Joining the Herd

With the news about what’s happening on the BirdSite… I thought it might be time to explore what the state of the art alternative is these days.

As an aside, this isn’t the first time I’ve looked at Twitter alternatives, back in the very early days of Twitter I build and ran a project called BlueTwit inside IBM. This was ground up clone that was there to see how a micro blogging platform (how quaint a term…) would work in a large organisation. It had a Firefox plugin client, supported 250+ characters long before Twitter. The whole was written in Java ran on a modest AMD64 box under my desk for many years and was even played with by the IBM Research team before similar functionality ended up in IBM/Lotus Connections. (Someday I should do a proper writup about it…)

Anyway back to the hear and now… I have a propensity for wanting to know how things work under the covers (which is why I run my own web, DNS, mail, SIP, finger and, gopher server). So I thought I’d have a go at running my own Mastodon server for a little while to see how it all fits together.

A little digging shows that a Mastodon instance isn’t just one thing, it needs the following:

  • Mastodon Web interface
  • Mastodon Streaming interface
  • Mastodon Job server (sidekiq)
  • A PostgreSQL instance (for persistence)
  • A Redis Instance
  • A Elastic Search Instance (optional)

Given this list of parts trying to run it in a containerised environment made sense. I have both a Docker Compose setup and a Kubernetes setup at home for testing FlowForge on while I’m working, so that wouldn’t be a major problem (though I understand I’m the outlier here). I decided to go with Kubernetes, because that cluster is a bit bigger and I like a challenge.

Deploying to Kubernetes

A bit of Googling turned up that while there isn’t a published Helm chart, there is one included in the project. So I cloned the project to my laptop.

git clone https://github.com/mastodon/mastodon.git

Configuration

I then started to create a local-values.yml file to contain my instances specific configuration details. To get a feel for what values I’d need I started by looking in the chart/values.yml file to see what the defaults are and what I could override.

I also started to read the Mastodon server install guide as it had explanations to what each option means.

The first choice was what to call the instance. I went with a suggestion from @andypiper for the server name, but I’ll have the users hosted at my root domain

This means that the server is called bluetoot.hardill.me.uk and the instance is called hardill.me.uk so users will be identified for example as @ben@hardill.me.uk. These are configured as local_domain and web_domain

Next up was setting up the details of my mail server so that thinks like alerts and password resets can be sent. That was all pretty self explanatory.

The first tricky bit was setting up some secrets for the instance. There are secret keys for authentication tokens and a seed for the OTP setup. The documentation says to use rake secret but that implies you have Ruby environment already setup. I don’t work with Ruby so this wasn’t available. A bit more searching suggested that OpenSSL could be used:

openssl rand -hex 64

The next set of secrets are the vapid public and private keys. Here the documentation again has a rake command, but this time it appears to be a Mastodon specific Gem. To get round this I decided to pull the pre-built docker image and see if I could run the command in the container.

docker pull tootsuite/mastodon
docker run -it --rm --entrypoint /bin/bash tootsuite/mastodon:latest
mastodon@ab417e0a893a:~$ RAILS_ENV=production bundle exec rake mastodon:webpush:generate_vapid_key
VAPID_PRIVATE_KEY=MdgBNkR98ctXtk3xSTbs7-KJBCcykvvw_q1aFGNfMgY=
VAPID_PUBLIC_KEY=BB-g5Lgund3gYi3UhGGn7Z1Yj06gy4DqdozXQXYxeCDJjpEUW9TXYau7Ifv9xK_676MgUE4JSOSh4XSsroBoHmo=

(These keys are purely for demonstration and have not been used)

I made the decision to skip the elastic search instance to start with just to try and limit just how many resources I need to provide.

There are a couple of other bits I tweaked to make things work with my environment (forcing the bitnami charts to run on the AMD64 nodes rather than the ARM64) but the local-values.yml ended up looking like

mastodon:
  createAdmin:
    enabled: true
    username: fat_controller
    email: mastodon@example.com
  local_domain: example.com
  web_domain: bluetoot.example.com
  persistence:
    assets:
      accessMode: ReadWriteMany
      resources:
        requests:
          storage: 10Gi
    system:
      accessMode: ReadWriteMany
      resources:
        requests:
          storage: 10Gi
  smtp:
    auth_method: plain
    delivery_method: smtp
    enable_starttls_auto: true
    port: 587
    server: mail.example.com
    login: user
    password: password
    from_address: mastodon@example.com
    domain: example.com

  secrets:
    secret_key_base: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
    otp_secret: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
    vapid:
      private_key: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
      public_key: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
elasticsearch:
  enabled: false

ingress:
  annotations:
  tls:
  hosts:
  - host: bluetoot.example.com
    paths:
    - path: '/'

postgresql:
  auth:
    password: xxxxxxx
  primary:
    nodeSelector:
      beta.kubernetes.io/arch: amd64

redis:
  password: xxxxxxx
  master:
    nodeSelector:
      beta.kubernetes.io/arch: amd64
  replica:
    nodeSelector:
      beta.kubernetes.io/arch: amd64

