AWS

Intro to Serverless Functions

Published Dec 31, 2020

Updated Feb 14, 2023

3 min read

This article was written over 18 months ago and may contain information that is out of date. Some content may be relevant but please refer to the relevant official documentation or available resources for the latest information.

Serverless functions are functions that are hosted on a managed infrastructure, otherwise known as the cloud. They take all the responsiblity of maintaining instances, uptime, and scaling onto themselves. As a developer, all I have to worry about is my functions that deliver business value. Can you imagine all the extra time you will have to code?!

Periodocally, I will get a great idea (or what I think is a great idea) and want to build it, but I don't want to spend money on it yet. In this scenerio, serverless functions have been a lifesaver. Most cloud providers will also allow you to have a certain amount of function calls per month for free, which means I can develop a live solution for free until it starts to get use. This is huge for startups!

The billing is really great, too. I only pay for the function when its running! I don't have to pay for an instance to just sit there! This may not mean a lot to a dev that is working in a big company, but this kind of information is very important if you ever want your business to adopt the technology. Financial savings are a huge part of the business.

There are many cloud computing companies that have serverless functions such as Google Cloud Platform, Amazon Web Services, and Microsoft Azure to name a few.

I can talk about serverless all day or I can build one and show you how it works. Feel free to follow along. For this example, I will be using Lambda Functions on Amazon Web Services, also commonly known as AWS.

Let's look at a simple hello world function:

const plainOldFunction = () => {
  console.log('Hello World');
}

module.plainOldFunction = plainOldFunction;

After invoking...

const { plainOldFunction } = require("./functions");

plainOldFunction();

We see the expected output of "Hello World"

Here is a function using an AWS handler as the function that is invoked in the cloud. We expect an event object (reference example below). We expect to have access to an object on the body of the event with the field with the name "firstName"

const awsHandler = (event, context) =>  {
  try {
    console.log(`Hello ${event.body.firstName}`);
  } catch (error) {
    return console.error(error);
  }
};

This is what an AWS event looks like as an argument when a Lambda function is invoked from an api gateway. More info about the api gateway can be found here.

{
    "resource": "/",
    "path": "/",
    "httpMethod": "GET",
    "requestContext": {
        "resourcePath": "/",
        "httpMethod": "GET",
        "path": "/Prod/",
        ...
    },
    "headers": {
        "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9",
        "accept-encoding": "gzip, deflate, br",
        "Host": "70ixmpl4fl.execute-api.us-east-2.amazonaws.com",
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.132 Safari/537.36",
        "X-Amzn-Trace-Id": "Root=1-5e66d96f-7491f09xmpl79d18acf3d050",
        ...
    },
    "multiValueHeaders": {
        "accept": [
            "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9"
        ],
        "accept-encoding": [
            "gzip, deflate, br"
        ],
        ...
    },
    "queryStringParameters": null,
    "multiValueQueryStringParameters": null,
    "pathParameters": null,
    "stageVariables": null,
    "body": {
      "firstName: "Josh"
    },
    "isBase64Encoded": false
}

We invoke the function locally (with the example event from above) to show the function works. I also recommend adding unit tests to cover the functions.


const event = require("./lambdaEvent.mock.json");

awsHandler(event, {});

We see the expected output of "Hello {provided first name from event}"

I hope this demystifies the world of serverless functions. I recommend taking a look at the AWS Lambda documentation at https://docs.aws.amazon.com/lambda/?id=docs_gateway for more in depth information. You can also read up on the various available AWS services at https://docs.aws.amazon.com/.

If you have any questions please feel free to reach out to me via Twitter or Github.

Happy Coding!

Josh Oppenheim is a Senior Software Engineer at Thisdot Labs.

Twitter: @JoshOppDev

Github: Syntactual

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

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How to automatically deploy your full-stack JavaScript app with AWS CodePipeline

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Deployment prerequisites To be able to deploy to S3 and Elastic Beanstalk, we need our CodeBuild job to be able to interact with those services. When we created the build, we created a service role for it. In this example, the service role is codebuild-aws-test-build-and-deploy-service-role. Let's go to the IAM page in the console and open the Roles page. Search for our codebuild role and let's add permissions to it. Click the Add permissions button and select Attach policies. We need two AWS-managed policies to be added to this service role. The AdministratorAccess-AWSElasticBeanstalk will allow us to deploy the API and the AmazonS3FullAccess will allow us to deploy the front-end. The CloudFrontFullAccess will allow us to invalidate the caches so CloudFront will send the new front-end files after the deployment is ready. Deployment Upload the front-end to S3 Uploading the front-end should be pretty straightforward. 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It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. 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2 mins

VueNuxtJS

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Intro to Serverless Functions

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