A while ago, I was asked to capture a LOT of data for a support case, where they wanted lots of commands to be run, like “kubectl get namespace” and then for each namespace, get all the pods with “kubectl get pods -n $namespace” and then describe each pod with “kubectl get pod -n namespace $podname”. Then do the same with all the services, deployments, ingresses and endpoints.
I wrote this function, and a supporting script to execute the actual checks, and just found it while clearing up!
#!/bin/bash
filename="$(echo $* | sed -E -e 's~[ -/\\]~_~g').log"
echo "\$ $@" | tee "${filename}"
$@ 2>&1 | tee -a "${filename}"
This script is quite simple, it does three things
Take the command you’re about to run, strip all the non-acceptable-filename characters out and replace them with underscores, and turn that into the output filename.
Write the command into the output file, replacing any prior versions of that file
Execute the command, and append the log to the output file.
So, how do you use this? Simple
log_result my-command --with --all --the options
This will produce a file called my-command_--with_--all_--the_options.log that contains this content:
$ my-command --with --all --the options
Congratulations, you ran my-command and turned on the options "--with --all --the options". Nice one!
… oh, and the command I ran to capture the data for the support case?
log_result kubectl get namespace
for TYPE in pod ingress service deployment endpoints
do
for ns in $(kubectl get namespace | grep -v NAME | awk '{print $1}' )
do
echo $ns
for item in $(kubectl get $TYPE -n $ns | grep -v NAME | awk '{print $1}')
do
log_result kubectl get $TYPE -n $ns $item -o yaml
log_result kubectl describe $TYPE -n $ns $item
done
done
done
I’ve been using Desktop Linux for probably 15 years, and Server Linux for more like 25 in one form or another. One of the things you learn to write pretty early on in Linux System Administration is Bash Scripting. Here’s a great example
#!/bin/bash
i = 0
until [ $i -eq 10 ]
do
print "Jon is the best!"
(( i += 1 ))
done
Bash scripts are pretty easy to come up with, you just write the things you’d type into the interactive shell, and it does those same things for you! Yep, it’s pretty hard not to love Bash for a shell script. Oh, and it’s portable too! You can write the same Bash script for one flavour of Linux (like Ubuntu), and it’s probably going to work on another flavour of Linux (like RedHat Enterprise Linux, or Arch, or OpenWRT).
But. There comes a point where a Bash script needs to be more than just a few commands strung together.
At work, I started writing a “simple” installer for a Kubernetes cluster – it provisions the cloud components with Terraform, and then once they’re done, it then starts talking to the Kubernetes API (all using the same CLI tools I use day-to-day) to install other components and services.
When the basic stuff works, it’s great. When it doesn’t work, it’s a bit of a nightmare, so I wrote some functions to put logs in a common directory, and another function to gracefully stop the script running when something fails, and then write those log files out to the screen, so I know what went wrong. And then I gave it to a colleague, and he ran it, and things broke in a way that didn’t make sense for either of us, so I wrote some more functions to trap that type of error, and try to recover from them.
And each time, the way I tested where it was working (or not working) was to just… run the shell script, and see what it told me. There had to be a better way.
Enter Python
Python earns my vote for a couple of reasons (and they might not be right for you!)
I’ve been aware of the language for some time, and in fact, had patched a few code libraries in the past to use Ansible features I wanted.
My preferred IDE (Integrated Desktop Environment), Visual Studio Code, has a step-by-step debugger I can use to work out what’s going on during my programming
It’s still portable! In fact, if anything, it’s probably more portable than Bash, because the version of Bash on the Mac operating system – OS X is really old, so lots of “modern” features I’d expect to be in bash and associate tooling isn’t there! Python is Python everywhere.
There’s an argument parsing tool built into the core library, so if I want to handle things like ./myscript.py --some-long-feature "option-A" --some-long-feature "option-B" -a -s -h -o -r -t --argument I can do, without having to remember how to write that in Bash (which is a bit esoteric!)
And lastly, for now at least!, is that Python allows you to raise errors that can be surfaced up to other parts of your program
Given all this, my personal preference is to write my shell scripts now in Python.
If you’ve not written python before, variables are written without any prefix (like you might have seen $ in PHP) and any flow control (like if, while, for, until) as well as any functions and classes use white-space indentation to show where that block finishes, like this:
def do_something():
pass
if some_variable == 1:
do_something()
and_something_else()
while some_variable < 2:
some_variable = some_variable * 2
Starting with Boilerplate
I start from a “standard” script I use. This has a lot of those functions I wrote previously for bash, but with cleaner code, and in a way that’s a bit more understandable. I’ll break down the pieces I use regularly.
