Serverless computing is a cloud computing model in which a cloud provider automatically manages the provisioning and allocation of compute resources. This contrasts with traditional cloud computing where the user is responsible for directly managing virtual servers.
A popular approach to running "serverless" web applications is to implement the application functionality as one or more functions in AWS Lambda and then expose these for public consumption using Amazon API Gateway.
This tutorial will show how to deploy such an architecture using Terraform. The tutorial assumes some basic familiarity with Lambda and API Gateway but does not assume any pre-existing deployment. It also assumes that you are familiar with the usual Terraform plan/apply workflow. If you're new to Terraform itself, refer first to the Getting Started tutorial.
This is a slightly-opinionated tutorial, which chooses to ignore the built-in versioning and staged deployment mechanisms in AWS Lambda and API Gateway. In many cases these features are not necessary when using Terraform because changes can be tracked and deployed by keeping the Terraform configuration in a version-control repository. It also uses API Gateway in a very simple way, proxying all requests to a single AWS Lambda function that is expected to contain its own request routing logic.
As usual, there are other valid ways to use these services that make different tradeoffs. We encourage readers to consult the official documentation for the respective services for additional context and best-practices. This tutorial can still serve as an introduction to the main resources associated with these services, even if you choose a different architecture.
»Preparation
In order to follow this tutorial you will need an AWS account and to have Terraform installed. Configure your credentials so that Terraform is able to act on your behalf.
For simplicity here we will assume you are already using a set of IAM credentials with suitable access to create Lambda functions and work with API Gateway. If you aren't sure and are working in an AWS account used only for development, the simplest approach to get started is to use credentials with full administrative access to the target AWS account.
In the following section we will manually emulate an automatic build process using the zip command line tool and the AWS CLI. The latter must also have access to your AWS credentials, and the easiest way to achieve this is to provide them via environment variables so that they can be used by both the AWS CLI and Terraform.
Warning: Following this tutorial will create objects in your AWS account that will cost you money against your AWS bill.
»Building the Lambda Function Package
AWS Lambda expects a function's implementation to be provided as an archive containing the function source code and any other static files needed to execute the function.
Terraform is not a build tool, so the zip file must be prepared using a separate build process prior to deploying it with Terraform. For a real application we recommend automating your build via a CI system, whose job is to run any necessary build actions (library installation, compilation, etc), produce the deployment zip file as a build artifact, and then upload that artifact into an Amazon S3 bucket from which it will be read for deployment.
For the sake of this tutorial we will perform these build steps manually and build a very simple AWS Lambda function. Start by creating a new directory called example that will be used to create the archive, and place in it a single source file. We will use the JavaScript runtime in this example, so our file is called main.js and will contain the following source code:
'use strict'
exports.handler = function (event, context, callback) {
var response = {
statusCode: 200,
headers: {
'Content-Type': 'text/html; charset=utf-8',
},
body: '<p>Hello world!</p>',
}
callback(null, response)
}
The above is the simplest possible Lambda function for use with API Gateway, returning a hard-coded "Hello world!" response in the object structure that API Gateway expects.
From your command prompt, change to the directory containing that file.
$ cd example
Next, zip up main.js and put the resulting file alongside your current working directory.
$ zip ../example.zip main.js
adding: main.js (deflated 33%)
Change directories back to your original directory.
$ cd ..
In a real build and deploy scenario we would have an S3 bucket set aside for staging our archive and would use this to "hand off" these artifacts between the build and deploy process. For the sake of this tutorial we will create a temporary S3 bucket using the AWS CLI. S3 bucket names are globally unique, so you may need to change the --bucket= argument in the following example and substitute your new bucket name throughout the rest of this tutorial.
$ aws s3api create-bucket --bucket=terraform-serverless-example --region=us-east-1
You can now upload your build artifact into this S3 bucket.
$ aws s3 cp example.zip s3://terraform-serverless-example/v1.0.0/example.zip
A version number is included in the object path to identify this build. Later we will demonstrate deploying a new version, which will create another separate object.
