AWS Not Authorized to Use Launch Template (in Terraform or in Console)

This is just a quick note for anyone facing this issue.

A few of us lost about a day debugging what we thought was a terraform issue originally.  While we were creating an auto scaling group (ASG), we were getting “Invalid details specified: You are not authorized to use launch template…”.

It turned out that the same error was presented in the AWS console when we tried to create the ASG there.

After some substantial debugging, it turned out that terraform was allowed to create a launch template with an AMI (Amazon Machine Image) that did not exist.  We had used the AMI ID from our non-prod account in our prod account, but AMIs must exist in each account with unique IDs – so it wasn’t working.

It took us a while to get to this point in our debugging because, frankly, we were very astounded that the error message was so miss-leading.  We spent a very long time trying to figure out everything that could trigger a permissions error on the template itself, not realizing that a missing resource used within the template would make the whole template present that error.

Terraform on Docker – Run Using Current Directory as Volume

Quick Tip

You can use the following command to run a terraform apply using the current directory as the volume. This is great if you, say, do a git checkout of your repository and want to just run the terraform files from the checkout folder.

docker run -it -v $(pwd):/workpace -w /workpace hashicorp/terraform:light apply


AWS + Terraform + Auto Scale Group + User Data Bash Script on Startup to Customize Image

User Data  – On Startup

If you want to customize your VM image on its first start-up, you may want to use “user data”.  You can basically think of this as a script that will be run right after boot-up the very first time.  You can also make it run every reboot apparently (with extra config).

Why would you need this?  Well, in my case, I was spawning up a Presto cluster.  I generally do this in a special HA way… but even if you did it the simple way, you would have 1 coordinator and N workers, and the N workers would have to point at your 1 coordinator.

So, there are 2 interesting things here:

  1. The coordinator and workers are identical barring some slightly different configuration in one file.
  2. The workers need to know about the coordinator in order to use it.

So, for both of these cases, we’d like to run a script on start-up!.

The Terraform Code

When you want to create an auto-scale-group, you have to start by creating a launch template:

You can use that template to spawn up multiple auto-scale groups when its is done.  The launch template itself has the user data though.  So, you are best off trying to make your user data script generic enough that it can work for all your cases.  It can be a bash file and can use variables, so this isn’t too hard.

If you do need multiple separate user data scripts you’ll have to use separate launch templates, which is not the end of the world either.

The launch template in the link above is very complete, so all I’m going to show you is how to pass a bash script that takes parameters to the user data.

Basically replace:

user_data = "${base64encode(...)}"

In their example with something like this:

user_data = base64encode(templatefile("${path.module}/", {coordinator_lb = "${aws_lb.coordinator.dns_name}", hive_thrift_csv = "${var.hive_thrift_csv}"}))

Assuming your worker-script has content like this:

echo "Hello World" > /tmp/test-output.txt

and you have the hive_thrift_csv variable defined in your variables file like this:

variable "hive_thrift_csv" {
type = "string"
default = "thrift://ip-addr-1:9083,thrift://ip-addr-2:9083"

you should be good. Note, the first variable, definition coordinator_lb = “${aws_lb.coordinator.dns_name}” is a reference to the DNS name from a load balancer created in another part of my terraform config. I left it in as its a good example for a more complex separate variable.

Running Terraform on Centos7/RHEL7 With Docker

Install Docker

Here is a lean version of the Docker site content that I tested on Centos 7.5.  It yum installs some pre-requisites, adds the stable Docker Community Edition repository to yum, and then installs and starts Docker.

sudo yum install -y yum-utils \
device-mapper-persistent-data \
sudo yum-config-manager \
--add-repo \
sudo yum install docker-ce
sudo systemctl start docker

Now Docker is started – but only the root user can really use it.  So, let’s create the docker group and add our current user to it.  That way we can use docker with our current user and avoid having to use sudo on every command.

These instructions from from here:

sudo groupadd docker
sudo usermod -aG docker $USER

After this, please re-log in (e.g. exit out of SSH and jump back into your server) so that your group memberships apply.

Now Docker is running and we can use it as ourselves.

Get Terraform Working in Docker

We will run Terraform as a single command inside of a Docker image.  So, let’s start by getting the latest Terraform image form Hashicorp:

docker pull hashicorp/terraform

Create a directory for your Terraform work and give ownership to your user. Also create a sub-directory to act as the Docker volume in which we will put your Terraform plans.

sudo mkdir /opt/terraform && sudo chown $USER:$USER /opt/terraform
cd /opt/terraform
mkdir tf-vol

Now let’s create a file at /opt/terraform/tf-vol/ with a sample Terraform plan (just a debug one).

output "test" {
  value = "Hello World!"

After this, we can run Terraform and tell docker to use that tf-vol directory as as a volume. Terraform will use it as the working directory, will find our plan, and will display “Hello World!”.

$ docker run -i -t -v /opt/terraform/tf-vol:/tf-vol/ -w /tf-vol/ hashicorp/terraform:light apply

Apply complete! Resources: 0 added, 0 changed, 0 destroyed.


test = Hello World!

So, we now have Docker installed, and Terraform running with it using an external volume to store our plans.

Azure + Terraform + Linux Custom Script Extension (Scale Set or VM)


Whether you are creating a virtual machine or a scale set in Azure, you can specify a “Custom Script Extension” to tailor the VM after creation.

Terraform Syntax

I’m not going to go into detail on how to do the entire scale set or VM, but here is the full extension block that should go inside either one of them.

resource "azurerm_virtual_machine_scale_set" "some-name" {
  # ... normal scale set config ...

  extension {
    name                 = "your-extension-name"
    publisher            = "Microsoft.Azure.Extensions"
    type                 = "CustomScript"
    type_handler_version = "2.0"

    settings = <<SETTINGS
    "fileUris": [""],
    "commandToExecute": "bash"

Things to notice include:

  1. The extension settings have to be valid JSON (e.g. no new-lines in strings, proper quoting).
  2. This can get frustrating, so it helps to use a bash “heredoc” style block to write it the JSON (to help avoid quote escaping, etc).
  3. Assuming you have a non-trivial use case, it is very beneficial to maintain your script(s) outside of your VM image.  After all… you don’t want to go make a new VM image every time you find a typo in your script.  This is what fileUris does; it lets you refer to a script in azure storage or in any reachable web location.
  4. You can easily create new Azure storage, create a blob container, and upload a file and mark it as public so that you can refer to it without authentication.  Don’t put anything sensitive in it in this case though; if you do, use a storage key instead.  I prefer to make it public but then pass any “secret” properties to it from the command-to-execute, that way all variables are managed by Terraform at execution time.
  5. The command-to-execute can call the scripts downloaded form the fileURIs.  When the extension is run on your VM or scale set VM(s) after deployment, the scripts are uploaded to /var/lib/waagent/custom-script/download/1/ and then run with the command-to-execute.  This location serves as the working directory.

Debugging Failures

Sometimes things can go wrong when running custom scripts; even things outside your control.  For example, on Centos7.5, I keep getting 40% of my VMs or so stuck on “creating” and they clearly haven’t run the scripts.

In this case, you can look at the following log file to get more information: