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Streaming video and audio of an USB webcam to multiple users of a website with SSL, basic authentication and in-video timestamps (FFmpeg, RTMP, NGINX, HLS and MPEG-Dash)


(Please don’t mind the mess)

Having an USB webcam connected to a Raspberry Pi 4, we’re going to use FFmpeg to securely (SSL and secret-key authentication) stream its video and audio (with CCTV-style timestamps) to a containerized NGINX server over RTMP/RTMPS protocol, and then use this same server to broadcast the stream to multiple users using both Apple HLS and MPEG-Dash. Users will be able to watch the stream on the browser, Android, iOS, Linux, Windows and MacOS.


Architecture layout

This is useful if you’re willing to have any video source (file or live feed) streaming on the internet or network, maybe also host the stream on your own website (embedded as HTML) to display public events/places, all-sky-cameras, CCTV, science experiments, etc. Or anything that won’t match with some of the major streaming platform policies. Or something that’s designed to broadcast 24/7. The cases are numerous. Whatever the use-case be, I have applied here simple but efficient methods to limit who can stream, who can watch and also to end-to-end encrypt everything. In this article I’m going to demonstrate how I configured it all using common “of-the-shelf” tools, and I hope this helps you.

But before we begin, note that using this configuration I’m experiencing about 50 seconds of delay between what’s recorded and what’s played with all devices communicating over a 54mpbs Wifi network. But the biggest bottleneck I assume it’s NGINX processing the stream.

Preparing the NGINX Server

The most important part of this setup is the NGINX server. This server is going to receive the media stream from the Raspberry Pi, decrypt it, authenticate and serve it as both HLS and Dash. However NGINX won’t support RTMP media stream by default, we’ll have to compile it with the RTMP module. This process is straight-forward but tedious, so I’ve built a Docker image to do that for us. In case you want to do it manually for some reason you can follow the commands of the Dockerfile:

For you to set this container up, first clone the Docker image repository:

$ git clone

Then cd into it:

cd nginx_rtmp_hls_dash

Now you will need Docker and Docker Compose installed on your machine. Installing them is pretty simple, so take a minute to do so if you don’t have them already.

Build the image:

$ docker-compose build broadcaster

And spawn up the NGINX server:

$ docker-compose up broadcaster

The server is now ready to accept streams and to start broadcasting them.

This will leave NGINX running attached to the 4080, 4443, 4080 and 4936 ports on the Docker host machine. Check the repository documentation for more information on those ports. Or better yet, take a look at the nginx.conf file:

You can change this file as you see fit, just be sure to check the “Warnings” section of the repository documentation.

As you can see on the nginx.conf, we have a rudimentar secret-key based authentication. It’s enough for the purpose of this article and for the most simple usage of media streaming. Mind though that it only makes sense to use this authentication over SSL/TLS encryption, because the keys will be sent as plain text otherwise.

The configuration also describes a SSL and a plain-text endpoint for each connection. So you can choose between encrypted and unencrypted on all steps.

Sending webcam audio and video to the NGINX server

Now that the server is up and running, we need to SSH into the Raspberry Pi (or any computer with an USB webcam). In my case the Pi has a generic USB webcam connected to it. Our goal is to find this webcam device and to use FFmpeg to start a RTPM and RTPMS audio/video stream to the NGINX server over the network. Be sure to have FFmpeg installed on your Pi/computer, this should be simple to do.

Also mind that here I’m going to be describing steps for a Linux computer. Although FFmpeg is multi-platform, the way the video data input is harvested may change depending on the OS. I’m going to use Video4Linux. On the repository you will find a more simple example for streaming .mp4 files.

The first step is to find the webcam device:

$ v4l2-ctl --list-devices

bcm2835-codec-decode (platform:bcm2835-codec):

bcm2835-isp (platform:bcm2835-isp):

GENERAL WEBCAM: GENERAL WEBCAM (usb-0000:01:00.0-1.3):

This output tells me that /dev/video0 is the webcam.

