- [Morgan] AWS Lambda is one of these serverless compute options you have available to you on AWS. Lambda allows you to package and upload your code to the Lambda service, creating what's called a Lambda function. Once you create a Lambda function, it isn't always running all of the time. Instead, Lambda functions run in response to triggers. You configure a trigger for when you want your function to run and from there, the Lambda service waits for the trigger. There's a long list of available triggers for AWS Lambda and new ones are being supported frequently. So, I won't run through them all now. However, a couple of common examples of triggers for Lambda functions are an HTTP request, an upload of a file to the storage service, Amazon S3, events originating from other AWS services or even in-app activity from mobile devices. When the trigger is detected, the code is automatically run in a managed environment, an environment you do not need to worry too much about because it is automatically scalable, highly available and all of the maintenance of the environment itself is done by AWS. You do, however, get to choose the language your Lambda function is coded in, the amount of memory and CPU your function is allocated in the environment, it's permissions, dependencies and many other aspects of how the function runs. If you have one or 1000 incoming triggers, AWS Lambda will scale your function to meet demand, each in its own isolated environment. Lambda is currently designed to run code that has a runtime of under 15 minutes. So, this isn't for long running processes like deep learning or batch jobs, you wouldn't host something like a WordPress site on AWS Lambda. It's more suited for quick processing, like a web backend for handling requests or a backend report processing service. One of the best things about Lambda, is that you aren't billed for code that isn't running, you only get billed for the resources that you use, down to 100 millisecond intervals. To understand Lambda more, let's run through a quick demo. In this demo, I will create a Lambda function that resizes images uploaded into the employee directory application to be a uniform thumbnail size. It makes sense to create this sort of logic with a Lambda function. You don't need an application to be running 24/7 on EC2 to resize photos uploaded to the application. You really only need the resize logic to run when a new photo is uploaded. So, here's our diagram for the app. What I want to do is add this. When a new photo is uploaded to Amazon S3, it triggers a Lambda function that resizes the image and uploads it to the same S3 bucket but to a different location than where the original image is stored. Okay, let's go ahead and build this out. You can see that I'm already in the AWS management console and I'm going to go ahead and click create function. From here, you have a couple of choices of how you want to create this function. We are going to author this one from scratch and I already have the code written and downloaded to my local machine. So we're going to author from scratch, we'll name this, resize photo. And then we get to select the runtime. And that's basically the language that you've coded your function to be. So for us, we coded this in Python. So I'm going to select the Python runtime. And then I'm also going to expand this permission section here and I'm going to use an existing IAM role for this code. The Python code that we wrote is using the AWS SDK to make API calls to S3. Therefore, those API calls must be signed and authenticated, as we discussed in earlier lessons. So, in order for this code to gain access to the temporary credentials needed to sign the request, we are going to associate an IAM role with the appropriate S3 permissions to this Lambda function. So, I'm going to go ahead and select the existing role which is called AWS Lambda S3 access, and then I'll click, create function. At this point, we are now seeing the designer view and I'm going to add a trigger for this Lambda function. I will select a trigger and you can see here as I scroll down, all of the different triggers that are available for Lambda - - there are quite a bit -- and there are also partner event sources powered by a service called Amazon EventBridge. But I'm going to go ahead and select S3. Then I will select the employee photos bucket which is the bucket that our app is using. And then I will use the prefix, employee-pic, because I know that that is the prefix where the function is uploading the images and then I will scroll down and I'm going to acknowledge this recursive invocation piece here. And this is essentially just letting us know that if I were to have this Lambda function read from the employee-pic prefix which is what triggered the event, and then process the image and upload it to the same spot, it would trigger another Lambda function run and that would kind of just go on forever. So, you want to make sure that you are using a different place for the output than you are for the input. So I'm going to go ahead and click add, and now I will scroll down and we are going to add the source code. So, if you have source code with no dependencies, you can just type it right in here which is really nice for experimentation but for us, we're using some outside dependencies for resizing the image in Python. So, I'm going to upload a zip file from my local machine. Then I'm going to click save. Then I'm going to scroll down and change this Lambda handler. Go ahead and check out the class notes if you want to learn more about Lambda handlers and how this works. I'm just changing this so that it's referring to my actual file and then I'll click save. And now what I want to do, is test this through the employee directory application. So I'm going to add a new employee, we'll name this Alana, locations, USA, role is Cloud Technologist. She is a Mac user and then we will upload her photo and click save. So now it's been saved. I want to then test to see if this worked. So I'm going to click monitoring here and then I want to view the logs in CloudWatch Logs. So, CloudWatch Logs is where the output goes from your Lambda function. I'm going to scroll down and click on the latest log stream which is just the latest collection of log files from the Lambda function. And we can see that we did get the image resized output here. So I know that this code ran in response to the incoming event from S3. And that's it. That's how you create an AWS Lambda function at a high level. You could host the entire employee directory application's backend on Lambda with some refactoring. But, I think I'm going to save that for a later conversation. If you're interested in the code that we used and more information about how Lambda works and handlers and how you write code for Lambda, I'll go ahead and include some resources for you in the reading section following this video.
