Google Cloud Run

I have a Next.js website application that uses a MongoDB database, and I am developing it using Docker containers. The original system was intended to be deployed on a NAS after development, and the NAS system is an ARM32 platform, not an Intel platform. As a result, the original program needs to be modified to support ARM32. The application’s image was changed from node:lts-alpine to arm32v7/node:lts-alpine, and the database image was changed from mongo:3.6.23 to apcheamitru/arm32v7-mongo:latest.

Local Docker Development

If you are not familiar Docker, please refer to

1. OpenAI GPT + Docker: Hello World,
2. Docker for full-stack developer

If you are not familiar with Next.js, please refer to these articles.

Here is the Docker-intel, use the command to generate a Docker image.

docker build -f Dockerfile-intel -t fluber/wood:latest . Reference: Docker-Hub.

FROM node:lts-alpine

# 設置工作目錄
WORKDIR /home/node/code/my-wood-co2-app

# 複製 package.json 和 package-lock.json
COPY ./code/test ./

# 安裝依賴（可選：加快安裝過程）
RUN npm ci || npm install

# 設置環境變數
ENV HOST=0.0.0.0
ENV CHOKIDAR_USEPOLLING=true
ENV CHOKIDAR_INTERVAL=100

# 開放應用使用的端口
EXPOSE 3001

# 啟動應用
CMD ["npm", "run", "start"]

Here is the Docker-arm32v7, use the command to generate a Docker image:.

docker build -f Dockerfile-arm32v7 -t fluber/wood:wood:armv7 .

# 使用 arm32v7 版本的 Node.js LTS Alpine 映像
FROM arm32v7/node:lts-alpine

# 設置工作目錄
WORKDIR /home/node/code/my-wood-co3-app

# 複製 package.json 和 package-lock.json
COPY ./code/arm32_test ./

# 安裝依賴（可選：加快安裝過程）
RUN npm ci || npm install

# 確保在正確的目錄中運行 build 步驟
WORKDIR /home/node/code/my-wood-co3-app

# 設置環境變數
ENV HOST=0.0.0.0
ENV CHOKIDAR_USEPOLLING=true
ENV CHOKIDAR_INTERVAL=100

# 開放應用使用的端口
EXPOSE 80

# 啟動應用
CMD ["npm", "run", "start"]

Here is the docker-compose-intel-production.yml

services:
  application:
    container_name: my-wood-co2-application
    image: fluber/wood:latest
    ports:
      - "3001:3001"
    working_dir: /home/node/code/my-wood-co2-app
    environment:
      - HOST=0.0.0.0
      - CHOKIDAR_USEPOLLING=true
      - CHOKIDAR_INTERVAL=100
      - MONGO_URI=mongodb://admin:secretpassword@mongodb:27017/wood_co2_db?authSource=admin
    tty: true
    depends_on:
      - mongodb

  mongodb:
    container_name: my-mongodb
    image: mongo:3.6.23
    restart: always
    ports:
      - "27017:27017"
    volumes:
      - mongodb_data:/data/db
    environment:
      - MONGO_INITDB_ROOT_USERNAME=admin
      - MONGO_INITDB_ROOT_PASSWORD=secretpassword
      - MONGO_INITDB_DATABASE=wood_co2_db
    healthcheck:
      test: ["CMD", "mongo", "--username", "admin", "--password", "secretpassword", "--eval", "db.adminCommand('ping')"]
      interval: 30s
      retries: 3
      start_period: 10s
      timeout: 5s

  mongo-express:
    container_name: my-mongo-express
    image: mongo-express
    restart: always
    ports:
      - "8081:8081"
    environment:
      - ME_CONFIG_MONGODB_ADMINUSERNAME=admin
      - ME_CONFIG_MONGODB_ADMINPASSWORD=secretpassword
      - ME_CONFIG_MONGODB_SERVER=my-mongodb
      - ME_CONFIG_BASICAUTH=false  # Disable login authentication
    depends_on:
      - mongodb

volumes:
  mongodb_data:

Here is the docker-compose-arm32-production.yml

services:
  application:
    container_name: my-wood-co2-application
    image: fluber/wood:armv7
    ports:
      - "80:80"
    working_dir: /home/node/code/my-wood-co3-app
    environment:
      - HOST=0.0.0.0
      - CHOKIDAR_USEPOLLING=true
      - CHOKIDAR_INTERVAL=100
      - MONGO_URI=mongodb://admin:secretpassword@mongodb:27017/wood_co2_db?authSource=admin
    tty: true
    depends_on:
      - mongodb

  mongodb:
    container_name: my-mongodb
    #image: mongo:latest
    image: apcheamitru/arm32v7-mongo:latest
    restart: always
    ports:
      - "27017:27017"
    volumes:
      - mongodb_data:/data/db
    environment:
      - MONGO_INITDB_ROOT_USERNAME=admin
      - MONGO_INITDB_ROOT_PASSWORD=secretpassword
      - MONGO_INITDB_DATABASE=wood_co2_db
    healthcheck:
      test: ["CMD", "mongo", "--username", "admin", "--password", "secretpassword", "--eval", "db.adminCommand('ping')"]
      interval: 30s
      retries: 3
      start_period: 10s
      timeout: 5s


volumes:
  mongodb_data:

λ docker compose -f docker-compose-intel-production.yml up -d

Prepare to deploy to Google Cloud Run

1. Use an external MongoDB service to replace the mongodb block in docker-compose.yml
2. You can use a Google account to activate Google Cloud services, which requires your credit card information.
3. You need to remove the MongoDB and mongo-express sections from the docker-compose.yml file and add the MONGO_URI environment variable to the application section to connect to an external MongoDB database.
4. Create a Google Cloud Project, and the system will provide a project ID, which will be used later.
5. Install the gcloud CLI if you haven’t: https://cloud.google.com/sdk/docs/install
6. Tag & Push Docker Image
7. Deploy to Cloud Run

Apply for MongoDB Atlas

For temporary testing, you can do it this way.

Create a Cloud Project

gcloud auth login

gcloud config set project wood-10958

gcloud auth configure-docker

# Tag the image for GCR
docker tag fluber/wood:latest gcr.io/wood-10958/wood-app

# Push it
docker push gcr.io/wood-10958/wood-app

#Try Using Artifact Registry Instead (Recommended)
gcloud services enable artifactregistry.googleapis.com

#Create a Docker Repository
gcloud artifacts repositories create wood-repo \
  --repository-format=docker \
  --location=us-central1 \
  --description="Docker repo for Wood app"

#Tag & Push to Artifact Registry
docker tag fluber/wood:latest us-central1-docker.pkg.dev/wood-10958/wood-repo/wood-app:latest  

docker push us-central1-docker.pkg.dev/wood-10958/wood-repo/wood-app:latest  

#Re-authenticate and Configure Docker
gcloud auth configure-docker us-central1-docker.pkg.dev

#Try Pushing Again
docker push us-central1-docker.pkg.dev/wood-10958/wood-repo/wood-app:latest

gcloud run deploy wood-app \
  --image=us-central1-docker.pkg.dev/wood-10958/wood-repo/wood-app:latest \
  --platform=managed \
  --region=us-central1 \
  --allow-unauthenticated \
  --port=3001 \
  --set-env-vars=HOST=0.0.0.0,CHOKIDAR_USEPOLLING=true,CHOKIDAR_INTERVAL=100,MONGODB_URI=mongodb+srv://fluber:<db_password>@cluster0.0smc2wa.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0

Google Cloud Run vs. AWS App Runner

In the chart, each column (IaaS, STaaS, PaaS, DaaS, FaaS, SaaS) represents a cloud service model, and each row (e.g., Datacenters, Networking, Storage, etc.) represents a layer of the technology stack needed to deliver an application.

• Gray-colored boxes indicate:
  🔧 Managed by the Cloud Provider (e.g., AWS)
  → You do not have to manage these components yourself.
• White-colored boxes indicate:
  👤 Managed by You (the customer/developer)
  → You are responsible for configuring, maintaining, or developing on that layer.

Why Google Cloud Run and AWS App Runner are PaaS (Platform as a Service):

1. No Infrastructure Management:
   You don’t need to manage servers or hardware. You just upload your application (usually in a container), and the platform takes care of running it for you.
2. Focus on Code:
   You focus on writing your application code. The platform handles everything else like scaling, networking, and managing servers.
3. Automatic Scaling:
   Both platforms automatically adjust the number of resources (like containers) based on traffic. You don’t need to worry about how many servers you need or when to add more.
4. Managed Services:
   They handle all the technical details for you, like monitoring, logging, and updating the system. You don’t have to deal with any of that.
5. Simple Deployment:
   You can easily deploy your code, often with tools that automate the process. You don’t need to worry about setting up servers or handling complex configurations.
6. No Server Worries:
   These platforms take care of things like server updates and maintenance. You don’t need to worry about keeping servers running or making sure they are up to date.