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

Requirements

Create an SST app

Let’s start by creating an SST app.

$ npx create-sst@latest --template=base/example rest-api-postgresql
$ cd rest-api-postgresql
$ npm install

By default, our app will be deployed to the us-east-1 AWS region. This can be changed in the sst.config.ts in your project root.

import { SSTConfig } from "sst";

export default {
  config(_input) {
    return {
      name: "rest-api-postgresql",
      region: "us-east-1",
    };
  },
} satisfies SSTConfig;

Project layout

An SST app is made up of two 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. packages/functions/ — App Code

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

Adding PostgreSQL

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

Replace the stacks/ExampleStack.ts with the following.

import { Api, RDS, StackContext } from "sst/constructs";

export function ExampleStack({ stack }: StackContext) {
  const DATABASE = "CounterDB";

  // Create the Aurora DB cluster
  const cluster = new RDS(stack, "Cluster", {
    engine: "postgresql10.14",
    defaultDatabaseName: DATABASE,
    migrations: "services/migrations",
  });
}

This creates an RDS Serverless cluster. We also set the database engine to PostgreSQL. The database in the cluster that we’ll be using is called CounterDB (as set in the defaultDatabaseName variable).

The migrations prop should point to the folder where your migration files are. The RDS construct uses Kysely to run and manage schema migrations. You can read more about migrations here.

Setting up the Database

Let’s create a migration file that creates a table called tblcounter.

Create a migrations folder inside the services/ folder.

Let’s write our first migration file, create a new file called first.mjs inside the newly created services/migrations folder and paste the below code.

import { Kysely } from "kysely";

/**
 * @param db {Kysely<any>}
 */
export async function up(db) {
  await db.schema
    .createTable("tblcounter")
    .addColumn("counter", "text", (col) => col.primaryKey())
    .addColumn("tally", "integer")
    .execute();

  await db
    .insertInto("tblcounter")
    .values({
      counter: "hits",
      tally: 0,
    })
    .execute();
}

/**
 * @param db {Kysely<any>}
 */
export async function down(db) {
  await db.schema.dropTable("tblcounter").execute();
}

Setting up the API

Now let’s add the API.

Add this below the cluster definition in stacks/ExampleStack.ts.

// Create a HTTP API
const api = new Api(stack, "Api", {
  defaults: {
    function: {
      bind: [cluster],
    },
  },
  routes: {
    "POST /": "packages/functions/src/lambda.handler",
  },
});

// Show the resource info in the output
stack.addOutputs({
  ApiEndpoint: api.url,
  SecretArn: cluster.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 packages/functions/src/lambda.ts will get invoked.

We’ll also bind our database cluster to our API.

Reading from our database

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

Replace packages/functions/src/lambda.ts with the following.

import { RDSDataService } from "aws-sdk";
import { Kysely } from "kysely";
import { DataApiDialect } from "kysely-data-api";
import { RDS } from "sst/node/rds";

interface Database {
  tblcounter: {
    counter: string;
    tally: number;
  };
}

const db = new Kysely<Database>({
  dialect: new DataApiDialect({
    mode: "postgres",
    driver: {
      database: RDS.Cluster.defaultDatabaseName,
      secretArn: RDS.Cluster.secretArn,
      resourceArn: RDS.Cluster.clusterArn,
      client: new RDSDataService(),
    },
  }),
});

export async function handler() {
  const record = await db
    .selectFrom("tblcounter")
    .select("tally")
    .where("counter", "=", "hits")
    .executeTakeFirstOrThrow();

  let count = record.tally;

  return {
    statusCode: 200,
    body: count,
  };
}

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

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

Let’s install the kysely and kysely-data-api in the packages/functions/ folder.

$ npm install kysely kysely-data-api

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.

$ npm run dev

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-ExampleStack: deploying...

 ✅  dev-rest-api-postgresql-ExampleStack


Stack dev-rest-api-postgresql-ExampleStack
  Status: deployed
  Outputs:
    SecretArn: arn:aws:secretsmanager:us-east-1:087220554750:secret:CounterDBClusterSecret247C4-MhR0f3WMmWBB-dnCizN
    ApiEndpoint: https://u3nnmgdigh.execute-api.us-east-1.amazonaws.com
    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.

Running migrations

You can run migrations from 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 RDS tab and click the Migrations button on the top right corner.

It will list out all the migration files in the specified folder.

Now to apply the migration that we created, click on the Apply button beside to the migration name.

list-of-migrations-in-the-stack

To confirm if the migration is successful, let’s display the tblcounter table by running the below query.

SELECT * FROM tblcounter

successful-migration-output

You should see the table with 1 row .

Test our API

Now that our table is created, let’s test our endpoint with the SST Console.

Go to the API tab and click Send button to send a POST 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 a 0 in the response body.

Writing to our table

So let’s update our table with the hits.

Add this above the return statement in packages/functions/src/lambda.ts.

await db
  .updateTable("tblcounter")
  .set({
    tally: ++count,
  })
  .execute();

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

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

api-explorer-invocation-response-after-update

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.

Run the below command to open the SST Console in prod stage to test the production endpoint.

npx sst console --stage prod

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

api-explorer-invocation-response-prod

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 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!