Tag: aws provider

Deploying an AWS instance from Marketplace images using Terraform

In a previous post we looked at network requirements required to deploy an instance in AWS. In this post we are going to look at what it takes to pull a Marketplace Amazon Machine Instance (AMI) from the marketplace and deploy it into a virtual private cloud with the appropriate network security group and subnet definitions.

If you go into the AWS Marketplace from the AWS Console you get a list of virtual machine images. We are going to deploy a Commvault CommServe server instance because it is relatively complex with networking requirements, SQL Server, IIS Server, and customization after the image is deployed. We could just as easily have done a Windows 2016 Server or Ubuntu 18 Server instance but wanted to do something a little more complex.

The Cloud Control is a Windows CommServe server installation. The first step needed is to open a PowerShell and connect to Amazon using the aws command line interface. This might require an Install-Module aws to get the aws command line installed and configured but once it is ready to connect to aws by typing in

aws configure

We can search for Marketplace images by doing an ec2 describe-images with a filter option

aws ec2 describe-images –executable-users all –filters “Name=name,Values=*Cloud Control*”

The describe-images command searches for an Amazon AMI that matches the description that we are looking for and returns an AMI ID. From this we can create a new instance pre-configured with a CommServe server. From here we can create out terraform files. It is important to note that the previous examples of main.tf and network.tf files do not need to be changed for this definition. We only need to create a virtual_machine.tf file to define our instance and have it created with the network configurations that we have previously defined.

We will need to create a new variable in our main.tf file that defines the private and public key that we are going to use to authenticate against our Windows server.

resource “aws_key_pair” “cmvlt2020” {
provider = aws.east
key_name = “cmvlt2020”
public_key = “AAAAB3NzaC1yc2EAAAADAQABAAABAQCtVZ7lZfbH8ZKC72A+ipNB6L/upQrj8pRxLwzQi7LVPrameil8/q4ROvWbC1KC9A3Ego”
}

A second element that needs to be defined is an aws_ami data declaration to reference an existing AMI. This can be done in the virtual_machines.tf file to isolate the variable and data declaration for virtual machine specific definitions. If we wanted to define an Ubuntu instance we would need to define the owner as well as the filter to use for an aws_ami search. In this example we are going to look for Ubuntu on an AMD 64-bit processor. The unusualness is the owners that needs to be used for Ubuntu since it is controlled by a third part Marketplace owner.

variable “ubuntu-version” {
type = string
default = “bionic”
# default = “xenial”
# default = “groovy”
# default = “focal”
# default = “trusty”
}

data “aws_ami” “ubuntu” {
provider = aws.east
most_recent = true
# owners = [“Canonical”]
owners = [“099720109477”]
filter {
name = “name”
values = [“ubuntu/images/hvm-ssd/ubuntu-${var.ubuntu-version}–amd64-server-“]
}
}

output “Ubuntu_image_name” {
value = “${data.aws_ami.ubuntu.name}”
}

output “Ubuntu_image_id” {
value = “${data.aws_ami.ubuntu.id}”
}

In this example we will be pulling the ubuntu-bionic-amd64-server image that has hardware virtualization running on a solid state disk. The variable ubuntu-version is mapped to the version of the Ubuntu kernel that is desired. The filter.values does the search in the Marketplace store to find the AMI ID. We restrict the search by searching in the region that we are deploying and use owner “099720109477” as the Marketplace provider.

