Introduction
GraphQL has become famous for APIs due to its flexibility and efficiency. Coupled with AWS Lambda, it provides a robust serverless architecture that can scale automatically and reduce costs. When paired with an AWS Application Load Balancer (ALB), it ensures high availability and improved performance. In this guide, we will explore the advantages and disadvantages of this setup and provide a step-by-step tutorial on how to implement a GraphQL Lambda function behind an ALB using Terraform.
Advantages of Using a GraphQL Lambda Function with an AWS Application Load Balancer
- Scalability: AWS Lambda automatically scales your GraphQL API based on the incoming traffic. This ensures that your application can handle numerous requests without manual intervention.
- Cost-Efficiency: With AWS Lambda, you only pay for the compute time you consume, which can result in significant cost savings compared to traditional server-based architectures.
- High Availability: The AWS Application Load Balancer distributes incoming traffic across multiple targets, ensuring high availability and reliability.
- Reduced Operational Overhead: Managing servers and infrastructure is simplified, as AWS handles most operational tasks, such as scaling, patching, and maintenance.
- Seamless Integration: The combination of GraphQL, AWS Lambda, and ALB integrates seamlessly with other AWS services, providing a robust and comprehensive solution for your application needs.
Disadvantages of Using a GraphQL Lambda Function with an AWS Application Load Balancer
- Cold Starts: AWS Lambda functions can experience cold starts, which may introduce latency during the initial invocation.
- Complexity: Setting up and configuring Lambda functions with an ALB using Terraform can be complex, especially for beginners.
- Resource Limits: AWS Lambda has certain limitations on execution time and memory, which might not be suitable for all workloads.
- Debugging and Monitoring: Debugging serverless functions can be more challenging than figuring out traditional servers, requiring additional tools and practices for effective monitoring.
How to Implement a GraphQL Lambda Function Behind an AWS Application Load Balancer Using Terraform
Prerequisites
- AWS account
- Terraform installed
- Basic knowledge of AWS Lambda, ALB, and Terraform
Step 1: Set Up the Terraform Configuration
Create a new directory for your Terraform configuration and navigate to it. Inside the directory, create a file named main.tf and add the following configuration:
provider “aws” {
region = “us-east-1”
}
resource “aws_lambda_function” “graphql_function” {
function_name = “graphql_lambda”
runtime = “nodejs14.x”
handler = “index.handler”
role = aws_iam_role.lambda_exec.arn
filename = “path/to/your/lambda/deployment/package.zip”
}
resource “aws_iam_role” “lambda_exec” {
name = “lambda_exec_role”
assume_role_policy = jsonencode({
Version = “2012-10-17”
Statement = [{
Action = “sts:AssumeRole”
Effect = “Allow”
Principal = {
Service = “lambda.amazonaws.com”
}
}]
})
}
resource “aws_iam_role_policy_attachment” “lambda_exec_policy” {
role = aws_iam_role.lambda_exec.name
policy_arn = “arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole”
}
resource “aws_lb” “graphql_alb” {
name = “graphql-alb”
internal = false
load_balancer_type = “application”
security_groups = [aws_security_group.alb_sg.id]
subnets = aws_subnet.public.*.id
}
resource “aws_security_group” “alb_sg” {
name = “alb_sg”
description = “Allow inbound HTTP traffic”
vpc_id = aws_vpc.main.id
ingress {
from_port = 80
to_port = 80
protocol = “tcp”
cidr_blocks = [“0.0.0.0/0”]
}
egress {
from_port = 0
to_port = 0
protocol = “-1”
cidr_blocks = [“0.0.0.0/0”]
}
}
resource “aws_lb_target_group” “graphql_tg” {
name = “graphql-tg”
port = 80
protocol = “HTTP”
vpc_id = aws_vpc.main.id
health_check {
path = “/health”
interval = 30
timeout = 5
healthy_threshold = 5
unhealthy_threshold = 2
}
}
resource “aws_lb_listener” “http” {
load_balancer_arn = aws_lb.graphql_alb.arn
port = 80
protocol = “HTTP”
default_action {
type = “forward”
target_group_arn = aws_lb_target_group.graphql_tg.arn
}
}
resource “aws_lambda_permission” “alb” {
statement_id = “AllowExecutionFromALB”
action = “lambda:InvokeFunction”
function_name = aws_lambda_function.graphql_function.function_name
principal = “elasticloadbalancing.amazonaws.com”
source_arn = aws_lb.graphql_alb.arn
}
resource “aws_vpc” “main” {
cidr_block = “10.0.0.0/16”
}
resource “aws_subnet” “public” {
count = 2
vpc_id = aws_vpc.main.id
cidr_block = “10.0.1.0/24”
availability_zone = data.aws_availability_zones.available.names[count.index]
}
data “aws_availability_zones” “available” {}
Step 2: Deploy the Resources
- Initialize the Terraform configuration:
terraform init
- Apply the configuration:
terraform apply
Confirm the apply action when prompted.
Step 3: Create the Lambda Deployment Package
Ensure your GraphQL Lambda function is packaged correctly. For a Node.js example, your index.js might look like this:
const { ApolloServer, gql } = require(‘apollo-server-lambda’);
const typeDefs = gql`
type Query {
hello: String
}
`;
const resolvers = {
Query: {
hello: () => ‘Hello, world!’,
},
};
const server = new ApolloServer({ typeDefs, resolvers });
exports.handler = server.createHandler();
Package your Lambda function:
zip -r function.zip .
Update the filename path in the Terraform configuration to point to this function.zip file.
Step 4: Testing
Once the resources are deployed, the ALB will have a DNS name. You can use this DNS name to test your GraphQL API.
Conclusion
Implementing a GraphQL Lambda function behind an AWS Application Load Balancer using Terraform offers numerous benefits, such as scalability, cost-efficiency, and high availability. However, it also comes with challenges, such as cold starts and complexity in setup. By following this guide, you can leverage the power of AWS and Terraform to build a robust and scalable GraphQL API.