In this example we will look at how to use Datadog to monitor the Lambda functions in your SST serverless application.

Requirements

What is Datadog

When a serverless app is deployed to production, it’s useful to be able to monitor your Lambda functions. There are a few different services that you can use for this. One of them is Datadog. Datadog offers an End-to-end Serverless Monitoring solution that works with Lambda functions.

Let’s look at how to set this up.

Create an SST app

Start by creating an SST app.

$ npx create-sst@latest --template=minimal/typescript-starter datadog
$ cd datadog
$ npm install

By default, our app will be deployed to an environment (or stage) called dev and the us-east-1 AWS region. This can be changed in the sst.json in your project root.

{
  "name": "datadog",
  "region": "us-east-1",
  "main": "stacks/index.ts"
}

Project layout

An SST app is made up of a couple of parts.

  1. stacks/ — App Infrastructure

    The code that describes the infrastructure of your serverless app is placed in the stacks/ directory of your project. SST uses AWS CDK, to create the infrastructure.

  2. services/ — App Code

    The code that’s run when your API is invoked is placed in the services/ directory of your project.

Create our infrastructure

Our app is going to be a simple API that returns a Hello World response.

Creating our API

Let’s add the API.

Add this in stacks/MyStack.ts.

import { StackContext, Api } from "@serverless-stack/resources";

export function MyStack({ stack, app }: StackContext) {
  // Create a HTTP API
  const api = new Api(stack, "Api", {
    routes: {
      "GET /": "functions/lambda.handler",
    },
  });

  // Show the endpoint in the output
  stack.addOutputs({
    ApiEndpoint: api.url,
  });
}

We are using the SST Api construct to create our API. It simply has one endpoint at the root. When we make a GET request to this endpoint the function called handler in services/functions/lambda.ts will get invoked.

Your services/functions/lambda.ts should look something like this.

import { APIGatewayProxyHandlerV2 } from "aws-lambda";

export const handler: APIGatewayProxyHandlerV2 = async (event) => {
  return {
    statusCode: 200,
    headers: { "Content-Type": "text/plain" },
    body: `Hello, World! Your request was received at ${event.requestContext.time}.`,
  };
};

Setting up our app with Datadog

Now let’s setup Datadog to monitor our API. Make sure Datadog has been configured with your AWS account.

Run the following in the services/ directory.

$ npm install --save-dev datadog-cdk-constructs-v2

Next, go to the API keys page of your Datadog dashboard and copy the API key.

Copy Datadog API key from dashboard

Create a .env.local file with the API key in your project root.

DATADOG_API_KEY=<API_KEY>

Note that, this file should not be committed to Git. If you are deploying the app through a CI service, configure the DATADOG_API_KEY as an environment variable in the CI provider. If you are deploying through Seed, you can configure this in your stage settings.

Next, you’ll need to import it into the stack and pass in the functions you want monitored.

Add the following above the stack.addOutputs line in stacks/MyStack.ts.

// Configure Datadog only in prod
if (!app.local) {
  // Configure Datadog
  const datadog = new Datadog(stack, "Datadog", {
    nodeLayerVersion: 65,
    extensionLayerVersion: 13,
    apiKey: process.env.DATADOG_API_KEY,
  });

  // Monitor all functions in the stack
  datadog.addLambdaFunctions(stack.getAllFunctions());
}

Also make sure to include the Datadog construct.

import { Datadog } from "datadog-cdk-constructs-v2";

Note that getAllFunctions gives you an array of all the Lambda functions created in this stack. If you want to monitor all the functions in your stack, make sure to call it at the end of your stack definition.

Deploying to prod

To wrap things up we’ll deploy our app to prod.

$ npx sst deploy --stage prod

This allows us to separate our environments, so when we are working in dev, it doesn’t break the app for our users.

Once deployed, you should see something like this.

 ✅  prod-datadog-my-stack


Stack prod-datadog-my-stack
  Status: deployed
  Outputs:
    ApiEndpoint: https://k40qchmtvf.execute-api.ap-south-1.amazonaws.com

The ApiEndpoint is the API we just created.

Let’s test our endpoint with the SST Console. The SST Console is a web based dashboard to manage your SST apps. Learn more about it in our docs.

Go to the API tab and click Send button to send a GET request.

Note, The API explorer lets you make HTTP requests to any of the routes in your Api construct. Set the headers, query params, request body, and view the function logs with the response.

API explorer invocation response

You should see the Hello World message.

Now head over to your Datadog dashboard to start exploring key performance metrics; invocations, errors, and duration from your function. The Serverless view aggregates data from all of the serverless functions running in your environment, enabling you to monitor their performance in one place. You can search and filter by name, AWS account, region, runtime, or any tag. Or click on a specific function to inspect its key performance metrics, distributed traces, and logs.

Datadog functions dashboard

Cleaning up

Finally, you can remove the resources created in this example using the following commands.

$ npx sst remove
$ npx sst remove --stage prod

Conclusion

And that’s it! We’ve got a serverless API monitored with Datadog. We also have a local development environment, to test and make changes. And it’s deployed to production as well, so you can share it with your users. Check out the repo below for the code we used in this example. And leave a comment if you have any questions!