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## Website change detection with headless Firefox and ImageMagick

This wasn't the script I had in mind in the previous blog post (so you can look forward to another blog post about it), but have you ever wanted to know when a web page changes? If it does change, it's almost impossible to tell where on the page it's changed. Recently, I was thinking about the problem, and realised a few things:

• Firefox can be operated headlessly (with --headless) to take screenshots
• ImageMagick must be advanced enough to diff images

With this in mind, I set about implementing a script. Before we continue, here's an example diff image:

It's rather tall because of the webpage I chose, but the bits that have changed appear in red. The script I've written also generates an animated PNG showing the difference too:

Again, it's very tall because of the page I tested with, but I think it's pretty cool!

If you'd like to check the script out for yourself, you find it in the following git repository: sbrl/url-diff

For the curious, the script in question is written in Bash. It uses apcalc (available in Debian / Ubuntu based Linux distributions with sudo apt install apcalc) to crunch the numbers, and headless Firefox + Imagemagick as described above to take the screenshots and do the image processing. It should in theory work on Windows, but you'll need to jump through a number of hoops:

• Install call url-diff.sh from [git bash]()
• Install [ImageMagick]() and make sure the binaries are in your PATH
• Install Firefox and make sure firefox is in your PATH
• Explicitly set the URLDIFF_STORAGE_DIR environment variable when calling the script (do this by prefixing the command at the bottom of this post with URLDIFF_STORAGE_DIR=path/to/directory)

With my fancy new embed system, I can show you the code behind it:

(Can't see the above? Check it out in the git repository.)

I'm working on line numbers (sadly the author of highlight.js doesn't like them, so an alternative solution is required).

Anyway, the basic layout of the script is as follows:

1. First, the settings are read in and the default values set
2. Then, I define some utility functions.
• The calculate_percentage_colour function is integral to the image change detection algorithm. It counts percentage of an image that is a given colour.
3. Next, the help text is displayed if necessary
4. The case statement that follows allows multiple subcommands to be implemented. Currently I only have a check subcommand, but you never know!
5. Inside this case statement, the screenshots are taken and compared.
• A new screenshot is taken with headless Firefox
• If we don't have a screenshot stored away already, we stash the new screenshot and exit
• If we do have a pre-existing screenshot, we continue with the comparison, starting by generating a diff image where pixels that have changed are given 1 colour, and pixels that haven't changed another
• It's at this point that calculate_percentage_colour is called to calculate how much of the image has changed - the diff image is passed in and the changed pixels are counted
• If more than 2% (by default) has changed, then we continue on to generate the output images
• The first output image consists of the new screenshot with the diff image overlaid - this is generated with some ImageMagick wizardry: -compose over -composite
• The second is an animated PNG comprised of the old and new screenshots. This is generated with ffmpeg - which supports animated PNGs
• Finally, the old screenshot that we have stored away is replaced with the new one

It sounds more complicated than it is - hopefully my above explanation makes sense (post a comment below if you're confused about something!).

You can call the script like so:

git clone https://git.starbeamrainbowlabs.com/sbrl/url-diff.git
cd url-diff;
./url-diff.sh check URL_HERE path/to/output_diff.png path/to/output.apng

....replacing URL_HERE with the URL to check, and the paths with the places you'd like to write the output images to.

## EmbedBox: Lightweight syntax-highlighted embeds

I was planning posting about something else yesterday, but I wanted to show some GitLab code in a syntax-highlighted embed. When I wasn't able to figure out how to do that, I ended up writing EmbedBox.

The whole thing is best explained with an example. Have an embed:

(Can't see the above? Check out the original file here)

Pretty cool, right? The above is the default settings file for EmbedBox. Given any URL (e.g. https://raw.githubusercontent.com/sbrl/EmbedBox/master/src/settings.default.toml), it will generate a syntax-highlighted embed for it.

It does so using highlight.php to do the syntax-highlighting server-side, Stash PHP for the cache, and without any Javascript in the embed itself.

It comes with a web interface that generates the embed code given the input URL and a few other settings and shows a preview of what it'll look like.

EmbedBox is open-source too (under the Mozilla Public Licence 2.0), so you're welcome to setup your own instance!

To do so, check out the code here: https://github.com/sbrl/EmbedBox/

The installation instructions should be pretty straightforward in theory, but if you get stuck please open an issue.

Now that I've implemented EmbedBox, you can expect to see it appear in future blog posts. I'm planning to write about my organise-photos script in the near future, so expect a blog post about it soon.

Found this interesting? Got a suggestion? Want to say hi? Comment below!

## Cluster, Part 8: The Shoulders of Giants | NFS, Nomad, Docker Registry

Welcome back! It's been a bit of a while, but now I'm back with the next part of my cluster series. As a refresher, here's a list of all the parts in the series so far:

In this one, we're going to look at running our first job on our Nomad cluster! If you haven't read the previous posts in this series, you'll probably want to go back and read them now, as we're going to be building on the infrastructure we've setup and the groundwork we've laid in the previous posts in this series.

Before we get to that though, we need to sort out shared storage - as we don't know which node in the cluster tasks will be running on. In my case, I'll be setting up NFS. This is hardly the only solution to the issue though - other options include:

If you're going to choose NFS like me though, you should be warned that it's neither encrypted not authenticated. You should ensure that NFS is only run on a trusted network. If you don't have a trusted network, use the WireGuard Mesh VPN trick in part 4 of this series.

### NFS: Server

Setting up a server is relatively easy. Simply install the relevant package:

sudo apt install nfs-kernel-server

....edit /etc/exports to look something like this:

/mnt/somedrive/subdirectory 10.1.2.0/24(rw,async,no_subtree_check)

/mnt/somedrive/subdirectory is the directory you'd like clients to be able to access, and 10.1.2.0/24 is the IP range that should be allowed to talk to your NFS server.

Next, open up the relevant ports in your firewall (I use UFW):

sudo ufw allow nfs

....and you're done! Pretty easy, right? Don't worry, it'll get harder later on :P

### NFS: Client

The client, in theory, is relatively straightforward too. This must be done on all nodes in the cluster - except the node that's acting as the NFS server (although having the NFS server as a regular node in the cluster is probably a bad idea). First, install the relevant package:

sudo apt install nfs-common

Then, update /etc/fstab and add the following line:

10.1.2.10:/mnt/somedrive/subdirectory   /mnt/shared nfs auto,nofail,noatime,intr,tcp,bg,_netdev 0   0

Again, 10.1.2.10 is the IP of the NFS server, and /mnt/somedrive/subdirectory must match the directory exported by the server. Finally, /mnt/shared is the location that we're going to mount the directory from the NFS server to. Speaking of, we should create that directory:

sudo mkdir /mnt/shared

I have yet to properly tune the options there on both the client and the server. If I find that I have to change anything here, I'll both come back and edit this and mention it in a future post that I did.

From here, you should be able to mount the NFS share like so:

sudo mount /mnt/shared

You should see the files from the NFS server located in /mnt/shared. You should check to make sure that this auto-mounts it on boot too (that's what the auto and _netdev are supposed to do).

If you experience issues on boot (like me), you might see something like this buried in /var/log/syslog:

mount[586]: mount.nfs: Network is unreachable

....then we can quickly hack this by creating a script in the directory /etc/network/if-up.d. It should read something like this should fix the issue:

#!/usr/bin/env bash
mount /mnt/shared

Save this to /etc/network/if-up.d/cluster-shared-nfs for example, not forgetting to mark it as executable:

sudo chmod +x /etc/network/if-up.d/cluster-shared-nfs

Alternatively, there's autofs that can do this more intelligently if you prefer.

### First Nomad Job: Docker Registry

Now that we've got shared storage online, it's time for the big moment. We're finally going to start our very first job on our Nomad cluster!

It's going to be a Docker registry, and in my very specific case I'm going to be marking it as insecure (gasp!) because it's only going to be accessible from the WireGuard VPN - which I figure provides the encryption and authentication for us to get started reasonably simply without jumping through too many hoops. In the future, I'll probably revisit this in a later post to tighten things up.

Tasks on a Nomad cluster take the form of a Nomad job file. These can written in JSON or HCL (Hashicorp Configuration Language). I'll be using HCL here, because it's easier to read and we're not after machine legibility yet at this stage.

Nomad job files work a little bit like Nginx config files, in that they have nested sequences of blocks in a hierarchical structure. They loosely follow the following pattern:

job > group > task

The job is the top-level block that contains everything else. tasks are the items that actually run on the cluster - e.g. a Docker container. groups are a way to logically group tasks in a job, and are not required as far as I can tell (but we'll use one here anyway just for illustrative purposes). Let's start with the job spec:

job "registry" {
datacenters = ["dc1"]
# The Docker registry *is* pretty important....
priority = 80

# If this task was a regular task, we'd use a constraint here instead & set the weight to -100
affinity {
attribute   = "${attr.class}" value = "controller" weight = 100 } # ..... } This defines a new job called registry, and it should be pretty straight forward. We don't need to worry about the datacenters definition there, because we've only got the 1 (so far?). We set a priority of 80, and get the job to prefer running on nodes with the controller class (though I observe that this hasn't actually made much of a difference to Nomad's scheduling algorithm at all). Let's move on to the real meat of the job file: the task definition! group "main" { task "registry" { driver = "docker" config { image = "registry:2" labels { group = "registry" } volumes = [ "/mnt/shared/registry:/var/lib/registry" ] port_map { registry = 5000 } } resources { network { port "registry" { static = 5000 } } } # ....... } } There's quite a bit to unpack here. The task itself uses the Docker driver, which tells Nomad to run a Docker container. In the config block, we define the Docker driver-specific settings. The docker image we're going to run is registry:2 where registry is the image name, and 2 is the tag. This will to automatically pulled from the Docker hub. Future tasks will pull docker images from our very own private Docker registry, which we're in the process of setting up :D We also mount a directory into the Docker container to allow it to persist the images that we push to it. This is done through a volume, which is the Docker word for bind-mounting a specific directory on the host system into a given location inside the guest container. For me I'm (currently) going to store the Docker registry data at /mnt/shared/registry - you should update this if you want to store it elsewhere. Remember this this needs to be a location on your shared storage, as we don't know which node in the cluster the Docker registry is going to run on in advance. The port_map allows us to tell Nomad the port(s) that our service inside the Docker container listens on, and attach a logical name to them. We can then expose them in the resources block. In this specific case, I'm forcing Nomad to statically allocate port 5000 on the host system to point to port 5000 inside the container, for reasons that will become apparent later. This is done with the static keyword there. If we didn't do this, Nomad would allocate a random port number (which is normally what we'd want, because then we can run lots of copies of the same thing at the same time on the same host). The last block we need to add to complete the job spec file is the service block. with a service block, Nomad will inform Consul that a new service is running, which will then in turn allow us to query it via DNS. service { name = "${TASK}"
tags = [ "infrastructure" ]

port = "registry"
check {
type        = "tcp"
port        = "registry"
interval    = "10s"
timeout     = "3s"
}

}

The service name here is pulled from the name of the task. We tell Consul about the port number by specifying the logical name we assigned to it earlier.

Finally, we add a health check, to allow Consul to keep an eye on the health of our Docker registry for us. This will appear as a green tick if all is well in the web interface, which we'll be getting to in a future post. The health check in question simply ensures that the Docker registry is listening via TCP on the port it should be.

Here's the completed job file:

job "registry" {
datacenters = ["dc1"]
# The Docker registry *is* pretty important....
priority = 80

# If this task was a regular task, we'd use a constraint here instead & set the weight to -100
affinity {
attribute   = "${attr.class}" value = "controller" weight = 100 } group "main" { task "registry" { driver = "docker" config { image = "registry:2" labels { group = "registry" } volumes = [ "/mnt/shared/registry:/var/lib/registry" ] port_map { registry = 5000 } } resources { network { port "registry" { static = 5000 } } } service { name = "${TASK}"
tags = [ "infrastructure" ]

port = "registry"
check {
type        = "tcp"
port        = "registry"
interval    = "10s"
timeout     = "3s"
}

}
}

//  driver = "docker"
//
//  config {
//      // We're going to have to build our own - the Docker image on the Docker Hub is amd64 only :-/
//      // See https://github.com/Joxit/docker-registry-ui
//      image = ""
//  }
// }
}
}

Save this to a file, and then run it on the cluster like so:

nomad job run path/to/job/file.nomad

I'm as of yet unsure as to whether Nomad needs the file to persist on disk to avoid it getting confused - so it's probably best to keep your job files in a permanent place on disk to avoid issues.

Give Nomad to start the job, and then you can check on it's status like so:

nomad job status

This will print a summary of the status of all jobs on the cluster. To get detailed information about our new job, do this:

nomad job status registry

It should show that 1 task is running, like this:

ID            = registry
Name          = registry
Submit Date   = 2020-04-26T01:23:37+01:00
Type          = service
Priority      = 80
Datacenters   = dc1
Namespace     = default
Status        = running
Periodic      = false
Parameterized = false

Summary
Task Group  Queued  Starting  Running  Failed  Complete  Lost
main        0       0         1        5       6         1

Latest Deployment
ID          = ZZZZZZZZ
Status      = successful
Description = Deployment completed successfully

Deployed
main        1        1       1        0          2020-06-17T22:03:58+01:00

Allocations
ID        Node ID   Task Group  Version  Desired  Status   Created   Modified
XXXXXXXX  YYYYYYYY  main        4        run      running  6d2h ago  2d23h ago

Ignore the Failed, Complete, and Lost there in my output - I ran into some snags while learning the system and setting mine up :P

You should also be able to resolve the IP of your Docker registry via DNS:

dig +short registry.service.mooncarrot.space

mooncarrot.space is the root domain I've bought for my cluster. I highly recommend you do the same if you haven't already. Consul exposes all services under the service subdomain, so in the future you should be able to resolve the IP of all your services in the same way: service_name.service.DOMAIN_ROOT.

Take care to ensure that it's showing the right IP address here. In my case, it should be the IP address of the wgoverlay network interface. If it's showing the wrong IP address, you may need to carefully check the configuration of both Nomad and Consul. Specifically, start by checking the network_interface setting in the client block of your Nomad worker nodes from part 7 of this series.

### Conclusion

We're getting there, slowly but surely. Today we've setup shared storage with NFS, and started our first Nomad job. In doing so, we've started to kick the tyres of everything we've installed so far:

• wesher, our WireGuard Mesh VPN
• Unbound, our DNS server
• Consul, our service discovery superglue

Truly, we are standing on the shoulders of giants: a whole host of open-source software that thousands of people from across the globe have collaborated together to produce which makes this all possible.

Moving forwards, we're going to be putting that Docker registry to good use. More immediately, we're going to be setting up Fabio (who's documentation is only marginally better than Traefik's, but just good enough that I could figure out how to use it....) in order to take a peek at those cool web interfaces for Nomad and Consul that I keep talking about.

We're also going to be looking at setting up Vault for secret (and certificate, if all goes well) management.

Until then, happy cluster configuration! If you're confused about anything so far, please leave a comment below. If you've got a suggestion to make it even better, please comment also! I'd love to know.

## Ensuring a Linux machine's network connection stays up with Bash

Recently, I had the unpleasant experience of my Lab machine at University dropping offline. It has a tendency to do this randomly - and normally I'd just reboot it myself, but since I'm working from home at the moment it meant that I couldn't go in to fix it. This unfortunately meant that I was stuck waiting for a generous technician to go in and reboot it for me.

With access now restored I decided that I really didn't want this to happen again, so I've written a simple Bash script to resolve the issue.

