Having got a VM stood up in Azure, I wanted to build a VM in AWS, after all, it’s more-or-less the same steps. Note, this is a work-in-progress, and shouldn’t be considered “Final” – this is just something to use as *your* starting block.
What do you need?
You need an AWS account for this. If you’ve not got one, signing up for one is easy, but bear in mind that while there are free resource on AWS (only for the first year!), it’s also quite easy to suddenly enable a load of features that cost you money.
Best practice suggests (or rather, INSISTS) you shouldn’t use your “root” account for AWS. It’s literally just there to let you define the rest of your admin accounts. Turn on MFA (Multi-Factor Authentication) on that account, give it an exceedingly complex password, write that on a sheet of paper, and lock it in a box. You should NEVER use it!
Create your admin account, log in to that account. Turn on MFA on *that* account too. Then, create an “Access Token” for your account. This is in IAM (Identity and Access Management). These are what we’ll use to let Terraform perform actions in AWS, without you needing to actually “log in”.
On my machine, I’ve put the credentials for this in /home/<MYUSER>/.aws/credentials and it looks like this:
This file should be chmod 600 and make sure it’s only your account that can access this file. With this token, Terraform can perform *ANY ACTION* as you, including anything that charges you money, or creating servers that can mine a “cryptocurrency” for someone malicious.
I’m using Windows Subsystem for Linux (WSL). I’m using the Ubuntu 18.04 distribution obtained from the Store. This post won’t explain how to get *that*. Also, you might want to run Terraform on Mac, in Windows or on Linux natively… so, yehr.
Next, we need to actually install Terraform. Excuse the long, unwrapped code block, but it gets what you need quickly (assuming the terraform webpage doesn’t change any time soon!)
Before you can build your first virtual machine on AWS, you need to stand up the supporting infrastructure. These are:
An SSH Keypair (no password logins here!)
A VPC (“Virtual Private Cloud”, roughly the same as a VNet on Azure, or somewhat like a L3 switch in the Physical Realm).
An Internet Gateway (if your VPC isn’t classed as “the default one”)
A Subnet.
A Security Group.
Once we’ve got these, we can build our Virtual Machine on EC2 (“Elastic Cloud Compute”), and associate a “Public IP” to it.
To quote my previous post:
One quirk with Terraform, versus other tools like Ansible, is that when you run one of the terraform commands (like terraform init, terraform plan or terraform apply), it reads the entire content of any file suffixed “tf” in that directory, so if you don’t want a file to be loaded, you need to either move it out of the directory, comment it out, or rename it so it doesn’t end .tf. By convention, you normally have three “standard” files in a terraform directory – main.tf, variables.tf and output.tf, but logically speaking, you could have everything in a single file, or each instruction in it’s own file.
For the sake of editing and annotating the files for this post, these code blocks are all separated, but on my machine, they’re all currently one big file called “main.tf“.
In that file, I start by telling it that I’m working with the Terraform AWS provider, and that it should target my nearest region.
If you want to risk financial ruin, you can put things like your access tokens in here, but I really wouldn’t chance this!
Next, we create our network infrastructure – VPC, Internet Gateway and Subnet. We also change the routing table.
I suspect, if I’d created the VPC as “The Default” VPC, then I wouldn’t have needed to amend the routing table, nor added an Internet Gateway. To help us make the routing table change, there’s a “data” block in this section of code. A data block is an instruction to Terraform to go and ask a resource for *something*, in this case, we need AWS to tell Terraform what the routing table is that it created for the VPC. Once we have that we can ask for the routing table change.
AWS doesn’t actually give “proper” names to any of it’s assets. To provide something with a “real” name, you need to tag that thing with the “Name” tag. These can be practically anything, but I’ve given semi-sensible names to everything. You might want to name everything “main” (like I nearly did)!
We’re getting close to being able to create the VM now. First of all, we’ll create the Security Groups. I want to separate out my “Allow Egress Traffic” rule from my “Inbound SSH” rule. This means that I can clearly see what hosts allow inbound SSH access. Like with my Azure post, I’m using a “data provider” to get my public IP address, but in a normal “live” network, you’d specify a collection of valid source address ranges.
