This repository was created for demo purposes and will not be kept up-to-date with future releases of CDK for Terraform (CDKTF); as such, it has been archived and is no longer supported in any way by HashiCorp. You are welcome to try out the archived version of the code in this example project, but there are no guarantees that it will continue to work with newer versions of CDKTF. We do not recommend directly using this sample code in production projects without extensive testing, and HashiCorp disclaims any and all liability resulting from use of this code.
This repository contains an end to end serverless web app hosted on AWS and deployed with CDK for Terraform in Python. In more application specific terms, we are deploying serverless infrastructure for a web app that has a list of posts and a modal to create a new post by specifying author and content. For more information regarding setup and the features of CDKTF please refer to these docs.
In order to run this example you must have CDKTF and it's prerequisites installed. For further explanation please see this quick start demo.
Additionally an AWS account and AWS credentials configured for use with Terraform are needed.
First run pipenv install in the root directory of the project to create a virtual environment with all the needed packages installed.
Then in that virtual environment cdktf deploy can be runned with the stacks that you wish to deploy e.g cdktf deploy posts-dev frontend-dev for deploying the dev environement or cdktf deploy posts-prod frontend-prod for deploying the production environment.
Frontend: React, Create React App, statically hosted via AWS S3 + CloudFront Backend API: AWS Lambda + API Gateway + DynamoDB
We will have two primary Stacks– PostsStack and FrontendStack
The Post and Frontend class encapsulate the finer details of infrastructure provisioned for each respective Stack. The first parameter denotes the scope of the infrastructure being provision– we use self to tie the infrastructure contained in Post/Frontend to the Stack in which it is contained, the same is true with AwsProvider.
class PostsStack(TerraformStack):
posts: Posts
def __init__(self, scope: Construct, name: str, environment: str, user: str):
super().__init__(scope,name)
AwsProvider(self, "aws",
region = "eu-central-1",
)
self.posts = Posts(self, "posts",
environment = environment,
userSuffix = user
)class FrontendStack(TerraformStack):
def __init__(self, scope: Construct, name: str, environment: str , apiEndPoint: str):
super().__init__(scope, name)
AwsProvider(self, "aws",
region = "eu-central-1",
)
LocalProvider(self, "local")
Frontend(self, "frontend",
environment = environment,
apiEndPoint = apiEndPoint,
)In using different Stacks to separate aspects of our infrastructure we allow for separation in state management of the frontend and backend– making alteration and redeployment of a specific piece of infrastructure a simpler process. Additionally, this allows for the instantiation of the same resource multiple times throughout.
For example…
# In main.py
postsDev = PostsStack(app, "posts-dev",
environment = "development",
user = os.getenv("CDKTF_USER")
)
frontendDev = FrontendStack(app, "frontend-dev",
environment = "development",
apiEndPoint = postsDev.posts.apiEndPoint,
)
postsProd = PostsStack(app, "posts-prod",
environment = "production",
user = os.getenv("CDKTF_USER")
)
frontendProd = FrontendStack(app, "frontend-prod",
environment = "production",
apiEndPoint = postsProd.posts.apiEndPoint,
)`Here we created separate instances of the infrastructure for the frontend and backend with different naming of the resources in each application environment (by ways of the environment param), with the ease of adding additional as needed.
The Posts class melds two elements together– the Dynamodb table coming from PostsStorage and our Lambda function and Apigateway coming from PostsApi that takes our new Dynamodb table for setting up the Lambda function environment.
class Posts(Resource):
apiEndPoint: str
def __init__(self, scope: Construct, id: str, environment: str, userSuffix: str ):
super().__init__(scope, id)
storage = PostsStorage(self, "storage",
environment = environment,
userSuffix = userSuffix
)
postsApi = PostsApi(self, "api",
environment = environment,
table = storage.table,
userSuffix = userSuffix
)
self.apiEndPoint = postsApi.apiEndPointIn PostsApi we create our Lambda function and Apigateway, along with the needed permissions/policies and IAM role.
Here we see a use of the environment variable– the one that was initially given in main.java. With this we provide greater description for the resources in each environment as well as avoiding naming conflicts.
role = IamRole(self, "lambda-exec",
name = f"sls-example-post-api-lambda-exec-{userSuffix if userSuffix is not None else ''}",
//...It is also in the IAM Role that we define certain policies for the role. It is important to note that policies that are denoted as taking Strings (in IamRole and elsewhere) are really JSON strings.
