Projectious Work Aibox Terraform Basics

Terraform Basics

Intro

Terraform (and OpenTofu) describes cloud infrastructure declaratively and reconciles reality with plan and apply. Pin provider versions, use a remote state backend, and never edit state by hand — get those three right and the rest is just resource documentation.

Overview

Resources and providers

Every project starts with a provider configuration. Pin provider versions in a required_providers block:

terraform {
  required_version = ">= 1.5"
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
  }
}

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

Resources are the core building blocks. Use descriptive names and set critical arguments explicitly rather than relying on defaults.

Variables and outputs

Use typed variables with descriptions and validation where appropriate:

variable "instance_type" {
  type        = string
  default     = "t3.micro"
  description = "EC2 instance size"
  validation {
    condition     = can(regex("^t3\\.", var.instance_type))
    error_message = "Only t3 instance types are allowed."
  }
}

output "instance_ip" {
  value       = aws_instance.web.public_ip
  description = "Public IP of the web server"
}

Variable types: string, number, bool, list(type), map(type), object({...}). Use terraform.tfvars or -var-file for environment-specific values. Never hardcode secrets — use environment variables or a secrets manager data source.

Data sources

Use data sources to reference existing infrastructure without managing it:

data "aws_ami" "ubuntu" {
  most_recent = true
  owners      = ["099720109477"]
  filter {
    name   = "name"
    values = ["ubuntu/images/hvm-ssd/ubuntu-jammy-22.04-amd64-server-*"]
  }
}

State management

State tracks the mapping between config and real infrastructure. Configure a remote backend for team use:

terraform {
  backend "s3" {
    bucket         = "myproject-tfstate"
    key            = "prod/terraform.tfstate"
    region         = "eu-central-1"
    dynamodb_table = "terraform-locks"
    encrypt        = true
  }
}

Never edit state files manually. Use terraform state list, terraform state show, and terraform state mv for inspection and refactoring. Use terraform import to bring existing resources under management.

Modules

Modules group related resources for reuse. Keep modules focused on a single concern:

module "vpc" {
  source  = "./modules/vpc"
  cidr    = "10.0.0.0/16"
  azs     = ["eu-central-1a", "eu-central-1b"]
}

Public modules from the registry use source = "terraform-aws-modules/vpc/aws" with a version pin. Always pin module versions in production.

Plan / apply / destroy

Always preview before applying:

terraform init               # download providers and modules
terraform fmt -check         # check formatting
terraform validate           # syntax and internal consistency
terraform plan -out=tfplan   # preview changes, save plan
terraform apply tfplan       # apply the saved plan exactly

Use terraform destroy with extreme caution — review the plan output first. For targeted operations, use -target=resource.name sparingly and only for debugging.

Gotchas

Agent-specific failure modes — provider-neutral pause-and-self-check items:

• terraform apply without a saved plan. Running terraform plan to review changes and then terraform apply (without -auto-approve tfplan) runs a fresh plan at apply time, which may differ from the plan you reviewed — a new resource may have appeared, or the plan may have changed due to drift. Always save the plan with terraform plan -out=tfplan and apply that exact file with terraform apply tfplan.
• Unpinned provider or module versions. A provider block without a version constraint will download the latest version on the next terraform init on any machine, silently changing behavior. Use version = "~> 5.0" for providers (allow patch and minor bumps within the major version) and exact pins for production modules. Lock files (terraform.lock.hcl) enforce this in CI.
• Editing state files manually. The state file is a JSON representation of Terraform's understanding of reality. Manual edits corrupt this mapping and cause plan to produce incorrect diffs. Use terraform state mv, terraform state rm, terraform import, and moved blocks for all state manipulation.
• Committing terraform.tfstate to version control. A local state file checked into git will diverge between contributors, has no locking, and exposes sensitive resource attributes in plain text. Use a remote backend (S3 + DynamoDB, GCS, Terraform Cloud) with state locking from the first day of the project.
• Monolithic root module with everything in one directory. A single module managing networking, compute, databases, and IAM in one terraform apply means a change to a Lambda function requires a plan that touches the VPC and RDS, increasing plan time and blast radius. Split by lifecycle: network infrastructure rarely changes; application resources change constantly. Separate state per lifecycle group.
• terraform state rm to "fix" drift instead of import. Removing a resource from state tells Terraform "this resource no longer exists under my management" — the next apply will attempt to create a duplicate. To bring a manually-created resource under Terraform management, use terraform import. state rm is correct only when deliberately abandoning management of a resource.
• Hardcoded secrets in .tf files or terraform.tfvars. Secrets committed to version control in Terraform files have the same exposure as any other code repository secret — and Terraform state files may also expose them in plaintext. Pass secrets via environment variables (TF_VAR_db_password), a secrets manager data source, or a vault provider — never as literal values in configuration files.

Full reference

Best practices

• One state per environment (dev/staging/prod) via workspaces or separate directories
• Use lifecycle { prevent_destroy = true } on critical resources (databases, S3 buckets holding data you cannot recreate)
• Tag all resources with project, environment, and owner
• Run terraform fmt and terraform validate before committing
• Use moved blocks for refactoring instead of destroy/recreate
• Keep modules small and focused — a module for VPC, another for IAM, another for RDS, rather than one "everything" module

Worked example: EC2 with security group

resource "aws_security_group" "web" {
  name_prefix = "web-"
  ingress {
    from_port   = 443
    to_port     = 443
    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_instance" "web" {
  ami                    = data.aws_ami.ubuntu.id
  instance_type          = var.instance_type
  vpc_security_group_ids = [aws_security_group.web.id]
  tags = {
    Name        = "web-server"
    Environment = "prod"
  }
}

Migrating state from local to S3

# 1. Add backend config to the terraform block
# 2. Run init with migration flag
terraform init -migrate-state
# 3. Verify state is accessible
terraform state list
# 4. Delete local terraform.tfstate after confirming

Importing an existing resource

# Find the resource ID in the cloud console or CLI
terraform import aws_s3_bucket.logs my-existing-log-bucket
# Write the matching resource block in .tf
terraform plan  # should show no changes if config matches

State surgery with state mv

When you rename a resource or move it into a module, Terraform sees it as "destroy old, create new" unless you update state:

terraform state mv aws_instance.web module.web.aws_instance.this
terraform state mv aws_instance.old aws_instance.new

Prefer a moved block in config when possible — it is checked into version control and replayed automatically:

moved {
  from = aws_instance.old
  to   = aws_instance.new
}

Anti-patterns

• Hardcoded secrets in .tf files — use env vars, -var, or a secrets manager data source
• Unpinned providers or modules — version = "~> 5.0" minimum; prefer exact pins in production
• Blind terraform apply without a saved plan — apply the plan you reviewed, not a fresh one
• terraform state rm to "fix" drift — you are lying to Terraform about what exists; reach for import or moved instead
• -target as normal workflow — it bypasses graph analysis and produces partial applies
• Committing terraform.tfstate — remote backend only; local state is a collaboration hazard
• Monolithic root module — split by lifecycle (network rarely changes, app changes daily); separate state per lifecycle
• Destroy/recreate when a moved block would do — destructive changes should be explicit, not accidental

Source: projectious-work/aibox — distributed by TomeVault.