Starting the script up
Here’s the first bit of code I always write, this goes at the top of everything
This makes sure this code is portable, but is always using Python3 and not Python2. It also starts to logging engine.
At the bottom I create a block which the “main” code will go into, and then run it.
def main():
logger.basicConfig(level=logging.DEBUG)
logger.debug('Started main')
if __name__ == "__main__":
main()
Adding argument parsing
There’s a standard library which takes command line arguments and uses them in your script, it’s called argparse and it looks like this:
#!/usr/bin/env python3
# It's convention to put all the imports at the top of your files
import argparse
import logging
logger = logging
def process_args():
parser=argparse.ArgumentParser(
description="A script to say hello world"
)
parser.add_argument(
'--verbose', # The stored variable can be found by getting args.verbose
'-v',
action="store_true",
help="Be more verbose in logging [default: off]"
)
parser.add_argument(
'who', # This is a non-optional, positional argument called args.who
help="The target of this script"
)
args = parser.parse_args()
if args.verbose:
logger.basicConfig(level=logging.DEBUG)
logger.debug('Setting verbose mode on')
else:
logger.basicConfig(level=logging.INFO)
return args
def main():
args=process_args()
print(f'Hello {args.who}')
# Using f'' means you can include variables in the string
# You could instead do printf('Hello %s', args.who)
# but I always struggle to remember in what order I wrote things!
if __name__ == "__main__":
main()
The order you put things in makes a lot of difference. You need to have the if __name__ == "__main__": line after you’ve defined everything else, but then you can put the def main(): wherever you want in that file (as long as it’s before the if __name__). But by having everything in one file, it feels more like those bash scripts I was talking about before. You can have imports (a bit like calling out to other shell scripts) and use those functions and classes in your code, but for the “simple” shell scripts, this makes most sense.
So what else do we do in Shell scripts?
Running commands
This is class in it’s own right. You can pass a class around in a variable, but it has functions and properties of it’s own. It’s a bit chunky, but it handles one of the biggest issues I have with bash scripts – capturing both the “normal” output (stdout) and the “error” output (stderr) without needing to put that into an external file you can read later to work out what you saw, as well as storing the return, exit or error code.
# Add these extra imports
import os
import subprocess
class RunCommand:
command = ''
cwd = ''
running_env = {}
stdout = []
stderr = []
exit_code = 999
def __init__(
self,
command: list = [],
cwd: str = None,
env: dict = None,
raise_on_error: bool = True
):
self.command = command
self.cwd = cwd
self.running_env = os.environ.copy()
if env is not None and len(env) > 0:
for env_item in env.keys():
self.running_env[env_item] = env[env_item]
logger.debug(f'exec: {" ".join(command)}')
try:
result = subprocess.run(
command,
cwd=cwd,
capture_output=True,
text=True,
check=True,
env=self.running_env
)
# Store the result because it worked just fine!
self.exit_code = 0
self.stdout = result.stdout.splitlines()
self.stderr = result.stderr.splitlines()
except subprocess.CalledProcessError as e:
# Or store the result from the exception(!)
self.exit_code = e.returncode
self.stdout = e.stdout.splitlines()
self.stderr = e.stderr.splitlines()
# If verbose mode is on, output the results and errors from the command execution
if len(self.stdout) > 0:
logger.debug(f'stdout: {self.list_to_newline_string(self.stdout)}')
if len(self.stderr) > 0:
logger.debug(f'stderr: {self.list_to_newline_string(self.stderr)}')
# If it failed and we want to raise an exception on failure, record the command and args
# then Raise Away!
if raise_on_error and self.exit_code > 0:
command_string = None
args = []
for element in command:
if not command_string:
command_string = element
else:
args.append(element)
raise Exception(
f'Error ({self.exit_code}) running command {command_string} with arguments {args}\nstderr: {self.stderr}\nstdout: {self.stdout}')
def __repr__(self) -> str: # Return a string representation of this class
return "\n".join(
[
f"Command: {self.command}",
f"Directory: {self.cwd if not None else '{current directory}'}",
f"Env: {self.running_env}",
f"Exit Code: {self.exit_code}",
f"nstdout: {self.stdout}",
f"stderr: {self.stderr}"
]
)
def list_to_newline_string(self, list_of_messages: list):
return "\n".join(list_of_messages)
So, how do we use this?
Well… you can do this: prog = RunCommand(['ls', '/tmp', '-l']) with which we’ll get back the prog object. If you literally then do print(prog) it will print the result of the __repr__() function:
Command: ['ls', '/tmp', '-l']
Directory: current directory
Env: <... a collection of things from your environment ...>
Exit Code: 0
stdout: total 1
drwx------ 1 root root 0 Jan 1 01:01 somedir
stderr:
So, I wrote all this up into a git repo, that you’re more than welcome to take your own inspiration from! It’s licenced under an exceptional permissive license, so you can take it and use it without credit, but if you want to credit me in some way, feel free to point to this blog post, or the git repo, which would be lovely of you.