For example…
role = IamRole(self, "lambda-exec",
#...
assume_role_policy = json.dumps({
"Version": "2012-10-17",
"Statement":
{
"Action": "sts:AssumeRole",
"Principal": {
"Service": "lambda.amazonaws.com",
},
"Effect": "Allow",
"Sid": "",
},
}),
#...This TerraformAsset helps manage our local Lambda function implementation. Providing necessary details for defining our Lambda.
asset = TerraformAsset(self, "lambda-asset",
path = Path.join(os.getcwd(), "posts/api/lambdaPy"),
type = AssetType.ARCHIVE,
)Now we get into the details of our Lambda function. It is here that we provide the Lambda with the role we created above as well as the Dynamodb table from the Storage object created alongside this PostsApi object in the Post class. We also provide other needed details (name of handler, runtime, local path to lambda implementation, ect.).
new_lambda = LambdaFunction(self, "api",
function_name = f"sls-example-posts-api-{userSuffix if userSuffix is not None else ''}",
handler = "index.lambda_handler",
runtime = "python3.9",
role = role.arn,
filename = asset.path,
source_code_hash = asset.asset_hash,
environment = LambdaFunctionEnvironment(
variables = {"DYNAMODB_TABLE_NAME":table.name}
)
)Our API Gateway will sit between our Frontend and Lambda function– both routing requests to our Lambda as well as returning the appropriate result. We give the API Gateway our Lambda function defined as its target.
api = Apigatewayv2Api(self, "api-gw",
name = f"sls-example-posts-{userSuffix if userSuffix is not None else ''}",
protocol_type = "HTTP",
target = new_lambda.arn,
cors_configuration = Apigatewayv2ApiCorsConfiguration(
allow_origins = ["*"],
allow_methods = ["*"],
allow_headers = ["*"]
)
)We then pass the API Gateway’s endpoint to the PostApi object– this will be later given to our Frontend.
self.apiEndPoint = api.api_endpointFinally we provide Permissions to our Lambda and additional policy for our IAM Role.
IamRolePolicyAttachment(self, "lambda-managed-policy",
policy_arn = "arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole",
role = role.name,
)
#...
LambdaPermission(self, "apigw-lambda",
function_name = new_lambda.function_name,
action = "lambda:InvokeFunction",
principal = "apigateway.amazonaws.com",
source_arn = "{}/*/*".format(api.execution_arn)
)In the Frontend class we provision a S3 Bucket as well as a Cloudfront distribution for our React app to be statically hosted.
bucket = S3Bucket(self, "bucket",
bucket_prefix = "sls-example-frontend-{}".format(environment),
tags = {
"hc-internet-facing": "true"
}
)
bucketWebsite = S3BucketWebsiteConfiguration(self, "website-configuration",
bucket = bucket.bucket,
index_document = S3BucketWebsiteConfigurationIndexDocument(suffix = "index.html"),
error_document = S3BucketWebsiteConfigurationErrorDocument(key = "index.html")
)The Cloudfront Distribution speeds up the distribution of our Frontend content and reduces the load on our S3 Bucket by caching its contents. It is here we define the behavior and permission of this cache as well as provide the endpoint of the S3 Bucket we defined above.
cf = CloudfrontDistribution(self, "cf",
comment = "Serverless example frontend for env={}".format(environment),
enabled = True,
default_cache_behavior = CloudfrontDistributionDefaultCacheBehavior(
allowed_methods = [
"DELETE",
"GET",
"HEAD",
"OPTIONS",
"PATCH",
"POST",
"PUT",
],
cached_methods = ["GET", "HEAD"],
target_origin_id = S3_ORIGIN_ID,
viewer_protocol_policy = "redirect-to-https",
forwarded_values = CloudfrontDistributionDefaultCacheBehaviorForwardedValues(
query_string = False,
cookies = CloudfrontDistributionDefaultCacheBehaviorForwardedValuesCookies(
forward = "none",
),
),
),
origin = [
CloudfrontDistributionOrigin(
origin_id = S3_ORIGIN_ID,
domain_name = bucketWebsite.website_endpoint,
custom_origin_config = CloudfrontDistributionOriginCustomOriginConfig(
origin_protocol_policy = "http-only",
http_port = 80,
https_port = 443,
origin_ssl_protocols = ["TLSv1.2", "TLSv1.1", "TLSv1"],
)
),
],
default_root_object = "index.html",
restrictions = CloudfrontDistributionRestrictions(
geo_restriction = CloudfrontDistributionRestrictionsGeoRestriction(
restriction_type = "none"
),
),
viewer_certificate = CloudfrontDistributionViewerCertificate(
cloudfront_default_certificate = True
)
)The file env.production.local provides the S3 Bucket and Backend endpoints to our React app.
File(self, "env",
filename = Path.join(os.getcwd(), "frontend", "code", ".env.production.local"),
content = "S3_BUCKET_FRONTEND={bucket}\nREACT_APP_API_ENDPOINT={endPoint}".format(bucket = bucket.bucket, endPoint = apiEndPoint)
)Finally we create a TerraformOutput that gives us the domain name of the application’s frontend.
TerraformOutput(self, "frontend_domainname",
value = cf.domain_name,
).add_override("value", "https://{}".format(cf.domain_name))