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- 建立 Gitea Actions CD pipeline (.gitea/workflows/cd.yaml) - 部署模式: rsync Python 檔案至 188 → docker restart (volume mount) - Dockerfile/requirements 變動時自動重建 Docker image - 部署通知: Telegram (開始/成功/失敗) - 健康檢查: https://mo.wooo.work/health (最多 5 次重試) - 同步最新 CLAUDE.md / ADR-008 / memory (2026-04-19) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
443 lines
8.0 KiB
Markdown
443 lines
8.0 KiB
Markdown
# Momo Pro System - Docker 部署指南
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## 方案一：本機測試 Docker 部署
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### 1. 確認環境
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```bash
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# 確認 Docker 已安裝
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docker --version
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docker-compose --version
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```
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### 2. 建立並啟動容器
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```bash
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# 建立映像並啟動服務
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docker-compose up -d
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# 查看日誌
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docker-compose logs -f
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# 查看容器狀態
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docker-compose ps
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```
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### 3. 測試訪問
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瀏覽器開啟：http://localhost
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### 4. 停止服務
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```bash
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# 停止容器
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docker-compose down
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# 停止並刪除 volumes
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docker-compose down -v
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```
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---
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## 方案二：部署到 GCP Cloud Run（推薦）
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**優點：**
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- 完全託管，自動擴展
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- 按使用付費（沒有流量時不收費）
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- 自動 HTTPS
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- 不需要管理 VM
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### 1. 設定 GCP 專案
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```bash
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# 設定專案 ID
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export PROJECT_ID="your-project-id"
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gcloud config set project $PROJECT_ID
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# 啟用必要的 API
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gcloud services enable \
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run.googleapis.com \
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cloudbuild.googleapis.com \
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artifactregistry.googleapis.com
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```
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### 2. 建立 Artifact Registry
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```bash
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# 建立 Docker repository
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gcloud artifacts repositories create momo-repo \
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--repository-format=docker \
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--location=asia-east1 \
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--description="Momo Pro System Docker Repository"
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```
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### 3. 建立並推送 Docker 映像
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```bash
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# 設定映像名稱
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export IMAGE_NAME="asia-east1-docker.pkg.dev/$PROJECT_ID/momo-repo/momo-app"
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# 建立映像
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docker build -t $IMAGE_NAME .
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# 推送到 Artifact Registry
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docker push $IMAGE_NAME
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```
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### 4. 部署到 Cloud Run
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```bash
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# 部署服務
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gcloud run deploy momo-pro-system \
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--image=$IMAGE_NAME \
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--platform=managed \
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--region=asia-east1 \
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--allow-unauthenticated \
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--port=5000 \
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--memory=2Gi \
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--cpu=2 \
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--min-instances=1 \
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--max-instances=10 \
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--timeout=300 \
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--set-env-vars="FLASK_ENV=production" \
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--set-secrets="DATABASE_URL=momo-db-url:latest"
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```
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### 5. 設定環境變數和 Secrets
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```bash
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# 建立 secret（例如：EMAIL_PASSWORD）
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echo -n "your-password" | gcloud secrets create email-password --data-file=-
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# 更新 Cloud Run 服務使用 secret
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gcloud run services update momo-pro-system \
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--region=asia-east1 \
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--set-secrets=EMAIL_PASSWORD=email-password:latest
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```
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### 6. 獲取服務 URL
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```bash
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gcloud run services describe momo-pro-system \
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--region=asia-east1 \
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--format='value(status.url)'
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```
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---
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## 方案三：部署到 GCP Compute Engine (VM)
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### 1. 建立 VM
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```bash
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# 建立具有 Docker 的 VM
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gcloud compute instances create momo-server \
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--zone=asia-east1-a \
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--machine-type=e2-medium \
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--image-family=cos-stable \
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--image-project=cos-cloud \
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--boot-disk-size=50GB \
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--tags=http-server,https-server
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```
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### 2. 設定防火牆規則
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```bash
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# 允許 HTTP 流量
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gcloud compute firewall-rules create allow-http \
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--allow=tcp:80 \
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--target-tags=http-server
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# 允許 HTTPS 流量
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gcloud compute firewall-rules create allow-https \
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--allow=tcp:443 \
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--target-tags=https-server
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```
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### 3. 上傳程式碼到 VM
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```bash
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# 上傳整個專案
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gcloud compute scp --recurse . momo-server:~/momo_pro_system \
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--zone=asia-east1-a
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```
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### 4. 連接到 VM 並啟動服務
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```bash
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# SSH 到 VM
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gcloud compute ssh momo-server --zone=asia-east1-a
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# 在 VM 上執行
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cd ~/momo_pro_system
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# 啟動服務
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docker-compose up -d
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# 查看日誌
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docker-compose logs -f
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```
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### 5. 設定自動啟動
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```bash
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# 建立 systemd service
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sudo tee /etc/systemd/system/momo-docker.service > /dev/null <<EOF
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[Unit]
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Description=Momo Pro System Docker
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Requires=docker.service
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After=docker.service
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[Service]
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Type=oneshot
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RemainAfterExit=yes
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WorkingDirectory=/home/$USER/momo_pro_system
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ExecStart=/usr/local/bin/docker-compose up -d
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ExecStop=/usr/local/bin/docker-compose down
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TimeoutStartSec=0
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[Install]
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WantedBy=multi-user.target
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EOF
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# 啟用自動啟動
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sudo systemctl enable momo-docker
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sudo systemctl start momo-docker
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```
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---
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## 方案四：使用 Google Kubernetes Engine (GKE)
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適合需要更複雜的擴展和管理需求。
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### 1. 建立 GKE 集群
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```bash
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gcloud container clusters create momo-cluster \