I have a pair of Proxmox servers, each with a single ZFS drive attached, with GlusterFS over the top to provide storage to the VMs.
Last week I had a power outage which took both nodes offline. When the power came back on, one node’s system drive had failed entirely and during recovery the second machine refused to restart some of the VMs.
Rather than try to fix things properly, I decided to “Nuke-and-Pave”, a decision I’m now regretting a little!
I re-installed one of the nodes OK, set up the new ZFS drive, set up Gluster and then started transferring the content from the old machine to the new one.
During the file transfer, I saw a couple of messages about failed blocks, and finally got a message from the cluster about how the pool was considered degraded, but as this was largely performed while I was asleep, I didn’t notice until I woke up… when the new node was offline.
I connected a Keyboard and Monitor to the box and saw a kernel panic. I rebooted the node, and during the boot sequence, just after the Systemd service that scanned the ZFS pool, it panicked again.
Unplugging the data drive from the machine and rebooting it, the node came up just fine.
I plugged the drive into my laptop and ran zpool import -d /dev/sdb1 -R /recovery/poolname poolname and my laptop crashed (although, I was running this in GUI mode, so I don’t know if it was a kernel panic or “just” a crash.)
Finally, I ran zpool import -d /dev/sdb1 -o read-only=on -R /recovery/poolname poolname and the drive came up in /recovery/poolname, so I could transfer files off to another drive until I figure out what’s going on!
Once I was done, I ran zfs unmount poolname and was able to detach the disk from the device.
How many times have you seen an instruction in a setup script which says “Now add source <(somescript completion bash) to your ~/.bashrc file” or “Add export SOMEVAR=abc123 to your .bashrc file”?
This is great when it’s one or two lines, but for a big chunk of them? Whew!
Instead, I created this block in mine:
if [ -d ~/.bash_extensions.d ]; then
for extension in ~/.bash_extensions.d/[a-zA-Z0-9]*
do
. "$extension"
done
fi
This dynamically loads all the files in ~/.bash_extensions.d/ which start with a letter or a digit, so it means I can manage when things get loaded in, or removed from my bash shell.
For example, I recently installed the pre-release of Atuin, so my ~/.bash_extensions.d/atuin file looks like this:
I’ve had a couple of issues with brown-outs recently which have interrupted my Proxmox server, and stopped my connected disks from coming back up cleanly (yes, I’m working on that separately!) but it’s left me in a state where several of my containers and virtual machines on the cluster are down.
It’s possible to point-and-click your way around this, but far easier to script it!
A failed state may look like this:
root@proxmox1:~# ha-manager status
quorum OK
master proxmox2 (active, Fri Mar 22 10:40:49 2024)
lrm proxmox1 (active, Fri Mar 22 10:40:52 2024)
lrm proxmox2 (active, Fri Mar 22 10:40:54 2024)
service ct:101 (proxmox1, error)
service ct:102 (proxmox2, error)
service ct:103 (proxmox2, error)
service ct:104 (proxmox1, error)
service ct:105 (proxmox1, error)
service ct:106 (proxmox2, error)
service ct:107 (proxmox2, error)
service ct:108 (proxmox1, error)
service ct:109 (proxmox2, error)
service vm:100 (proxmox2, error)
Once you’ve fixed your issue, you can do this on each node:
for worker in $(ha-manager status | grep "($(hostnamectl hostname), error)" | cut -d\ -f2)
do
echo "Disabling $worker"
ha-manager set $worker --state disabled
until ha-manager status | grep "$worker" | grep -q disabled ; do sleep 1 ; done
echo "Restarting $worker"
ha-manager set $worker --state started
until ha-manager status | grep "$worker" | grep -q started ; do sleep 1 ; done
done
Note that this hasn’t been tested, but a scan over it with those nodes working suggests it should. I guess I’ll be updating this the next time I get a brown-out!
I wrote this post in January 2023, and it’s been languishing in my Drafts folder since then. I’ve had a look through it, and I can’t see any glaring reasons why I didn’t publish it so… it’s published… Enjoy 😁
If you’ve ever built a private subnet in AWS, you know it can be a bit tricky to get updates from the Internet – you end up having a NAT gateway or a self-managed proxy, and you can never be 100% certain that the egress traffic isn’t going somewhere you don’t want it to.
In this case, I wanted to ensure that outbound HTTPS traffic was being blocked if the SNI didn’t explicitly show the DNS name I wanted to permit through, and also, I only wanted specific DNS names to resolve. To do this, I used AWS Network Firewall and Route 53 DNS Firewall.