Deploying

The first part is to run helm

helm upgrade --install  --namespace mastodon --create-namespace mastodon ../mastodon/chart -f ./local-values.yml

The first time I ran this it failed to start most of the Mastodon pods with a bunch of errors around the PostgreSQL user password. I tracked it down to a recent Pull Request so I raised a new issue and a matching PR to fix things. This got merged really quickly which was very nice to see.

Once I’d modified the helm chart templates a little it deployed cleanly.

Proxying

Next up was setting up my Internet facing web proxy. The Ingress controller on my Kubernetes cluster is not directly exposed to the internet so I needed to add an extra layer of proxying.

First up was to setup an new host entry and renew my LetsEncrypt certificate with the new SAN entry.

location / {
    proxy_pass https://kube-three.local;
    proxy_ssl_verify off;
    proxy_ssl_server_name on;
    proxy_redirect off;
    proxy_read_timeout 900;
    proxy_set_header Host $http_host; # host:$server_port;
    proxy_set_header X-Real-IP $remote_addr;
    proxy_set_header X-Forwarded-Proto $scheme;
    proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    proxy_set_header X-NginX-Proxy true;
    proxy_set_header Upgrade $http_upgrade;
    proxy_set_header Connection "Upgrade";
    proxy_http_version 1.1;
}

It was important to make sure that the proxy target was over https otherwise the mastodon web app would trigger a redirect loop. the proxy_ssl_verify off; is because I’m using the default https certificate on the kubernetes ingress controller so it will fail the hostname check.

The other bit that needed doing was to add a proxy on the hardill.me.uk server for the .well-known/webfinger path to make sure user discovery works properly.

location /.well-known/webfinger {
    return 301 https://bluetoot.example.com$request_uri;
}

First Login

Now all the proxying and https setup is complete I can point my browser at https://bluetoot.hardill.me.uk and I get the intial sigin/signup screen.

As part of the configuration I created an admin user (fat_controller) but didn’t have a way to login as I don’t know what the generated password is. I tried follwing the lost password flow but couldn’t get it to work so followed the documentation about using the admin cli to do a password reset. I did this by using kubectl to get a shell in the mastodon web app pod in the cluster.

kubectl -n mastodon exec -it mastodon-web-5dd6764d4-mvwnl /bin/bash
kubectl exec [POD] [COMMAND] is DEPRECATED and will be removed in a future version. Use kubectl exec [POD] -- [COMMAND] instead.
mastodon@mastodon-web-5dd6764d4-mvwnl:~$ RAILS_ENV=production bin/tootctl accounts modify fat_controller --reset-password
OK
New password: fc20b54eddb312bf9462abfec77a27d8

I could then log in as the administrator, update things like the contact email address, change the default user creation to require approval for new users.

What next?

I’m not sure if I’m going to keep this instance yet. At the moment it’s a testing environment and I’m not sure yet how much I’m going to actually use Mastodon. A lot will depend on what Mr Musk decides to do with his new play thing and if/when the communities I follow move.

But I will probably play with writing a few bots for some of the home automation things I have on the go, e.g. as I write this I’m waiting for a WiFi CO2 monitor to be delivered. Having a bot that Toots the level in my office sounds like a good first project.

Taking a RISC

As a result of the Chip Crisis getting hold of Raspberry Pi SBCs has been tricky for the last year or so. If you manage to find a retailer that has some in stock before they get cleaned out by the scalpers they come bundled with PSU, pre-installed SD card, case and HDMI adapters (I understand why, it makes them more attractive to the original target audience).

At the same time there has been a growing number of new SBCs based on the RISC-V architecture. RISC-V is an open CPU architecture and looking to try and compete with the close ARM architecture which is found just about everywhere (Phones, new Macs, Watches, Set Top boxes, …)

So it seamed like a good time to see what boards were available and have a play. I had a bit of a search, and found a Sipeed Nezha RV64 Development Board on Amazon. It has the same basic layout as a Pi, all the same ports (plus some extra), a Debian Linux variant to run on it, the only thing lacking is RAM at 1GB, but that should be enough to get started.

Single Board Computer

Having ordered one, the delivery date was for mid October, early November but it turned up the 3rd weekend in September.