It works by checking for an Internet connection every hour by pinging starbeamrainbowlabs.com - and if it doesn't manage to do so successfully, then it will reboot. A simple concept, but I discovered a number of things that needed considering while writing it:

1. To avoid detecting transient network issues, we should make multiple attempts before giving up and rebooting
2. Those multiple attempts need to be delayed to be effective
3. We mustn't reboot more than once an hour to avoid getting into a 'reboot loop'
4. If we're running an experiment, we need a way to temporarily delay it from doing it's checks that will resume automatically
5. We could try and diagnose the network error or turn the networking of and on again, but if it gets stuck halfway through then we're locked out (very undesirable) - so it's easier / safer to just reboot

With these considerations in mind, I came up with this: ensure-network.sh (link to part of a GitHub Gist, as it's quite long)

This script requires Bash version 4+ and has a number of environment variables that can configure its behaviour:

Environment Variable Description
CHECK_EXTERNAL_HOST The domain name or IP address to ping to check the connection
CHECK_INTERVAL The interval to check the connection in seconds
CHECK_TIMEOUT Wait at most this long for a reply to our ping
CHECK_RETRIES Retry this many times before giving up and rebooting
CHECK_RETRY_DELAY Delay this many seconds in between retries
CHECK_DRY_RUN If true, then don't actually reboot (useful for testing)
CHECK_REBOOT_DELAY Leave at least this many minutes in between reboots
CHECK_POSTPONE_FILE If this file exists and has a recent last-modified time (mtime), don't actually reboot
CHECK_POSTPONE_MAXAGE The maximum age in minutes of the CHECK_POSTPONE_FILE to consider it fresh and avoid rebooting

With these environment variables, it covers all 4 points in the above list. To expand on CHECK_POSTPONE_FILE, if I'm running an experiment for example and I don't want it to reboot in the middle of said experiment, then I can simply run touch /path/to/postpone_file to delay network connection-related reboots for 7 days (by default). After this time, it will automatically start rebooting again if it drops off the network. This ensures that it will always restart monitoring eventually - as if I had a more manual system I'd forget to re-enable it and then loose access.

Another consideration is that the /var/cache directory must exist. This is because an empty tracking file is created there to keep track of when the last network connection-related reboot occurred.

With the script written, then next step is to have it run automatically on boot. For systemd-based systems such as my lab machine, a systemd service is the order of the day. This is relatively simple:

[Unit]
Description=Reboot if the network connection is down
After=network.target

[Service]
Type=simple
# Because it needs to be able to reboot
User=root
Group=root
EnvironmentFile=-/etc/default/ensure-network
ExecStartPre=/bin/sleep 60
ExecStart=/bin/bash "/usr/local/lib/ensure-network/ensure-network.sh"
SyslogIdentifier=ensure-access
StandardError=syslog
StandardOutput=syslog

[Install]
WantedBy=multi-user.target

This assumes that the ensure-network.sh script is located at /usr/local/lib/ensure-network/ensure-network.sh. It also allows for an environment file to optionally be created at /etc/default/ensure-network, so that you can customise the parameters. Here's an example environment file:

CHECK_EXTERNAL_HOST=example.com
CHECK_INTERVAL=60

The above example environment file checks against example.com every minute instead of the default starbeamrainbowlabs.com every hour. You can, of course, specify any (or all) of the environment variables detailed above in the environment file if you wish.

That completes my setup - so hopefully I don't encounter any more network-related issues that lock me out of accessing my lab machine remotely! To install it yourself, you can do this:

# Create the directory for the script to live in
sudo mkdir /usr/local/lib/ensure-network
sudo curl -L -O /usr/local/lib/ensure-network/ensure-network.sh https://gist.githubusercontent.com/sbrl/08e13f2ceedafe35ac7f8dbdfb8bfde7/raw/cc5ab4226472c08b09e448a257256936cc749193/ensure-network.sh
sudo curl -L -O /etc/systemd/system/ensure-network.service https://gist.githubusercontent.com/sbrl/08e13f2ceedafe35ac7f8dbdfb8bfde7/raw/adf5ed4009b3e1a09f857936fceb3581897072f4/ensure-network.service
# Start the service & enable it on boot
sudo systemctl start ensure-network.service
sudo systemctl enable ensure-network.service

You might need to replace the URLs there with the latest ones that download the raw content from the GitHub Gist.

Did you find this useful? Got a suggestion to make it better? Running into issues? Comment below!

## Analysing logs with lnav

Before I forget about it, I want to make a note on here about lnav. It's available in the default Ubuntu repositories, and I discovered it a while back.

(Above: a screenshot of lnav. The pixellated bits are the IPs, which I've hidden for privacy.)

Essentially, it's a tool to make reading and analysing log files much easier. It highlights the interesting bits, and also allows you to filter log lines in or out with regular expressions. It even allows you to query your logs with SQLite if they are in any of the well-known formats that it can parse - and you can write your own log line parser definitions too with a JSON configuration file!

I find it a great tool to us every now and then to get an overview of my various devices that I manage to see if there are any issues I need to take care of. The error and warning message highlighting (while not perfect) is also rather useful to help in spotting the things that require my attention.

If you're on a Debian-based distribution of Linux, you should be able to install it like so:

sudo apt install lnav

Then, to analyse some log files:

lnav path/to/log/files

You can also use Bash's glob-star feature to specify multiple log files. it can also automatically unpack gzipped logfiles too:

lnav /var/log/syslog*

Of course, don't forget to prefix with sudo if you require it to read a given logfile.

## PhD Update 4: Ginormous Data

Hello again! In the last PhD update blog post, I talked about patching HAIL-CAESAR to improve performance and implementing a Temporal Convolutional Neural Net (Temporal CNN).

Since making that post, I've had my PhD Panel 1 (very useful, thanks to everyone who was on that panel!). I've also got an initial - albeit untested - implementation of a Temporal CNN. I've also been wrangling lots of data in more ways than one. I'm definitely seeing the Big Data aspect of my project title now.

### HAIL-CAESAR

I ran HAIL-CAESAR initially at 50m per pixel. This went ok, and generated lots of data out, but in 2 weeks of real time it barely hit 43 days worth of simulation time! The other issue I discovered due to the way I compressed the output of HAIL-CAESAR, for some reason it compressed the output files before HAIL-CAESAR had finished writing to them. This resulted in the data being cut off randomly in the output files for each time step.

Big problem - clearly another approach is in order.

To tackle these issues, I've done several things. Firstly, I patched HAIL-CAESAR again to support writing the output water depth files to the standard output. As a refresher, they are actually identical in format to the heightmap, which looks a bit like this:

ncols 4
nrows 3
xllcorner 400000
yllcorner 300000
cellsize 1000
1 2 3 4
1 1 2 3
0 1 1 2

The above is a 4x3 grid of points, with the bottom-left corner being at (400000, 300000) on the Ordnance Survey National Grid (I know, latitude / longitude would be so much better, but all the data I'm working with is on the OS national grid :-/). Each point represents a 1km square area.

To this end, I realised that it doesn't actually matter if I concatenate multiple files in this format together - I can guarantee that I can tell them apart. As soon as I detect a metadata line that the current file has already declared, then I know that the next file is starting and we're starting to read the next file along. To this end, I implemented a new Terrain50.ParseStream() function that is an async generator that will take a stream, and then iteratively yield the Terrain50 instances it parses out of the stream. In this way, I can split 1 big continuous stream back up again into the individual parts.

By patching HAIL-CAESAR such that it outputs the data in 1 continuous stream, it also means that I can pipe it to a single compression program. This has 2 benefits:

• It avoids the "compressing the individual files before HAIL-CAESAR is ready" problem (the observant might note that inotifywait would solve this issue neatly too, but it isn't installed on Viper)
• It allows for more efficient compression, as the compression program can use data from other time step files as context

Finding a compression tool was next. I wanted something CPU efficient, because I wanted to ensure that the maximum number of CPU cycles were dedicated to HAIL-CAESAR for running the simulation, rather than compressing the output it generates - since it is the bottleneck after all.

I ended up using lz4 in the end, an extremely fast compression algorithm. It compiles easily too, which is nice as I needed to compile it from source automatically on Viper.

