Serverless User Data Processing with FusionAuth Lambdas

Question

Pulumi · Accepted Answer

To create a serverless user data processing system using FusionAuth Lambdas, we will utilize AWS Lambda functions with a DynamoDB table as the data store. The operation is triggered by an authentication or authorization event in the FusionAuth identity management system.

In this case, FusionAuth Lambdas allow you to create custom logic that is executed during various phases of the authentication process. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers, automatically scaling your workload, and only charging for compute time when the code is executed.

Here's the overview of how the components will work together:

- **FusionAuth Lambda**: This resource is specific to the FusionAuth identity platform and allows you to define functions that execute at specific stages of the authentication process, such as login, user registration, or token refresh. This step is beyond the scope of AWS as it's specific to FusionAuth's implementation.

- **AWS Lambda**: It will contain the business logic that processes user data. The incoming data from FusionAuth hits the Lambda function, which then updates the DynamoDB table accordingly.

- **Amazon DynamoDB**: A fully-managed NoSQL database that provides fast and predictable performance with seamless scalability. It will store user data and can be accessed or modified by the Lambda function.

Below is a Pulumi program written in Python, that sets up an AWS Lambda function along with the necessary IAM role and policies to access a DynamoDB table:

```python
import pulumi
import pulumi_aws as aws
import json

# Create an IAM role for your Lambda function
iam_role = aws.iam.Role("lambdaRole", 
    assume_role_policy=json.dumps({
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Principal": {"Service": "lambda.amazonaws.com"},
            "Effect": "Allow",
        }],
    })
)

# Attach the AWS managed policy for Lambda functions which allows logging to CloudWatch
log_policy_attachment = aws.iam.RolePolicyAttachment("lambdaLogs", 
    role=iam_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole"
)

# IAM policy to allow your Lambda function to access DynamoDB
dynamo_policy = aws.iam.RolePolicy("lambdaDynamo", 
    role=iam_role.name,
    policy=pulumi.Output.all(iam_role.arn).apply(lambda role_arn: json.dumps({
        "Version": "2012-10-17",
        "Statement": [{
            "Action": [
                "dynamodb:GetItem",
                "dynamodb:PutItem",
                "dynamodb:UpdateItem",
                "dynamodb:Query",
                "dynamodb:Scan",
                "dynamodb:DeleteItem",
                "dynamodb:BatchWriteItem",
                "dynamodb:BatchGetItem",
                "dynamodb:DescribeTable",
                "dynamodb:ConditionCheckItem"
            ],
            "Resource": "*",  # Best practice is to limit this to the specific table you will access
            "Effect": "Allow"
        }]
    }))
)

# Create a DynamoDB table
dynamo_table = aws.dynamodb.Table("userData",
    attributes=[
        {"name": "UserID", "type": "S"}
    ],
    hash_key="UserID",
    read_capacity=1,
    write_capacity=1,
    billing_mode="PROVISIONED"
)

# Define the Lambda function
lambda_function = aws.lambda_.Function("userProcessor",
    runtime="python3.8",
    role=iam_role.arn,
    handler="processor.handler",  # Assumes a file named `processor.py` with a `handler` function
    code=pulumi.FileArchive("./function"),  # Points to a folder with your Lambda code
    timeout=30,
    memory_size=512,
    environment={
        "variables": {
            "DYNAMODB_TABLE": dynamo_table.name
        }
    }
)

# Note: The FusionAuth Lambda function resource is beyond the scope of Pulumi's AWS resources. 
# To connect AWS Lambda with FusionAuth, you would generally use FusionAuth's APIs or SDKs. 
# Typically, you'd grab an event in FusionAuth, call an AWS Lambda function via AWS's SDK in response, 
# and handle that in your AWS Lambda function code.

# Export the ARN of the Lambda function and the name of the DynamoDB table
pulumi.export("lambda_function_arn", lambda_function.arn)
pulumi.export("dynamo_table_name", dynamo_table.name)
```

Before deploying this code with Pulumi, please ensure you have the following:

1. **Pulumi CLI installed**: Make sure you have Pulumi CLI installed on your machine and you are logged in.
2. **AWS Credentials configured**: Pulumi uses your AWS credentials to deploy the resources, so make sure your credentials are appropriately configured on your development machine.
3. **Python Environment**: Set up your Python environment including the `requirements.txt` with the necessary Pulumi and AWS SDK libraries.
4. **FusionAuth Setup**: To use FusionAuth with AWS Lambda, you need to set up FusionAuth and define FusionAuth Lambdas within your FusionAuth Application. Connect these to your AWS Lambda functions using FusionAuth's capability to trigger webhooks (AWS Lambdas here) during authentication events.

Please replace `"./function"` with the path to your Lambda's code and ensure that the handler is correctly named after the relative location and method within your function's code.

After setting up the above resources with Pulumi, you will need to configure FusionAuth to trigger your AWS Lambda function during specific events. This typically involves setting up a webhook in FusionAuth that points to your AWS Lambda's invocation URL. This URL can be created using API Gateway or as an HTTP endpoint exposed through AWS Lambda itself. This lies outside of the Pulumi context but is an essential part of hooking up FusionAuth with AWS Lambda.