Unboxing

We get a reasonably nice box which contains the following

  • Sipeed Nezha 64bit 1GB Development Board
  • 32gb SD card preloaded with Debain Linux
  • 2 USB-A to USB-C cables
  • USB to Serial break out cable
  • American style USB Power supply
  • 4 Stand off feet

I don’t think I’ll be making use of the USB PSU, unbranded Chinese PSUs like this have a bad reputation and I don’t fancy digging out my UK to US step down transformer. Luckily when the Postman delivered this he also delivered one of the new Google Chromecast HDs which came with a suitable PSU that I don’t need as the TV has a USB socket I can use with the Chromcast.

The USB-A to USB-C cables also look a little strange, the USB-A end has a purple insert, but doesn’t look to have the USB-3 extra tracks and the data tracks look really thin. I’ll give them a go to power the board, but will probably use one of my existing cables with the USB-C OTG port on the board when I get round to seeing if I can drive it as a USB Gadget like I’ve been doing with Raspberry Pi 4 and Pi Zero boards.

I can’t find a matching case for this board online, while it’s mainly laid out like a Pi, the ports are not quite the same, so most Pi cases would need modifying. I may see if I can find somebody with a 3D printer to run one up for me. In the mean time I’ve screwed in the stand off feet to hold it up off the desk.

Powering up

As the board only has one USB-A socket I stuck a wireless keyboard/mouse dongle in to get started, along with a HDMI cable, ethernet and power.

When I plugged the power in I got absolutely nothing out to the display, but the power LED lit up and the link and data lights on the ethernet socket started to flash. I had a quick look in the DHCP table on my router and found a new device with a hostname of sipeed. I fired up SSH to connect as the sipeed user and entered the password printed on the inside of the box.

This got me logged into a bash shell. I found a shell script in the home directory called test_lcd.sh which when I examined hinted that i might be able to switch between a built in LCD driver and the HDMI output. Running the script at root with the argument of hdmi kicked the hooked up display into life.

X Windows running on half a screen

I appears (after a little searching) that the default X config is for a tablet sized screen in portrait mode, which is why it’s only using the left 3rd of the screen.

I’ll try and find some time to set up the display properly later, but for now we’ve proved nothing is broken.

Next I tried checking for any OS updates with sudo apt-get update but it threw errors to do with missing GPG keys.

root@sipeed:~# apt-get update
Get:1 http://ftp.ports.debian.org/debian-ports sid InRelease [69.6 kB]
Err:1 http://ftp.ports.debian.org/debian-ports sid InRelease
  The following signatures couldn't be verified because the public key is not available: NO_PUBKEY E852514F5DF312F6
Fetched 69.6 kB in 2s (34.0 kB/s)
Reading package lists... Done
W: An error occurred during the signature verification. The repository is not updated and the previous index files will be used. GPG error: http://ftp.ports.debian.org/debian-ports sid InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY E852514F5DF312F6
W: Failed to fetch http://ftp.ports.debian.org/debian-ports/dists/sid/InRelease  The following signatures couldn't be verified because the public key is not available: NO_PUBKEY E852514F5DF312F6
W: Some index files failed to download. They have been ignored, or old ones used instead.

I’m pretty sure I can import them, just need to find the right key server to pull them from.

(From Redit)

$ busybox wget -O- https://www.ports.debian.org/archive_2022.key | apt-key add

Once I get the updates installed I’ll see if I can get NodeJS and give Node-RED and FlowForge a go.

That’s a basic sniff done. Will post some more when I’ve had time to poke round some more.

I’ve found some Fedora images for this board, here that I’ll probably boot up next, then give Ubuntu a go from here.

Full NGINX Logging Example

In a previous post I talked about how to setup NGINX to log both the request and response headers and bodies to help with debugging. It’s a lot easier to set up a HTTP reverse proxy than sort out all the private keys when trying to capture HTTPS with wireshark.

I needed to use that again recently and it took me a little while to remember exactly how to put it all together again, so this is just a really short follow up post with a full minimal example of the nginx.conf file needed.