With all this in place, I ran HAIL-CAESAR again 2 more times. The first run was at the same resolution as before, and generated 303 GiB (!) of data.

The second run was at 500m per pixel (10 times lower resolution), which generated 159 GiB (!) of data and, by my calculations, managed to run through ~4.3 years in simulation time in 5 days of real time. Some quick calculations suggest that to get through all 13 years of rainfall radar data I have it would take just over 11 days, so since I've got everything setup already, I'm going to be contacting the Viper administrators to ask about running a longer job to allow it to complete this process if possible.

### Temporal CNN Preprocessing

The other major thing I've been working on since the last post is the Temporal CNN. I've already got an initial implementation setup, and I'm currently in the process of ironing out all the bugs in it.

I ran into a number of interesting bugs. One of these was to do with incorrectly specifying the batch size (due to a typo), which resulted in the null values you may have noticed in the model summary in the last post. With those fixed, it looks much more sensible:

_________________________________________________________________
Layer (type)                 Output shape              Param #
=================================================================
conv3d_1 (Conv3D)            [32,2096,3476,124,64]     16064
_________________________________________________________________
conv3d_2 (Conv3D)            [32,1046,1736,60,64]      512064
_________________________________________________________________
conv3d_3 (Conv3D)            [32,521,866,28,64]        512064
_________________________________________________________________
pooling (AveragePooling3D)   [32,521,866,1,64]         0
_________________________________________________________________
reshape (Reshape)            [32,521,866,64]           0
_________________________________________________________________
conv2d_output (Conv2D)       [32,517,862,1]            1601
_________________________________________________________________
reshape_end (Reshape)        [32,517,862]              0
=================================================================
Total params: 1041793
Trainable params: 1041793
Non-trainable params: 0
_________________________________________________________________

This model is comprised of the following:

• 3 x 3D convolutional layers
• 1 x pooling layer to average out the temporal dimension
• 1 x reshaping layer to remove the redundant dimension
• 1 x 2D convolutional layer that will produce the output
• 1 x reshaping layer to remove another redundant dimension

I'll talk about this model in more detail in a future post in this series once I've managed to get it running and I've played around with it a bit.

Another significant one I ran into was to do with stacking tensors like an image. I ended up asking on Stack Overflow: How do I reorder the dimensions of a rank 3 tensor in Tensorflow.js?

The input to the above model is comprised of a sliding window that moves along the rainfall radar time steps. Each time step contains a 2D array, representing the amount of rain that has fallen in a given area. This needs to be combined with the heightmap, so that the AI model knows what the terrain that the rain is falling on looks like.

The heightmap doesn't change, but I'm including a copy of it with every rainfall radar time step because of the way the 3D convolutional layer works in Tensorflow.js. 2D convolutional layers in Tensorflow.js, for example, take in a 2D array of data as a tensor. They can also take in multiple channels though, much like pixels in an image. The pixels in an image might look something like this:

R1 G1 B1 A1 R2 G2 B2 A2 R3 G3 B3 A3 .....

As you might have seen in the Stack Overflow answer I linked to above, Tensorflow.js does support stacking multiple 2D tensors in this fashion. It is unfortunately extremely slow however. It is for this reason that I've been implementing a multi-process program to preprocess the data to do this stacking in advance.

As I'm writing this though, I've finally understood what the dataFormat option is for in the conv3d and conv2d layers is for, and I think I might have been barking up the wrong tree......

### What's next

From here, I'm going to investigate that dataFormat option for the TemporalCNN - it would hugely simplify my setup and remove the need for me to preprocess the data beforehand, since stacking tensors directly 1 after another is very quick - it's just stacking them along a different dimension that's slow.

I'm also hoping to do a longer run of that 500m per pixel HAIL-CAESAR simulation. More data is always good, right? :P

After I've looked into the dataFormat option, I'd really like to get the Temporal CNN set off training and all the bugs ironed out. I'm so close I can almost taste it!

Finally, if I have time, I want to go looking for a baseline model of sorts. By this, I mean an existing model that's the closest thing to the task I'm doing - even though they might not be as performant or designed for my specific task.

Found this interesting? Got a suggestion of something I could do better? Confused about something I've talked about? Comment below!

## PhD Aside: Reading a file descriptor line-by-line from multiple Node.js processes

Phew, that's a bit of a mouthful. We're taking a short break from the cluster series of posts (though those will be back next week I hope), because I've just run into a fascinating problem, the solution to which I thought I'd share here - since I didn't find a solution elsewhere on the web.

For my PhD, I've got a big old lump of data, and it all needs preprocessing before I train an AI model (or a variant thereof, since I'm effectively doing video-to-image translation). Unfortunately, one of the preprocessing steps is really slow. And because I'll naturally be training my AI for multiple epochs, the problem is multiplied.....

The solution, of course, is to do all the preprocessing up front such that I can just read the data in and push it directly into a Tensor in the right format. However, doing this on such a large dataset would take forever if I did the items 1 by 1. The thing is that Javascript isn't inherently multithreaded. I like this quote, as it describes the situation rather well:

In Javascript everything runs in parallel... except your code

In other words, when Node.js is reading or writing to and from the network, disk, or other places it can do lots of things at the same time because it does them asynchronously. The Javascript that gets executed though is only done on a single thread though.

This is great for io-bound tasks (such as a web server), as Node.js (a Javascript runtime) can handle many requests at the same time. On a side note, this is also the reason why Nginx is more efficient than Apache (because Nginx is event based too like Javascript, unlike Apache which is thread based).

It's not so great though for CPU bound tasks, such as the one I've got on my hands. All is not lost though, because Node.js has a number of useful functions inbuilt that we can use to tackle the issue.

Firstly, Node.js has a clever forking system. By using child_process.fork(), a single Node.js process can create multiple copies of itself to act as workers:

// main.js
import child_process from 'child_process';
import os from 'os';

let workers = [];

for(let i = 0; i &lt; os.cpus().length; i++) {
workers.push(
child_process.fork("worker.mjs")
);
}
// worker.js
console.log(Hello, world from a child process!);

Very useful! The next much more sticky problem though is how to actually preprocess the data in a performant manner. In my specific case, I'm piping the data in from a shell script that decompresses a number of gzip archives in a specific order (as of the time of typing I have yet to implement this).

Because this is a single pipe we're talking about here, the question now arises of how to allow all the child processes to access the data that's coming in from the standard input of the master process.

I've actually encountered an issue like this one before. I initially tried reading it in on the master process, and then using worker.send(message) to send it to the worker processes for processing. This didn't end up working very well, because the master process became a bottleneck as it couldn't read from the standard input and send stuff to the workers fast enough.

With this in mind, I came up with a new plan. In Node.js, when you're forking to create a worker process, you can supply it with some custom file descriptors upon initialisation. So long as it has at least IPC (inter-process communication) channel for passing messages back and forth with the .send() and .on("message", (message) => ....) method and listeners, it doesn't actually care what you do with the others.

Cue file descriptor cloning:


// main.js
import child_process from 'child_process';
import os from 'os';

let workers = [];

for(let i = 0; i 

I've highlighted the key line here (line 10 for those who can't see it). Here we tell it to clone file descriptors 0, 1, and 2 - which refer to stdin, stdout, and stderr respectively. This allows the worker processes direct access to the master process' stdin, stdout, and stderr.

With this, we can read from the same pipe with as many worker processes as we like - so long as they do so 1 at a time.

With this sorted, it gives rise to the next issue: reading line-by-line. Packages exist on npm (such as nexline, my personal favourite) to read from a stream line-by-line, but they have the unfortunate side-effect of maintaining a read buffer. While this is great for performance, it's not so great in my situation because it ends up scrambling the input! This is because said read buffer would be local to each worker process, so when the next worker along reads, it will skip a random number of bytes and start reading from the next bit along.

This means that I need to implement a custom method that reads a single line from a given file descriptor without maintaining a read buffer. I came up with this:

import fs from 'fs';

//  .....