Last steps before we create the Virtual Machine. We need to upload our SSH key, and we need to find the “AMI” (AWS Machine ID) of the image we’ll be using. To create the key, in this directory, along side the .tf files, I’ve put my SSH public key (called id_rsa.pub), and we load that key when we create the “my_key” resource. To find the AMI, we need to make another data call, this time asking the AMI index to find the VM with the name containing ubuntu-bionic-18.04 and some other stuff. AMIs are region specific, so the image I’m using in eu-west-2 will not be the same AMI in eu-west-1 or us-east-1 and so on. This filtering means that, as long as the image exists in that region, we can use “the right one”. So let’s take a look at this file.
So, now we have everything we need to create our VM. Let’s do that!
In here, we specify a “user_data” file to upload, in this case, the contents of a file – CloudDev.sh, but you can load anything you want in here. My CloudDev.sh is shown below, so you can see what I’m doing with this file :)
So, having created all this lot, you need to execute the terraform workload. Initially you do terraform init. This downloads all the provisioners and puts them into the same tree as these .tf files are stored in. It also resets the state of the terraform discovered or created datastore.
Next, you do terraform plan -out tfout. Technically, the tfout part can be any filename, but having something like tfout marks it as clearly part of Terraform. This creates the tfout
file with the current state, and whatever needs to change in the
Terraform state file on it’s next run. Typically, if you don’t use a
tfout file within about 20 minutes, it’s probably worth removing it.
Finally, once you’ve run your plan stage, now you need to apply it. In this case you execute terraform apply tfout. This tfout is the same filename you specified in terraform plan. If you don’t include -out tfout on your plan (or even run a plan!) and tfout in your apply, then you can skip the terraform plan stage entirely.
Once you’re done with your environment, use terraform destroy to shut it all down… and enjoy :)
SemVer, short for Semantic Versioning is an easy way of numbering your software versions. They follow the model Major.Minor.Patch, like this 0.9.1 and has a very opinionated view on what is considered a Major “version bump” and what isn’t.
Sometimes, when writing a library, it’s easy to forget what version you’re on. Perhaps you have a feature change you’re working on, but also bug fixes to two or three previous versions you need to keep an eye on? How about an easy way of figuring out what that next bump should be?
In a recent conversation on the McrTech slack, Steven [0] mentioned he had a simple bash script for incrementing his SemVer numbers, and posted it over. Naturally, I tweaked it to work more easily for my usecases so, this is *mostly* Steven’s code, but with a bit of a wrapper before and after by me :)
Late Edit: 2022-11-19 ictus4u spotted that I wasn’t handling the reset of PATCH to 0 when MINOR gets a bump. I fixed this in the above gist.
So how do you use this? Dead simple, use nextver in a tree that has an existing git tag SemVer to get the next patch number. If you want to bump it to the next minor or major version, try nextver minor or nextver major. If you don’t have a git tag, and don’t specify a SemVer number, then it’ll just assume you’re starting from fresh, and return 0.0.1 :)
One of the things I miss about Jekyll when I’m working with Ansible is the ability to fragment my data across multiple files, but still have it as a structured *whole* at the end.
For example, given the following directory structure in Jekyll:
+ _data
|
+---+ members
| +--- member1.yml
| +--- member2.yml
|
+---+ groups
+--- group1.yml
+--- group2.yml
The content of member1.yml and member2.yml will be rendered into site.data.members.member1 and site.data.members.member2 and likewise, group1 and group2 are loaded into their respective variables.