worker_processes 1;
load_module modules/ndk_http_module.so;
load_module modules/ngx_http_lua_module.so;
pcre_jit on;

events {
  worker_connections 1024;
}

error_log /dev/stderr;

http {
  log_format log_req_resp '$remote_addr - $remote_user [$time_local] '
  '"$request" $status $body_bytes_sent '
  '"$http_referer" "$http_user_agent" $request_time req_body:"$request_body" resp_body:"$resp_body" '
  'req_headers:"$req_header" resp_headers:"$resp_header"';

  server {
    listen 80;
    access_log /dev/stdout log_req_resp;

    root /var/www/html;

    lua_need_request_body on;

    set $resp_body "";
    body_filter_by_lua '
      local resp_body = string.sub(ngx.arg[1], 1, 1000)
      ngx.ctx.buffered = (ngx.ctx.buffered or "") .. resp_body
      if ngx.arg[2] then
        ngx.var.resp_body = ngx.ctx.buffered
      end
    ';

    set $req_header "";
    set $resp_header "";
    header_filter_by_lua '
      local h = ngx.req.get_headers()
      for k, v in pairs(h) do
        if (type(v) == "table") then
          ngx.var.req_header = ngx.var.req_header .. k.."="..table.concat(v,",").." "
        else
          ngx.var.req_header = ngx.var.req_header .. k.."="..v.." "
        end
      end
      local rh = ngx.resp.get_headers()
      for k, v in pairs(rh) do
        ngx.var.resp_header = ngx.var.resp_header .. k.."="..v.." "
      end
      ';

  }
}

It also has a small improvement to allow for duplicate HTTP headers in the request (which is in spec). It will now concatenate the values in to a comma separated list.

This is intended to be used with the following Docker container (the default nginx container doe not have the lua module installed)

FROM debian:latest

RUN apt-get update && apt-get install -y libnginx-mod-http-lua libnginx-mod-http-ndk

CMD ["nginx", "-g", "daemon off;"]

Run as follows

docker run --rm -v `pwd`/nginx.conf:/etc/nginx/nginx.conf:ro -p 80:80 custom-nginx

Contributing to Upstream

Over the last couple of weeks I’ve run into a few little bugs/niggles in Open Software components we are using at FlowForge. As a result I have been opening Pull Requests and Feature Requests against those projects to get them fixed.

mosquitto

First up was a really small change for the Mosquitto MQTT broker when using MQTT over WebSockets. In FlowForge v0.8.0 we added support to use MQTT to communicate between different projects in the same team and also as a way for devices to send state back to the core Forge App. To support this we have bundled the Mosquitto MQTT broker as part of the deployment.

When running on Docker or Kubernetes we use MQTT over WebSockets and the MQTT broker is exposed via the same HTTP reverse proxy as the Node-RED projects. I noticed that in the logs all the connections were coming from the IP address of the HTTP reverse proxy, not the actual clients. This makes sense because as far as mosquitto is concerned this is the source of the connection. To work around this the proxy usually adds a HTTP Header with the original clients IP address as follows:

X-Forwarded-For: 192.168.1.100

Web applications normally have a flag you can set to tell them to trust the proxy to add the correct value and substitute this IP address in any logging. Mosquitto uses the libwebsocket library to handle the set up of the WebSocket connection and this library supports exposing this HTTP Header when a new connection is created.

I submitted a Pull Request (#2616) to allow mosquitto to make use of this feature. Which adds the following code in src/websockets.c here.

...
    if (lws_hdr_copy(wsi, ip_addr_buff, sizeof(ip_addr_buff), WSI_TOKEN_X_FORWARDED_FOR) > 0) {
        mosq->address = mosquitto__strdup(ip_addr_buff);
    } else {
        easy_address(lws_get_socket_fd(wsi), mosq);
    }
...

This will use the HTTP header value if it exists, or fallback to the remote socket address if not.

Roger Light reviewed and merged this for me pretty much straight away and then released mosquitto v2.0.15, which was amazing.

mosquitto-go-auth

To secure our mosquitto broker we make use of the mosquitto-go-auth plugin which allows us to dynamically create users and ACL entries as we add/remove projects or users from the system. To make life easy this project publishes a Docker container with mosquitto, libwebsocket and the plugin all pre-built and setup to work together.

I had earlier run into a problem with the container not always working well when using MQTT over WebSockets connections. This turned out to be down to the libwebsockets instance in the container not being compiled with a required flag.

To fix this I submitted a Pull Request (#241) that

  • Updated the version of libwebsockets built into the container
  • Added the missing compile time flag when building libwebsockets
  • Bumped the mosquitto version to the just released v2.0.15

Again the project maintainer was really responsive and got the Pull Request turned round and release in a couple of days.

This means the latest version of the plugin container works properly with MQTT over WebSocket connections and will log the correct IP address of the connecting clients.

grafana

As I mentioned earlier I spotted the problem with the client IP addresses while looking at the logs from the MQTT broker of our Kubernetes deployment. To gather the logs we make use of Grafana Loki. Loki gathers the logs from all the different components and these can then be interrogated from the Gafana front end.

To deploy Grafana and Loki I made use of the Helm charts provided by the project. This means that all the required parts can be installed and configured with a single command and a file containing the specific customisations required.