// Global buffer to avoid unnecessary memory churn
let buffer = Buffer.alloc(4096);
let i = 0;
while(true) {
if(bytes_read !== 1 || buffer[i] == 0x0A) {
if(i == 0 && bytes_read == null) return null;
return buffer.toString("utf-8", 0, i); // This is not inclusive, so we can abuse it to trim the \n off the end
}

i++;
if(i == buffer.length) {
let new_buffer = new Buffer(Math.ceil(buffer.length * 1.5));
buffer.copy(new_buffer);
buffer = new_buffer;
}
}
}

I read from the given file descriptor character by character directly into a buffer. As soon as it detects a new line character (\n, or character code 0x0A), it returns the new line. If we run out of space in the buffer, then we create a new larger one, copy the old buffer's contents into it, and keep going.

I maintain a global buffer here, because this helps to avoid unnecessary memory churn. In my case, the lines I'm reading in a rather long (hence the need to clone the file descriptor in the first place), and if I didn't keep a shared buffer I'd be allocating and deallocating a new pretty large buffer every time.

This also has the nice side-effect that we keep the largest buffer we've had to use so far around for next time, avoiding the need for subsequent copies to larger and larger buffers.

Finally, we can also guarantee that it won't be a problem if we call this multiple times, because as I explained above Javascript is single-threaded, so if we call the function multiple times in quick succession each read will happen 1 after another.

With this chain of Node.js features, we can read a large amount of data from and efficiently process the content of a pipe. The trick from here is to implement a proper messaging and locking system to avoid reading from the stream at the same time, and avoid write to the standard output at the same time.

Taking this further, I ended up with this:

(Licence: Mozilla Public Licence 2.0)

This correctly ensures that only 1 worker process reads from the stream at the same time. It doesn't do anything with the result though except log a message to the console, but when I implement that I'll implement a similar messaging system to ensure that only 1 process writes to the output at once.

On that note, my data is also ordered, so I'll have to implement a complicated cache system // ordering system to ensure that I write them to the standard output in the same order I read them in. When I do implement that, I'll probably blog about that too....

The main problem I still have with this solution is that I'm reading from the input stream. I haven't done any proper testing, but I'm pretty sure that doing so will be really slow. I not sure I can avoid this though and read a few KiBs at a time, because I don't currently know of any way to put the extra characters back into the input stream.

If anyone has a solution to that that increases performance, I'd love to know. Leave a comment below!

## Pure CSS spoilers with the CSS :target selector

For 1 reason or another, I've been working on some parser improvements for Pepperminty Wiki recently. Pepperminty Wiki uses Markdown for the page content syntax - specifically Parsedown. Markdown has a number of variations and extensions, some of which are more widely accepted than others. For Pepperminty Wiki, I try to stick as closely to existing Markdown conventions as possible (such as the CommonMark spec). Where that's not possible, I try to make sure there's an existing precedent (e.g. internal links use the same syntax as MediaWiki).

Anyway, as part of this I thought it would be cool to implement a spoiler tag. The problem here is that nobody can agree on the canonical syntax. Discord has recently implemented a vertical bar syntax like a spoiler wall:

Some text ||spoiler text|| more text

Reddit, on the other hand, uses a different syntax:

Some text >!spoiler text!< more text

Anyway, I've ended up supporting both of the above 2 syntaxes. My Parsedown extension generates something like the following HTML:

<p>Some text <a class="spoiler" id="spoiler-RSSZTkNA30-OGJQf_7VivKtJAaoNhbx" href="#spoiler-RSSZTkNA30-OGJQf_7VivKtJAaoNhbx" title="Click / tap to reveal spoiler">spoiler text</a> more text</p>

The next question here is how to make it function as a spoiler. If you're not already aware, to reveal to text in a spoiler, one first has to click on it or perform some other action. Personally, I'd prefer to avoid Javascript if possible for this, as not all users have it enabled and it complicates matters in Pepperminty Wiki.

To this end, if you search for "Pure CSS spoiler" with your favourite search engine, you'll find loads of different solutions out there. Some require Javascript, and others only show the text in a tooltip on hover (which doesn't work on mobile). All this isn't very cool, so I decided to implement my own solution and share it here :-)

It's actually pretty concise:

.spoiler {
background: #333333;
color: transparent;
cursor: pointer;
}
.spoiler:target {
background: transparent;
color: inherit;
}

By setting the text colour to transparent and the background to an obvious colour, we can give the user an obvious hint that there's a spoiler that can be clicked on. Setting the cursor to a hand on platforms with a mouse further helps to support this suggestion.

When the link is clicked, it sets the anchor to spoiler-RSSZTkNA30-OGJQf_7VivKtJAaoNhbx, which is also the id of the spoiler. This triggers the :target selector, which makes the spoiler text visible.

Here's a demo:

See the Pen Pure CSS Spoiler by Starbeamrainbowlabs (@sbrl) on CodePen.

The only issue here is that it doesn't support accessibility tools such as screen readers very well. Using a trick I've found on the Mozilla Developer Net, we can do this to improve that:

.spoiler::before, .spoiler::after {
clip-path: inset(100%);
clip: rect(1px, 1px, 1px, 1px);
height: 1px;
overflow: hidden;
position: absolute;
white-space: nowrap;
width: 1px;
}
.spoiler::before {
content: " [spoiler start] ";
}
.spoiler::after {
content: " [spoiler end] ";
}

...but this still doesn't "fix" the issue, because we're only giving the user warning. Not being a screen-reader user myself, I'm not sure whether this is adequate (is there a 'skip' command that allows skipping to the end of the element or something?) and what isn't.

If you've got a better idea for screen-reader users, please do comment below - I'd love to know.

Found this useful? Got a suggestion to make it even better? Comment below!

## Cluster, Part 7: Wrangling... boxes? | Expanding the Hashicorp stack with Docker and Nomad

Welcome back to part 7 of my cluster configuration series. Sorry this one's a bit late - the last one was a big undertaking, and I needed a bit of a rest :P

Anyway, I'm back at it with another part to my cluster series. For reference, here are all the posts in this series so far:

Don't forget that you can see all the latest posts in the cluster tag right here on my blog.

Last time, we lit the spark for the bonfire so to speak, that keeps track of what is running where. We also tied it into the internal DNS system that we setup in part 4, which will act as the binding fabric of our network.

In this post, we're going to be doing 4 very important things:

• Installing Docker
• Installing and configuring Hashicorp Nomad

This is set to be another complex blog post that builds on the previous ones in this series (remember that benign rabbit hole from a few blog posts ago?).

Above: Nomad is a bit like a railway network manager. It decides what is going to run where and at what time. Picture taken by me.

### Installing Docker

Let's install Docker first. This should be relatively easy. According to the official Docker documentation, you can install Docker like so:

curl https://get.docker.com/ | sudo sh

I don't like piping to sh though (and neither should you), so we're going to be doing something more akin to the "install using the repository". As a reminder, I'm using Raspberry Pi 4s running Raspbian (well, DietPi - but that's a minor detail). If you're using a different distribution or CPU architecture, you'll need to read the documentation to figure out the specifics of installing Docker for your architecture.

For Raspberry Pi 4s at least, it looks a bit like this:

echo 'deb [arch=armhf] https://download.docker.com/linux/raspbian buster stable' | sudo tee /etc/apt/sources.list.d/docker.list
sudo apt update
sudo apt install docker-ce

Don't forget that if you're running an apt caching server, you'll need to tweak that https to be plain-old http. For the curious, my automated script for my automated Ansible replacement (see the "A note about cluster management" in part 6) looks like this:

#!/usr/bin/env bash
RUN "sudo apt-get update";
RUN "sudo apt-get install --yes docker-ce";

Docker should install without issue - note that you need to install it on all nodes in the cluster. We can't really do anything meaningful with it yet though, as we don't yet have Nomad installed. Let's move on and install that then!

Nomad is what's known as a workload orchestrator. This means that it, given a bunch of jobs, decides what is going to run where. If a host goes down, it is also responsible for shuffling things around to compensate.