I’ve written this blog post, and followed along with this, I’ve created a set of terraform files to represent the steps I’ve taken.
The Setup
Let’s start this story from a simple VPC with three private subnets for my compute resources, and three private subnets for the VPC Endpoints for Systems Manager (SSM).
Here’s our network diagram, with the three subnets containing the VPC Endpoints at the top, and the three instances at the bottom.
At this point, none of those instances can reach anything outside the network, with the exception of the SSM environment. So, we can’t install any packages, we can’t get data from outside the network or anything similar.
Getting Protected Internet Access
In order to get internet access, we need to add 4 things;
An internet gateway
A NAT gateway in each AZ
Which needs three new subnets
And three Elastic IP addresses
Route tables in all the subnets
To clarify, a NAT gateway acts like a DSL router. It hides the source IP address of outbound traffic behind a single, public IP address (using an Elastic IP from AWS), and routes any return traffic back to wherever that traffic came from. To reduce inter-AZ data transfer rates, I’m putting one in each AZ, but if there’s not a lot of outbound traffic or the outbound traffic isn’t critical enough to require resiliency, this could all be centralised to a single NAT gateway. To put a NAT gateway in each AZ, you need a subnet in each AZ, and to get out to the internet (by whatever means you have), you need an internet gateway and route tables for how to reach the NAT and internet gateways.
We also should probably add, at this point, four additional things.
The Network Firewall
Subnets for the Firewall interfaces
Stateless Policy
Stateful Policy
The Network Firewall acts like a single appliance, and uses a Gateway Load Balancer to present an interface into each of the availability zones. It has a stateless policy (which is very fast, but needs to address both inbound and outbound traffic flows) to do IP and Port based filtering (referred to as “Layer 3” filtering) and then specific traffic can be passed into a stateful policy (which is slower) to do packet and flow inspection.
In this case, I only want outbound HTTPS traffic to be passed, so my stateless rule group is quite simple;
VPC range on any port → Internet on TCP/443; pass to Stateful rule groups
Internet on TCP/443 → VPC range on any port; pass to Stateful rule groups
I have two stateful rule groups, one is defined to just allow access out to example.com and any relevant subdomains, using the “Domain List” stateful policy item. The other allows access to example.org and any relevant subdomains, using a Suricata stateful policy item, to show the more flexible alternative route. (Suricata has lots more filters than just the SNI value, you can check for specific SSH versions, Kerberos CNAMEs, SNMP versions, etc. You can also add per-rule logging this way, which you can’t with the Domain List route).
These are added to the firewall policy, which also defines that if a rule doesn’t match a stateless rule group, or an established flow doesn’t match a stateful rule group, then it should be dropped.
New network diagram with more subnets and objects, but essentially, as described in the paragraphs above. Traffic flows from the instances either down towards the internet, or up towards the VPCe.
So far, so good… but why let our users even try to resolve the DNS name of a host they’re not permitted to reach. Let’s turn on DNS Firewalling too.
Turning on Route 53 DNS Firewall
You’ll notice that in the AWS Network Firewall, I didn’t let DNS out of the network. This is because, by default, AWS enables Route 53 as it’s local resolver. This lives on the “.2” address of the VPC, so in my example environment, this would be 198.18.0.2. Because it’s a local resolver, it won’t cross the Firewall exiting to the internet. You can also make Route 53 use your own DNS servers for specific DNS resolution (for example, if you’re running an Active Directory service inside your network).
Any Network Security Response team members you have working with you would appreciate it if you’d turn on DNS Logging at this point, so I’ll do it too!
In March 2021, AWS announced “Route 53 DNS Firewall”, which allow this DNS resolver to rewrite responses, or even to completely deny the existence of a DNS record. With this in mind, I’m going to add some custom DNS rules.
The first thing I want to do is to only permit traffic to my specific list of DNS names – example.org, example.com and their subdomains. DNS quite likes to terminate DNS names with a dot, signifying it shouldn’t try to resolve any higher up the chain, so I’m going to make a “permitted domains” DNS list;
And then build a DNS Firewall Policy which allows access to the “permitted domains”, “VPCe” lists, but blocks resolution of any “default deny” entries.
So there we have it. While the network is not “secure” (there’s still a few gaps here) it’s certainly MUCH more secure than it was, and it certainly would take a lot more work for anyone with malicious intent to get your content out.
Feel free to have a poke around, and leave comments below if this has helped or is of interest!
I keep trundling back to a collection of WordPress plugins that I really love. And sometimes I want to contribute patches to the plugin.
I don’t want to develop against this server (that would be crazy… huh… right… no one does that… *cough*) but instead, I want a nice, fresh and new WordPress instance to just check that it works the way I was expecting.