One of the customisations is the setting up a Kubernetes Ingress entry for the Grafana frontend to make it accessible outside the cluster. The documentation says that you can pass a list of hostnames using the grafana.ingress.hosts key. If you just set this then most things work properly until you start to build dashboards based on the logs at which point you get some strange behaviour where you get redirected to http://localhost which doesn’t work. This is because to get the redirects to work correctly you also need to pass the hostname in the grafana."grafana.ini".server.domain setting.

In order to get this to work cleanly you have to know that you need to pass the same hostname in two different places. To try and make this a little simpler for the default basic case I have submitted a PR (#1689) that will take the first enty in the grafana.ingress.hosts list and use it for the value of grafana."grafana.ini".server.domain if there isn’t one provided.

Many thanks to Marcus Noble for helping me with the Go Templating needed for this.

This PR is currently undergoing review and hopefully will be merged soon.

eksctl

And finally this isn’t a code contribution, but I opened a feature request against WeaveWorks’ eksctl command. This is a tool create and managed Kubernetes clusters on AWS systems.

A couple of weeks ago we received a notice from AWS that one of the EC2 machines that makes up the FlowForge Cloud instance was due to be restarted as part of some EC2 planned maintenance.

We had a couple of options as to how to approach this

  1. Just leave it alone, the nodegroup would automatically replace the node when it was shutdown, but this would lead to downtime as the new EC2 instance was spun up.
  2. Add an extra node to the impacted nodegroup and migrate the running workload to the new node once it was fully up and running.

We obviously went with option 2, but this left us with an extra node in the group after maintenance. eksctl has options to allow the group size to be reduced but it doesn’t come with a way to say which node should be removed, so there was no way to be sure it would be the (new) node with no pods scheduled that is removed.

I asked a question on AWS’s re:Post forum as to how to remove a specific node and got details of how to do this with the awscli tool. While this works it would be really nice to be able to do it all from eksctl as a single point of contact.

I’ve raised a feature request (#5629) asking if this can be added and it’s currently waiting review and hopefully design and planning.

Home MicroK8s Cluster

I started to write about my home test environment for FlowForge a while ago, having just had to rebuild my K8s cluster due to a node failure I thought I should come back to this and document how I set it up (as much for next time as to share).

Cluster Nodes

I’m using the following nodes

Base OS

I’m working with Ubuntu 20.04 as this is the default OS of choice for MicroK8s and it’s available for both x86_64 and Arm8 for the Raspberry Pi 4.

Installing MicroK8s

$ sudo snap install microk8s --classic --channel=1.24

Once deployed on all 3 nodes, then we need to pick one of the nodes as the manager. In this case I’m using the Intel Celeron machine as the master and will run the following:

$ microk8s add-node
From the node you wish to join to this cluster, run the following:
microk8s join 192.168.1.59:25000/52bfa563603b3018770f88cadf606920/0e6fa3fb9ed3

Use the '--worker' flag to join a node as a worker not running the control plane, eg:
microk8s join 192.168.1.59:25000/52bfa563603b3018770f88cadf606920/0e6fa3fb9ed3 --worker

If the node you are adding is not reachable through the default interface you can use one of the following:
microk8s join 192.168.1.59:25000/52bfa563603b3018770f88cadf606920/0e6fa3fb9ed3

And then on the other 2 nodes run the following

$ microk8s join 192.168.1.59:25000/52bfa563603b3018770f88cadf606920/0e6fa3fb9ed3 --worker

You can verify the nodes are joined to the cluster with:

$ microk8s.kubectl get nodes
NAME         STATUS   ROLES    AGE    VERSION
kube-two     Ready    <none>   137m   v1.24.0-2+f76e51e86eadea
kube-one     Ready    <none>   138m   v1.24.0-2+f76e51e86eadea
kube-three   Ready    <none>   140m   v1.24.0-2+59bbb3530b6769

Once the nodes are added to the cluster we need to enable a bunch of plugins, on the master node run:

$ microk8s enable dns:192.168.1.xx ingress helm helm3

dns:192.168.1.xx overrides the default of using Google’s 8.8.8.8 DNS server to resolve names outside the cluster. This is important because I want it to point to my local DNS as I have set *.flowforge.loc and *.k8s.loc to point to the cluster IP addresses for Ingress.

Install kubectl and helm

By default Microk8s ships with a bunch of tools baked in, these include kubectl and helm that can be accessed as microk8s.kubectl and microk8s.helm respectively.

kubectl

Instructions for installing standalone kubectl can be found here. Once installed you can generate the config by running the following on the master node:

$ microk8s config > ~/.kube/config

This can be copied to other machines that you want to be able to administrate the cluster.

helm

Instructions for installing helm can be found standalone here.