This kind of structure isn’t possible in Ansible, because all the data files are compressed into one vars value that we can read. To work around this on a few different projects I’ve worked on, I’ve ended up doing the following:
- set_fact:
my_members: |-
[
{%- for var in vars | dict2items -%}
{%- if var.key | regex_search(my_regex) is not none -%}
"{{ var.key | regex_replace(my_regex, '') }}":
{%- if var.value | string %}"{% endif -%}
{{ var.value }}
{%- if var.value | string %}"{% endif %},
{%- endif -%}
{%- endfor -%}
]
vars:
my_regex: '^member_'
So, what this does is to step over all the variables defined (for example, in host_vars\*, group_vars\*, from the gathered facts and from the role you’re in – following Ansible’s loading precedence), and then checks to see whether the key of that variable name (e.g. “member_i_am_a_member” or “member_1”) matches the regular expression (click here for more examples). If it does, the key (minus the regular expression matching piece [using regex_replace]) is added to a dictionary, and the value attached. If the value is actually a string, then it wraps it in quotes.
So, while this doesn’t give me my expressive data structure that Jekyll does (no site.data.members.member1.somevalue for me), I do at least get to have my_members.member1.somevalue if I put the right headers in! :)
I’ll leave extending this model for doing other sorts of building variables out (for example, something like if var.value['variable_place'] | default('') == 'my_members.member' + current_position) to the reader to work out how they could use something like this in their workflows!
I’m strongly in the “Ansible is my tool, what needs fixing” camp, when it comes to Infrastructure as Code (IaC) but, I know there are other tools out there which are equally as good. I’ve been strongly advised to take a look at Terraform from HashiCorp. I’m most familiar at the moment with Azure, so this is going to be based around resources available on Azure.
Late edit: I want to credit my colleague, Pete, for his help getting started with this. While many of the code samples have been changed from what he provided me with, if it hadn’t been for these code samples in the first place, I’d never have got started!
Late edit 2: This post was initially based on Terraform 0.11, and I was prompted by another colleague, Jon, that the available documentation still follows the 0.11 layout. 0.12 was released in May, and changes how variables are reused in the code. This post now *should* follow the 0.12 conventions, but if you spot something where it doesn’t, check out this post from the Terraform team.
As with most things, there’s a learning curve, and I struggled to find a “simple” getting started guide for Terraform. I’m sure this is a failing on my part, but I thought it wouldn’t hurt to put something out there for others to pick up and see if it helps someone else (and, if that “someone else” is you, please let me know in the comments!)
Pre-requisites
You need an Azure account for this. This part is very far outside my spectrum of influence, but I’m assuming you’ve got one. If not, look at something like Digital Ocean, AWS or VMWare :) For my “controller”, I’m using Windows Subsystem for Linux (WSL), and wrote the following notes about getting my pre-requisites.
Building the file structure
One quirk with Terraform, versus other tools like Ansible, is that when you run one of the terraform commands (like terraform init, terraform plan or terraform apply), it reads the entire content of any file suffixed “tf” in that directory, so if you don’t want a file to be loaded, you need to either move it out of the directory, comment it out, or rename it so it doesn’t end .tf. By convention, you normally have three “standard” files in a terraform directory – main.tf, variables.tf and output.tf, but logically speaking, you could have everything in a single file, or each instruction in it’s own file. Because this is a relatively simple script, I’ll use this standard layout.
The actions I’ll be performing are the “standard” steps you’d perform in Azure to build a single Infrastructure as a Service (IAAS) server service:
Create your Resource Group (RG)
Create a Virtual Network (VNET)
Create a Subnet
Create a Security Group (SG) and rules
Create a Public IP address (PubIP) with a DNS name associated to that IP.
Create a Network Interface (NIC)
Create a Virtual Machine (VM), supplying a username and password, the size of disks and VM instance, and any post-provisioning instructions (yep, I’m using Ansible for that :) ).
I’m using Visual Studio Code, but almost any IDE will have integrations for Terraform. The main thing I’m using it for is auto-completion of resource, data and output types, also the fact that control+clicking resource types opens your browser to the documentation page on terraform.io.
So, creating my main.tf, I start by telling it that I’m working with the Terraform AzureRM Provider (the bit of code that can talk Azure API).
This simple statement is enough to get Terraform to load the AzureRM, but it still doesn’t tell Terraform how to get access to the Azure account. Use az login from a WSL shell session to authenticate.
Next, we create our basic resource, vnet and subnet resources.
