Easily Scale Your Apps with Cloud Run

Want to build and deploy apps without worrying about the backend? Google Cloud Run is here to help. It's a simple, managed platform that lets you focus on coding while it handles the rest. Let's explore how Cloud Run can make your life easier.

What is Cloud Run?

• Pack Your App

  Put your app and its dependencies into a single container.

• No Server Worries

  Cloud Run manages the servers for you, scaling up or down as needed.

• Works for All Apps

  Whether your app stores data or not, Cloud Run supports both types.

Benefits You'll Love

1. Scale Automatically

   Your app grows, Cloud Run grows with it.

2. Secure by Default

   Enjoy top-notch security with easy access controls.

3. Easy Monitoring

   Understand your app's performance at a glance.

4. Pay Only for What You Use

   No surprise bills, just fair pricing.

Real-Life Uses of Cloud Run

• Web Apps

  Grow your user base without backend headaches.

  /* Example code for web app deployment */

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• APIs & Microservices

  Easily integrate and scale individual services.

• Event-Driven Apps

  React to events in real-time with Cloud Run.

• AI/ML Models

  Deploy machine learning models as easily as web apps.

Example: "Image Resizer" - A Serverless Image Processing Service

Scenario:

• Company:

  GreenEarth, an e-commerce platform for eco-friendly products.

• Challenge:

  1. Ensure all product images are resized to three standard dimensions (thumbnail, medium, large) for consistent display across their website and mobile app.
  2. Offload image processing from their main application to improve performance and reduce latency.

Solution:

Deploy an "Image Resizer" service on Cloud Run.

How it Works:

1. Trigger:

   Whenever a new product image is uploaded to GreenEarth's Cloud Storage bucket.

2. Cloud Run Service: "Image Resizer"

   • Containerized Application:

     • Written in Node.js using Sharp (image processing library).
     • Listens for new image uploads in the designated Cloud Storage bucket.

   • Functionality:

     1. Downloads the newly uploaded image.
     2. Resizes the image into three predefined dimensions (thumbnail, medium, large).
     3. Uploads the resized images back to a separate Cloud Storage bucket.

3. Output:

   Three resized images for each uploaded product image, stored in Cloud Storage.

Cloud Run Configuration:

• Platform:

  Cloud Run (fully managed)

• Container:

  Node.js 14 with Sharp library

• Trigger:

  Cloud Storage bucket upload trigger

• Scaling:

  Automatic scaling with a minimum of 0 instances and a maximum of 10 instances

• Memory Allocated:

  512 MB

• Timeout:

  5 minutes (to handle large image processing)

Benefits for GreenEarth:

• Scalability:

  Automatically handles increased image uploads during sales or new product launches.

• Performance:

  Offloads resource-intensive image processing, ensuring the main application remains responsive.

• Cost-Effectiveness:

  Only pays for the actual compute time used by the "Image Resizer" service.

• Security:

  Leveraging Cloud Run's built-in security features and automatic updates.

Containers and Microservices

Scaling E-commerce Success with Cloud Run: The Story of "FashionFrenzy"

Background:

FashionFrenzy, a trendy online clothing store, was launched in 2020 by two entrepreneurs, Emma and Ryan. Initially, the website was built on a traditional monolithic architecture, hosted on a single virtual machine. As the brand gained popularity, especially among young adults, the site faced scalability issues, leading to frequent crashes during peak shopping seasons (e.g., Black Friday, Holiday Sales).

Step 1: Create a Simple "Hello World" Flask App

1. Create a new directory for your project and navigate into it:

   mkdir flask-cloudrun-example
     cd flask-cloudrun-example

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2. Create a file named app.py with the following content:

   from flask import Flask
     app = Flask(__name__)
     
     @app.route("/")
     def hello_world():
         return "Hello, World from Cloud Run!"
     
     if __name__ == "__main__":
         app.run(debug=True, host="0.0.0.0", port=8080)

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Step 2: Dockerize Your Flask Application

1. Create a Dockerfile in the same directory with the following content:

   FROM python:3.9-slim
     
     WORKDIR /app
     
     COPY . .
     
     # Install any needed packages specified in requirements.txt
     RUN pip install --trusted-host pypi.python.org flask
     
     # Make port 8080 available to the world outside this container
     EXPOSE 8080
     
     # Define environment variable
     ENV NAME World
     
     # Run app.py when the container launches
     CMD ["python", "app.py"]

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Step 3: Build Your Docker Image

1. Build your Docker image by running:

   docker build -t hello-world .
     docker tag hello-world gcr.io/[YOUR_GCP_PROJECT_ID]/hello-world

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   Replace [YOUR_GCP_PROJECT_ID] with your project ID.