»Creating the Lambda Function
With the source code artifact built and uploaded to S3, we can now write our Terraform configuration to deploy it. In a new directory, create a file named lambda.tf containing the following configuration.
terraform {
required_providers {
aws = {
source = "hashicorp/aws"
}
}
}
provider "aws" {
region = "us-east-1"
}
resource "aws_lambda_function" "example" {
function_name = "ServerlessExample"
# The bucket name as created earlier with "aws s3api create-bucket"
s3_bucket = "terraform-serverless-example"
s3_key = "v1.0.0/example.zip"
# "main" is the filename within the zip file (main.js) and "handler"
# is the name of the property under which the handler function was
# exported in that file.
handler = "main.handler"
runtime = "nodejs10.x"
role = aws_iam_role.lambda_exec.arn
}
# IAM role which dictates what other AWS services the Lambda function
# may access.
resource "aws_iam_role" "lambda_exec" {
name = "serverless_example_lambda"
assume_role_policy = <<EOF
{
"Version": "2012-10-17",
"Statement": [
{
"Action": "sts:AssumeRole",
"Principal": {
"Service": "lambda.amazonaws.com"
},
"Effect": "Allow",
"Sid": ""
}
]
}
EOF
}
Each Lambda function must have an associated IAM role which dictates what access it has to other AWS services. The above configuration specifies a role with no access policy, effectively giving the function no access to any AWS services, since our example application requires no such access.
Before you can work with a new configuration directory, it must be initialized using terraform init, which in this case will install the AWS provider:
$ terraform init
Initializing provider plugins...
- Checking for available provider plugins on https://releases.hashicorp.com...
- Downloading plugin for provider "aws" (1.9.0)...
# ...
Terraform has been successfully initialized!
# ...
Now apply the configuration as usual:
$ terraform apply
# ....
aws_iam_role.lambda_exec: Creating...
arn: "" => "<computed>"
assume_role_policy: "" => "{\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Action\": \"sts:AssumeRole\",\n \"Principal\": {\n \"Service\": \"lambda.amazonaws.com\"\n },\n \"Effect\": \"Allow\",\n \"Sid\": \"\"\n }\n ]\n}\n"
create_date: "" => "<computed>"
force_detach_policies: "" => "false"
name: "" => "serverless_example_lambda"
path: "" => "/"
unique_id: "" => "<computed>"
aws_iam_role.lambda_exec: Creation complete after 1s (ID: serverless_example_lambda)
aws_lambda_function.example: Creating...
arn: "" => "<computed>"
function_name: "" => "ServerlessExample"
handler: "" => "main.handler"
invoke_arn: "" => "<computed>"
last_modified: "" => "<computed>"
memory_size: "" => "128"
publish: "" => "false"
qualified_arn: "" => "<computed>"
role: "" => "arn:aws:iam::123456:role/serverless_example_lambda"
runtime: "" => "nodejs6.10"
s3_bucket: "" => "terraform-serverless-example"
s3_key: "" => "v1.0.0/example.zip"
source_code_hash: "" => "<computed>"
timeout: "" => "3"
tracing_config.#: "" => "<computed>"
version: "" => "<computed>"
aws_lambda_function.example: Still creating... (10s elapsed)
aws_lambda_function.example: Creation complete after 11s (ID: ServerlessExample)
Apply complete! Resources: 2 added, 0 changed, 0 destroyed.
After the function is created successfully, try invoking it using the AWS CLI.
$ aws lambda invoke --region=us-east-1 --function-name=ServerlessExample output.txt
{
"StatusCode": 200,
"ExecutedVersion": "$LATEST"
}
Check the output for success.
$ cat output.txt
{
"statusCode":200,
"headers":{
"Content-Type":"text/html; charset=utf-8"
},
"body":"<p>Hello world!</p>"
}
With the function working as expected, the next step is to create the API Gateway REST API that will provide access to it.
»Configuring API Gateway
API Gateway's name reflects its original purpose as a public-facing frontend for REST APIs, but it was later extended with features that make it easy to expose an entire web application based on AWS Lambda. These later features will be used in this tutorial. The term "REST API" is thus used loosely here, since API Gateway is serving as a generic HTTP frontend rather than necessarily serving an API.
Create a new file api_gateway.tf in the same directory as our lambda.tf from the previous step. First, configure the root "REST API" object, as follows:
resource "aws_api_gateway_rest_api" "example" {
name = "ServerlessExample"
description = "Terraform Serverless Application Example"
}
The "REST API" is the container for all of the other API Gateway objects we will create.
All incoming requests to API Gateway must match with a configured resource and method in order to be handled. Append the following to the lambda.tf file to define a single proxy resource:
resource "aws_api_gateway_resource" "proxy" {
rest_api_id = aws_api_gateway_rest_api.example.id
parent_id = aws_api_gateway_rest_api.example.root_resource_id
path_part = "{proxy+}"
}
resource "aws_api_gateway_method" "proxy" {
rest_api_id = aws_api_gateway_rest_api.example.id
resource_id = aws_api_gateway_resource.proxy.id
http_method = "ANY"
authorization = "NONE"
}
The special path_part value "{proxy+}" activates proxy behavior, which means that this resource will match any request path. Similarly, the aws_api_gateway_method block uses a http_method of "ANY", which allows any request method to be used. Taken together, this means that all incoming requests will match this resource.