But wait, I hear you cry, what are those var.something bits in there? I mentioned before that in the “standard” set of files is a “variables.tf” file. In here, you specify values for later consumption. I have recorded variables for the resource group name and location, as well as the VNet name and subnet name. Let’s add those into variables.tf.
When you’ve specified a resource, you can capture any of the results from that resource to use later – either in the main.tf or in the output.tf files. By creating the resource group (called “rg” here, but you can call it anything from “demo” to “myfirstresourcegroup”), we can consume the name or location with azurerm_resource_group.rg.name and azurerm_resource_group.rg.location, and so on. In the above code, we use the VNet name in the subnet, and so on.
After the subnet is created, we can start adding the VM specific parts – a security group (with rules), a public IP (with DNS name) and a network interface. I’ll create the VM itself later. So, let’s do this.
BUT WAIT, what’s that ${trimspace(data.http.icanhazip.body)}/32 bit there?? Any resources we want to load from the terraform state, but that we’ve not directly defined ourselves needs to come from somewhere. These items are classed as “data” – that is, we want to know what their values are, but we aren’t *changing* the service to get it. You can also use this to import other resource items, perhaps a virtual network that is created by another team, or perhaps your account doesn’t have the rights to create a resource group. I’ll include a commented out data block in the overall main.tf file for review that specifies a VNet if you want to see how that works.
In this case, I want to put the public IP address I’m coming from into the NSG Rule, so I can get access to the VM, without opening it up to *everyone*. I’m not that sure that my IP address won’t change between one run and the next, so I’m using the icanhazip.com service to determine my IP address. But I’ve not defined how to get that resource yet. Let’s add it to the main.tf for now.
So, we’re now ready to create our virtual machine. It’s quite a long block, but I’ll pull certain elements apart once I’ve pasted this block in.
So, this is broken into four main pieces.
Virtual Machine Details. This part is relatively sensible. Name RG, location, NIC, Size and what happens to the disks when the machine powers on. OK.
OS basics: VM Hostname, username of the first user, and it’s password. Note, if you want to use an SSH key, this must be stored for Terraform to use without passphrase. If you mention an SSH key here, as well as a password, this can cause all sorts of connection issues, so pick one or the other.
And lastly, provisioning. I want to use Ansible for my provisioning. In this example, I have a basic playbook stored locally on my Terraform host, which I transfer to the VM, install Ansible via pip, and then execute ansible-playbook against the file I uploaded. This could just as easily be a git repo to clone or a shell script to copy in, but this is a “simple” example.
This part of code is done in three parts – create upload path, copy the files in, and then execute it. If you don’t create the upload path, it’ll upload just the first file it comes to into the path specified.
Each remote-exec and file provisioner statement must include the hostname, username and either the password, or SSH private key. In this example, I provide just the password.
So, having created all this lot, you need to execute the terraform workload. Initially you do terraform init. This downloads all the provisioners and puts them into the same tree as these .tf files are stored in. It also resets the state of the terraform discovered or created datastore.
Next, you do terraform plan -out tfout. Technically, the tfout part can be any filename, but having something like tfout marks it as clearly part of Terraform. This creates the tfout file with the current state, and whatever needs to change in the Terraform state file on it’s next run. Typically, if you don’t use a tfout file within about 20 minutes, it’s probably worth removing it.
Finally, once you’ve run your plan stage, now you need to apply it. In this case you execute terraform apply tfout. This tfout is the same filename you specified in terraform plan. If you don’t include -out tfout on your plan (or even run a plan!) and tfout in your apply, then you can skip the terraform plan stage entirely.
When I ran this, with a handful of changes to the variable files, I got this result:
Once you’re done with your environment, use terraform destroy to shut it all down… and enjoy :)
The full source is available in the associated Gist. Pull requests and constructive criticism are very welcome!
Featured image is “Seca” by “Olearys” on Flickr and is released under a CC-BY license.
What we do here is to start an action with an “async” time (to give the Schedule an opportunity to register itself) and a “poll” time of 0 (to prevent the Schedule from waiting to be finished). We then tell it that it’s “never changed” (changed_when: False) because otherwise it always shows as changed, and to register the scheduled item itself as a “sleeper”.
