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Build an Automated Image Resizer with AWS Lambda

Learn how to build an automated image-resizing workflow using Amazon S3 and AWS Lambda. This tutorial explains how to automatically process and resize images when they are uploaded to an S3 bucket, using a simple serverless architecture

Images are an important part of modern web and mobile applications, but large image files can increase storage requirements and slow down applications. Manually resizing every uploaded image can also become time-consuming.
In this tutorial, we’ll explore how to build an automated image-resizing workflow using Amazon S3 and AWS Lambda. Whenever a new image is uploaded to an S3 bucket, Lambda can automatically process it and create a resized version.

Architecture Overview

The workflow consists of three main steps:
  1. A user uploads an image to an Amazon S3 bucket.
  2. The S3 upload event triggers an AWS Lambda function.
  3. Lambda processes the image and saves the resized version back to S3.
This is an example of an event-driven serverless architecture. There is no need to keep a server running and waiting for new images.

AWS Services Used

Amazon S3

Amazon S3 provides object storage for the images. You can use one bucket with separate folders, or separate buckets, for original and processed images.
For example:
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images/
├── original/
│ └── photo.jpg
└── resized/
└── photo.jpg
Keeping the original and resized images separate makes the workflow easier to manage.

AWS Lambda

AWS Lambda runs the image-processing code when an image is uploaded. The function can download the image, resize it using an image-processing library, and upload the result back to S3.
Lambda is useful here because the processing only needs to happen when an image arrives. You don't need a continuously running server.

Amazon CloudWatch

AWS Lambda automatically integrates with Amazon CloudWatch for logging. CloudWatch can help you check whether the function executed successfully and investigate errors when something goes wrong.

Creating the Workflow

First, create an S3 bucket to store your images. You can create an original folder for uploaded images and a resized folder for the processed images.
Next, create a Lambda function. The function should be configured to receive an S3 event whenever a new image is uploaded.
The Lambda function can then:
  • Read the S3 event.
  • Identify the uploaded image.
  • Download the image from S3.
  • Resize the image to a predefined width or height.
  • Upload the new image to the resized location.
A common approach is to use a Python image-processing library such as Pillow inside the Lambda function.

Important Considerations

When configuring the workflow, make sure the Lambda execution role has the appropriate permissions to read the original image and write the resized image.
You should also avoid creating an event loop. For example, if Lambda writes the resized image into the same location that triggers the function, the new file could trigger Lambda again. Using separate prefixes such as original/ and resized/ helps prevent this problem.
For production applications, you may also want to consider image dimensions, file types, error handling, and large image processing requirements.

Benefits of This Approach

This serverless architecture provides several advantages:
  • Automation: Images are processed automatically after upload.
  • Scalability: Lambda can process events without managing traditional servers.
  • Lower operational effort: There is no server to maintain or patch.
  • Flexible storage: S3 can store both original and processed images.
  • Easy monitoring: CloudWatch provides logs for Lambda executions.

Conclusion

An automated image resizer is a practical example of how AWS services can work together to create a serverless application. By combining Amazon S3, AWS Lambda, and Amazon CloudWatch, you can build an event-driven workflow that automatically processes images as they are uploaded.
This project is also a good starting point for exploring more advanced serverless applications, such as thumbnail generation, image optimization, and automated media-processing pipelines.
Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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