This will make use of the same ~/.kube/config credentials file as kubectl.

NFS Persistent Storage

In order to have a consistent persistence storage pool across all 3 nodes I’m using a NFS share from my NAS. This is controlled using the nfs-subdir-external-provisioner. This creates a new directory on the NFS share for each volume created.

All the nodes need to have all the NFS client tools installed, this can be achieved with:

$ sudo apt-get install nfs-common

This is deployed using helm

$ helm repo add nfs-subdir-external-provisioner https://kubernetes-sigs.github.io/nfs-subdir-external-provisioner/
$ helm install nfs-subdir-external-provisioner nfs-subdir-external-provisioner/nfs-subdir-external-provisioner \
    --set nfs.server=192.168.1.7 \
    --set nfs.path=/volume1/kube

To set this as the default StorageClass run the following:

kubectl patch storageclass standard -p '{"metadata": {"annotations":{"storageclass.kubernetes.io/is-default-class":"true"}}}'

Conclusion

That is enough for the basic Kubernetes cluster setup, there are some FlowForge specific bits that are needed (e.g. tagging nodes) but I’ll leave that for the FlowForge on Kubernetes install docs (which I have to finish writing before the next release).

Building a Kubernetes Test Environment

Over the last couple of weekends I’ve been noodling around with my home lab set up to build a full local environment to test out FlowForge with both the Kubernetes Container and Docker Drivers.

The other reason to put all this together is to help to work the right way to put together a proper CI pipeline to build, automatically test and deploy to our staging environment.

Components

NPM Registry

This is somewhere to push the various FlowForge NodeJS modules so they can then be installed while building the container images for the FlowForge App and the Project Container Stacks.

This is a private registry so that I can push pre-release builds without them slipping out in to the public domain, but also so I can delete releases and reuse version numbers which is not allowed on the public NPM registry.

I’m using the Verdaccio registry as I’ve used this in the past to host custom Node-RED nodes (which it will probably end up doing again in this set up as things move forwards). This runs as Docker container and I use my Nginx instance to reverse proxy for it.

As well as hosting my private builds it can proxy for the public npmjs.org regisry which speeds up local builds.

Docker Container Registry

This is somewhere to push the Docker containers that represent both the FlowForge app it’s self and the containers that represent the Project Stacks.

Docker ship a container image available that will run a registry.

As well as the registry I’m also running second container with this web UI project to help keep track of what I’ve pushed to the registry and also allows me to delete tags which is useful when testing

Again my internet facing Nginx instance is proxying for both of these (on the same virtual host since their routes do not clash and it makes CORS easier since the UI is all browser side JavaScript)

Helm Chart Repository

This isn’t really needed, as you can generate all the required files with the helm command and host the results on any Web server, but this lets me test the whole stack end to end.

I’m using a package called ChartMuseum which will automatically generate index.yaml manifest file when charts are uploaded via it’s simple UI.

Nginx Proxy

All of the previous components have been stood up as virtual hosts on my public Nginx instance so that they can get HTTPS certificates from LetsEncrypt. This is makes things a lot easier because both Docker and Kubernetes basically require the container registry be secure by default.

While it is possible to add exceptions for specific registries, these days it’s just easier to do it “properly” up front.

MicroK8s Cluster

And finally I need a Kubernetes cluster to run all this on. In this case I have a 3 node cluster made up of

  • 2 Raspberry Pi 4s with 8gb of RAM each
  • 1 Intel Celeron based mini PC with 8gb of RAM

All 3 of these are running 64bit Ubuntu 20.04 and MicroK8s. The Intel machine is needed at the moment because the de facto standard PostrgresSQL Helm Chat only have amd64 based containers at the moment so won’t run on the Raspberry Pi based nodes.

The cluster uses the NFS Persistent Volume provisioner to store volumes on my local NAS so they are available to all the nodes.

Usage

I’ll write some more detailed posts about how I’ve configured each of these components and then how I’m using them.

As well as testing the full Helm chart builds, I can also use this to run the FlowForge app locally and have the Kubernetes Container driver running locally on my development machine and have it create Projects in the Kubernetes cluster.

New Daily Driver

I got my first Dell XPS13 back in 2016 and a second one in 2020. I really like them but with the new job I’ve been using it for both personal and work use. So I decided to grab a second machine to help with keeping things separate, it’s easier to switch off at the end of the day if I can leave the “work” machine in the office.

Lenovo X1 Carbon

I’ve gone for a Lenovo X1 Carbon. It’s a machine I looked at when I got the second XPS13 as it was another machine that can be ordered with Linux pre-installed. Lenovo are now offering both Ubuntu and Fedora as options. In my case I knew I wouldn’t have any problems installing the OS myself so I ordered a bare machine and installed Fedora 35. Because I got to do a clean install I could also enable LUKS out of the box to encrypt the drive.

Also running both a deb and rpm based distro will help me stay current with both and make testing a little easier without running VMs all the time.

I used to run Fedora when I was at IBM and even worked with the internal team that packaged some of the tools we needed on a day to day basis (like Lotus Notes and the IBM JVM). I decided it would be good to give it a try again especially as Fedora releases tend to move a little quicker than Ubuntu LTS and are more aggressive at picking up new versions.

The main hardware differences to the XPS13 are double the RAM at 32gb and double the storage with a 1TB SSD. The screen is the same resolution but slightly larger and without a touch screen (but that’s not something I make a lot of use of). It also comes with a Lenovo trademark track point as well as a trackpad. The CPU is still a 4 core ( with Hyper Threading) but the base clock speeds are better (Dell, Lenovo)

The only niggle I’ve found so far is that the USB-C port layout doesn’t work as well as with the XPS13 on my desk. The XPS13 has USB-C ports on both sides of the case, where as the X1 Carbon only has 2 on the left hand edge. But it does have a full sized HDMI port and 2 USB 3.1 A ports which means I don’t need the little USB-C to USB-A hub I’d been using. This also makes pluggin in a SD card reader a little easier as the Lenovo doesn’t have one built in.

The keyboard feels a little nicer (just got to get used to the ctrl and fn keys being swapped, though there is bios setting to flip them)

Pimoroni Keybow Upgrade

I’ve had a little 3 key Pimoroni Keybow sat on my desk for a while. It was running the same basic config I had setup when I bought it, namely mapping the three buttons to volume down, mute and volume up respectively.

Pimoroni Keybow Mini

While this was useful, it felt like there where better uses for it.

With more and more time being spent in video meetings having quick shortcuts to mute the mic or toggle the camera on/off sounded like a good idea. But then I wondered if I could find a way to switch the key mapping on the fly.

The key mapping is done by editing a short Lua script. This is stored on the sdcard that the Pi Zero that holds the Keybow boots from. This means the layout is normally fixed. Except the latest version (0.0.4) of the sdcard image on the Pimoroni Github page added support for starting a USB serial link as well as the HID used to send the keyboard events. This is exposed in the Lua environment so I managed to build the following script.

require "keybow"

-- Keybow MINI volume/zoom controls --

function setup()
    keybow.use_mini()
    keybow.auto_lights(false)
    keybow.clear_lights()
    keybow.set_pixel(0, 0, 255, 255)
    keybow.set_pixel(1, 255, 0, 255)
    keybow.set_pixel(2, 0, 255, 255)
end

-- Key mappings --

state = 'zoom'

function handle_minikey_02(pressed)
    if state == 'zoom' then
	if pressed then
            keybow.set_modifier(keybow.LEFT_ALT, keybow.KEY_DOWN)
            keybow.tap_key("v")
            keybow.set_modifier(keybow.LEFT_ALT, keybow.KEY_UP)
        end
    elseif state == 'media' then
        keybow.set_media_key(keybow.MEDIA_VOL_UP, pressed)
    end
end

function handle_minikey_01(pressed)
    if state == 'zoom' then
	if pressed then
            keybow.set_modifier(keybow.LEFT_ALT, keybow.KEY_DOWN)
            keybow.tap_key("a")
            keybow.set_modifier(keybow.LEFT_ALT, keybow.KEY_UP)
        end
    elseif state == 'media' then
        keybow.set_media_key(keybow.MEDIA_MUTE, pressed)
    end
end

function handle_minikey_00(pressed)
    if state == 'zoom' then
	if pressed then
             keybow.set_modifier(keybow.LEFT_ALT, keybow.KEY_DOWN)
             keybow.tap_key("n")
             keybow.set_modifier(keybow.LEFT_ALT, keybow.KEY_UP)
        end
    elseif state == 'media' then
        keybow.set_media_key(keybow.MEDIA_VOL_DOWN, pressed)
    end
end

local function isempty(s)
  return s == nil or s == ''
end

function tick()
    local line
    line = keybow_serial_read()
    if not isempty(line) then 
        -- keybow_serial_write( line .. "\n" )
        if line == 'zoom' then
            keybow.clear_lights()
            keybow.set_pixel(0, 0, 255, 255)
            keybow.set_pixel(1, 255, 0, 255)
            keybow.set_pixel(2, 0, 255, 255)
            state = 'zoom'
        elseif line == 'media' then
            keybow.clear_lights()
            keybow.set_pixel(0, 255, 0, 255)
            keybow.set_pixel(1, 0, 255, 255)
            keybow.set_pixel(2, 255, 0, 255)
            state = 'media'
        end
    end

end

The serial port gets setup on /dev/ttyACM0 on my laptop so I’m toggling between the 2 modes with echo media > /dev/ttyACM0 and echo zoom > /dev/ttyACM0.