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--zone=asia-east1-a \
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--num-nodes=2 \
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--machine-type=e2-medium
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```
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### 2. 建立 Kubernetes 部署配置
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建立 `k8s-deployment.yaml`：
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```yaml
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: momo-app
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spec:
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replicas: 2
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selector:
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matchLabels:
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app: momo
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template:
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metadata:
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labels:
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app: momo
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spec:
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containers:
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- name: momo-app
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image: asia-east1-docker.pkg.dev/PROJECT_ID/momo-repo/momo-app
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ports:
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- containerPort: 5000
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resources:
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requests:
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memory: "512Mi"
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cpu: "500m"
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limits:
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memory: "2Gi"
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cpu: "1000m"
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---
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apiVersion: v1
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kind: Service
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metadata:
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name: momo-service
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spec:
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type: LoadBalancer
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selector:
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app: momo
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ports:
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- port: 80
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targetPort: 5000
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```
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### 3. 部署到 GKE
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```bash
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# 部署應用
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kubectl apply -f k8s-deployment.yaml
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# 查看狀態
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kubectl get pods
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kubectl get services
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```
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---
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## 資料持久化
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### Cloud Run（使用 Cloud SQL）
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```bash
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# 建立 Cloud SQL 實例
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gcloud sql instances create momo-db \
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--database-version=POSTGRES_14 \
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--tier=db-f1-micro \
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--region=asia-east1
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# 建立資料庫
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gcloud sql databases create momo \
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--instance=momo-db
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# 連接 Cloud Run 到 Cloud SQL
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gcloud run services update momo-pro-system \
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--add-cloudsql-instances=$PROJECT_ID:asia-east1:momo-db
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```
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### VM/GKE（使用 Persistent Disk）
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```bash
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# 建立持久化磁碟
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gcloud compute disks create momo-data \
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--size=50GB \
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--zone=asia-east1-a
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# 掛載到 VM
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gcloud compute instances attach-disk momo-server \
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--disk=momo-data \
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--zone=asia-east1-a
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```
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---
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## 備份策略
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### 自動備份腳本
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```bash
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# 在容器內設定 cron job
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docker exec momo-pro-system sh -c "echo '0 2 * * * /app/backup.sh' | crontab -"
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```
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### 備份到 Cloud Storage
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```bash
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# 建立 Cloud Storage bucket
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gsutil mb -l asia-east1 gs://momo-backups
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# 備份資料庫
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docker exec momo-pro-system tar -czf - /app/data | \
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gsutil cp - gs://momo-backups/backup-$(date +%Y%m%d-%H%M%S).tar.gz
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```
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---
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## 監控和日誌
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### Cloud Run
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```bash
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# 查看日誌
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gcloud logging read "resource.type=cloud_run_revision AND resource.labels.service_name=momo-pro-system" \
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--limit=50 \
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--format=json
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# 設定監控警報
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gcloud alpha monitoring policies create \
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--notification-channels=CHANNEL_ID \
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--display-name="Momo High Error Rate" \
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--condition-display-name="Error rate > 5%" \
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--condition-threshold-value=5
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```
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### VM/Docker
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```bash
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# 查看容器日誌
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docker-compose logs -f --tail=100
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# 使用 Google Cloud Logging
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docker plugin install gcplogs --alias gcplogs
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```
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---
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## 成本估算
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### Cloud Run（最省錢）
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- 免費額度：每月 200 萬請求
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- CPU: $0.00002400 / vCPU 秒
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- 記憶體: $0.00000250 / GiB 秒
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- **預估：約 $10-30 / 月**
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### Compute Engine (e2-medium)
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- VM: ~$25 / 月
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- 儲存: ~$2 / 月
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- 網路: ~$5 / 月
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- **預估：約 $32 / 月**
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### GKE
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- 集群管理費: $73 / 月
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- 節點: ~$50 / 月
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- **預估：約 $123 / 月**
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---
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## 推薦方案
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**對於 Momo Pro System，建議使用 Cloud Run：**
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1. ✅ 成本最低
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2. ✅ 自動擴展
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3. ✅ 免維護
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4. ✅ 自動 HTTPS
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5. ✅ 快速部署
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**快速部署命令：**
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```bash
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# 一鍵部署
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gcloud run deploy momo-pro-system \
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--source . \
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--region=asia-east1 \
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--allow-unauthenticated \
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--port=5000
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```
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---
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## 故障排除
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### 容器無法啟動
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```bash
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# 查看詳細日誌
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docker-compose logs momo-app
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# 進入容器調試
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docker exec -it momo-pro-system bash
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```
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### 資料庫連接問題
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```bash
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# 檢查環境變數
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docker exec momo-pro-system env | grep DATABASE
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# 測試資料庫連接
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docker exec momo-pro-system python -c "import sqlite3; print(sqlite3.connect('/app/data/momo_database.db'))"
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```
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### Cloud Run 超時
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```bash
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# 增加超時時間
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gcloud run services update momo-pro-system \
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--timeout=900 \
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--region=asia-east1
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```
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