If we compare this to a CentOS deployment the centos-version variable has a different string definition and a different owner.

variable “centos-version” {
type = string
default = “Linux 7 x86_64”
# default = “Linux 6 x86_64”
}

data “aws_ami” “centos” {
provider = aws.east
most_recent = true
owners = [“aws-marketplace”]

filter {
name = “name”
values = [“CentOS ${var.centos-version}*”]
}
}

output “CentOS_image_name” {
value = “${data.aws_ami.centos.name}”
}

output “CentOS_image_id” {
value = “${data.aws_ami.centos.id}”
}

For CentOS we can deploy 6 or version 7 by changing the centos-version.default definition. It is important to note that the owner of this AMI is not Amazon and uses the aws-marketplace definition to perform the filter. The same is true for the Commvault image that we are looking at.

data “aws_ami” “commvault” {
provider = aws.east
most_recent = true
# owners = [“Canonical”]
owners = [“aws-marketplace”]

filter {
name = “name”
values = [“*Cloud Control*”]
}
}

output “Commvault_CommServe_image_name” {
value = “${data.aws_ami.commvault.name}”
}

output “Commvault_CommServe_image_id” {
value = “${data.aws_ami.amazon.id}”
}

Note the filter uses a leading wildcard with the name “Cloud Control” followed by a wildcard to look for the instance that we are looking for. Once we have the AMI we can use the AMI.id from our search to define the aws_instance definition.

resource “aws_instance” “commserve” {
provider = aws.east
ami = data.aws_ami.commvault.id
associate_public_ip_address = true
instance_type = “m5.xlarge”
key_name = “cmvlt2020”
vpc_security_group_ids = [aws_security_group.cmvltRules.id]
subnet_id = aws_subnet.mySubnet.id
tags = {
Name = “TechEnablement test”
environment = var.environment
createdby = var.createdby
}
}

output “test_instance” {
value = aws_instance.commserve.public_ip
}

If we take the aws_instance declaration piece by piece the provider defines which AWS region that we will provision into Amazon. The vpc_security_group_ids and subnet_id defines what network that this instance will join. The new declarations are

  • ami – AWS AMI id to use as the source to clone
  • associate_public_ip_address – do we want a public or private only IP address with this instance
  • instance_type – this is the size. We need to reference the documentation or our users to figure out how large or how small this server needs to be. From the Commvault documentation the smallest recommended size is an m5.xlarge.
  • key_name – this is the public and private key names that will be used to connect to the Windows instance.

The remainder of the variables like disk, is this a Windows instance, and all the regular required parameters we saw with a vsphere_virtual_machine are provided by the AMI definition.

With these files we can execute from the following files

  • aws configure
  • terraform init
  • terraform plan
  • terraform apply

In summary, pulling an AMI ID from the marketplace works well and allows us to dynamically create virtual machines from current or previous builds. The terraform apply finishes quickly but the actual spin up of the Windows instance takes a little longer. Using Marketplace instances like the Commvault AMI provides a good foundation for a proof of concept or demo platform. The files used in this example are available in github.com.

AWS networking with Terraform

In our previous blog we talked about provisioning an AWS Provider into Terraform. It was important to note that it differed from the vSphere provider in that you can create multiple AWS providers for different regions and give an alias to each region or login credentials as desired. With vSphere you can only have one provider and no aliases.

Once we have a provider defined we need to create elements inside the provider. If our eventual goal is to create a database using software as a service or a virtual machine using infrastructure as a service then we need to create a network to communicate with these services. With AWS there are basically two layers of network that you can define and two components associated with these networks.

The first layer is the virtual private network which defines an address range and access rights into the network. The network can be completely closed and private. The network can be an extension of your existing datacenter through a virtual private network connection. The network can be an isolated network that has public access points allowing clients and consumers access to websites and services hosted in AWS.

Underneath the virtual private network is either a public or private subnet that segments the IP addresses into smaller chunks and allows for instances to be addressed on the subnet network. Multiple subnet definitions can be created inside a virtual private network to segment communications with the outside world and private communications between servers (for example a database server and applications server). The application server might need a public IP address and an private IP address while the database server typically will only have a private IP address.

Associated with the network and subnets are a network security group and internet gateway to restrict access to servers in the AWS cloud. A diagram of this configurations with a generic compute instance is shown below.