After all the async jobs get queued, we then check the status of all the scheduled items with the async_status module, passing it the registered job ID. This lets me spin up a lot more items in parallel, and then “just” confirm afterwards that they’ve been run properly.
It’s not perfect, and it can make for rather messy code. But, it does work, and it’s well worth giving it the once over, particularly if you’ve got some slow-to-run tasks in your playbook!
An Ansible project I’ve been working on has tripped me up this week. I’m working with some HTTP APIs and I need to check early whether I can reach the host. To do this, I used a simple Ansible Core Module which lets you call an HTTP URI.
And this breaks the uri module, because it tries to punt everything through the proxy if the “no_proxy” contains CIDR values (like 192.0.2.0/24) (there’s a bug raised for this)… So here’s my fix!
The key part to this script is that we need to override the no_proxy environment variable with the IP address that we’re trying to address (so that we’re not putting 16M addresses for 10.0.0.0/8 into no_proxy, for example). To do that, we use the exact same URI block, except for the environment line at the end.
In turn, the set_fact block steps through the no_proxy values, looking for IP Addresses to check ({% if no_proxy | ipaddr ... %} says “if the no_proxy value is an IP Address, return it, but if it isn’t, return a ‘None’ value”) and if it’s an IP address or subnet mask, it checks to see whether the IP address of the host you’re trying to reach falls inside that IP Address or Subnet Mask ({% if ansible_host | ipaddr(no_proxy) ... %} says “if the ansible_host address falls inside the no_proxy range, then return it, otherwise return a ‘None’ value”). Both of these checks say “If this previous check returns anything other than a ‘None’ value, do the next thing”, and on the last check, the “next” thing is to set the flag ‘match’ to ‘true’. When we get to the environment variable, we say “if match is not true, it’s false, so don’t put a value in there”.
So that’s that! Yes, I could merge the set_fact block into the environment variable, but I do end up using that a fair amount. And really, if it was merged, that would be even MORE complicated to pick through.
See, one of the things I (mis-)use Ansible for is to build Azure, AWS and OpenStack environments (instead of, perhaps, using Terraform, Cloud Formations or Heat Stacks). As a result, I frequently want to set complex passwords that are unique to *that environment* but that aren’t new for each build. My way of doing this is to run a delegated task to generate files in host_vars. Here’s a version of the playbook I use for that!
In the same gist as that block has been sourced from I have some example output from “20 hosts” – one of which has a pre-defined password in the inventory, and the rest of which are generated by the script.
I hope this is useful to someone!
Late Edit – 2019-05-19: Encrypting the values you generate
Following this post, a friend of mine – Jeremy mentioned on Linked In that I should have a look at Ansible Vault. Well, *ideally*, yes, however, when I looked at this code, I couldn’t work out a way of forcing the session to run Vault against a value I’ve just created, short of running something a raw or a shell module like “ansible-vault encrypt {{ file_containing_password }}“. Realistically, if you’re doing a lot with these passwords, you should probably use an external password vault, such as HashiCorp’s Vault or PasswordStore.org’s Pass. Neither of which I tend to use, because it’s just not part of my life yet – but I’ve heard good things about both!
Featured image is “Matrix” by “Paul Downey” on Flickr and is released under a CC-BY license.
Recently, I’ve been migrating my POV (proof of value) and POC (proof of concept) environment from K5 to Azure to be able to test vendor products inside Azure. I ran a few tests to build the environment using the native tools (the powershell scripts) and found that the Powershell way of delivering Azure environments seems overly complicated… particularly as I’m comfortable with how Ansible works.
To be fair, I also need to look at Terraform, but that isn’t what I’m looking at today :)
So, let’s start with the scaffolding. Any Ansible Playbook which deals with creating virtual machines needs to have some extra modules installed. Make sure you’ve got ansible 2.7 or later and the python azure library 2.0.0 or later (you can get both with pip for python).
Next, let’s look at the group_vars for this playbook.
