Cloud Run for containerized services in GCP - Mini Project: Build & Apply

Cloud Run for containerized services

📖 Scenario: You are a developer who wants to deploy a simple containerized web service on Google Cloud using Cloud Run. Cloud Run lets you run containers without managing servers, scaling automatically based on traffic.In this project, you will create the necessary configuration to deploy a container image to Cloud Run with basic settings.

🎯 Goal: Build a Cloud Run service configuration that deploys a container image with a specified service name, region, and allows unauthenticated access.

📋 What You'll Learn

Create a Cloud Run service configuration dictionary with the service name and container image

Add a region configuration variable for deployment location

Add the main deployment configuration including container port and environment variables

Complete the configuration by enabling unauthenticated access and setting the service to use the specified region

💡 Why This Matters

🌍 Real World

Cloud Run is widely used to deploy containerized applications quickly without managing servers. This project simulates setting up a basic Cloud Run service configuration.

💼 Career

Understanding how to configure and deploy services on Cloud Run is essential for cloud engineers and developers working with serverless container platforms.

1

Create initial Cloud Run service configuration

Create a dictionary called cloud_run_service with these exact entries: 'name': 'my-cloud-run-service' and 'image': 'gcr.io/my-project/my-container:latest'.

GCP

# Create the cloud_run_service dictionary with name and image
# Your code here

Hint

Use a Python dictionary with keys 'name' and 'image' and assign the exact string values.

2

Add deployment region configuration

Create a variable called deployment_region and set it to the string 'us-central1' to specify the Cloud Run deployment region.

GCP

cloud_run_service = {
    'name': 'my-cloud-run-service',
    'image': 'gcr.io/my-project/my-container:latest'
}
# Set the deployment_region variable
# Your code here

Hint

Assign the exact string 'us-central1' to the variable deployment_region.

3

Add container port and environment variables

Add a key 'container_config' to the cloud_run_service dictionary. Its value should be another dictionary with 'port' set to 8080 and 'env' set to a dictionary with 'ENV' key and value 'production'.

GCP

cloud_run_service = {
    'name': 'my-cloud-run-service',
    'image': 'gcr.io/my-project/my-container:latest'
}
deployment_region = 'us-central1'
# Add container_config with port and env to cloud_run_service
# Your code here

Hint

Update the cloud_run_service dictionary to include container_config with the specified port and environment variable.

4

Enable unauthenticated access and set region

Add two keys to the cloud_run_service dictionary: 'allow_unauthenticated' set to True and 'region' set to the variable deployment_region.

GCP

cloud_run_service = {
    'name': 'my-cloud-run-service',
    'image': 'gcr.io/my-project/my-container:latest',
    'container_config': {
        'port': 8080,
        'env': {
            'ENV': 'production'
        }
    }
}
deployment_region = 'us-central1'
# Add allow_unauthenticated and region keys to cloud_run_service
# Your code here

Hint

Set 'allow_unauthenticated' to True and 'region' to deployment_region in the cloud_run_service dictionary.

Practice

1. What is the main benefit of using Cloud Run for your containerized applications?

easy

A. It needs you to configure load balancers yourself.

B. It automatically scales your app based on traffic without managing servers.

C. It only works with virtual machines, not containers.

D. It requires you to manage virtual machines manually.

Solution

1. Step 1: Understand Cloud Run's serverless nature

   Cloud Run runs containers without requiring you to manage servers or infrastructure.

2. Step 2: Recognize automatic scaling feature

   Cloud Run automatically adjusts the number of container instances based on incoming traffic.

3. Final Answer:

   It automatically scales your app based on traffic without managing servers. -> Option B

4. Quick Check:

   Serverless + auto-scale = D [OK]

Hint: Cloud Run = serverless + auto scaling [OK]

Common Mistakes:

• Thinking you must manage servers manually
• Confusing Cloud Run with VM services
• Assuming manual load balancer setup is needed

2. Which of the following is the correct command to deploy a container image to Cloud Run?

easy

A. gcloud run deploy --image=gcr.io/my-project/my-image

B. gcloud compute instances create --image=gcr.io/my-project/my-image

C. kubectl run deploy --image=gcr.io/my-project/my-image

D. docker run deploy --image=gcr.io/my-project/my-image

Solution

1. Step 1: Identify the correct CLI tool for Cloud Run deployment

   The gcloud run deploy command is used to deploy container images to Cloud Run.