Each method on an API gateway resource has an integration which specifies where incoming requests are routed. Add the following configuration to specify that requests to this method should be sent to the Lambda function defined earlier:
resource "aws_api_gateway_integration" "lambda" {
rest_api_id = aws_api_gateway_rest_api.example.id
resource_id = aws_api_gateway_method.proxy.resource_id
http_method = aws_api_gateway_method.proxy.http_method
integration_http_method = "POST"
type = "AWS_PROXY"
uri = aws_lambda_function.example.invoke_arn
}
The AWS_PROXY integration type causes API gateway to call into the API of another AWS service. In this case, it will call the AWS Lambda API to create an "invocation" of the Lambda function.
Unfortunately the proxy resource cannot match an empty path at the root of the API. To handle that, a similar configuration must be applied to the root resource that is built in to the REST API object:
resource "aws_api_gateway_method" "proxy_root" {
rest_api_id = aws_api_gateway_rest_api.example.id
resource_id = aws_api_gateway_rest_api.example.root_resource_id
http_method = "ANY"
authorization = "NONE"
}
resource "aws_api_gateway_integration" "lambda_root" {
rest_api_id = aws_api_gateway_rest_api.example.id
resource_id = aws_api_gateway_method.proxy_root.resource_id
http_method = aws_api_gateway_method.proxy_root.http_method
integration_http_method = "POST"
type = "AWS_PROXY"
uri = aws_lambda_function.example.invoke_arn
}
Finally, you need to create an API Gateway "deployment" in order to activate the configuration and expose the API at a URL that can be used for testing:
resource "aws_api_gateway_deployment" "example" {
depends_on = [
aws_api_gateway_integration.lambda,
aws_api_gateway_integration.lambda_root,
]
rest_api_id = aws_api_gateway_rest_api.example.id
stage_name = "test"
}
With all of the above configuration changes in place, run terraform apply again to create these new objects:
$ terraform apply
# ...
aws_api_gateway_rest_api.example: Creating...
created_date: "" => "<computed>"
description: "" => "Terraform Serverless Application Example"
name: "" => "ServerlessExample"
root_resource_id: "" => "<computed>"
aws_api_gateway_rest_api.example: Creation complete after 1s (ID: bkqhuuz8r8)
# ...etc, etc...
Apply complete! Resources: 5 added, 0 changed, 0 destroyed.
After the creation steps are complete, the new objects will be visible in the API Gateway console.
The integration with the Lambda function is not functional yet because API Gateway does not have the necessary access to invoke the function. The next step will address this, making the application fully-functional.
»Allowing API Gateway to Access Lambda
By default any two AWS services have no access to one another, until access is explicitly granted. For Lambda functions, access is granted using the aws_lambda_permission resource, which should be added to the lambda.tf file created in an earlier step:
resource "aws_lambda_permission" "apigw" {
statement_id = "AllowAPIGatewayInvoke"
action = "lambda:InvokeFunction"
function_name = aws_lambda_function.example.function_name
principal = "apigateway.amazonaws.com"
# The "/*/*" portion grants access from any method on any resource
# within the API Gateway REST API.
source_arn = "${aws_api_gateway_rest_api.example.execution_arn}/*/*"
}
In order to test the created API you will need to access its test URL. To make this easier to access, add the following output to api_gateway.tf:
output "base_url" {
value = aws_api_gateway_deployment.example.invoke_url
}
Apply the latest changes with terraform apply:
$ terraform apply
# ...
aws_lambda_permission.apigw: Creating...
statement_id: "" => "AllowAPIGatewayInvoke"
action: "" => "lambda:InvokeFunction"
function_name: "" => "ServerlessExample"
# ...
aws_lambda_permission.apigw: Creation complete after 1s
Apply complete! Resources: 1 added, 0 changed, 1 destroyed.
Outputs:
base_url = https://bkqhuuz8r8.execute-api.us-east-1.amazonaws.com/test
Load the URL given in the output from your run in your favorite web browser. If everything has worked, you will see the text "Hello world!". This message is being returned from the Lambda function code uploaded earlier, via the API Gateway endpoint.
This is a good milestone! The first version of the application is deployed and accessible. Next we will see how to deploy a new version of the application.
»A New Version of the Lambda Function
For any real application there will inevitably be changes to the application code over time, which must then be deployed to AWS Lambda in place of the previous version.
Returning to the example directory containing the main.js from earlier, update the source code to change the message. For example:
'use strict'
exports.handler = function (event, context, callback) {
var response = {
statusCode: 200,
headers: {
'Content-Type': 'text/html; charset=utf-8',
},
body: '<p>Bonjour au monde!</p>',
}
callback(null, response)
}
Change to the directory containing main.js.
