Load-Balanced AI API Gateway with Target Groups

Question

Pulumi · Accepted Answer

To create a load-balanced AI API Gateway with target groups, we need to set up the following resources:

1. **AI API Gateway**: This service sits at the front of your backend services, processing requests from clients before they are routed to your backend services.

2. **Load Balancer (ALB)**: In AWS, Application Load Balancer (ALB) is commonly used for routing HTTP/HTTPS traffic. It will distribute incoming application traffic across multiple targets, such as EC2 instances, in multiple Availability Zones.

3. **Target Group**: This is a group of destinations for routed traffic, identified by things like IP addresses and ports. The target group allows the load balancer to route requests to registered targets within one of these groups.

4. **EC2 Instances (or Containers or Lambda Functions)**: The actual servers where your AI API will be running. Depending on your architecture, you could also be using containers in ECS or Fargate, or even serverless functions with AWS Lambda.

Here's a high-level overview of the process:

- Define a Target Group.
- Register EC2 Instances (or other compute resources) with the Target Group.
- Create an Application Load Balancer (ALB).
- Associate the Target Group with the Load Balancer.
- Create an AWS API Gateway and integrate it with the Load Balancer.

Below is the Pulumi program that creates this setup using AWS infrastructure:

```python
import pulumi
import pulumi_aws as aws

# Create a new VPC for our infrastructure.
# For simplicity, we're using default settings. In a real-world scenario, you'd want to customize your VPC.
vpc = aws.ec2.Vpc("my_vpc", cidr_block="10.0.0.0/16")

# Create an Internet Gateway for our VPC.
igw = aws.ec2.InternetGateway("my_igw", vpc_id=vpc.id)

# Create a subnet to launch our instances into.
subnet = aws.ec2.Subnet("my_subnet",
    vpc_id=vpc.id,
    cidr_block="10.0.1.0/24",
    map_public_ip_on_launch=True,
)

# Our default security group to allow HTTP and SSH traffic.
security_group = aws.ec2.SecurityGroup("my_security_group",
    vpc_id=vpc.id,
    description="Allow HTTP and SSH inbound traffic",
    ingress=[
        {
            "description": "HTTP",
            "from_port": 80,
            "to_port": 80,
            "protocol": "tcp",
            "cidr_blocks": ["0.0.0.0/0"],
        },
        {
            "description": "SSH",
            "from_port": 22,
            "to_port": 22,
            "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"],
        }
    ])

# An Application Load Balancer (ALB) to distribute http and https traffic across our instances.
load_balancer = aws.lb.LoadBalancer("my_load_balancer",
    internal=False,
    security_groups=[security_group.id],
    subnets=[subnet.id])

# Target Group for the Load Balancer.
target_group = aws.lb.TargetGroup("my_target_group",
    port=80,
    protocol="HTTP",
    vpc_id=vpc.id,
    health_check={
        "enabled": True,
        "interval": 30,
        "path": "/",
        "protocol": "HTTP",
        "timeout": 3,
        "healthy_threshold": 2,
        "unhealthy_threshold": 2,
        "matcher": "200-299",
    })

# Listener for the Load Balancer that checks the target group.
listener = aws.lb.Listener("my_listener",
    load_balancer_arn=load_balancer.arn,
    port=80,
    default_actions=[{
        "type": "forward",
        "target_group_arn": target_group.arn,
    }])

# Sample code to launch an EC2 instance and register it to the target group.
# In a real-world scenario, you would loop over your instances, or use an Auto Scaling Group.
instance = aws.ec2.Instance("my_instance",
    instance_type="t2.micro",
    vpc_security_group_ids=[security_group.id],
    ami="ami-0c55b159cbfafe1f0", # Update this with a valid AMI ID.
    subnet_id=subnet.id)

target_group_attachment = aws.lb.TargetGroupAttachment("my_target_group_attachment",
    target_group_arn=target_group.arn,
    target_id=instance.id,
    port=80)

# The AWS API Gateway to act as the entry point for requests.
api_gateway = aws.apigatewayv2.Api("my_api_gateway",
    protocol_type="HTTP",
    target=listener.arn)

# Exporting the API Gateway's endpoint so it can be accessed.
pulumi.export('api_url', api_gateway.api_endpoint)
```

This program does the following:

- Sets up a new virtual private cloud (VPC) and related networking resources.
- Creates a security group to control traffic to instances.
- Defines an AWS Application Load Balancer.
- Configures a target group and attaches an EC2 instance to it.
- Sets up an API Gateway to route incoming HTTP requests to the load balancer.

Each step is illustrated with an AWS resource being instantiated via Pulumi's Python-based infrastructure as code.

Once you run this Pulumi program, it will output the API Gateway's URL which you can use to access your API.

Remember, in a production setup, you'd want to configure finer details such as tailor-made networking rules, scaling policies for your EC2 instances, and more exhaustive health checks.