
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- echo "===== 开始构建版本：${DRONE_TAG} ====="
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- cd /data/ai_flow_backend-prod/source
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# 删除旧镜像
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- docker rmi ai-flow-backend-prod:latest || true
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# 构建新镜像
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- docker build -f Dockerfile-prod -t ai-flow-backend-prod:${DRONE_TAG} -t ai-flow-backend-prod:latest .
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# ===== 清理旧单实例容器 =====
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- |
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if docker ps -a --format '{{.Names}}' | grep -q '^ai-flow-backend-prod$'; then
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echo "检测到旧单实例容器 ai-flow-backend-prod，先清理"
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docker stop ai-flow-backend-prod || true
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docker rm ai-flow-backend-prod || true
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fi
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# ===== 部署单实例 =====
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- echo "===== 部署单实例 ai-flow-backend-prod ====="
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# 启动新容器（宿主机映射为8004，容器内为8002）
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- |
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docker run -d \
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--name ai-flow-backend-prod \
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--restart always \
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-p 8004:8002 \
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-v /data/ai_flow_backend-prod/uploads:/app/uploads \
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-v /data/ai_flow_backend-prod/logs:/app/logs \
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ai-flow-backend-prod:${DRONE_TAG}
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# 等待容器启动
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- |
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HEALTH_OK=false
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for i in $(seq 1 30); do
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# 如果有健康检查接口，请替换为 curl，例如：curl -sf http://127.0.0.1:8002/health
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# 这里先用 docker inspect 判断是否处于 running 状态
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if [ "$(docker inspect -f '{{.State.Running}}' ai-flow-backend-prod)" = "true" ]; then
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echo "ai-flow-backend-prod 启动成功"
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HEALTH_OK=true
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break
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fi
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echo "等待容器启动... ($i/30)"
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sleep 2
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done
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if [ "$HEALTH_OK" != "true" ]; then
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echo "ERROR: ai-flow-backend-prod 启动超时"
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exit 1
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fi
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# ===== 清理无用的悬空镜像 =====
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- docker image prune -f
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- echo "===== 单实例部署完成：ai-flow-backend-prod:${DRONE_TAG} ====="
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trigger:
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event:
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- tag
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