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AWS LAMBDA IMAGE RESIZE CODE
extends the capability of Lambda to the edge by allowing you to run the code in multiple AWS locations closer to the viewer.įor each cache behavior in a Amazon CloudFront distribution, you can add up to four triggers (associations) that cause a Lambda function to execute when one or more of the following CloudFront events occur: Lambda scales automatically and is highly available. You pay only for the compute time you consume – there is no charge when your code is not running.
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Setting the Scene with Lambda lets you run code without provisioning or managing servers. To learn more about edge networking with AWS, click here. What if you could just do all these things on the fly, as needed, without having to preprocess each image manually? enables you to do each of these things which will result in a better experience for your users while also reducing the bandwidth usage. Have you ever been in a situation where you have lots of images and you want to modify their dimensions, apply watermark for new design layouts, or optimize their formats based on browser support? Then, using a Lambda function with NodeJS runtime, we’ll write code which will use the ( ) to resize the image into two sizes - one 200px wide, and another 800px wide, and finally, store it in another S3 bucket.*This blog was updated with a new section, Cost and Performance Considerations. We’ll create a simple image optimization pipeline which will get triggered whenever a new image is added to our S3 bucket. I’ve mostly focused on the Lambda code and the concept of the pipeline, so I’m assuming the reader is familiar with common workflows in the AWS ecosystem.
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So, this post describes a way to tackle this problem in an efficient way using AWS services. (If you want to read about how images are being uploaded directly to S3 from client, I’ve written about it here) So, doing the conversion on API side was not an option. I was using AWS S3 for storing the pictures, and the pictures were being uploaded to S3 from the client side itself, and the API would just receive the URL for uploaded image. Taking this one step ahead, let’s make two copies of the picture in different sizes - one relatively high res (for profile picture, yearbook picture, etc) and another a bit low res (for displaying on scraps where small size picture is needed) to improve performance. Ideally, what should happen is that once user uploads the picture, it should be resized, compressed and then used. It’s not just the performance, but the cost skyrockets as well, since that large image gets downloaded by so many other users multiple times. Also, if some user uploads a picture in wrong dimensions, it would ruin the UI for other users. If some user uploads a high quality 5 MB picture, the whole application would potentially slow down because of that. One mistake I made and realized a bit late after we got many users, is that I was directly storing the user uploaded profile pictures and then showing them as-is throughout the application. When a user registers on the platform, they upload an image as their profile picture, which is then used on their profile, as well as on others’ profile if they post a scrap for other users. So, we’re planning to work some more on GradGoggles and reach even more users this time. Since the pandemic hasn’t receded, even the batch of 2021 is going to graduate virtually. We recieved a very good response from users regarding GradGoggles.
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You can read the case study for the project here. Last year, I worked on an interesting project which was a virtual yearbook and scrapbook for the graduating batch of 2020 in my college.