An Example of Auto Scaling Using AWS Lambda

This article presents An Example of Auto Scaling Using AWS Lambda.

Auto Scaling with AWS Lambda typically involves dynamically adjusting the number of function instances (containers) based on the incoming workload. AWS Lambda automatically manages the scaling process, allowing you to focus on writing code rather than managing infrastructure. Here’s a simplified example of how Auto Scaling works with AWS Lambda.


  1. AWS Account:
    • Ensure you have an AWS account and appropriate permissions to create Lambda functions and configure Auto Scaling.
  2. AWS CLI or AWS Management Console:
    • You can use either the AWS CLI or the AWS Management Console to create and manage resources.


Let’s consider a scenario where you have a Lambda function that processes images uploaded to an S3 bucket. The workload may vary, and you want to automatically scale the number of function instances based on the number of incoming image uploads.

1. Create a Lambda Function

Create a simple Lambda function using the AWS Management Console or AWS CLI. This function processes images and stores the results in another S3 bucket. For example, in Python.

import boto3

s3 = boto3.client('s3')

def lambda_handler(event, context):
    for record in event['Records']:
        bucket = record['s3']['bucket']['name']
        key = record['s3']['object']['key']

        # Process the image (simplified for illustration)
        result = process_image(bucket, key)

        # Store the result in another S3 bucket (simplified for illustration)

def process_image(bucket, key):
    # Image processing logic (simplified)
    return f"Processed image {key}"

def store_result(result):
    # Store result in another S3 bucket (simplified)

2. Create an S3 Bucket for Image Uploads

Create an S3 bucket where users can upload images. This will be the source of the events triggering the Lambda function.

3. Configure Lambda Trigger

Configure the S3 bucket to trigger the Lambda function when a new object is created. This can be done using the AWS Management Console or the AWS CLI.

4. Configure Auto Scaling

Use AWS Auto Scaling to dynamically adjust the number of function instances based on the incoming workload.

  • Open the Lambda function in the AWS Management Console.
  • Scroll down to the “Concurrency” section.
  • Click “Edit” to configure Auto Scaling.
  • Enable “Auto scaling” and set the desired concurrency level, along with maximum and minimum instances.

5. Test Auto Scaling

Upload a batch of images to the S3 bucket and monitor how AWS Lambda automatically scales the number of function instances based on the incoming workload.


  • Auto Scaling for AWS Lambda is based on the number of concurrent executions, not the number of function instances.
  • You can use CloudWatch metrics to monitor Lambda function invocations and adjust Auto Scaling settings accordingly.

This is a simplified example, and real-world scenarios may involve more complex processing logic, additional AWS services, and considerations for security, error handling, and performance optimization. Always follow best practices for production deployments.

Further Reading

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Boto3 and its Features

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GetObject and PutObject Permissions in Amazon S3

Features of AWS Lambda

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