This file has several pieces. We define the project settings (anything prefixed project_ is a project setting), including the prefix used for all resources we create (in this case “env01“), and a standard password used for all VMs we create (in this case “My$uper$ecret$Passw0rd“).
Next we define the standard images to load from the Marketplace. You can extend this with other images, these are just the “easiest” ones that I’m most familiar with (your mileage may vary). Next up is the networks to build inside the VNet, and lastly we define the actual machines we want to build. If you’ve got questions about any of the values we define here, just let me know in the comments below :)
Here we start by pulling in the variables we might want to override, and we do this by reading system environment variables (ANSIBLE_PREFIX and BREAKGLASS) and using them if they’re set. If they’re not, use the project defaults, and if that hasn’t been set, use some pre-defined values… and then tell us what they are when we’re running the tasks (those are the debug: lines).
This block is where we create our “Static Assets” – individual items that we will be consuming later. This shows a clear win here over the Powershell methods endorsed by Microsoft – here you can create a Resource Group (RG) as part of the playbook! We also create a single Storage Account for this RG and a single VNET too.
These creation rules are not suitable for production use, as this defines an “Any-Any” Security group! You should tailor your security groups for your need, not for blanket access in!
This is where things start to get a bit more interesting – We’re using the “async/async_status” pattern here (and the rest of these sections) to start creating the resources in parallel. As far as I can tell, sometimes you’ll get a case where the async doesn’t quite get set up fast enough, then the async_status can’t track the resources properly, but re-running the playbook should be enough to sort that out, without slowing things down too much.
But what are we actually doing with this block of code? A UDR is a “User Defined Route” or routing table for Azure. Effectively, you treat each network interface as being plumbed directly to the router (none of this “same subnet broadcast” stuff works here!) so you can do routing at the router for all the networks.
By default there are some existing network routes (stuff to the internet flows to the internet, RFC1918 addresses are dropped with the exception of any RFC1918 addresses you have covered in your VNETs, and each of your subnets can reach each other “directly”). Adding a UDR overrides this routing table. The UDRs we’re creating here are applied at a subnet level, but currently don’t override any of the existing routes (they’re blank). We’ll start putting routes in after we’ve added the UDRs to the subnets. Talking of which….
Again, this block is not really suitable for production use, and assumes the VNET supernet of /8 will be broken down into several /24’s. In the “real world” you might deliver a handful of /26’s in a /24 VNET… or you might even have lots of disparate /24’s in the VNET which are then allocated exactly as individual /24 subnets… this is not what this model delivers but you might wish to investigate further!
Now that we’ve created our subnets, we can start adding the routing table to the UDR. This is a basic one – add a 0.0.0.0/0 route (internet access) from the “protected” network via the firewall. You can get a lot more specific than this – most people are likely to want to add the VNET range (in this case 10.0.0.0/8) via the firewall as well, except for this subnet (because otherwise, for example, 10.0.0.100 trying to reach 10.0.0.101 will go via the firewall too).
Without going too much into the intricacies of network architecture, if you are routing your traffic between subnets to the firewall, it’s probably better to get an appliance with more interfaces, so you can route traffic across the appliance, rather than going across a single interface as this will halve your traffic bandwidth (it’s currently capped 1Gb/s – so 500Mb/s).
Having mentioned “The Internet” – let’s give our firewall a public IP address, and create the rest of the interfaces as well.
This script creates a public IP address by default for each interface unless you explicitly tell it not to (see lines 40, 53 and 62 in the group_vars file I rendered above). You could easily turn this around by changing the lines which contain this:
item.1.public is not defined or (item.1.public is defined and item.1.public == 'true')
into lines which contain this:
item.1.public is defined and item.1.public == 'true'
OK, having done all that, we’re now ready to build our virtual machines. I’ve introduced a “Priority system” here – VMs with priority 0 go first, then 1, and 2 go last. The code snippet below is just for priority 0, but you can easily see how you’d extrapolate that out (and in fact, the full code sample does just that).
