Deploying EC2 Instances in Multiple Availability Zones with a Load Balancer Using Terraform

Deploying EC2 instances across multiple Availability Zones (AZs) is a key practice for ensuring high availability and fault tolerance of your applications on AWS. Using Terraform, an infrastructure-as-code (IaC) tool, you can automate this process and manage your infrastructure efficiently. In this blog, we will walk you through the steps to deploy EC2 instances in multiple AZs and set up a load balancer to distribute traffic among them.

Prerequisites

Before we begin, make sure you have the following:

1.AWS Account: An active AWS account.

2.Terraform: Installed on your local machine. You can download it from here.

3.AWS CLI: Installed and configured with your AWS credentials. You can download it from here.

Step 1: Set Up Your Terraform Project

First, create a directory for your Terraform project. Inside this directory, create a new file called main.tf.

mkdir terraform-ec2-multi-az
cd terraform-ec2-multi-az
touch main.tf

Step 2: Define the Provider and Variables

In your main.tf, start by defining the AWS provider and necessary variables.

provider "aws" {
  region = "us-west-2"  # Replace with your preferred region
}

variable "instance_count" {
  description = "Number of EC2 instances"
  default     = 2
}

variable "instance_type" {
  description = "Type of EC2 instance"
  default     = "t2.micro"
}

variable "vpc_id" {
  description = "VPC ID where instances will be deployed"
}

variable "subnet_ids" {
  description = "List of Subnet IDs in different AZs"
  type        = list(string)
}

variable "ami_id" {
  description = "AMI ID for the EC2 instances"
}

Step 3: Create a Security Group

Create a security group to allow necessary traffic to your EC2 instances.

resource "aws_security_group" "web_sg" {
  name        = "web_sg"
  description = "Allow web traffic"
  vpc_id      = var.vpc_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"]
  }
}

Step 4: Launch EC2 Instances

Use a for_each loop to deploy EC2 instances across multiple AZs.

resource "aws_instance" "web" {
  count = var.instance_count

  ami                         = var.ami_id
  instance_type               = var.instance_type
  subnet_id                   = element(var.subnet_ids, count.index)
  security_groups             = [aws_security_group.web_sg.name]

  tags = {
    Name = "web-instance-${count.index + 1}"
  }
}

Step 5: Create a Load Balancer

Set up an Elastic Load Balancer (ELB) to distribute traffic among your EC2 instances.

resource "aws_elb" "web_elb" {
  name               = "web-load-balancer"
  availability_zones = var.subnet_ids

  listener {
    instance_port     = 80
    instance_protocol = "HTTP"
    lb_port           = 80
    lb_protocol       = "HTTP"
  }

  health_check {
    target              = "HTTP:80/"
    interval            = 30
    timeout             = 5
    healthy_threshold   = 2
    unhealthy_threshold = 2
  }

  instances = [for instance in aws_instance.web : instance.id]

  tags = {
    Name = "web-elb"
  }
}

Step 6: Apply the Configuration

Initialize Terraform, review the execution plan, and apply the configuration to create your resources.

terraform init
terraform plan
terraform apply

Conclusion

By following these steps, you have successfully deployed EC2 instances in multiple Availability Zones and set up a load balancer using Terraform. This setup ensures high availability and fault tolerance for your application, providing a resilient architecture on AWS.

Terraform makes infrastructure management more predictable and less error-prone by allowing you to define your infrastructure as code. Keep exploring Terraform’s capabilities to manage your AWS infrastructure more efficiently.

Feel free to expand this setup with additional features like auto-scaling, advanced monitoring, and integrating with other AWS services.

-Hope you like this blog! Happy Learning!