Guided Lab: Understanding Visibility Timeout in Amazon SQS

1. Navigate to the Amazon SQS service:

2. Create a new queue using the following configurations:

• Click on Create Queue.
• Choose Standard as the queue type and configure the following:
  • Queue Name: MyVisibilityTimeoutQueue
• Under Configuration, set the Default Visibility Timeout to 30 seconds.

• Leave the other settings at their default values and click Create Queue.

1. Navigate to AWS Lambda: Search for “Lambda” in the AWS Management Console.

2. Create a new Lambda function using the following configurations:

• Choose Author from scratch.
• Function name: myLambdaFunction
• Select Python 3.12 as the runtime.
• Execution role:
  • Select Use an Existing Role: PlayCloud-Sanbox

• Click Create function

3. Replace the existing code with the following:

import time

def lambda_handler(event, context):
    for record in event['Records']:
        print("Processing message:", record['body'])
        time.sleep(45)  # Simulate processing delay (longer than visibility timeout)
        print("Finished processing")

This delay (45 seconds) exceeds the queue’s visibility timeout (30 seconds), allowing us to test message redelivery.

4.  Click Deploy to save the changes.

5. Go to the Configurations tab > General configuration > click Edit and change the Timeout to 30 sec

6. In the Amazon SQS Console, go to MyMainQueue and configure the Lambda function as a trigger:

• Go to Lambda triggers and click Configure Lambda function trigger.

• Choose myLambdaFunction as the function and Save.
• Wait for the status to change to Enabled.

1. Select MyVisibilityTimeoutQueue and click on Send and receive messages.

2. In the Message body field, enter a sample message (e.g., { "order_id": "12345" }).

3. Click Send message to add it to the queue.

1. After adding the trigger, the Lambda function will attempt to process the message.

• Since the function’s processing time (45 seconds) is longer than the visibility timeout (30 seconds), the message will reappear in the queue after 30 seconds.
• Wait for a few minutes.
• Observe the MyVisibilityTimeoutQueue queue in the SQS console.

• Go to the CloudWatch Logs Console to view the logs for the myLambdaFunction function. You should see the message being processed multiple times as it becomes visible again due to the visibility timeout.

1. Return to the AWS Lambda Console and open the lambda function created earlier.

2.Modify the function code with:

import json

def lambda_handler(event, context):
    for record in event['Records']:
        print("Successfully processed message:", record['body'])
    return {
        'statusCode': 200,
        'body': 'Success'
    }

This updated Lambda function processes messages from an SQS queue without the previous delay (sleep). It iterates over each record in the event, logs the message content, and returns a 200 status with "Success" to confirm processing. This setup is now optimized for faster processing without any intentional delay.

3. Click Deploy to save changes.

1. After updating the Lambda Function code, check the SQS Queue again. The Message in flight should be Zero ( 0 ) by now.

2. Go to the CloudWatch Logs Console to view the logs for the myLambdaFunction function again. Open the latest log stream to see the latest log event. You should see successful message and no repeated processing logs.

That’s it. Congratulations! You have successfully tested the Amazon SQS visibility timeout feature by observing both successful message processing within the timeout and redelivery when the timeout expires. Understanding visibility timeout behavior is crucial for reliable message processing, especially for handling scenarios where messages may need to be retried if not processed on time.

Key Takeaways:

• If processing exceeds the visibility timeout, messages reappear for reprocessing.
• Successfully processed messages within the visibility timeout are deleted from the queue.
• Adjusting visibility timeout settings can help manage processing duration to ensure messages are only redelivered when necessary.

As a final step, remember it’s always a best practice to clean up resources after completing a lab. Deleting unused resources will help maintain a clutter-free AWS environment.

Happy learning!!!