Cloud Tasks for async processing in GCP - Mini Project: Build & Apply

Cloud Tasks for async processing

📖 Scenario: You are building a web application that needs to handle user requests without making users wait for long processing tasks. To do this, you will use Google Cloud Tasks to send tasks to a queue for asynchronous processing.

🎯 Goal: Create a Cloud Tasks queue, configure a task payload, and enqueue tasks for asynchronous processing using Google Cloud Platform.

📋 What You'll Learn

Create a Cloud Tasks queue named my-queue

Define a task payload with a specific message

Enqueue the task to the my-queue queue

Configure the task with a target HTTP endpoint URL

💡 Why This Matters

🌍 Real World

Cloud Tasks help web applications handle long-running or delayed jobs without making users wait, improving user experience.

💼 Career

Understanding Cloud Tasks is essential for cloud engineers and developers building scalable, responsive applications on Google Cloud Platform.

1

Create a Cloud Tasks queue

Write a command to create a Cloud Tasks queue named my-queue in the us-central1 location using the gcloud CLI.

GCP

# Your command here to create the queue named my-queue

Need a hint?

Use gcloud tasks queues create followed by the queue name and location flag.

2

Define the task payload

Create a JSON string variable called payload with the exact content {"message": "Process this task asynchronously"} in Python.

GCP

gcloud tasks queues create my-queue --location=us-central1

# Create a variable called payload with the JSON string
# Your code here

Need a hint?

Use single quotes outside and double quotes inside for the JSON string.

3

Create the task dictionary

Create a Python dictionary variable called task with a key http_request that contains a nested dictionary with keys http_method set to POST, url set to https://example.com/task_handler, and body set to the base64-encoded payload variable. Use base64.b64encode(payload.encode()) and decode it to utf-8 for the body.

GCP

gcloud tasks queues create my-queue --location=us-central1

payload = '{"message": "Process this task asynchronously"}'

# Create a dictionary called task with http_request containing http_method, url, and body
# Your code here

Need a hint?

Remember to import the base64 module before encoding.

4

Enqueue the task using Cloud Tasks client

Write Python code to enqueue the task to the my-queue queue in the us-central1 location using the Google Cloud Tasks client. Create a client with client = tasks_v2.CloudTasksClient(), build the queue path with client.queue_path("my-project", "us-central1", "my-queue"), and call client.create_task(parent=parent, task=task). Use my-project as the project ID.

GCP

gcloud tasks queues create my-queue --location=us-central1

payload = '{"message": "Process this task asynchronously"}'

import base64

from google.cloud import tasks_v2

# Create client, build queue path, and enqueue the task
# Your code here

Need a hint?

Use the Cloud Tasks client library to create and send the task.