The first element that needs to be defined is the AWS Provider.

provider “aws” {
version = “> 2”
profile = “default”
region = “us-east-1”
alias = “east”
}

The second component would be the virtual private cloud or aws_vpc.

resource “aws_vpc” “myNet” {
cidr_block = “10.0.0.0/16”
provider = aws.east
tags = {
Name = “myNet”
environment = var.environment
createdby = var.createdby
}
}

Note that the only required attribute for the aws_vpc is the cidr_block. Everything else is optional. It is important to note that the aws_vpc can be defined as a resource or as a data element that does not create or destroy the network definition in AWS with terraform apply and destroy. With the data declaration the cidr_block is optional given that it has already been defined and the only only attribute needed to match the existing VPC is the name or the ID of the existing VPC.

Once the VPC has been created an aws_subnet can be defined and the two required elements for a resource definition are the cidr_block and the vpc_id. If you want to define the aws_subnet as a data element the only required resource is the vpc_id.

resource “aws_subnet” “MySubnet” {
provider = aws.east
vpc_id = aws_vpc.myNet.id
cidr_block = “10.0.1.0/24”
tags = {
Name = “MySubnet”
environment = var.environment
createdby = var.createdby
}
}

The provider declaration is not required but does help with debugging and troubleshooting at a later date. It is important to note that the VPC was defined with a /16 cidr_block and the subnet was a more restrictive /24 cidr_block. If we were going to place a database in a private network we would create another subnet definition and use a different cidr_block to isolate the network.

Another element that needs to be defined is an aws_internet_gateway to define access from one network (public or private) to another network. The only required element that is needed for the resource declaration is the internet gateway id. If you define the aws_internet_gateway as a data declaration then the name or the vpc_id is required to map to an existing gateway declaration.

resource “aws_internet_gateway” “igw” {
provider = aws.aws
vpc_id = aws_vpc.myNet.id
tags = {
Name = “igw”
environment = var.environment
createdby = var.createdby
}
}

The final element that we want to define is the network security group which defines ports that are open inbound and outbound. In the following example we define inbound rules for ports 80, 443, and 8400-8403, ssh (port 22), and rdp (port 3389) as well as outbound traffic for all ports.

resource “aws_security_group” “cmvltRules” {
provider = aws.aws
name = “cmvltRules”
description = “allow ports 80, 443, 8400-8403 inbound traffic”
vpc_id = aws_vpc.myNet.id

ingress {
description = “Allow 443 from anywhere”
from_port = 443
to_port = 443
protocol = “tcp”
cidr_blocks = [“0.0.0.0/0”]
}

ingress {
description = “Allow 80 from anywhere”
from_port = 80
to_port = 80
protocol = “tcp”
cidr_blocks = [“0.0.0.0/0”]
}

ingress {
description = “Allow 8400-8403 from anywhere”
from_port = 8400
to_port = 8403
protocol = “tcp”
cidr_blocks = [“0.0.0.0/0”]
}

ingress {
description = “Allow ssh from anywhere”
from_port = 22
to_port = 22
protocol = “tcp”
cidr_blocks = [“0.0.0.0/0”]
}

ingress {
description = “Allow rdp from anywhere”
from_port = 3389
to_port = 3389
protocol = “tcp”
cidr_blocks = [“0.0.0.0/0”]
}

egress {
description = “Allow all to anywhere”
from_port = 0
to_port = 0
protocol = “-1”
cidr_blocks = [“0.0.0.0/0”]
}

tags = {
Name = “cmvltRules”
environment = var.environment
createdby = var.createdby
}
}

For the security group the protocol and from_port are the only required definitions when defining an aws_security_group resource. If you declare an aws_security_group data declaration then the name is the only required element. For the declaration shown above the provider and vpc_id to help identify the network that the roles are associated with for debugging and troubleshooting.

This simple video looks at the AWS console to see the changes defined by terraform using the main.tf and network.tf files saved in github.com.

