In this example we will look at how to use PostgreSQL in our serverless app using Serverless Stack (SST). We’ll be creating a simple hit counter using Amazon Aurora Serverless.


Create an SST app

Let’s start by creating an SST app.

$ npx create-serverless-stack@latest rest-api-postgresql
$ cd rest-api-postgresql

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": "rest-api-postgresql",
  "stage": "dev",
  "region": "us-east-1"

Project layout

An SST app is made up of two parts.

  1. lib/ — App Infrastructure

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

  2. src/ — App Code

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

Adding PostgreSQL

Amazon Aurora Serverless is an auto-scaling managed relational database that supports PostgreSQL.

Replace the lib/MyStack.js with the following.

import * as cdk from "@aws-cdk/core";
import * as ec2 from "@aws-cdk/aws-ec2";
import * as rds from "@aws-cdk/aws-rds";
import * as sst from "@serverless-stack/resources";

export default class MyStack extends sst.Stack {
  constructor(scope, id, props) {
    super(scope, id, props);

    const defaultDatabaseName = "CounterDB";

    // Create the VPC needed for the Aurora Serverless DB cluster
    const vpc = new ec2.Vpc(this, "CounterVPC");

    // Create the Serverless Aurora DB cluster
    const cluster = new rds.ServerlessCluster(this, "CounterDBCluster", {
      // Set the engine to Postgres
      engine: rds.DatabaseClusterEngine.AURORA_POSTGRESQL,
      parameterGroup: rds.ParameterGroup.fromParameterGroupName(
      // Optional, disable the instance from pausing after 5 minutes
      scaling: { autoPause: cdk.Duration.seconds(0) },

This creates a VPC and uses that to create our Aurora cluster. We also set the database engine to PostgreSQL. The databsse in the cluster that we’ll be using is called CounterDB (as set in the defaultDatabaseName variable).

Setting up the API

Now let’s add the API.

Add this below the rds.ServerlessCluster definition in lib/MyStack.js.

// Create a HTTP API
const api = new sst.Api(this, "Api", {
  routes: {
    "POST /": {
      function: {
        handler: "src/lambda.handler",
        environment: {
          dbName: defaultDatabaseName,
          clusterArn: cluster.clusterArn,
          secretArn: cluster.secret.secretArn,

// Grant access to the cluster from the Lambda function
cluster.grantDataApiAccess(api.getFunction("POST /"));

// Show the resource info in the output
  ApiEndpoint: api.url,
  SecretArn: cluster.secret.secretArn,
  ClusterIdentifier: cluster.clusterIdentifier,

Our API simply has one endpoint (the root). When we make a POST request to this endpoint the Lambda function called handler in src/lambda.js will get invoked.

We also pass in the name of our database, the ARN of the database cluster, and the ARN of the secret that’ll help us login to our database. An ARN is an identifier that AWS uses. You can read more about it here.

We then allow our Lambda function to access our database cluster. Finally, we output the endpoint of our API, ARN of the secret and the name of the database cluster. We’ll be using these later in the example.

We need to install a couple of CDK packages that we are using.

From the project root run the following.

$ npx sst add-cdk @aws-cdk/aws-ec2 @aws-cdk/aws-rds

The reason we are using the add-cdk command instead of using an npm install, is because of a known issue with AWS CDK. Using mismatched versions of CDK packages can cause some unexpected problems down the road. The sst add-cdk command ensures that we install the right version of the package.

Reading from our database

Now in our function, we’ll start by reading from our PostgreSQL database.

Replace src/lambda.js with the following.

import client from "data-api-client";

const db = client({
  database: process.env.dbName,
  secretArn: process.env.secretArn,
  resourceArn: process.env.clusterArn,

export async function handler() {
  const { records } = await db.query(
    "SELECT tally FROM tblCounter where counter='hits'"

  let count = records[0].tally;

  return {
    statusCode: 200,
    body: count,

We are using the Data API. It allows us to connect to our database over HTTP using the data-api-client.

For now we’ll get the number of hits from a table called tblCounter and return it.

Let’s install the data-api-client.

$ npm install data-api-client

And test what we have so far.

Starting your dev environment

SST features a Live Lambda Development environment that allows you to work on your serverless apps live.

$ npx sst start

The first time you run this command it’ll take a couple of minutes to deploy your app and a debug stack to power the Live Lambda Development environment.

 Deploying app

Preparing your SST app
Transpiling source
Linting source
Deploying stacks
dev-rest-api-postgresql-my-stack: deploying...

 ✅  dev-rest-api-postgresql-my-stack

Stack dev-rest-api-postgresql-my-stack
  Status: deployed
    SecretArn: arn:aws:secretsmanager:us-east-1:087220554750:secret:CounterDBClusterSecret247C4-MhR0f3WMmWBB-dnCizN
    ClusterIdentifier: dev-rest-api-postgresql-counterdbcluster09367634-1wjmlf5ijd4be

The ApiEndpoint is the API we just created. While the SecretArn is what we need to login to our database securely. The ClusterIdentifier is the id of our database cluster.

Before we can test our endpoint let’s create the tblCounter table in our database.

Creating our table

To create our table we’ll use the query editor in the AWS console. First let’s grab the secret ARN to login to our database.

Head over to the Amazon RDS part of the console.

Amazon RDS console

Here click on Query Editor. Now you’ll be asked to connect to your database.

  • In the Database instance or cluster dropdown select the one matching the ClusterIdentifier in our app outputs.
  • For the Database username select, Connect with a Secrets Manager ARN.
  • Paste the SecretArn from your app outputs in the Secret manager ARN field.
  • And paste the CounterDB (or the defaultDatabaseName variable in lib/MyStack.js) as the name of the database.

Then click Connect to database.

Amazon RDS Query Editor connect to a database

Paste the following queries. This will create our table and insert a row to keep track of our hits.

CREATE TABLE tblCounter (
 counter text UNIQUE,
 tally integer

INSERT INTO tblCounter VALUES ('hits', 0);

Hit Run.

Amazon RDS Query Editor run query

Test our API

Now that our table is created, let’s test our endpoint. Run the following in your terminal.

$ curl -X POST

This makes a POST request to our API.

You should see a 0 printed out. Of course, if you call it again, nothing changes.

Writing to our table

So let’s update our table with the hits.

Add this above the return statement in src/lambda.js.

await db.query(`UPDATE tblCounter set tally=${++count} where counter='hits'`);

Here we are updating the hits row’s tally column with the increased count.

And now if you head over to your terminal and make a request to our API. You’ll notice the count increase!

$ curl -X POST

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 API for our users.

Cleaning up

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

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


And that’s it! We’ve got a completely serverless hit counter. And we can test our changes locally before deploying to AWS! Check out the repo below for the code we used in this example. And leave a comment if you have any questions!