In media mode it works exactly the same as before, but in zoom mode it toggles the camera on/off, toggles mute on/off and cycles through the available cameras.

This worked but keybow_serial_read() call added 1 second of latency to each call to the tick function which really wasn’t great as it was possible to miss key presses.

A bit of digging in the git rep turned up the file that implemented the serial access and this bit of code:

int serial_open(){
    if(port_fd > -1) return 0;

    port_fd = open(KEYBOW_SERIAL, O_RDWR);

    if(port_fd > -1){
        printf("Open success\n");
        tcgetattr(port_fd, &termios);
        termios.c_lflag &= ~ICANON;
        termios.c_cc[VTIME] = 10;
        termios.c_cc[VMIN] = 0;
        tcsetattr(port_fd, TCSANOW, &termios);
    }
    return 0;
}

The termios.c_cc[VTIME] = 10; was what was causing the delay. I rebuilt the library changing the value to 1 and 0. The value is in deciseconds (1/10 seconds)

With 1 the delay was cut to a tenth of a second, which was OK, but meant you had to be very deliberate in pushing the button to make sure it didn’t get missed, which with a mute toggle is a little risky.

With 0 it worked perfectly.

The script also changes the backlight colour for the keys based on mode so I can see which is active. It should be possible to add more modes as needed.

Next up is to see if I can script the toggling the mode based on if Zoom is the currently active window. Looks like it should be possible with tools like xprop or xdotool.

D11 Label Printer

A couple of weeks ago I was rearranging my collection of Raspberry Pi’s that live in the attic (A Kubernetes cluster, LoRa gateway and a few other things) and I was having problems remembering exactly which was which as a few of them have the same Pimoroni Pibow case in the same colours. I decided it was time to actually label them all to make life a little easier.

My first thought was a Dynmo device, but I decided that I didn’t want one of the original plastic tape embossing machine and the newer printers get expensive quickly. I did go down a rabbit hole around Brother label printers that come with Linux printer drivers, but decided I didn’t actually need that level of support.

I ended up grabbing a D11 Bluetooth Thermal Label Printer from Amazon. It comes with a roll of stickers 12×40 mm and can print text, numbers emoji, barcodes or QR codes. There are many different sized stickers with a selection of borders, background prints or transparent and even a glow in the dark version.

The rolls have NFC tags to identify the size and type of stickers currently installer in the printer and it updates the app with this when you try to print.

It uses an Android (and iOS) app to create the labels and then send them to the printer. The app is pretty intuitive, the only slight niggle is that if you want to save pre-built layouts you need to sign up to a online account. This is not a problem if you are just doing one off labels for different things.

Summary

It has solved the problem I bought it for, we will see how the thermal paper holds up over time but most of the labels are on the underside of the Pibow cases so should be out of direct light most of the time.

FlowForge v0.1.0

So it’s finally time to talk a bit more about what I’ve been up to for the last few months since joining FlowForge Inc.

The FlowForge platform is a way to manage multiple instances of Node-RED at scale and to control user access to those instances.

The platform comes with 3 different backend drivers

  • LocalFS
  • Docker Compose
  • Kubernetes

LocalFS

This is the driver to use for evaluating the platform or as a home user that doesn’t want to install all the overhead that is required for the other 2 drivers. I starts Projects (Node-RED instances) as separate processes on the same machine and runs each one on a separate port. It keeps state in a local SQLite database.

Docker Compose

This version is a little more complicated, it uses the Docker runtime to start containers for the FlowForge runtime, a PostgreSQL database and Nginx reverse proxy. Each Project lives in it’s own container and is accessed by a unique hostname prepended to a supplied hostname. This can still run on a single machine (or multiple if Docker Swarm mode is used)

Kubernetes

This is the whole shebang, similar to Docker Compose the FlowForge platform all runs in containers and the Projects end up in their own containers. But the Kubernetes platform provides more ways to manage the resources behind the containers and to scale to even bigger deployments.

Release

Today we have released version 0.1.0 and made all the GitHub projects public.

The initial release is primarily focused on getting the core FlowForge platform out there for feedback and we’ve tried to make the LocalFS install experience as smooth as possible. There are example installers for the Docker and Kubernetes drivers but the documentation around these will improve very soon.

You can read the official release announcement here which has a link to the installer and also includes a walk through video.