So, I created a little Vagrant environment, just for testing WordPress plugins. I clone the repository for the plugin, and create a “TestingEnvironment” directory in there.
I then create the following Vagrantfile.
Vagrant.configure("2") do |config|
config.vm.box = "ubuntu/jammy64"
# This will create an IP address in the range 192.168.64.0/24 (usually)
config.vm.network "private_network", type: "dhcp"
# This loads the git repo for the plugin into /tmp/git_repo
config.vm.synced_folder "../", "/tmp/git_repo"
# If you've got vagrant-cachier, this will speed up apt update/install operations
if Vagrant.has_plugin?("vagrant-cachier")
config.cache.scope = :box
end
config.vm.provision "shell", inline: <<-SHELL
# Install Dependencies
apt-get update
apt-get install -y apache2 libapache2-mod-fcgid php-fpm mysql-server php-mysql git
# Set up Apache
a2enmod proxy_fcgi setenvif
a2enconf "$(basename "$(ls /etc/apache2/conf-available/php*)" .conf)"
systemctl restart apache2
rm -f /var/www/html/index.html
# Set up WordPress
bash /vagrant/root_install_wordpress.sh
SHELL
end
Next, let’s create that root_install_wordpress.sh file.
#! /bin/bash
# Allow us to run commands as www-data
chsh -s /bin/bash www-data
# Let www-data access files in the web-root.
chown -R www-data:www-data /var/www
# Install wp-cli system-wide
curl -s -S -O https://raw.githubusercontent.com/wp-cli/builds/gh-pages/phar/wp-cli.phar
mv wp-cli.phar /usr/local/bin/wp
chmod +x /usr/local/bin/wp
# Slightly based on
# https://www.a2hosting.co.uk/kb/developer-corner/mysql/managing-mysql-databases-and-users-from-the-command-line
echo "CREATE DATABASE wp;" | mysql -u root
echo "CREATE USER 'wp'@'localhost' IDENTIFIED BY 'wp';" | mysql -u root
echo "GRANT ALL PRIVILEGES ON wp.* TO 'wp'@'localhost';" | mysql -u root
echo "FLUSH PRIVILEGES;" | mysql -u root
# Execute the generic install script
su - www-data -c bash -c /vagrant/user_install_wordpress.sh
# Install any plugins with this script
su - www-data -c bash -c /vagrant/customise_wordpress.sh
# Log the path to access
echo "URL: http://$(sh /vagrant/get_ip.sh) User: admin Password: password"
Now we have our dependencies installed and our database created, let’s get WordPress installed with user_install_wordpress.sh.
#! /bin/bash
# Largely based on https://d9.hosting/blog/wp-cli-install-wordpress-from-the-command-line/
cd /var/www/html
# Install the latest WP into this directory
wp core download --locale=en_GB
# Configure the database with the credentials set up in root_install_wordpress.sh
wp config create --dbname=wp --dbuser=wp --dbpass=wp --locale=en_GB
# Skip the first-run-wizard
wp core install --url="http://$(sh /vagrant/get_ip.sh)" --title=Test --admin_user=admin --admin_password=password --admin_email=example@example.com --skip-email
# Setup basic permalinks
wp option update permalink_structure ""
# Flush the rewrite schema based on the permalink structure
wp rewrite structure ""
Excellent. This gives us a working WordPress environment. Now we need to add our customisation – the plugin we’re deploying. In this case, I’ve been tweaking the “presenter” plugin so here’s the customise_wordpress.sh code:
Actually, that /tmp/git_repo path is a call-back to this line in the Vagrantfile: config.vm.synced_folder "../", "/tmp/git_repo".
And there you have it; a vanilla WordPress install, with the plugin installed and ready to test. It only took 4 years to write up a blog post for it!
As an alternative, you could instead put the plugin you’re working with in a subdirectory of the Vagrantfile and supporting files, then you’d just need to change that git clone /tmp/git_repo line to git clone /vagrant/MyPlugin – but then you can’t offer this to the plugin repo as a PR, can you? 😀
Tale as old as time, the compute instance type you want to use in AWS is highly contested (or worse yet, not as available in every availability zone in your region)! You plead with your TAM or AM “Please let us have more of that instance type” only to be told “well, we can put in a request, but… haven’t you thought about using a range of instance types”?
And yes, I’ve been on both sides of that conversation, sadly.
