Building a Multi-Tier Serverless Application in AWS
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In this course, we will create a multi-tier serverless architecture on AWS using Amazon API Gateway, AWS Lambda, AWS Step Functions, and Amazon Polly text to speech. This is a hands-on course where you will learn how to create serverless functions, data access, business logic, and integration layers on AWS. Also, you will learn how to create a presentation layer for your application using the client SDK generated by Amazon API Gateway. We will then host the application on Amazon S3.
By the end of this course, you will be able to recognize and implement an end to end workflow built in the Amazon Cloud using Serverless components.
This course is intended for developers or DevOps engineers who want to create serverless applications on AWS, or who may be considering migrating their existing web applications to AWS.
The GitHub repository for this course is available here: https://github.com/cloudacademy/advanced-api-gateway-resources.
Hello and welcome back. In this lecture, we'll create the data and data access layers for our application, which will consist of DynamoDB and Lambda functions. Let's look at our application workflow before we head to create the Lambda functions. In our application, the user will select Menu Items, Delivery Options and a Payment Type, and then submit the order. Once the order is submitted, the payment will be processed, and either approved or rejected. Based on that, the order will be further processed or canceled, and the user will be notified accordingly. This workflow can be easily translated into Step Functions or a State Machine, and we'll see that in the next video. For now, we can just think of each step in the workflow as a Lambda function which does a discreet task, and passes the results to the next function. To start, we'll first need to create a table in DynamoDB to which our Lambda functions will write to and re-prompt. I'll go to our DynamoDB console and create a table, let's call it Orders. I'll choose Order ID as a primary key. We don't need to do anything else here since it's a NoSQL Database, and we can insert our records later through the Lambda function in the format we like. We'll create our Lambda functions using a CloudFormation template. But before that, I would need to place my code on S3 so that the CloudFormation template can access it. Let's create a bucket in S3 and call it gopizza-lamba-functions. All of the Lambda functions are in .js files, and I'll zip them all up as Lambda Functions.zip and upload to the bucket. From here, the CloudFormation template can access the code. Okay, now here is my CloudFormation template that I've already created using YAML. This template creates a role which our Lambda functions will use. It has a AssumeRolePolicyDocument property, which is a trust policy associated with this role, which will allow Lambda functions to assume its role. It also has these ManagePolicyArns that give it full access to S3, DynamoDB and Amazon Polly, as required by our Lambda functions. Then we create our five Lambda functions through the template. If you look at the first Lambda function, PlaceOrder, it has the type of Lambda function, and properties including description, function name, a handler, which is the function Lambda will invoke, and since we've saved our Node.js function as PlaceOrder.js, the handler will be PlaceOrder.handler. For role, we'll get the ARN of the role created earlier in the template. For code, we'll specify the S3 Bucket where our code is located, and the name of the ZIP file which contains all the functions. So, I'll go to CloudFormation Console and execute the template to create our functions in us-east-2 region. If you plan to use Step Functions, make sure you choose a region where it support it. The template also creates an IAM role, so I'll need to acknowledge that, and then click on create. Our stack is successfully created, now let's look at each Lambda function to understand it's functionality. The first function to be executed is PlaceOrder. This function simply puts all the data from the input event, including Order Type, Menu Items, and Payment Details to the Orders table in DynamoDB and passes over the information to the next Lambda function in the workflow which is ProcessPayment. ProcessPayment is just a stub method here, with some hard-coded logic for demo. It only passes the payment if credit card number is all ones, all the others are rejected. In the real world, of course, this method would be much sophisticated to correctly process the payment details. This function returns two objects, one is a paymentSuccess boolean value, and the other is the Params from the event. The paymentSuccess variable allows us to determine whether the order was successfully placed or canceled, and will be used in the choice state later in the State Machine. After that we have two more functions, ConfirmOrder and CancelOrder. Both update the order status of the record, where they confirm or cancel the value, based on the paymentSuccess value. After that we have NotifyUser function, which just returns the order status in text format. And the last function, NotifyWithPolly, converts the text order status to speech in the form of MP3 file, and stores it on S3 for the front-end page to playback so the user can hear the order status in live-like speech format from our front-end app. Okay, now we have all our Lambda functions in place, so it's time to put them in the correct order of execution. We'll do that using the step functions in our next video.
Tehreem is a Sr. Software Engineer with passion in Cloud Technologies, Big Data analytics, Software Testing and Automation. She has over 10 years of work experience comprising of her tenure at ServiceNow, Microsoft and Harmonic Inc. Most recently she has been developing learning content in-line with the emergence of Public Clouds and XaaS platforms with focus on AWS, Microsoft Azure and GCP. Tehreem resides in BayArea, CA with her family and when not working she enjoys nature/outdoors, movies and fine dining.