Provision and Manage Amazon Aurora Databases with Terraform

Introduction

Amazon Aurora is a high-performance, scalable relational database service compatible with MySQL and PostgreSQL. With Terraform, you can efficiently automate the provisioning and management of your Aurora databases.

In this blog, we will walk through setting up an Aurora cluster using Terraform, including configuration, file structure, and deployment steps.

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Prerequisites

Before starting, ensure you have:

Basic knowledge of Terraform and AWS services.
Terraform is installed on your local machine.
AWS CLI configured with appropriate credentials.
Access to an AWS account with permissions to create Amazon RDS clusters.

File Structure and Configuration

main.tf

The main.tf file contains the primary configuration for your Aurora cluster and instances.

resource "aws_rds_cluster" "aurora" {
  cluster_identifier          = "${var.environment}-aurora-cluster"
  engine                      = "aurora-mysql"
  database_name               = var.db_name
  master_username             = var.db_username
  master_password             = var.db_password
  engine_mode                 = "provisioned"
  backup_retention_period     = 7
  preferred_backup_window     = "09:00-10:00"
  preferred_maintenance_window = "Tue:07:00-Tue:09:00"
  serverlessv2_scaling_configuration {
    max_capacity = var.max_capacity
    min_capacity = var.min_capacity
  }

  vpc_security_group_ids       = [var.security_group_id]
  db_subnet_group_name         = var.db_subnet_group_name

  tags = {
    Name        = "${var.environment}-aurora-cluster"
    Description = var.description
  }
}

resource "aws_rds_cluster_instance" "writer" {
  cluster_identifier           = aws_rds_cluster.aurora.id
  identifier                   = var.environment == "dev" ? "test" : "${aws_rds_cluster.aurora.id}-instance"
  instance_class               = "db.serverless"
  engine                       = aws_rds_cluster.aurora.engine
  preferred_maintenance_window = "sat:08:00-sat:08:30"
  publicly_accessible          = false
  tags = {
    Name              = "${aws_rds_cluster.aurora.id}-instance"
    Environment       = var.environment
    "Stoppable"       = "False"
    "Restart"         = "False"
  }
}

resource "aws_rds_cluster_instance" "reader" {
  cluster_identifier           = aws_rds_cluster.aurora.id
  identifier                   = var.environment == "dev" ? "test" : "${aws_rds_cluster.aurora.id}-reader"
  instance_class               = "db.serverless"
  engine                       = aws_rds_cluster.aurora.engine
  preferred_maintenance_window = "sat:08:00-sat:08:30"
  publicly_accessible          = false
  tags = {
    Name              = "${aws_rds_cluster.aurora.id}-instance"
    Environment       = var.environment
    "Stoppable"       = "False"
    "Restart"         = "False"
  }
}

The main.tf file contains the primary configuration for your Aurora cluster and instances.

resource "aws_rds_cluster" "aurora" {

cluster_identifier = "${var.environment}-aurora-cluster"

engine = "aurora-mysql"

database_name = var.db_name

master_username = var.db_username

master_password = var.db_password

engine_mode = "provisioned"

backup_retention_period = 7

preferred_backup_window = "09:00-10:00"

preferred_maintenance_window = "Tue:07:00-Tue:09:00"

serverlessv2_scaling_configuration {

max_capacity = var.max_capacity

min_capacity = var.min_capacity

}

vpc_security_group_ids = [var.security_group_id]

db_subnet_group_name = var.db_subnet_group_name

tags = {

Name = "${var.environment}-aurora-cluster"

Description = var.description

}

resource "aws_rds_cluster_instance" "writer" {

cluster_identifier = aws_rds_cluster.aurora.id

identifier = var.environment == "dev" ? "test" : "${aws_rds_cluster.aurora.id}-instance"

instance_class = "db.serverless"

engine = aws_rds_cluster.aurora.engine

preferred_maintenance_window = "sat:08:00-sat:08:30"

publicly_accessible = false

tags = {

Name = "${aws_rds_cluster.aurora.id}-instance"

Environment = var.environment

"Stoppable" = "False"

"Restart" = "False"

}

resource "aws_rds_cluster_instance" "reader" {

cluster_identifier = aws_rds_cluster.aurora.id

identifier = var.environment == "dev" ? "test" : "${aws_rds_cluster.aurora.id}-reader"

instance_class = "db.serverless"

engine = aws_rds_cluster.aurora.engine

preferred_maintenance_window = "sat:08:00-sat:08:30"

publicly_accessible = false

tags = {

Name = "${aws_rds_cluster.aurora.id}-instance"

