How to Build a Serverless Architecture with Terraform

Are you ready to take your cloud infrastructure to the next level? If you're interested in creating a serverless architecture with Terraform, you're in luck. We're going to walk you through the process, step by step!

What is Serverless Architecture?

Before we dive in, let's discuss what serverless architecture actually means.

Serverless architecture is a way to build and run applications and services without having to manage servers. With a serverless architecture, the cloud provider takes care of the infrastructure, so you can focus on your code and your application.

The benefits of serverless architecture include lower costs and easier scalability. You only pay for what you use, and the provider takes care of adding more resources as needed.

Why Use Terraform?

Now that you know what serverless architecture is, let's talk about why we're using Terraform.

Terraform is a tool for building, changing, and versioning infrastructure safely and efficiently. With Terraform, you can create, modify, and destroy infrastructure components, such as virtual machines, storage, and networking.

Using Terraform makes it easy to manage your infrastructure as code. This means you can version control your infrastructure, making it easier to collaborate with others and make changes without fear of breaking things.

Building a Serverless Architecture with Terraform

Now that we've covered the basics, let's get started building a serverless architecture with Terraform! Here are the steps we'll be following:

1. Define an S3 bucket to store our lambda function.
2. Create IAM roles and policies for our lambda function.
3. Create a lambda function using our code.
4. Expose our lambda function as an API Gateway.

Defining an S3 Bucket

The first step is to define an S3 bucket that we'll use to store our lambda function. We'll use the aws_s3_bucket resource to do this.

resource "aws_s3_bucket" "example_bucket" {
  bucket = "example-bucket"
  acl    = "private"

  tags = {
    Name        = "example-bucket"
    Environment = "production"
  }
}

In this example, we're defining a bucket called "example-bucket." We're setting the ACL to "private," which means that only the owner of the bucket can access it.

We're also adding some tags to our bucket, which can be useful for tracking the purpose and ownership of our infrastructure components.

Creating IAM Roles and Policies

Next, we need to create IAM roles and policies for our lambda function. IAM stands for Identity and Access Management, and it's a way to control who has access to your AWS resources.

We'll use the aws_iam_role and aws_iam_policy resources to create our roles and policies.

resource "aws_iam_role" "example_lambda_role" {
  name = "example-lambda-role"

  assume_role_policy = jsonencode({
    Statement = [
      {
        Action    = "sts:AssumeRole"
        Effect    = "Allow"
        Principal = {
          Service = "lambda.amazonaws.com"
        }
      }
    ],
    Version   = "2012-10-17"
  })
}

resource "aws_iam_policy" "example_lambda_policy" {
  name        = "example-lambda-policy"
  description = "Policy for example lambda function"
  policy      = jsonencode({
    Version   = "2012-10-17"
    Statement = [
      {
        Action   = [
          "logs:CreateLogStream",
          "logs:PutLogEvents"
        ]
        Effect   = "Allow"
        Resource = "arn:aws:logs:*:*:/aws/lambda/*"
      }
    ]
  })
}

resource "aws_iam_role_policy_attachment" "example_lambda_policy_attachment" {
  policy_arn = aws_iam_policy.example_lambda_policy.arn
  role       = aws_iam_role.example_lambda_role.name
}

In this example, we're creating an IAM role called "example-lambda-role" that allows our lambda function to run. We're also creating an IAM policy called "example-lambda-policy" that allows our lambda function to write logs to CloudWatch.

Finally, we're attaching the policy to the role using the aws_iam_role_policy_attachment resource.

Creating a Lambda Function

With our S3 bucket and IAM roles and policies in place, we're ready to create our lambda function.

We'll use the aws_lambda_function resource to do this.

resource "aws_lambda_function" "example_lambda_function" {
  filename       = "example_lambda_function.zip"
  function_name  = "example-lambda-function"
  role           = aws_iam_role.example_lambda_role.arn
  handler        = "lambda_function.example_handler"
  runtime        = "python3.8"
  source_code_hash = filebase64sha256("example_lambda_function.zip")

  environment {
    variables = {
      ENVIRONMENT = "production"
    }
  }

  tags = {
    Environment = "production"
  }
}

In this example, we're creating a lambda function called "example-lambda-function." We're setting the filename parameter to a zip file that contains our lambda function code.

We're also setting the role parameter to the ARN of the IAM role we created earlier.

The handler parameter is the name of the function inside our code that we want to execute.

The runtime parameter specifies the programming language runtime we're using.

Finally, we're setting some environment variables and adding some tags to our lambda function.

Exposing our Lambda Function as an API Gateway

Now that we've created our lambda function, we need to expose it as an API Gateway.

We'll use the aws_apigatewayv2_api, aws_apigatewayv2_integration, and aws_apigatewayv2_route resources to do this.

resource "aws_apigatewayv2_api" "example_api_gateway" {
  name          = "example-api-gateway"
  protocol_type = "HTTP"
}

resource "aws_apigatewayv2_integration" "example_api_gateway_integration" {
  api_id             = aws_apigatewayv2_api.example_api_gateway.id
  integration_type   = "AWS_PROXY"
  integration_uri    = aws_lambda_function.example_lambda_function.invoke_arn
  integration_method = "POST"
}

resource "aws_apigatewayv2_route" "example_api_gateway_route" {
  api_id    = aws_apigatewayv2_api.example_api_gateway.id
  route_key = "ANY /{proxy+}"

  target = "integrations/${aws_apigatewayv2_integration.example_api_gateway_integration.id}"
}

In this example, we're creating an API Gateway called "example-api-gateway." We're setting the protocol_type to "HTTP."

Next, we're creating an integration between the API Gateway and our lambda function. We're using the integration_uri parameter to specify the ARN of our lambda function.

Finally, we're creating a route for the API Gateway that maps to our lambda function integration.

Wrapping Up

Congratulations! You've successfully created a serverless architecture using Terraform. This is just the beginning of what you can accomplish with Terraform and serverless architecture. Give yourself a pat on the back, and keep exploring!

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