Infrastructure as Code (IaC) is the practice of managing and provisioning your cloud resources through code, rather than manual operations or one-off scripts. Essentially, it lets you script your infrastructure, the same way you script your application code. In doing so, IaC allows you to apply the principles of software development, such as version control and continuous integration, to your infrastructure.

Why is IaC Needed?

• Consistency: IaC allows for a consistent and standardized environment, which reduces errors that can occur when infrastructure is set up manually.
• Scalability: IaC makes it easier to scale infrastructure up or down in response to demand.
• Version Control: Infrastructure can be treated like any other codebase, enabling you to use version control systems to track changes and rollback if needed.
• Collaboration: Developers and operations can work together more seamlessly. Infrastructure becomes part of the application development process.
• Cost-Efficiency: By automating repetitive tasks, you save both time and resources, thus reducing operational costs.

The Evolution from Manual Operations to IaC

• Manual Operations: Initially, system administrators would manually configure servers and other resources. This was time-consuming and error-prone.
• Scripting: Automating configurations through scripts improved the speed but still lacked standardization and could be hard to manage for complex systems.
• Configuration Management Tools: Tools like Ansible, Chef, and Puppet brought more structure but were often specific to certain types of operations.
• Infrastructure as Code: IaC brings a holistic approach, where the entire environment is coded, versioned, and automated.

Terraform is an open-source tool created by HashiCorp that allows you to define and provide infrastructure as code (IaC). It uses its own domain-specific language known as HashiCorp Configuration Language (HCL) and can manage infrastructure across multiple cloud service providers.

Why Choose Terraform?

• Provider Agnostic: Terraform isn’t limited to a single cloud. With extensive provider support, you can manage a multi-cloud setup using one framework.

• Immutable Infrastructure: Terraform encourages the creation of unchangeable infrastructure. If updates are needed, the current resources are replaced, ensuring consistency and reducing potential drift.

• Declarative Syntax: Describe what you want with Terraform’s declarative configuration, and let it handle how to achieve that state.

• State Management: Terraform uses a state file to map real-world resources to your configuration, ensuring resources are managed correctly.

Core Concepts in Terraform

• Resource: The primary component in a Terraform configuration. A resource represents an infrastructure object, such as a VM or network.

• Provider: These are plugins that Terraform uses to interact with cloud service APIs, allowing it to manage resources in those clouds.

• Variables: Parameterize your configurations using variables, making your IaC modular and reusable.

• State: Terraform’s state maps configurations to real-world resources, tracking the resources it manages.

Terraform has a structured workflow:

1. Initialization: Prepare a Terraform working directory with necessary providers using the terraform init command.
2. Planning: Preview infrastructure changes with terraform plan to ensure alignment with your goals.
3. Applying: Implement the desired infrastructure state using terraform apply.
4. Destroy: To dismantle the infrastructure, use terraform destroy.

For further details, dive into Terraform’s official documentation to enhance your understanding and capabilities.

Now that you understand the basics of Terraform and the need for Infrastructure as Code, let’s try an exercise that doesn’t interact with any cloud providers. We’ll use the null_resource for demonstration and learning purposes.

Example: Using null_resource

This example uses Terraform’s null_resource.

resource "null_resource" "example" {
  provisioner "local-exec" {
    command = "echo Hello, World"
  }
}

In this example, we define a null_resource with the name example. The local-exec provisioner will run the command specified (echo Hello, World) on your local machine when you apply this Terraform configuration.

What Are Terraform Modules?

Terraform modules encapsulate a piece of Terraform configuration into a reusable, shareable unit. This allows for better organization and abstraction of Terraform code. More details can be found in the Terraform Modules Documentation.

Why Are Terraform Modules Needed?

• Reuse of Code: Avoid repetition of similar blocks of code in different parts of the project.
• Simplified Configuration: Hide the complexity of your setup by exposing only the most relevant variables.
• Version Control: Modules can be versioned, and you can specify which version to use in your main configuration.

Why Are Modules Good?

1. Modularity: As the name suggests, Terraform modules bring modularity to your infrastructure. You can encapsulate a whole set of functionalities and features into one module.
2. Reusability: Once you’ve written a module for a specific piece of infrastructure, you can reuse it across multiple environments or even multiple projects.
3. Maintainability: Using modules makes your Terraform configuration easier to maintain. If a change is needed for a specific piece of infrastructure, you can make the change in just one place.