The commented terraform
# This is your legacy instance_type variable. Ideally we'd have
# a warning we could raise at this point, telling you not to use
# this variable, but... it's not ready yet.
variable "instance_type" {
description = "The legacy single-instance size, e.g. t3.nano. Please migrate to instance_types ASAP. If you specify instance_types, this value will be ignored."
type = string
default = null
}
# This is your new instance_types value. If you don't already have
# some sort of legacy use of the instance_type variable, then don't
# bother with that variable or the locals block below!
variable "instance_types" {
description = "A list of instance sizes, e.g. [t2.nano, t3.nano] and so on."
type = list(string)
default = null
}
# Use only this locals block (and the value further down) if you
# have some legacy autoscaling groups which might use individual
# instance_type sizes.
locals {
# This means if var.instance_types is not defined, then use it,
# otherwise create a new list with the single instance_type
# value in it!
instance_types = var.instance_types != null ? var.instance_types : [ var.instance_type ]
}
resource "aws_launch_template" "this" {
# The prefix for the launch template name
# default "my_autoscaling_group"
name_prefix = var.name
# The AMI to use. Calculated outside this process.
image_id = data.aws_ami.this.id
# This block ensures that any new instances are created
# before deleting old ones.
lifecycle {
create_before_destroy = true
}
# This block defines the disk size of the root disk in GB
block_device_mappings {
device_name = data.aws_ami.centos.root_device_name
ebs {
volume_size = var.disksize # default "10"
volume_type = var.disktype # default "gp2"
}
}
# Security Groups to assign to the instance. Alternatively
# create a network_interfaces{} block with your
# security_groups = [ var.security_group ] in it.
vpc_security_group_ids = [ var.security_group ]
# Any on-boot customizations to make.
user_data = var.userdata
}
resource "aws_autoscaling_group" "this" {
# The name of the Autoscaling Group in the Web UI
# default "my_autoscaling_group"
name = var.name
# The list of subnets into which the ASG should be deployed.
vpc_zone_identifier = var.private_subnets
# The smallest and largest number of instances the ASG should scale between
min_size = var.min_rep
max_size = var.max_rep
mixed_instances_policy {
launch_template {
# Use this template to launch all the instances
launch_template_specification {
launch_template_id = aws_launch_template.this.id
version = "$Latest"
}
# This loop can either use the calculated value "local.instance_types"
# or, if you have no legacy use of this module, remove the locals{}
# and the variable "instance_type" {} block above, and replace the
# for_each and instance_type values (defined as "local.instance_types")
# with "var.instance_types".
#
# Loop through the whole list of instance types and create a
# set of "override" values (the values are defined in the content{}
# block).
dynamic "override" {
for_each = local.instance_types
content {
instance_type = local.instance_types[override.key]
}
}
}
instances_distribution {
# If we "enable spot", then make it 100% spot.
on_demand_percentage_above_base_capacity = var.enable_spot ? 0 : 100
spot_allocation_strategy = var.spot_allocation_strategy
spot_max_price = "" # Empty string is "on-demand price"
}
}
}
So what is all this then?
This is two Terraform resources; an aws_launch_template and an aws_autoscaling_group. These two resources define what should be launched by the autoscaling group, and then the settings for the autoscaling group.
You will need to work out what instance types you want to use (e.g. “must have 16 cores and 32 GB RAM, have an x86_64 architecture and allow up to 15 Gigabit/second throughput”)
When might you use this pattern?
If you have been seeing messages like “There is no Spot capacity available that matches your request.” or “We currently do not have sufficient <size> capacity in the Availability Zone you requested.” then you need to consider diversifying the fleet that you’re requesting for your autoscaling group. To do that, you need to specify more instance types. To achieve this, I’d use the above code to replace (something like) one of the code samples below.
Then this new method is a much better idea :) Even more so if you had two launch templates to support spot and non-spot instance types!
Hat-tip to former colleague Paul Moran who opened my eyes to defining your fleet of variable instance types, as well as to my former customer (deliberately unnamed) and my current employer who both stumbled into the same documentation issue. Without Paul’s advice with my prior customer’s issue I’d never have known what I was looking for this time around!
I have a small server running Docker for services at home. There are several services which will want to use HTTP, but I can’t have them all sharing the same port without a reverse proxy to manage how to route the traffic to the containers!
This is my guide to how I got Traefik set up to serve HTTP and HTTPS traffic.
The existing setup for one service
Currently, I have phpIPAM which has the following docker-compose.yml file:
The moment I want to bind another service to TCP/80, I get an error because we’ve already used TCP/80 for phpIPAM. Enter Traefik. Let’s stop the docker container with docker compose down and build our Traefik setup.