Nomad works on the concept of 'jobs', which can be handled any any 1 of a number of drivers. In our case, we're going to be using the built-in Docker driver, as we want to manage the running of lots of Docker containers across multiple hosts in our cluster.

After installing Consul last time, we can build on that with Nomad. The 2 actually integrate really nicely with each other. Nomad will, by default, seek out a local Consul daemon, use it to discover other hosts in the cluster, and hang it's own cluster from Consul. Neat!

Also like Consul, Nomad functions with servers and clients. The servers all talk to each other via the Raft consensus algorithm, and the clients a lightweight daemons that do that the servers tell them to. I'm going to have 3 servers and 2 clients, in the following layout:

1 Server Server
2 Server + Client Client
3 Server + Client Client
4 Client Server + Client
5 Client Server + Client

Just for the record, according to thee Nomad documentation it's not recommended that servers also act as clients, but I don't have enough hosts to avoid this yet.

With this in mind, let's install Nomad. Again, as last time, I've packaged Nomad in my at repository. If you haven't already, go and set it up now. Then, install nomad like so:

sudo apt install hashicorp-nomad

Also as last time, I've deliberately chosen a different name then the existing nomad package that you'll probably find in your distribution's repositories to avoid confusion during updates. If you're a systemd user, then I've also got a trio of packages that provide a systemd service file:

Package Name Config file location
hashicorp-nomad-systemd-server /etc/nomad/server.hcl
hashicorp-nomad-systemd-client /etc/nomad/client.hcl
hashicorp-nomad-systemd-both /etc/nomad/both.hcl

They all conflict with each other (such that you can only have 1 installed at a time), and the only difference between them is where the configuration file is located.

Install 1 of these (if required) now too with your package manager. If you're not a systemd user, consul your service manager's documentation and write a service definition. If you're willing, comment below and I'll include a note about it here!

Speaking of configuration files, we should write one for Nomad. Let's start off with the bits that will be common across all the config file variants:

bind_addr = "{{ GetInterfaceIP \"wgoverlay\" }}"

# Increase log verbosity
log_level = "INFO"

# Setup data dir
# The data directory used to store state and other persistent data. On client
# machines this is used to house allocation data such as downloaded artifacts
# used by drivers. On server nodes, the data dir is also used to store the
# replicated log.
data_dir = "/srv/nomad"

A few things to note here. log_level is mostly personal preference, so do whatever you like there. I'll probably tune it myself as I get more familiar with how everything works.

data_dir needs to be a path to a private root-owned directory on disk for the Nomad agent to store stuff locally to that node. It should not be shared with other nodes. If you installed one of the systemd packages above, /srv/nomad is created and properly permissed for you.

bind_addr tells Nomad which network interface to send management clustering traffic over. For me I'm using a WireGuard mesh VPN I setup in [part 4](), so I specify wgoverlay here.

Next, let's look at the server config:

# Enable the server
server {
enabled = true

# We've got 3 servers in the cluster at the moment
bootstrap_expect = 3

# Note that Nomad finds other servers automagically through the consul cluster

# TODO: Enable this. Before we do we need to figure out how to move this sekret into vault though or something
# encrypt = "SOME_VALUE_HERE"
}

Not much to see here. Don't forget to change the bootstrap_expect to a different value if you are going to have a different number of servers in your cluster (nodes that are just clients don't count).

Note that this isn't the complete server configuration file - you need to take both this and the above common bit to make the complete server configuration file.

Now, let's look at the client configuration:

client {
enabled = true
# Note that Nomad finds other servers automagically through the consul cluster

# Just a worker, nothing special going on here
node_class = "worker"

# use wgoverlay for network fingerprinting and forwarding
network_interface = "wgoverlay"

# Nobody is allow to run as root - even if you *are* inside a container.....
# For 1 thing it'll trigger a permission denied when writing to the NFS share
options = {
"user.blacklist" = "root"
}
}

This is more interesting.

network_interface is really important if you're using a WireGuard mesh VPN like wesher that I setup and configured in part 4. By default, Nomad port forwards over all interfaces that make sense, and in this case gets it wrong.

This fixes that by telling it to explicitly port forward containers over the wgoverlay interface. If your network interface has a different name, this is the place to change it. It's a fairly common practice from what I can tell to have both a 'public' and a 'private' network in a cluster environment. The private network is usually trusted, and as such has lots of management traffic running over it. The public network is the one that's locked down that requests come in to from outside.

The "user.blacklist" = "root" here is a precaution that I may end up having to remove in future. It blocks any containers from running on this client from running as root inside the Docker container. This is actually worth remembering, because it's a bit of a security risk. This is a fail-safe to remind myself that it's a Bad Idea.

Apparently there are tactics that can be deployed to avoid running containers as root - even when you might think you need to. In addition, if there's no other way to avoid it, apparently there's a clever user namespace remapping trick one can deploy to avoid a container from having root privileges if it breaks out of it's container.

Another thing to note is that NFS shares often don't like you reading or writing files owned by root either, so if you're going to be saving data to a shared NFS partition like me, this is yet another reason to avoid root in your containers.

At this point it's also probably a good idea to talk a little bit about usernames - although we'll talk in more depth about this later. From my current understanding, the usernames inside a container aren't necessarily the same as those outside the container.

Every process runs under a specified username, but each username is backed by a given user id. It's this user id that is translated back into a username on the client machine when reading files from an NFS mount - hence why usernames in NFS shares can be somewhat odd.

Docker containers often have custom usernames created inside the containers for running processes inside the container with specific user ids. More on this later, but I plan to dive into this in the process of making my own Docker container images.

Anyway, we now have our configuration files for Nomad. For a client node, take the client config and the common config from the top of this section. For a server, take the server and common sections. For a node that's to act as both a client and a server, take all 3 sections.

Now that we've got that sorted, we should be able to start the Nomad agent:

sudo systemctl enable --now nomad.service

This is the same for all nodes in the cluster - regardless as to whether it's a client, a server, or both (this is also the reason that you can't have more than 1 of the systemd apt packages installed at once that I mentioned above).

If you're using the UFW firewall, then that will need configuring. For me, I'm allowing all traffic on the wgoverlay network interface that's acting as my trusted network:

sudo ufw allow in on wgoverlay

If you'd prefer not to do that, then you can allow only the specific ports through like so:

sudo ufw allow 4646/tcp comment nomad-http
sudo ufw allow 4647/tcp comment nomad-rpc
sudo ufw allow 4648/tcp comment nomad-serf

Note that this allows the traffic on all interfaces - these will need tweaking if you only want to allow the traffic in on a specific interface (which, depending on your setup, is probably a wise decision).

Anyway, you should now be able to ask the Nomad cluster for it's status like so:

nomad node status

...execute this from any server node in the cluster. It should give output like this:

ID        DC   Name         Class   Drain  Eligibility  Status
75188064  dc1  piano        worker  false  eligible     ready
9eb7a7a5  dc1  harpsicord   worker  false  eligible     ready
c0d23e71  dc1  saxophone    worker  false  eligible     ready
a837aaf4  dc1  violin       worker  false  eligible     ready

If you see this, you've successfully configured Nomad. Next, I recommend reading the Nomad tutorial and experimenting with some of the examples. In particular the Getting Started and Deploy and Manage Jobs topics are worth investigating.

### Conclusion

In this post, we've installed Docker, and installed and configured Nomad. We've also touched briefly on some of the security considerations we need to be aware of when running things in Docker containers - much more on this in the future.

In future posts, we're going to look at setting up shared storage, so that jobs running on Nomad can be safely store state and execute on any client / worker node in the cluster while retaining access to said state information.

On the topic of Nomad, we're also going to look at running our first real job: a Docker registry, so that we can push our own custom Docker images to it when we've built them.

You may have noticed that both Nomad and Consul also come with a web interface. We're going to look at these too, but in order to do so we need a special container-aware reverse-proxy to act as a broker between 'cluster-space' (in which everything happens 'somewhere', and we don't really know nor do we particularly care where), and 'normal-network-space' (in which everything happens in clearly defined places).

