Using gcp sql with cloudfunctionsv2

Question

Pulumi · Accepted Answer

To accomplish your goal of using Google Cloud SQL with Cloud Functions (2nd generation) on GCP using Pulumi, you would first create a Cloud SQL instance to host your database. You will then create a Cloud Function that can connect to the Cloud SQL database to perform operations such as querying data or updating records.

Here’s a detailed breakdown of the steps we are going to take:

1. **Create a Cloud SQL Instance**: This will be your database server where the data will be stored. We will use `gcp.sql.DatabaseInstance` to create a MySQL instance. This instance will contain databases and can be accessed by authorized users.

2. **Create a SQL Database**: Within the SQL Instance, we need to create a database which will be done using `gcp.sql.Database`.

3. **Create a Database User**: For our Cloud Function to connect to the database, it will need credentials. We will create a user with `gcp.sql.User` and grant it the necessary permissions.

4. **Create a Cloud Function**: We will use `gcp.cloudfunctionsv2.Function` to create a Cloud Function that will have the logic to interact with our Cloud SQL database. This function will be triggered by an HTTP request.

5. **Set Up IAM Policy for the Cloud Function**: To ensure that our Cloud Function has the necessary permissions to interact with the Cloud SQL instance, we will set up an IAM policy. We will use `gcp.cloudfunctionsv2.FunctionIamPolicy` to grant the Cloud Function service account the required SQL client role.

Let's proceed with the actual Pulumi program written in TypeScript:

```typescript
import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";

// Create a Google Cloud SQL database instance
const databaseInstance = new gcp.sql.DatabaseInstance("my-instance", {
    databaseVersion: "MYSQL_5_7",
    settings: {
        tier: "db-f1-micro",
    },
});

// Create a SQL database inside our database instance
const myDatabase = new gcp.sql.Database("my-database", {
    instance: databaseInstance.name,
    // Define any additional properties such as charset or collation here if necessary
});

// Create a user for our SQL database
const sqlUser = new gcp.sql.User("my-user", {
    instance: databaseInstance.name,
    password: "my-strong-password"
});

// Create a Cloud Function to interact with the database
const myFunction = new gcp.cloudfunctionsv2.Function("my-function", {
    buildConfig: {
        entryPoint: "main",
        runtime: "nodejs16",
        // Define the location of your Cloud Functions code, can be a local path or a cloud source repository
        source: {
            storageSource: {
                bucket: "my-bucket-name",
                object: "path/to/function/source.zip",
            },
        },
    },
    serviceConfig: {
        // Define any additional properties such as environment variables here
    },
});

// Assign the `cloudsql.client` role to the Cloud Function's service account to enable access to the Cloud SQL instance
const iamPolicy = new gcp.cloudfunctionsv2.FunctionIamPolicy("my-function-iam", {
    project: myFunction.project,
    location: myFunction.location,
    cloudFunction: myFunction.name,
    policyData: JSON.stringify({
        bindings: [{
            role: "roles/cloudsql.client",
            members: [
                `serviceAccount:${myFunction.serviceConfig.serviceAccountEmail}`,
            ],
        }],
    }),
});

// Export the connection name of the instance to use it in your function's connection strings
export const instanceConnectionName = databaseInstance.connectionName;
// Export the HTTP trigger URL for the Cloud Function
export const functionUrl = myFunction.serviceConfig.uri;

```

Let's walk through the code:

- We first create a Cloud SQL instance using the `DatabaseInstance` class. This is a MySQL 5.7 instance at the smallest available size for demonstration purposes.
- Next, we create a database within that instance with `Database`.
- A user is then created using the `User` class with a placeholder password (please use a strong, secure password in actual deployments).
- We define a Cloud Function with `Function` class, specifying the entry point and runtime environment. You should place your cloud function code in a storage bucket which is referenced in the `storageSource`.
- We then set the IAM policy for the Cloud Function to allow it to interact with the Cloud SQL Database. The policy is provided in JSON format.

After you complete this code, you will have a Cloud SQL instance with a database and a user that can be accessed by a Cloud Function to work with the data stored in Google Cloud SQL.

Please note, before running this program, ensure you have configured your Pulumi CLI with the correct GCP credentials and project information. The function code itself (`main` function referenced in the code) must also be written to interact with the SQL database as desired, it is not provided with this configuration. The storage bucket and object paths are placeholders and should be replaced with your actual GCF code location details.