   

Step 4: Push Your Image to Google Container Registry (GCR)

1. Configure Docker to use gcloud as a credential helper, then push your image:

   gcloud auth configure-docker
     docker push gcr.io/[YOUR_GCP_PROJECT_ID]/hello-world

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   Replace [YOUR_GCP_PROJECT_ID] with your project ID.

   

Step 5: Deploy to Cloud Run (Console)

1. First, open Cloud Run console and click "Deploy Container":
   


2. Select the container from Google Container Registry:
   


3. Give a name to your service and click "Create":
   


4. After deployment, click on the link to view your "Hello, World from Cloud Run!" message:
   
   

Step 6: Verify Your Deployment

1. After deployment, the gcloud run deploy command will output a URL.
2. Visit this URL in your web browser to see your "Hello, World from Cloud Run!" message:
   
3. Alternatively, you can find the URL in the Cloud Console under the Cloud Run section for your service.

Congratulations! You've successfully deployed a Flask "Hello World" application as a Docker container to Google Cloud Run.

Environment Variables: Setup and Usage in Cloud Run

Setting Up Environment Variables in Cloud Run

Using the Google Cloud Console

1. Navigate to Cloud Run: Open the Google Cloud Console, select your project, and click on Cloud Run.
2. Select Your Service: Choose the service for which you want to set environment variables.
3. Click on "Variables": In the service's left sidebar, click on Variables (under Settings).
4. Add Variable:
   • Click Add Variable.
   • Enter the Key (name) and Value for your environment variable.
   • Optionally, select Is secret to encrypt the value.
   • Click Save.

Using the gcloud Command-Line Interface (CLI)

1. Install/Update gcloud CLI: Ensure you have the latest gcloud CLI installed.
2. Set Environment Variable:

   gcloud run services update  --set-env-vars =

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   Replace , , and with your actual service name, environment variable key, and value, respectively.

const myVariable = process.env.MY_VARIABLE;

Scaling and Traffic Splitting

Configuring Autoscaling in Cloud Run

Using the Google Cloud Console

1. Navigate to Cloud Run, select your service, and click on Settings.
2. Scroll to "Scaling": Adjust the scaling settings as desired (e.g., change the Minimum Instances, Maximum Instances, or Target CPU Utilization).
3. Click Save.

Using the gcloud CLI

gcloud run services update  --platform managed --region  \
          --min-instances  --max-instances  \
          --cpu-threshold  --cpu-target-utilization-percentage 

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Replace placeholders with your actual values.

Handling Multiple Versions with Traffic Splitting

What is Traffic Splitting?

Traffic Splitting allows you to divert a percentage of incoming traffic to different versions of your service, enabling A/B testing, canary releases, or rolling out updates with minimal risk.

Configuring Traffic Splitting in Cloud Run

Using the Google Cloud Console

1. Navigate to Cloud Run, select your service, and click on Traffic Splitting.
2. Add a New Split:
   • Select the Service Version to split traffic to.
   • Set the Percentage of traffic to divert.
   • Click Save.

Using the gcloud CLI

gcloud run services update-traffic  --platform managed --region  \
          --to-revisions =

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Replace , , , and with your actual values (e.g., my-service, us-central1, my-revision, and 20 for 20%).

Configuring Autoscaling in Cloud Run

Using the Google Cloud Console

1. Navigate to Cloud Run, select your service, and click on Settings.
2. Scroll to "Scaling": Adjust the scaling settings as desired (e.g., change the Minimum Instances, Maximum Instances, or Target CPU Utilization).
3. Click Save.

gcloud run services update  --platform managed --region  \
    --min-instances  --max-instances  \
    --cpu-threshold  --cpu-target-utilization-percentage 

Handling Multiple Versions with Traffic Splitting

Configuring Traffic Splitting in Cloud Run

Using the Google Cloud Console

1. Navigate to Cloud Run, select your service, and click on Traffic Splitting.
2. Add a New Split:
   • Select the Service Version to split traffic to.
   • Set the Percentage of traffic to divert.
   • Click Save.

Using the gcloud CLI

gcloud run services update-traffic  --platform managed --region  \
    --to-revisions =

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Replace , , , and with your actual values (e.g., my-service, us-central1, my-revision, and 20 for 20%).