Late edit 2020-01-16: The fantastic Jerry Steel, my co-host on The Admin Admin podcast looked at what I wrote, and made a few suggestions. I’ve updated the code in the git repo, and I’ll try to annotate below when I’ve changed something. If I miss it, it’s right in the Git repo!
One of the challenges I set myself this Christmas was to learn enough about Docker to put an arbitrary PHP application, that I would previously have misused Vagrant to contain.
Just before I started down this rabbit hole, I spoke to my Aunt about some family tree research my father had left behind after he died, and how I wished I could easily share the old tree with her (I organised getting her a Chromebook a couple of years ago, after fighting with doing remote support for years on Linux and Windows laptops). In the end, I found a web application for genealogical research called HuMo-gen, that is a perfect match for both projects I wanted to look at.
HuMo-gen was first created in 1999, with a PHP version being released in 2005. It used MySQL or MariaDB as the Database engine. I was reasonably confident that I could have created a Vagrantfile to deliver this on my home server, but I wanted to try something new. I wanted to use the “standard” building blocks of Docker and Docker-Compose, and some common containers to make my way around learning Docker.
I started by looking for some resources on how to build a Docker container. Much of the guidance I’d found was to use Docker-Compose, as this allows you to stand several components up at the same time!
In contrast to how Vagrant works (which is basically a CLI wrapper to many virtual machine services), Docker isolates resources for a single process that runs on a machine. Where in Vagrant, you might run several processes on one machine (perhaps, in this instance, nginx, PHP-FPM and MariaDB), with Docker, you’re encouraged to run each “service” as their own containers, and link them together with an overlay network. It’s possible to also do the same with Vagrant, but you’ll end up with an awful lot of VM overhead to separate out each piece.
So, I first needed to select my services. My initial line-up was:
MariaDB
PHP-FPM
Apache’s httpd2 (replaced by nginx)
I was able to find official Docker images for PHP, MariaDB and httpd, but after extensive tweaking, I couldn’t make the httpd image talk the way I wanted it to with the PHP image. Bowing to what now seems to be conventional wisdom, I swapped out the httpd service for nginx.
One of the stumbling blocks for me, particularly early on, was how to build several different Dockerfiles (these are basically the instructions for the container you’re constructing). Here is the basic outline of how to do this:
In this docker-compose.yml file, I tell it that to create the yourservice service, it needs to build the docker container, using the file in ./relative/path/to/Dockerfile. This file in turn contains an instruction to import an image.
Each service stacks on top of each other in that docker-compose.yml file, like this:
Late edit 2020-01-16: This previously listed Dockerfile/service1, however, much of the documentation suggested that Docker gets quite opinionated about the file being called Dockerfile. While docker-compose can work around this, it’s better to stick to tradition :) The docker-compose.yml files below have also been adjusted accordingly. I’ve also added an image: somehost:1234/image_name line to help with tagging the images for later use. It’s not critical to what’s going on here, but I found it useful with some later projects.
To allow containers to see ports between themselves, you add the expose: command in your docker-compose.yml, and to allow that port to be visible from the “outside” (i.e. to the host and upwards), use the ports: command listing the “host” port (the one on the host OS), then a colon and then the “target” port (the one in the container), like these:
Now, let’s take a quick look into the Dockerfiles. Each “statement” in a Dockerfile adds a new “layer” to the image. For local operations, this probably isn’t a problem, but when you’re storing these images on a hosted provider, you want to keep these images as small as possible.
I built a Database Dockerfile, which is about as small as you can make it!
FROM mariadb:10.4.10
Yep, one line. How cool is that? In the docker-compose.yml file, I invoke this, like this:
OK, so this one is a bit more complex! I wanted it to build my Dockerfile, which is “mariadb/Dockerfile“. I wanted it to restart the container whenever it failed (which hopefully isn’t that often!), and I wanted to inject some specific environment variables into the file – the root and user passwords, a user account and a database name. Initially I was having some issues where it wasn’t building the database with these credentials, but I think that’s because I wasn’t “building” the new database, I was just using it. I also expose the MariaDB (MySQL) port, 3306 to the other containers in the docker-compose.yml file.
Let’s take a look at the next part! PHP-FPM. Here’s the Dockerfile:
FROM php:7.4-fpm
RUN docker-php-ext-install pdo pdo_mysql
ADD --chown=www-data:www-data public /var/www/html
There’s a bit more to this, but not loads. We build our image from a named version of PHP, and install two extensions to PHP, pdo and pdo_mysql. Lastly, we copy the content of the “public” directory into the /var/www/html path, and make sure it “belongs” to the right user (www-data).
I’d previously tried to do a lot more complicated things with this Dockerfile, but it wasn’t working, so instead I slimmed it right down to just this, and the docker-compose.yml is a lot simpler too.
See! Loads simpler! Now we need the complicated bit! :) This is the Dockerfile for nginx.
FROM nginx:1.17.7
COPY nginx/default.conf /etc/nginx/conf.d/default.conf
COPY public /var/www/html
Weirdly, even though I’ve added version numbers for MariaDB and PHP, I’ve not done the same for nginx, perhaps I should!Late edit 2020-01-16: I’ve put a version number on there now, previously where it said nginx:1.17.7 it actually said nginx:latest.
I’ve created the configuration block for nginx in a single “RUN” line.Late edit 2020-01-16: This Dockerfile now doesn’t have a giant echo 'stuff' > file block either, following Jerry’s advice, and I’m using COPY instead of ADD on his advice too. I’ll show that config file below. There’s a couple of high points for me here!
server_name _;means “use this block for all unnamed requests”.
access_log /proc/self/fd/1; and error_log /proc/self/fd/2;These are links to the “stdout” and “stderr” file descriptors (or pointers to other parts of the filesystem), and basically means that when you do docker-compose logs, you’ll see the HTTP logs for the server! These two files are guaranteed to be there, while /dev/stderr isn’t!