I've actually been experiencing some issues with this, as I initially wanted to use Traefik for this purpose - but I ran into a number of serious difficulties with reading their (lack of) documentation. After getting thoroughly confused I'm now experimenting with Fabio (git repository) instead, which I'm getting on much better with. It's a shame really, I even got as far as writing the automated packaging script for Traefik - as evidenced by the traefik packages in my apt repository.

Found this interesting? Found a mistake? Confused about something? Comment below!

## Cluster, Part 6: Superglue Service Discovery | Setting up Consul

Hey, welcome back to another weekly installment of cluster configuration for colossal computing control. Apparently I'm managing to keep this up as a weekly series every Wednesday.

Last week, we sorted out managing updates to the host machines in our cluster to keep them fully patched. We achieved this by firstly setting up an apt caching server with apt-cacher-ng. Then we configured our host machines to use it. Finally, we setup automated updates with unattended-upgrades so we don't have to keep installing them manually all the time.

For reference, here are all the posts in this series so far:

In this part, we're going to install and configure Consul - the first part of the Hashicorp stack. Consul doesn't sound especially exciting, but it is an extremely important part of our (diabolical? I never said that) plan. It serves a few purposes:

• Clusters together, so Nomad (the task scheduler) can find other nodes
• Keeps track of which services are running where

It uses the Raft Consensus Algorithm (like wesher from part 4; they actually use the same library under-the-hood it would appear) to provide a relatively decentralised approach to the problem, allowing for some nodes to fail without impacting the cluster's operation as a whole.

It also provides a DNS API, which we'll be tying into with Unbound later in this post.

Before continuing, you may find reading through the official Consul guides a useful exercise. Try out some of the examples too to get your head around what Consul is useful for.

(Above: Nasa's DSN dish in Canberra, Australia just before major renovations are carried out. Credit: NASA/Canberra Deep Space Communication Complex)

### Installation and Preamble

To start with, we need to install it. I've done the hard work of packaging it already, so you can install it from my apt repository - which, if you've been following this series, you should have it setup already (if not, follow the link and read the instructions there).

Install consul like this:

sudo apt install consul

Next, we need a systemd service file. Again, I have packages in my apt repository for this. There are 2 packages:

• hashicorp-consul-systemd-client
• hashicorp-consul-systemd-server

The only difference between the 2 packages is where it reads the configuration file from. The client package reads from /etc/consul/client.hcl, and the server from /etc/consul/server.hcl. They also conflict with each other, so you can't install both at the same time. This is because - as far as I can tell - servers can expose the client interface in just the same way as any other normal client.

To get a feel for the bigger picture, let's talk architecture. Because Consul uses the Raft Consensus Algorithm, we'll need an odd number of servers to avoid issues (if you use an even number of servers, then you run the risk of a 'split brain', where there's no clear majority vote as to who's the current leader of the cluster). In my case, I have 5 Raspberry Pi 4s:

• 1 x 2GB RAM (controller)
• 4 x 4GB RAM (workers)

In this case, I'm going to use the controller as my first Consul server, and pick 2 of the workers at random to be my other 2, to make up 3 servers in total. Note that in future parts of this series you'll need to keep track of which ones are the servers, since we'll be doing this all over again for Nomad.

With this in mind, install the hashicorp-consul-systemd-server package on the nodes you'll be using as your servers, and the hashicorp-consul-systemd-client package on the rest.

### A note about cluster management

This is probably a good point to talk about cluster management before continuing. Specifically the automation of said management. Personally, I have the goal of making the worker nodes completely disposable - which means completely automating the setup from installing the OS right up to being folded into the cluster and accepting jobs.

To do this, we'll need a tool to help us out. In my case, I've opted to write one from scratch using Bash shell scripts. This is not something I can recommend to anyone else, unless you want to gain an understanding of how such tools work. My inspiration was efs2, which appears to be a Go program - and Docker files. As an example, my job file for automating the install of a Consul client agent install looks like this:

#!/usr/bin/env bash

SCRIPT "\${JOBFILE_DIR}/common.sh";

COPY "../consul/server.hcl" "/tmp/server.hcl"

RUN "sudo mv /tmp/server.hcl /etc/consul/server.hcl";
RUN "sudo chown root:root /etc/consul/server.hcl";
RUN "sudo apt-get update";
RUN "sudo apt-get install --yes hashicorp-consul-systemd-server";

RUN "sudo systemctl enable consul.service";
RUN "sudo systemctl restart consul.service";

...I'll be going through all steps in a moment. Of course, if there's the demand for it then I'll certainly write a post or 2 about my shell scripting setup here (comment below), but I still recommend another solution :P

Note that the firewall configuration is absent here - this is because I've set it to allow all traffic on the wgoverlay network interface, which I talked about in part 4. If you did want to configure the firewall, here are the rules you'd need to create:

sudo ufw allow 8301 comment consul-serf-lan;
sudo ufw allow 8300/tcp comment consul-rpc;
sudo ufw allow 8600 comment consul-dns;

Many other much more mature tools exist - you should use one of those instead of writing your own:

• Ansible - uses YAML configuration files; organises things logically into 'playbooks' (personally I really seriously can't stand YAML, which is another reason for writing my own)
• Puppet
• and more

The other thing to be aware of is version control. You should absolutely put all your configuration files, scripts, Ansible playbooks, etc under version control. My preference is Git, but you can use anything you like. This will help provide a safety net in case you make an edit and break everything. It's also a pretty neat way to keep it all backed up by pushing it to a remote such as your own Git server (you do have automated backups, right?), GitHub, or GitLab.

### Configuration

Now that we've got that sorted out, we need to deal with the configuration files. Let's do the server configuration file first. This is written in the Hashicorp Configuration Language. It's probably a good idea to get familiar with it - I have a feeling we'll be seeing a lot of it. Here's my full server configuration (at the time of typing, anyway - I'll try to keep this up-to-date).

bind_addr = "{{ GetInterfaceIP \"wgoverlay\" }}"

# When we have this many servers in the cluster, automatically run the first leadership election
# Remember that the Hashicorp stack uses the Raft consensus algorithm.
bootstrap_expect = 3
server = true
ui = true

client_addr = "127.0.0.1 {{ GetInterfaceIP \"docker0\" }}"

data_dir = "/srv/consul"
log_level = "INFO"

domain = "mooncarrot.space."

retry_join = [
// "172.16.230.100"
"bobsrockets",
"seanssatellites",
"tillystelescopes"
]

This might look rather unfamiliar. There's also a lot to talk about, so let's go through it bit by bit. If you haven't already, I do suggest coming up with an awesome naming scheme for your servers. You'll thank me later.

The other thing you'll need to do before continuing is buy a domain name. It sounds silly, but it's actually really important. As we talked about in part 3, we're going to be running our own internal DNS - and Consul is a huge part of this.

By default, Consul serves DNS under the .consul top-level-domain, which both unregistered and very bad practice (because it's unregistered). Someone could come along tomorrow and regsister and start using the .consul top-level domain, and then things would get awkward if you ever wanted to visit an external domain that ends in .consul that you're using internally.

I've chosen mooncarrot.space myself, but if you don't yet have one, I recommend taking your time and coming up with a really clever one you like - since you'll be using it for a long time - and updating it later is a pain in the behind. If you're looking for a recommendation for a DNS provider, I've been finding Gandi great so far (much better than GoDaddy, who have tons of hidden charges).

Once you've decided on a domain name (and bought it, if necessary), set it in the server configuration file via the domain directive:

domain = "mooncarrot.space."

Don't forget that trailing dot. As we learned in part 3, it's important, since it indicates an absolute domain name.

Also note that I'm using a subdomain of the domain in question here. This is because of an issue whereby I'm unable to get Unbound to forward that Consul is unable to resolve on to CloudFlare.

Another thing of note is the data_dir directive. Note that this is the data storage directive local to the specific node, not shared storage (we'll be tackling that in a future post).