Traefik Setup
I always store my docker compose files in /opt/docker/<servicename>, so let’s create a directory for traefik; sudo mkdir -p /opt/docker/traefik
The (“dynamic”) configuration file
Next we need to create a configuration file called traefik.yaml
# Ensure all logs are sent to stdout for `docker compose logs`
accessLog: {}
log: {}
# Enable docker provider but don't switch it on by default
providers:
docker:
exposedByDefault: false
# Select this as the docker network to connect from traefik to containers
# This is defined in the docker-compose.yaml file
network: web
# Enable the API and Dashboard on TCP/8080
api:
dashboard: true
insecure: true
debug: true
# Listen on both HTTP and HTTPS
entryPoints:
http:
address: ":80"
http: {}
https:
address: ":443"
http:
tls: {}
With the configuration file like this, we’ll serve HTTPS traffic with a self-signed TLS certificate on TCP/443 and plain HTTP on TCP/80. We have a dashboard on TCP/8080 served over HTTP, so make sure you don’t expose *that* to the public internet!
The Docker-Compose File
Next we need the docker-compose file for Traefik, so let’s create docker-compose.yaml
There are a few parts here which aren’t spelled out on the Traefik quickstart! Firstly, if you don’t define a network, it’ll create one using the docker-compose file path, so probably traefik_traefik or traefik_default, which is not what we want! So, we’ll create one called “web” (but you can call it whatever you want. On other deployments, I’ve used the name “traefik” but I found it tedious to remember how to spell that each time). This network needs to be “attachable” so that other containers can use it later.
You then attach that network to the traefik service, and expose the ports we need (80, 443 and 8080).
And then start the container with docker compose up -d
alpine-docker:/opt/docker/traefik# docker compose up -d
[+] Running 2/2
✔ Network web Created 0.2s
✔ Container traefik-traefik-1 Started 1.7s
alpine-docker:/opt/docker/traefik#
Adding Traefik to phpIPAM
Going back to phpIPAM, So that Traefik can reach the containers, and so that the container can reach it’s database, we need two network statements now; the first is the “external” network for the traefik connection which we called “web“. The second is the inter-container network so that the “web” service can reach the “db” service, and so that the “cron” service can reach the “db” service. So we need to add that to the start of /opt/docker/phpipam/docker-compose.yaml, like this;
We then need to add both networks that to the “web” container, like this:
services:
web:
image: phpipam/phpipam-www:latest
networks:
- ipam
- web
# ...... and the rest of the config
Remove the “ports” block and replace it with an expose block like this:
services:
web:
# ...... The rest of the config for this service
## Don't bind to port 80 - we use traefik now
# ports:
# - "80:80"
## Do expose port 80 for Traefik to use
expose:
- 80
# ...... and the rest of the config
And just the inter-container network to the “cron” and “db” containers, like this:
cron:
image: phpipam/phpipam-cron:latest
networks:
- ipam
# ...... and the rest of the config
db:
image: mariadb:latest
networks:
- ipam
# ...... and the rest of the config
There’s one other set of changes we need to make in the “web” service, which are to enable Traefik to know that this is a container to look at, and to work out what traffic to send to it, and that’s to add labels, like this:
services:
web:
# ...... The rest of the config for this service
labels:
- traefik.enable=true
- traefik.http.routers.phpipam.rule=Host(`phpipam.homenet`)
# ...... and the rest of the config
Right, now we run docker compose up -d
alpine-docker:/opt/docker/phpipam# docker compose up -d
[+] Running 4/4
✔ Network ipam Created 0.4s
✔ Container phpipam-db-1 Started 1.4s
✔ Container phpipam-cron-1 Started 2.1s
✔ Container phpipam-web-1 Started 2.6s
alpine-docker:/opt/docker/phpipam#
If you notice, this doesn’t show to the web network being created (because it was already created by Traefik) but does bring up the container.
Checking to make sure it’s working
If we head to the Traefik dashboard (http://your-docker-server:8080) you’ll see the phpipam service identified there… yey!
Better TLS with Lets Encrypt
So, at home I actually have a DNS suffix that is a real DNS name. For the sake of the rest of this documentation, assume it’s homenet.sprig.gs (but it isn’t 😁).
This DNS space is hosted by Digital Ocean, so I can use a DNS Challenge with Lets Encrypt to provide hostnames which are not publically accessible. If you’re hosting with someone else, then that’s probably also available – check the Traefik documentation for your specific variables. The table on that page (as of 2023-12-30) shows the environment variables you need to pass to Traefik to get LetsEncrypt working.
As you can see here, I just need to add the value DO_AUTH_TOKEN, which is an API key. I went to the Digital Ocean console, and navigated to the API panel, and added a new “Personal Access Token”, like this:
Notice that the API key needed to provide both “Read” and “Write” capabilities, and has been given a name so I can clearly see it’s purpose.