2. Step 2: Confirm the correct syntax

   The command includes the --image flag followed by the container image URL.

3. Final Answer:

   gcloud run deploy --image=gcr.io/my-project/my-image -> Option A

4. Quick Check:

   Cloud Run deploy uses gcloud run deploy [OK]

Hint: Use 'gcloud run deploy' to deploy containers [OK]

Common Mistakes:

• Using compute or kubectl commands instead of gcloud run
• Confusing docker run with Cloud Run deployment
• Missing the --image flag

3. Given this Cloud Run service configuration snippet, what will happen when traffic increases?

apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  name: my-service
spec:
  template:
    spec:
      containers:
      - image: gcr.io/my-project/my-image
        resources:
          limits:
            cpu: 1
            memory: 512Mi

medium

A. The service will scale down to zero immediately.

B. Cloud Run will reject new requests once CPU limit is reached.

C. You must manually add more instances in the configuration.

D. Cloud Run will automatically create more container instances to handle the traffic.

Solution

1. Step 1: Understand Cloud Run's scaling behavior

   Cloud Run automatically scales container instances based on incoming traffic demand.

2. Step 2: Recognize resource limits do not prevent scaling

   Resource limits set max CPU/memory per instance but do not stop Cloud Run from adding instances.

3. Final Answer:

   Cloud Run will automatically create more container instances to handle the traffic. -> Option D

4. Quick Check:

   Auto scaling = more instances with traffic [OK]

Hint: Cloud Run auto scales instances, resource limits per instance [OK]

Common Mistakes:

• Thinking resource limits block scaling
• Assuming manual scaling is required
• Confusing scale down with scale up

4. You deployed a container to Cloud Run but get an error: Container failed to start. What is a common cause to check first?

medium

A. The container image does not expose the port Cloud Run expects.

B. You forgot to create a virtual machine instance.

C. Cloud Run requires a YAML file to deploy containers.

D. The container image must be in Docker Hub only.

Solution

1. Step 1: Understand Cloud Run port requirements

   Cloud Run expects the container to listen on port 8080 by default or the port set in the PORT environment variable.

2. Step 2: Check container image configuration

   If the container does not expose or listen on the expected port, it will fail to start properly.

3. Final Answer:

   The container image does not expose the port Cloud Run expects. -> Option A

4. Quick Check:

   Port mismatch causes start failure [OK]

Hint: Check container listens on port 8080 [OK]

Common Mistakes:

• Assuming VM instances are needed
• Thinking YAML is mandatory for deployment
• Believing container must be on Docker Hub

5. You want to deploy a containerized web app on Cloud Run that must handle sudden traffic spikes and also keep costs low when idle. Which setup is best?

hard

A. Deploy on Compute Engine with fixed VM instances.

B. Deploy on Cloud Run with minimum instances set to 5 to handle spikes.

C. Deploy on Cloud Run with autoscaling enabled and minimum instances set to zero.

D. Deploy on Cloud Run with manual scaling to 3 instances.

Solution

1. Step 1: Understand Cloud Run autoscaling and cost model

   Cloud Run automatically scales instances up and down based on traffic, and setting minimum instances to zero means no cost when idle.

2. Step 2: Compare options for cost and scalability

   Setting minimum instances above zero incurs cost even when idle; Compute Engine fixed VMs do not scale automatically; manual scaling requires more management.

3. Final Answer:

   Deploy on Cloud Run with autoscaling enabled and minimum instances set to zero. -> Option C

4. Quick Check:

   Auto scale + zero min instances = cost efficient [OK]

Hint: Zero min instances saves cost, autoscale handles spikes [OK]

Common Mistakes:

• Setting minimum instances above zero increases cost
• Using fixed VMs lacks automatic scaling
• Manual scaling requires extra management