Environment = var.environment

"Stoppable" = "False"

"Restart" = "False"

}

variables.tf

The variables.tf file defines the variables used in the Terraform configuration.

variable "environment" {
  description = "The environment (dev, staging, prod, etc.)"
  type        = string
}

variable "db_name" {
  description = "The name of the database"
  type        = string
}

variable "db_username" {
  description = "The username for the database"
  type        = string
}

variable "db_password" {
  description = "The password for the database"
  type        = string
}

variable "security_group_id" {
  description = "The security group ID for the Aurora cluster"
  type        = string
}

variable "db_subnet_group_name" {
  description = "The name of the DB subnet group"
  type        = string
}

variable "min_capacity" {
  description = "The minimum capacity for Aurora Serverless"
  type        = number
}

variable "max_capacity" {
  description = "The maximum capacity for Aurora Serverless"
  type        = number
}

variable "auto_pause_timeout" {
  description = "The timeout in seconds before automatically pausing Aurora Serverless"
  type        = number
}

variable "description" {
  description = "Description tag for resources"
  type        = string
}

variable "environment" {

description = "The environment (dev, staging, prod, etc.)"

type = string

}

variable "db_name" {

description = "The name of the database"

type = string

}

variable "db_username" {

description = "The username for the database"

type = string

}

variable "db_password" {

description = "The password for the database"

type = string

}

variable "security_group_id" {

description = "The security group ID for the Aurora cluster"

type = string

}

variable "db_subnet_group_name" {

description = "The name of the DB subnet group"

type = string

}

variable "min_capacity" {

description = "The minimum capacity for Aurora Serverless"

type = number

}

variable "max_capacity" {

description = "The maximum capacity for Aurora Serverless"

type = number

}

variable "auto_pause_timeout" {

description = "The timeout in seconds before automatically pausing Aurora Serverless"

type = number

}

variable "description" {

description = "Description tag for resources"

type = string

}

outputs.tf

The outputs.tf file defines the output values for the Terraform configuration.

output "aurora_cluster_id" {
  description = "The ID of the Aurora cluster"
  value       = aws_rds_cluster.aurora.id
}

output "aurora_writer_instance_id" {
  description = "The ID of the Aurora writer instance"
  value       = aws_rds_cluster_instance.writer.id
}

output "aurora_reader_instance_id" {
  description = "The ID of the Aurora reader instance"
  value       = aws_rds_cluster_instance.reader.id
}

output "aurora_cluster_id" {

description = "The ID of the Aurora cluster"

value = aws_rds_cluster.aurora.id

}

output "aurora_writer_instance_id" {

description = "The ID of the Aurora writer instance"

value = aws_rds_cluster_instance.writer.id

}

output "aurora_reader_instance_id" {

description = "The ID of the Aurora reader instance"

value = aws_rds_cluster_instance.reader.id

}

terraform.tfvars

The terraform.tfvars file contains the variable values.

environment         = "dev"
db_name             = "mydatabase"
db_username         = "admin"
db_password         = "password123"
security_group_id   = "security-grp-id"
db_subnet_group_name = "my-db-subnet-group"
min_capacity        = 2
max_capacity        = 8
auto_pause_timeout  = 300
description         = "Aurora cluster for dev environment"

environment = "dev"

db_name = "mydatabase"

db_username = "admin"

db_password = "password123"

security_group_id = "security-grp-id"

db_subnet_group_name = "my-db-subnet-group"

min_capacity = 2

max_capacity = 8

auto_pause_timeout = 300

description = "Aurora cluster for dev environment"

Deploying the Aurora Cluster

Initialize Terraform: Run terraform init to initialize the Terraform configuration and download the necessary provider plugins.
Plan the Deployment: Execute terraform plan to review the planned changes and ensure everything is configured correctly.
Apply the Configuration: Use terraform apply to create the Aurora cluster and instances based on your configuration.

Conclusion

In this blog, we covered creating an Amazon Aurora database cluster using Terraform. We outlined the necessary Terraform configuration files, provided code examples, and completed the deployment steps. Following this guide, you can efficiently set up an Amazon Aurora cluster tailored to your environment and requirements.

Drop a query if you have any questions regarding Amazon Aurora and we will get back to you quickly.

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