Changing the traefik docker-compose.yaml file
In /opt/docker/traefik/docker-compose.yaml we need to add that new environment variable; DO_AUTH_TOKEN, like this:
services:
traefik:
# ...... The rest of the config for this service
environment:
DO_AUTH_TOKEN: dop_v1_decafbad1234567890abcdef....1234567890
# ...... and the rest of the config
Changing the traefik.yaml file
In /opt/docker/traefik/traefik.yaml we need to tell it to use Let’s Encrypt. Add this block to the end of the file:
Obviously change the email address to a valid one for you! I hit a few issues with the value specified in the documentation for delayBeforeCheck, as their value of “0” wasn’t long enough for the DNS value to be propogated around the network – 1 minute is enough though!
I also had to add the resolvers, as my local network has a caching DNS server, so I’d never have seen the updates! You may be able to remove both those values from your files.
Now you’ve made all the changes to the Traefik service, restart it with docker compose down ; docker compose up -d
Changing the services to use Lets Encrypt
We need to add one final label to the /opt/docker/phpipam/docker-compose.yaml file, which is this one:
services:
web:
# ...... The rest of the config for this service
labels:
- traefik.http.routers.phpipam.tls.certresolver=letsencrypt
# ...... and the rest of the config
Also, update your .rule=Host(`hostname`) to use the actual DNS name you want to be able to use, then restart the docker container.
phpIPAM doesn’t like trusting proxies, unless explicitly told to, so I also had add an environment variable IPAM_TRUST_X_FORWARDED=true to the /opt/docker/phpipam/docker-compose.yaml file too, because phpIPAM tried to write the HTTP scheme for any links which came up, based on what protocol it thought it was running – not what the proxy was telling it it was being accessed as!
Debugging any issues
If you have it all setup as per the above, and it isn’t working, go into /opt/docker/traefik/traefik.yaml and change the stanza which says log: {} to:
log:
level: DEBUG
Be aware though, this adds a LOT to your logs! (But you won’t see why your ACME requests have failed without it). Change it back to log: {} once you have it working again.
Adding your next service
I now want to add that second service to my home network – WordPress. Here’s /opt/docker/wordpress/docker-compose.yaml for that service;
alpine-docker:/opt/docker/wordpress# docker compose up -d
[+] Running 3/3
✔ Network wordpress Created 0.2s
✔ Container wordpress-mariadb-1 Started 3.0s
✔ Container wordpress-php-1 Started 3.8s
alpine-docker:/opt/docker/wordpress#
Tada!
One final comment – I never did work out how to make connections forceably upgrade from HTTP to HTTPS, so instead, I shut down port 80 in Traefik, and instead run this container.
I recently got talking to a colleague about how people prefer to work and how they prefer to be contacted. It’s obvious in an office – if Bob isn’t there, then he’s not around, but when some of the team is remote, some are hybrid working, then it’s a lot harder.
There are three things I’ve found are really useful to know when trying to reach someone, and I’ve written this up in a simple page stored on our internal wiki;
What’s your baseline – where do you live and when are you usually in the office.
What are your usual working hours – how accurate is your calendar for non-meetings? do you have fixed meetings that happen every week, or a school run that you typically do? Do you need to be away from your desk at certain times for religious reasons?
What’s the best way to contact you – if you’ve got a choice of tools (like Slack Hangouts or Google Meet) which would you rather use, and why. Is it best to drop in a 15 minute appointment, or just call you?
Once you’ve got these three items, in something everyone can access, add it to your directory profile, bio on slack, your email signature (for internal emails) and so on.
From here to the end of the post is a mildly sanitised version of my internally posted profile. I hope it’s useful to you!
Baseline
I am based in the UK, using the Europe/London time zone. I am remote based with very infrequent visits to the London office.
Typical Working Hours Patterns
I work from Monday to Friday, normally starting at X and finishing at X. During school term times, I will be out of the office between 3:00PM and 3:45PM to do school drop-off and pick ups. On Monday to Thursday, I am in a stand-up from X until Y. I will typically take my lunch break between X and Y. On Friday I have a weekly one-to-one which starts at X and finishes at Y. I will then take lunch until 1:00PM.
During school holidays, the start and end times will need to be a bit more flexible, and drop-off and pick-up slots will vary based on day-to-day activities.
I will keep my calendar up-to-date accordingly.
Contact Preference
I prefer being contacted by Slack mention or DM, however, I will often follow-up with a request for a DM chat or call, especially if I have been typing a lot during the day, or am trying to resolve an issue which I expect will require a lot of interaction.
I am happy to use Google Meetings, Slack Huddles, Microsoft Teams or Amazon Chime, all of which I have tested and work on my computer. I personally prefer to use Microsoft Teams because the presenter can allow participants to interact with the presenter’s screen or Slack Huddles because that allows participants to draw on the presenters screen, and because I can see more of your screen by default.
