GKE vs Cloud Run decision in GCP - Hands-On Comparison

GKE vs Cloud Run Decision Guide

📖 Scenario: You are a cloud architect helping a small company decide how to deploy their containerized applications on Google Cloud Platform. They want to understand when to use Google Kubernetes Engine (GKE) versus Cloud Run.

🎯 Goal: Build a simple Python dictionary that lists application types and their recommended deployment option (GKE or Cloud Run). Then add a configuration variable for workload size, write logic to select the deployment based on workload, and finally complete the decision guide with a summary statement.

📋 What You'll Learn

Create a dictionary named app_deployments with exact keys and values for application types and deployment options

Add a variable named workload_size with a string value representing workload size

Write a function named choose_deployment that takes an application type and workload size and returns the recommended deployment

Add a final variable named decision_summary that stores a summary string using the function

💡 Why This Matters

🌍 Real World

This project helps beginners understand how to decide between GKE and Cloud Run for deploying containerized applications based on workload and application complexity.

💼 Career

Cloud architects and developers often need to choose the right Google Cloud service for deployment. This exercise builds foundational decision-making skills for cloud infrastructure.

1

Create the application deployment dictionary

Create a dictionary called app_deployments with these exact entries: 'microservice': 'Cloud Run', 'batch job': 'Cloud Run', 'complex app': 'GKE', 'stateful app': 'GKE'

GCP

# Create the app_deployments dictionary with exact entries
# Your code here

Need a hint?

Use curly braces to create a dictionary with the exact keys and values.

2

Add workload size configuration

Add a variable called workload_size and set it to the string 'small'

GCP

app_deployments = {
    'microservice': 'Cloud Run',
    'batch job': 'Cloud Run',
    'complex app': 'GKE',
    'stateful app': 'GKE'
}
# Add workload_size variable below
# Your code here

Need a hint?

Assign the string 'small' to the variable workload_size.

3

Write deployment decision function

Write a function called choose_deployment that takes parameters app_type and workload. It should return 'GKE' if workload is 'large', otherwise return the deployment from app_deployments[app_type]

GCP

app_deployments = {
    'microservice': 'Cloud Run',
    'batch job': 'Cloud Run',
    'complex app': 'GKE',
    'stateful app': 'GKE'
}
workload_size = 'small'
# Define choose_deployment function below
# Your code here

Need a hint?

Use an if-else statement inside the function to check workload size.

4

Add final decision summary

Add a variable called decision_summary that stores the string "For a microservice with small workload, deploy to " plus the result of choose_deployment('microservice', workload_size)

GCP

app_deployments = {
    'microservice': 'Cloud Run',
    'batch job': 'Cloud Run',
    'complex app': 'GKE',
    'stateful app': 'GKE'
}
workload_size = 'small'

def choose_deployment(app_type, workload):
    if workload == 'large':
        return 'GKE'
    else:
        return app_deployments[app_type]

# Add decision_summary variable below
# Your code here

Need a hint?

Use string concatenation with the function call to build the summary.