Because nginx is “just” caching the web content, and I know the content doesn’t need to be written to from nginx, I knew I didn’t need to do the chown action, like I did with the PHP-FPM block.
Lastly, I need to configure the docker-compose.yml file for nginx:
I’ve gone for a slightly unusual ports configuration when I deployed this to my web server… you see, I already have the HTTP port (TCP/80) configured for use on my home server – for running the rest of my web services. During development, on my home machine, the ports line instead showed “1980:80” because I was running this on Instead, I’m running this application bound to “localhost” (127.0.0.1) on a different port number (1980 selected because it could, conceivably, be a birthday of someone on this system), and then in my local web server configuration, I’m proxying connections to this service, with HTTPS encryption as well. That’s all outside the scope of this article (as I probably should be using something like Traefik, anyway) but it shows you how you could bind to a separate port too.
Anyway, that was my Docker journey over Christmas, and I look forward to using it more, going forward!
Around 2-3 years ago, Slack– the company who produces Slack the IM client, started working on a meshed overlay network product, called Nebula, to manage their environment. After two years of running their production network on the back of it, they decided to open source it. I found out about Nebula via a Medium Post that was mentioned in the HangOps Slack Group. I got interested in it, asked a few questions about Nebula in the Slack, and then in the Github Issues for it, and then recently raised a Pull Request to add more complete documentation than their single (heavily) commented config file.
So, let’s go into some details on why this is interesting to me.
1. Nebula uses a flat IPv4 network to identify all hosts in the network, no matter where in the network those hosts reside.
This means that I can address any host in my (self allocated) 198.18.0.0/16 network, and I don’t need to worry about routing tables, production/DR sites, network tromboneing and so on… it’s just… Flat.
2. Nebula has host-based firewalling built into the configuration file.
This means that once I know how my network should operate (yes, I know that’s a big ask), I can restrict my servers from being able to reach my laptops, or I can stop my web server from being able to talk to my database server, except for on the database ports. Lateral movement becomes a LOT harder.
This firewalling also looks a lot like (Network) Security Groups (for the AWS and Azure familiar), so you have a default “Deny” rule, and then layer “Allow” rules on top. You also have Inbound and Outbound rules, so if you want to stop your laptops from talking anything but DNS, SSH, HTTPS and ICMP over Nebula…. well, yep, you can do that :)
3. Nebula uses a PKI environment. Where you can have multiple Certificate Authorities.
This means that I have a central server running my certificate authority (CA), with a “backup” CA, stored offline – in case of dire disaster with my primary CA, and push both CA’s to all my nodes. If I suddenly need to replace all the certificates that my current CA signed, I can do that with minimal interruption to my nodes. Good stuff.
Nebula also created their own PKI attributes to identify the roles of each device in the Nebula environment. By signing that as part of the certificate on each node too, means your CA asserts that the role that certificate holds is valid for that node in the network.
Creating a node’s certificate is a simple command:
This certificate has the IP address of the node baked in (it’s 198.18.0.1) and the groups it’s part of (admin, laptop and support), as well as the host name (jon-laptop). I can use any of these three items in my firewall rules I mentioned above.
4. It creates a peer-to-peer, meshed VPN.
While it’s possible to create a peer-to-peer meshed VPN with commercial products, I’ve not seen any which are as light-weight to deploy as this. Each node finds all the other nodes in the network by using a collection of “Lighthouses” (similar to Torrent Seeds or Skype Super Nodes) which tells all the connecting nodes where all the other machines in the network are located. These then initiate UDP connections to the other nodes they want to talk to. If they are struggling (because of NAT or Double NAT), then there’s a NAT Punching process (called, humourously, “punchy”) which lets you signal via the Lighthouse that you’re trying to reach another node that can’t see your connection, and asks it to also connect out to you over UDP… thereby fixing the connection issue. All good.
5. Nebula has clients for Windows, Mac and Linux. Apparently there are clients for iOS in the works (meh, I’m not on Apple… but I know some are) and I’ve heard nothing about Android as yet, but as it’s on Linux, I’m sure some enterprising soul can take a look at it (the client is written in Go).
2 VPCs (AWS) and 1 VNet (Azure) 6 subnets (3 public, 3 private) 1 public AWX (the upstream project from Ansible Tower) Server 1 private Nebula Certificate Authority 2 public Web Servers (one in AWS, one in Azure) 2 private Database Servers (one in AWS, one in Azure) 2 public Bastion Servers (one in AWS, one in Azure) – that lets AWX reach into the Private sections of the network, without exposing SSH from all the hosts.
If you don’t want to provision the Azure side, just remove load_web2_module.tf from the Terraform directory in that repo… Job’s a good’n!
I have plans to look at a couple of variables, like Nebula’s closest rival, ZeroTier, and to look at using SaltStack instead of Ansible, to reduce the need for the extra Bastion servers.
I recently needed to create a Certificate Authority with an Intermediate Certificate to test some TLS inspection stuff at work. This script (based on a document I found at jamielinux.com) builds a Certificate Authority and creates an Intermediate Certificate Authority using the root.
I’ve also done something similar with Ansible before, but I’ve not got that to hand :)
Late edit, 2019-08-21: Found it! Needs some tweaks to add the sub-CA or child certs, but so-far it would work :)
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.