The client_addr directive here tells Consul which network interfaces to bind the client API to. In our case, we're binding it to the local loopback (127.0.0.1) and the docker0 network interface by dynamically grabbing it's IP address - so that docker containers on the host can use the API.

The bind_addr directive is similar, but for the inter-node communication interfaces. This tells Consul that the other nodes in the Cluster are accessible over the wgoverlay interface that we setup in part 4. This is important, since Consul doesn't encrypt or authenticate it's traffic by default as far as I can tell - and I haven't yet found a good way to do this that doesn't involve putting a password directly into a configuration file.

In this way the WireGuard mesh VPN provides the encryption & authentication that Consul lacks by default (though I'm certainly going to be looking into it anyway).

bootstrap_expect is also interesting. If you've decided on a different number of Consul server nodes, then you should change this value to equal the number of server nodes you're going to have. 3, 5, and 7 are all good numbers - though don't go overboard. More servers means more overhead. Servers take more computing power than clients, so try not to have too many of them.

Finally, retry_join is also very important. It should contain the domain name of all the servers in the cluster. In my case, I'm using the be the hostnames of the other servers in the network, since Wesher (our WireGuard mesh VPN program) automatically adds the machine names of all the nodes in the VPN cluster to your /etc/hosts file. In this way we ensure that the Cluster always talks over the wgoverlay VPN network interface.

Oh yeah, and I should probably note here that your servers should not use FQDNs (Fully Qualified Domain Names) as their hostnames. I found out the hard way: it messes with Consul, and it ends up serving node IPs via DNS on something like harpsichord.mooncarrot.space.node.mooncarrot.space instead something sensible like harpsichord.node.mooncarrot.space. If anyone has a solution for this that doesn't involve using non-FQDNs as hostnames, I'd love to know (since FQDNs as hostnames is my preference).

That was a lot of words. Next, let's do the client configuration file:

bind_addr = "{{ GetInterfaceIP \"wgoverlay\" }}"

bootstrap = false
server = false

domain = "mooncarrot.space."

client_addr = "127.0.0.1 {{ GetInterfaceIP \"docker0\" }}"

data_dir = "/srv/consul"
log_level = "INFO"

retry_join = [
"wopplefox",
"spatterling",
]

Not much to talk about here, since the configuration is almost indentical to that of the server, except you don't have to tell it how many servers there are, and retry_join should contain the names of the servers that the client should try to join, as explained above.

Once you've copied the configuration files onto all the nodes in your cluster (/etc/consul/server.hcl for servers; /etc/consul/client.hcl for clients), it's now time to boot up the cluster. On all nodes (probably starting with the servers), do this:

# Enable the Consul service on boot
sudo systemctl enable consul.service
# Start the Consul service now
sudo systemctl start consul.service
# Or, you can do it all in 1 command:
sudo systemctl enable --now consul.service

It's probably a good idea to follow the cluster's progress along in the logs. On a server node, do this after starting the service & enabling start on boot:

sudo journalctl -u consul --follow

You'll probably see a number of error messages, but be patient - it can take a few minutes after starting Consul on all nodes for the first time for them to start talking to each other, sort themselves out, and calm down.

Now, you should have a working Consul cluster! On one of the server nodes, do this to list all the servers in the cluster:

consul members

If you like, you can also run this from your local machine. Simply install the consul package (but not the systemd service file), and make some configuration file adjustments. Update your ~/.bashrc on your local machine to add something like this:

export CONSUL_HTTP_ADDR=http://consul.service.mooncarrot.space:8500;

....replacing mooncarrot.space with your own domain, of course :P

Next, update the server configuration file to make the client_addr directive look like this:

client_addr = "127.0.0.1 {{ GetInterfaceIP \"docker0\" }} {{ GetInterfaceIP \"wgoverlay\" }}"

Upload the new version to your servers, and restart them one at a time (unless you're ok with downtime - I'm trying to practice avoiding downtime now so I know all the processes for later):

sudo systemctl restart consul.service

At this point, we've got a fully-functioning Consul cluster. I recommend following some of the official guides to learn more about how it works and what you can do with it.

### Unbound

Before we finish for today, we've got 1 more task to complete. As I mentioned back in part 3, we're going to configure our DNS server to conditionally forward queries to Consul. The end result we're aiming for is best explained with a diagram:

In short:

1. Try the localzone data
2. If nothing was found there (or it didn't match), see if it matches Consul's subdomain
3. If so, forward the query to Consul and return the result
4. If Consul couldn't resolve the query, forward it to CloudFlare via DNS-over-TLS

The only bit we're currently missing of this process is the Consul bit, which we're going to do now. Edit /etc/unbound/unbound.conf on your DNS server (mine is on my controller node), and insert the following:

###
# Consul
###
forward-zone:
name: "node.mooncarrot.space."
forward-zone:
name: "service.mooncarrot.space."
forward-addr: 127.0.0.1@8600

...replace mooncarrot.space. with your domain name (not forgetting the trailing dot, of course). Note here that we have 2 separate forward zones here.

Unfortunately, I can't seem to find a way to get Unbound to fall back to a more generic forward zone in the event that a more specific one is unable to resolve the query (I've tried both a forward-zone and a stub-zone). To this end, we need to define multiple more specific forward-zones if we want to be able to forward queries out to CloudFlare for additional DNS records. Here's an example:

1. tuner.service.mooncarrot.space is an internal service that is resolved by Consul
2. peppermint.mooncarrot.space is an externally defined DNS record defined with my registrar

If we then ask Unbound to resolve then both, only #1 will be resolved correctly. Unbound will do something like this for #2:

• Check the local-zone for a record (not found)
• Return error

If you are not worried about defining DNS records in your registrar's web interface / whatever they use, then you can just do something like this instead:

###
# Consul
###
forward-zone:
name: "mooncarrot.space."
forward-addr: 127.0.0.1@8600

For advanced users, the Consul's documentation on the DNS interface is worth a read, as it gives the format of all DNS records Consul can service.

Note also here that the recursors configuration option is an alternative solution, but I don't see an option to force DNS-over-TLS queries there.

If you have a better solution, please get in touch by commenting below or answering my ServerFault question.

With this done, you should be able to ask Consul what the IP address of any node in the cluster is like so:

dig +short harpsichord.node.mooncarrot.space
dig +short grandpiano.node.mooncarrot.space
dig +short oboe.node.mooncarrot.space
dig +short some_machine_name_here.node.mooncarrot.space

Again, you'll need to replace mooncarrot.space of course with your domain name.

### Conclusion

Phew! There was a lot of steps and moving parts to setup in this post. Take your time, and re-read this post a few times to make sure you've got all your ducks in a row. Make sure to test your new Consul cluster by reading the official guides as mentioned above too, as it'll cause lots of issues later if you've got bugs lurking around in Consul.

I can't promise that it's going to get easier in future posts - it's probably going to get a lot more complicated with lots more to keep track of, so make sure you have a solid understanding of what we've done so far before continuing.

To summarise, we've managed to setup a brand-new Consul cluster. We've configured Unbound to forward queries to Consul, to enable seamless service discovery (regardless of which host things are running on) later on.

We've also picked an automation framework for automating the setup and configuration of the various services and such we'll be configuring. I recommend taking some time to get to know your chosen framework - many have lots of built-in modules to make common tasks much easier. Try going back to previous posts in this series (links at the top of this post) and implementing them in your chosen framework.

Finally, we've learnt a bit about version control and it's importance in managing configuration files.

In the next few posts in this series (unless I get distracted - likely - or have a change of plans), we're going to be setting up Nomad, the task scheduler that will be responsible for managing what runs where and informing Consul of this. We're also going to be setting up and configuring a private Docker registry and Traefik - the latter of which is known as an edge router (more on that in future posts).

See you next time, when we dive further down into what's looking less like a rabbit hole and more like a cavernous sinkhole of epic proportions.

Found this useful? Confused about something? Got a suggestion? Comment below! It's really awesome to hear that my blog posts have helped someone else out.

Art by Mythdael