In summary, network definitions on AWS are radically different and more secure than a typical vSphere provider definition with undefined network configurations. Understanding network configurations in Terraform help build a more predictable and secure deployment in the cloud. If you are part of a larger organization you might need to use data declarations rather than resource declarations unless you are creating your own sandbox. You might need to join a corporate VPC or dedicated subnet assigned to your team. Once networking is defined, new and creating things like moving dev/test to the cloud or testing database as a service to reduce license costs. The only step missing from these configuration files are setting up the aws configure and authentication using the AWS CLI interface. Terraform does a good job leveraging the command line authentication so that the public and private keys don’t need to be stored in files or configuration templates.

aws provider vs vsphere provider

In a previous post we talked about the vsphere provider and what is needed to define a connection to create a virtual machine. In this blog we will start to look at what is needed to setup a similar environment to do the same thing in AWS EC2. Think of it as a design challenge. Your client comes to you and says “I want a LAMP or WAMP stack or Tomcat Server that I can play with. I want one local as well as one in the cloud. Can you make that happen?”. You look around and find out that they do have a vSphere server and figure out how to log into it and create a Linux instance to build a LAMP stack and a Windows instance to create a WAMP stack then want to repeat this same configuration in AWS, Azure, and/or Google GCP. Simple, right?

If you remember, to create a vSphere provider declaration in Terraform you basically need a username, password, and IP address of the vSphere server.

provider “vsphere” {
user = var.vsphere_user
password = var.vsphere_password
vsphere_server = var.vsphere_server
version = “1.12.0”

allow_unverified_ssl = true
}

The allow_unverified_ssl is to get around that most vSphere installations in a lab don’t have a certified certificate but a self-signed certificate and the version is to help us keep control of syntax changes in our IaC definitions that will soon follow.

The assumptions that you are making when connecting to a vSphere server when you create a virtual machine are

  1. Networking is setup for you. You can connect to a pre-defined network interface from vSphere but you really can’t change your network configuration beyond what is defined in your vSphere instance.
  2. Firewalls, subnets, and routing is all defined by a network administrator and you really don’t have control over the configuration inside Terraform unless you manage your switches and routers from Terraform as well. The network is what it is and you can’t really change it. To change routing rules and blocked or open ports on a network typically requires reconfiguration of a switch or network device.
  3. Disks, memory, and CPUs are limited by server configurations. In my home lab, for example, I have two 24 core servers with 48 GB of RAM on one system and 72 GB of RAM on the other. One system has just under 4 TB of disk while the other has just over 600 GB of disk available.
  4. Your CPU selection is limited to what is in your lab or datacenter. You might have more than just an x86 processer here and there but the assumption is that everything is x86 based and not Sparc or PowerPC. There might be an ARM processor as an option but not many datacenters have access to this unless they are developing for single board computers or robotics projects. There might be more advanced processors like a GPU or Nvidia graphics accelerated processor but again, these are rare in most small to midsize datacenters.

Declaring a vsphere provider gives you access to all of these assumptions. If you declare an aws or azure provider these assumptions are not true anymore. You have to define your network. You can define your subnet and firewall configurations. You have access to almost unlimited CPU, memory, and disk combinations. You have access to more than just an x86 processor and you have access to multiple datacenters that span the globe rather than just a single cluster of computers that are inside your datacenter.

The key difference between declaring a vsphere provider and an aws provider is that you can declare multiple aws providers and use multiple credentials as well as different regions.

provider “aws” {
version = “> 2”
profile = “default”
region = “us-east-1”
alias = “aws”
}

Note we don’t connect to a server. We don’t have a username or password. We do define a version and have three different parameters that we pass in. So the big question becomes how do we connect and authenticate? Where is this done if not in the provider connection? We could have gotten by with just provider “aws” {} and that would have worked as well.

To authenticate using the Hashicorp aws provider declaration you need to

  • declare the access_key and secret_key in the declaration (not advised)
  • declare the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY_ID as environment variables
  • or point to a configuration file with the shared_credentials_file declaration or AWS_SHARED_CREDENTIALS_FILE environment variable leveraging the profile declaration or PROFILE environment variable.
  • automatic loading of the ~/.aws/credentials or ~/.aws/config files

The drawback to using the environment variables is that you can only have one login into your aws console but can connect to multiple regions with the same credentials. If you have multiple accounts you need to declare the access_key and secret_key or the more preferred method of using the shared_credentials_file declaration.