For best practices on writing modules, check the Terraform Module Best Practices.

Structure of a Terraform Module

A typical module directory structure looks like this:

my_terraform_module/
|-- main.tf
|-- variables.tf
|-- outputs.tf
|-- README.md

• main.tf contains the core resource declarations.
• variables.tf contains any input variables that the module accepts.
• outputs.tf contains any output variables that the module will produce.
• README.md contains documentation for the module.

Example: An AWS S3 Bucket Module

To help you understand how a Terraform module works, let’s create an example of an AWS S3 bucket module. The example includes two parts:

1. The code for the module itself.
2. How to use the module in a Terraform script.

Code for the Module

First, let’s define the module. Create a new directory for the module and inside it, create a main.tf file with the following content:

provider "aws" {
  region = "eu-west-1"
}

resource "aws_s3_bucket" "this" {
  bucket = var.bucket_name
  acl    = "private"

  tags = {
    Name        = var.bucket_name
    Environment = var.environment
  }
}

output "bucket_arn" {
  value = aws_s3_bucket.this.arn
}

Create a variables.tf file to declare the input variables:

variable "bucket_name" {
  description = "The name of the bucket"
  type        = string
}

variable "environment" {
  description = "The environment this bucket will be part of"
  type        = string
  default     = "dev"
}

Using the Module

To use this module in a Terraform script, create a main.tf in a different directory and refer to the module like this:

module "s3_bucket" {
  source       = "./path/to/module/directory"
  bucket_name  = "my-new-bucket"
  environment  = "prod"
}

output "new_bucket_arn" {
  value = module.s3_bucket.bucket_arn
}

Run terraform init and terraform apply to create the S3 bucket using your module.

This example should give you a good understanding of how to create a basic Terraform module and how to use it. For more detailed information, you can refer to the Terraform AWS S3 Bucket Documentation and Terraform Modules Documentation.

What is Terraform State?

Terraform uses a state file to keep track of the current status of your infrastructure. This state file maps the resources in your configuration to real-world resources, providing a way to store attributes and manage resource properties. It is crucial to understand how Terraform handles this state, especially as you work on larger projects.

Local vs. Remote State

By default, Terraform stores the state file in the local filesystem. However, for any significant project or team-based work, a remote backend is recommended. Remote backends like AWS S3, Azure Blob Storage, or Google Cloud Storage allow you to store the Terraform state in a centralized, shared, and secure location.

Locking State

State locking prevents others from acquiring the lock and ensures that multiple users can’t make conflicting changes. Locking is automatically handled in some backends like AWS S3 when used in conjunction with a DynamoDB table.

AWS S3 as a Backend with DynamoDB Locking

Here is a basic example of how you can set up AWS S3 as a backend for storing your Terraform state file, and use AWS DynamoDB for state locking:

terraform {
  backend "s3" {
    bucket  = "my-terraform-state-bucket"
    key     = "terraform.tfstate"
    region  = "eu-west-1"
    dynamodb_table = "my-lock-table"
  }
}

This configuration tells Terraform to use an S3 bucket named my-terraform-state-bucket and a DynamoDB table named my-lock-table for state locking.

For more details on various aspects of Terraform state management, you can read the following sections from the official Terraform documentation:

• General State Management
• S3 Backend Configuration
• State Locking with DynamoDB

These resources will give you a comprehensive understanding of how to manage Terraform state effectively, including using remote backends like AWS S3 and state locking mechanisms like DynamoDB.

In software development, we break down complex projects into smaller, manageable parts, which we work on for a week or two. These periods are called “sprints.”

A sprint backlog is like a to-do list. It lists what the team has decided to work on this sprint. It’s chosen from a larger list, usually called the “product backlog,” which holds the entire project to-do list.

For your course, the mandatory work has been scheduled already. You will add any other tickets you want to work on to your backlog and schedule them according to your own goals and capacity. Use your planning board to do this.

You will find your backlog in the Backlog view on every sprint.

Copy the tickets you are working on to your own backlog. Organise your tickets on your board and move them to the right column as you work through them. It’s a flowchart:

flowchart LR
    Backlog --> Ready
    Ready --> in_progress
    in_progress[In Progress] --> in_review
    in_review[In Review] --> Done