$ cd example
Next, update the zip file.
$ zip ../example.zip main.js
updating: main.js (deflated 33%)
Change to the directory that contains that zip file.
$ cd ..
Finally, upload a new version to the artifact S3 bucket.
$ aws s3 cp example.zip s3://terraform-serverless-example/v1.0.1/example.zip
Notice that a different version number was used in the S3 object path, so the previous archive is retained.
In order to allow easy switching between versions you can define a variable to allow the version number to be chosen dynamically.
Add the following to lambda.tf.
variable "app_version" {
}
Then locate the aws_lambda_function resource defined earlier and change its s3_key argument to include the version variable:
resource "aws_lambda_function" "example" {
function_name = "ServerlessExample"
# The bucket name as created earlier with "aws s3api create-bucket"
s3_bucket = "terraform-serverless-example"
- s3_key = "v1.0.0/example.zip"
+ s3_key = "v${var.app_version}/example.zip"
# (leave the remainder unchanged)
}
The terraform apply command now requires a version number to be provided:
$ terraform apply -var="app_version=1.0.1"
# ...
Terraform will perform the following actions:
~ aws_lambda_function.example
s3_key: "v1.0.0/example.zip" => "v1.0.1/example.zip"
Plan: 0 to add, 1 to change, 0 to destroy.
# ...
After the change has been applied, visit again the test URL and you should see the updated greeting message.
»Rolling Back to an Older Version
Sometimes new code doesn't work as expected and the simplest path is to return to the previous version. Because all of the historical versions of the artifact are preserved on S3, the original version can be restored with a single command:
$ terraform apply -var="app_version=1.0.0"
After this apply completes, the test URL will return the original message again.
»Conclusion
In this tutorial you created an AWS Lambda function that produces a result compatible with Amazon API Gateway proxy resources and then configured API Gateway.
Although the AWS Lambda function used in this tutorial is very simple, in more practical applications it is possible to use helper libraries to map API Gateway proxy requests to standard HTTP application APIs in various languages, such as Python's WSGI or the NodeJS Express Framework.
When combined with an automated build process running in a CI system, Terraform can help to deploy applications as AWS Lambda functions, with suitable IAM policies to connect with other AWS services for persistent storage, access to secrets, etc.
»Cleaning Up
Once you are finished with this tutorial, you can destroy the example objects with Terraform. Since our configuration requires a version number as an input variable, provide a placeholder value to destroy:
$ terraform destroy -var="app_version=0.0.0"
Since the artifact zip files and the S3 bucket itself were created outside of Terraform, they must also be cleaned up outside of Terraform. This can be done via the S3 console. Note that all of the objects in the bucket must be deleted before the bucket itself can be deleted.
»Further Reading
The following Terraform resource types are used in this tutorial:
- aws_lambda_function
- aws_lambda_permission
- aws_api_gateway_rest_api
- aws_api_gateway_resource
- aws_api_gateway_method
- aws_api_gateway_integration
- aws_iam_role
The reference page for each resource type provides full details on all of its supported arguments and exported attributes.
»Custom Domain Names and TLS Certificates
For the sake of example, this tutorial uses the test URLs offered by default by API Gateway. In practice, most applications will be deployed at a custom hostname.
To use a custom domain name you must first register that domain and configure DNS hosting for it. You must also either create an Amazon Certificate Manager certificate or register a TLS certificate with a third-party certificate authority.
Configuring the domain name is beyond the scope of this tutorial, but if you already have a hostname and TLS certificate you wish to use then you can register it with API Gateway using the aws_api_gateway_domain_name resource type.
A registered domain name is then mapped to a particular "REST API" object using aws_api_gateway_base_path_mapping. The configured domain name then becomes an alias for a particular deployment stage.
»Making Changes to the API Gateway Configuration
This tutorial creates a very simple API Gateway Configuration with a single resource that passes through all requests to a single destination. The upgrade steps then modify only the AWS Lambda function, leaving the API Gateway configuration unchanged.
Due to API Gateway's staged deployment model, if you do need to make changes to the API Gateway configuration you must explicitly request that it be re-deployed by "tainting" the deployment resource:
$ terraform taint aws_api_gateway_deployment.example
This command flags that this object must be re-created in the next Terraform plan, so a subsequent terraform apply will then replace the deployment and thus activate the latest configuration changes.
Please note that this "re-deployment" will cause some downtime, since Terraform will need to delete the stage and associated deployment before re-creating it. Downtime can be avoided by triggering the deployment action via the API Gateway console, outside of Terraform. The approach covered in this tutorial intentionally minimizes the need to amend the API Gateway configuration over time to mitigate this limitation. Better support for this workflow will be added to Terraform's AWS provider in a future release.