For the aws provider, all parameters are optional. The provider is flexible enough to make some assumptions and connect to AWS based on environment variables and optional parameters defined. If something is defined with a parameter it is used over the environment variable. If you define both a key and a shared_credentials_file, Terraform will throw an error. If you have environment variables define and a ~/.aws/credentials file, the environment variables will be used first.

If we dive a little deeper into our vsphere variables.tf file we note that we need to run a script or manually generate the declarations for vsphere_datacenter, vsphere_host, and vsphere_resource_pool prior to defining a virtual machine. With the aws provider we only need to define the region to define all of these elements. Unfortunately, we also need to define the networking connections, subnet definitions, and potential firewall exceptions to be able to access our new virtual machine. It would be nice if we could take our simple vsphere virtual machine definition defined in our vsphere main.tf file and translate it directly into an aws_instance declaration. Unfortunately, there is very little that we can translate from one environment to the other.

The aws provider and aws_instance declaration does not allow us to clone an existing instance. We need to go outside of Terraform and create an AMI to use as a reference for aws_instance creation. We don’t pick a datacenter and resource_pool but select a region to run our instance. We don’t need to define a datastore to host the virtual machine disks but we do need to define the disk type and if it is a high speed (higher cost) or spinning disk (lower cost) to host the operating system or data.

We can’t really take our existing code and run it through a scrubber and spit out aws ready code unfortunately. We need to know how to find a LAMP, WAMP, and Tomcat AMI and reference it. We need to know the network configurations and configure connections to another server like a database or load balancer front end. We also need to know what region to deploy this into and if we can run these services using low cost options like spot instances or can shut off the running instance during times of the day to save money given that a cloud instance charges by the minute or hour and a vsphere instance is just consuming resources that you have already paid for.

One of the nice things about an aws provider declaration is that you can define multiple providers in the same file which generated an error for a vsphere provider. You can reference different regions using an alias. In the declaration shown above we would reference the provider with

provider = aws.aws

If we wanted to declare that the east was our production site and the west was our dev site we could use the declaration

provider “aws” {
version = “> 2”
profile = “default”
region = “us-east-1”
alias = “east”
}
provider “aws” {
version = “> 2”
profile = “default”
region = “us-west-1”
alias = “west”
}

If we add a declaration of a network component (aws_vpc) we can populate our state file and see that the changes were pushed to our aws account.

We get the .terraform tree populated for our Windows desktop environment as well as the terraform.tfstate created. Looking at our AWS VPC console we see that Prod-1 was created in US-East-1 (and we could verify that Dev-1 was created in US-West-1 if we wanted).

Note that the CIDR block was correctly defined as 10.0.0.0/16 as desired. If we run the terraform destroy command to clean up this vpc will be destroyed since it was created and is controlled by our terraform declaration.

Looking at our terraform state file we can see that we did create two VPC instances in AWS and the VPC ID should correspond to what we see in the AWS console.

In summary, using Terraform to provision and manage resources in Amazon AWS is somewhat easier and somewhat harder than provisioning resources in a vSphere environment. Unfortunately, you can’t take a variables.tf or main.tf declaration from vSphere and massage it to become a AWS definition. The code needs to be rewritten and created using different questions and parameters. You don’t need to get down to the SCSI target level with AWS but you do need to define the network connection and where and how the resource will be declared with a finer resolution. You can’t clone an existing machine inside of Terraform but you can do it leveraging private AMI declarations in AWS similar to the way that templates are created in vSphere. Overall an AWS managed state with Terraform is easy to start and allows you to create a similar environment to an on-premises environment as long as you understand the differences and cost implications between the two. Note that the aws provider declaration is much simpler and cleaner than the vsphere provider. Less is needed to define the foundation but more is needed as far as networking and how to create a virtual instance with AMIs rather than cloning.

The variables.tf and terraform.state files are available on github to review.