There are a few blocks here to draw attention to :) I’ve re-jigged them a bit here so it’s clearer to understand, but when you see them in the main playbook they’re a bit more compact. Let’s start with looking at the Network Interfaces section!
network_interfaces: |
[
{%- for nw in item.value.ports -%}
'{{ prefix }}{{ item.value.name }}port{{ nw.subnet.name }}'
{%- if not loop.last -%}, {%- endif -%}
{%- endfor -%}
]
In this part, we loop over the ports defined for the virtual machine. This is because one device may have 1 interface, or four interfaces. YAML is parsed to make a JSON variable, so here we can create a JSON variable, that when the YAML is parsed it will just drop in. We’ve previously created all the interfaces to have names like this PREFIXhostnamePORTsubnetname (or aFW01portWAN in more conventional terms), so here we construct a JSON array, like this: ['aFW01portWAN'] but that could just as easily have been ['aFW01portWAN', 'aFW01portProtect', 'aFW01portMGMT', 'aFW01portSync']. This will then attach those interfaces to the virtual machine.
Next up, custom_data. This section is sometimes known externally as userdata or config_disk. My code has always referred to it as a “Provision Script” – hence the variable name in the code below!
custom_data: |
{%- if item.value.provision_script is defined and item.value.provision_script != '' -%}
{%- include(item.value.provision_script) -%}
{%- elif item.value.image.provision_script is defined and item.value.image.provision_script != '' -%}
{%- include(item.value.image.provision_script) -%}
{%- else -%}
{{ omit }}
{%- endif -%}
Let’s pick this one apart too. If we’ve defined a provisioning script file for the VM, include it, if we’ve defined a provisioning script file for the image (or marketplace entry), then include that instead… otherwise, pretend that there’s no “custom_data” field before you submit this to Azure.
One last quirk to Azure, is that some images require a “plan” to go with it, and others don’t.
plan: |
{%- if item.value.image.plan is not defined -%}{{ omit }}{%- else -%}
{'name': '{{ item.value.image.sku }}',
'publisher': '{{ item.value.image.publisher }}',
'product': '{{ item.value.image.offer }}'
}
{%- endif -%}
So, here we say “if we’ve not got a plan, omit the value being passed to Azure, otherwise use these fields we previously specified. Weird huh?
The very last thing we do in the script is to re-render the standard password we’ve used for all these builds, so that we can check them out!
One of my colleagues has asked me for some help with an Ansible script he’s writing to push some policy to a cloud hosted FortiGate appliance. Unfortunately, he kept getting some very weird error messages, like this one:
This same colleague came across a post on the Fortinet Developer Network site (access to the site requires vendor approval), which said “this might be an internal bug, but to debug it, use the following”
fgt # diagnose debug enable
fgt # diagnose debug cli 8
Debug messages will be on for 30 minutes.
And then run your API commands. Your error message will be surfaced there… so here’s mine! (Mapped port doesn’t match extport in a vip).
0: config firewall vip
0: edit "vip8080"
0: unset src-filter
0: unset service
0: set extintf "port1"
0: set portforward enable
0: unset srcintf-filter
0: set mappedip "192.0.2.1-192.0.2.1"
0: unset extport
0: set extport 8080-8081
0: unset mappedport
0: set mappedport 8080
-651: end
Late edit 2020-03-27: I spotted a bug in the Ansible issues tracker today, and I added a note to the end of that bug mentioning that as well as diagnose debug cli 8, if that doesn’t give you enough logs to figure out what’s up, you can also try diagnose debug application httpsd -1 but this enables LOTS AND LOTS of logs, so really think twice before turning that one on!
Oh, and if 30 minutes isn’t enough, try diagnose debug duration 480 or however many minutes you think you need. Beware that it will write event logs out to the serial console even when you’ve logged out.
In my day job, I sometimes need to use a self-signed certificate when building a box. As I love using Ansible, I wanted to make the self-signed certificate piece something that was part of my Ansible workflow.
Here follows a bit of basic code that you could use to work through how the process of creating a self-signed certificate would work. I would strongly recommend using something more production-ready (e.g. LetsEncrypt) when you’re looking to move from “development” to “production” :)