This webcam have a built-in microphone and we also want to stream the audio, so we need to find its audio interface:

$ arecord -L

    Discard all samples (playback) or generate zero samples (capture)
    Rate Converter Plugin Using Samplerate Library
    Rate Converter Plugin Using Speex Resampler
    JACK Audio Connection Kit
    Open Sound System
    PulseAudio Sound Server
    Plugin using Speex DSP (resample, agc, denoise, echo, dereverb)
    Plugin for channel upmix (4,6,8)
    Plugin for channel downmix (stereo) with a simple spacialization
    Default ALSA Output (currently PulseAudio Sound Server)
    bcm2835 Headphones
    USB Stream Output
    Default Audio Device
    Front output / input
    USB Stream Output

It’s the one set as the default: sysdefault:CARD=WEBCAM.

We also need to discover the supported resolutions for the webcam:

$ ffmpeg  -hide_banner -f video4linux2 -list_formats all -i /dev/video0

[video4linux2,v4l2 @ 0xaaaac7504320] Compressed:       mjpeg :          Motion-JPEG : 1920x1080 1440x1080 1280x720 800x600 800x480 720x480 640x480 640x360 480x270 320x240 176x144
[video4linux2,v4l2 @ 0xaaaac7504320] Raw       :     yuyv422 :           YUYV 4:2:2 : 1280x720 800x600 800x480 720x480 640x480 640x360 480x270 320x240 176x144

I think 1280x720 is a good choice for this experiment.

Now a quick side note: I decided to play with webcam streaming to help me in a weather project for astronomy data acquisition, so for me a CCTV-style timestamp and the ability to write text on the video was essential. Luckily FFmpeg has it all covered (check the drawtext option on the next command).

As my laptop’s network IP (running the NGINX) is, this is how I composed my RTMPS webcam streaming with in-video timestamps, from the Raspberry Pi:

ffmpeg \
    -f video4linux2 -framerate 25 -video_size 1280x720 -i /dev/video0 \
    -f alsa -ac 2 -i sysdefault:CARD=WEBCAM \
    -c:v libx264 -b:v 1600k -preset ultrafast \
    -x264opts keyint=50 -g 25 -pix_fmt yuv420p \
    -c:a aac -b:a 128k \
    -vf "drawtext=fontfile=/usr/share/fonts/dejavu/DejaVuSans-Bold.ttf: \
text='CLOUD DETECTION CAMERA 01 UTC-3 %{localtime\:%Y-%m-%dT%T}': fontcolor=white@0.8: fontsize=16: x=10: y=10: box=1: boxcolor=black: boxborderw=6" \
    -f flv "rtmps:"

Note the rtmps: address, sending the RTMPS to the NGINX server.

Be sure to read the repository to learn more about the address format and the stream-key.

After running it, the RPi4 is already streaming the webcam audio and video to the NGINX server.

Watching it: HLS or MPEG-Dash?

Many articles go deep into the difference between the two. My take is that unless you have a reason, use MPEG-Dash over HLS, as it’s newer and “smart” enough to adapt the video quality to match the viewer’s connection bandwidth.

So from now on in this article I’m going to be covering only MPEG-Dash examples. If you want to know how to watch HLS, check the repository documentation.

Watching the stream on VLC (Windows, Linux and MacOS)

If you have VLC installed, you can open it and press Ctrl + n or Cmd + n. On the network address input, enter:

Replace with your NGINX server address.

Hit enter, accept the certificate issue (it’s a self signed SSL certificate) and wait for the stream to begin:

VLC on Linux

Watching the stream on mobile (Android and iOS)

For that any MPEG-Dash (and HLS) compatible mobile client will work. I don’t have an iOS device here to test, but I’m sure there’s plenty of clients to choose. For Android I’m going to use the ExpressPlayer.

For some reason this app does not implement HTTPS (shame!), so I’m going to use HTTP in this example.

Open the app, select “CUSTOM INPUT”, then on “Media/MS3 URL …” add:

Click “Play” and select “WV-DASH-M4F”:

Playing on Android

Watching the stream on the web (Google Chrome, Firefox, etc.)

There are two ways I know of for playing MPEG-Dash on browsers: Dash.js and Google’s Shaka Player, both JavaScript implementations.

Here I’m going to display Google’s Shaka in action.

Also, as we’re using a self signed certificate, Shaka will complain about not being able to verify the authority, so here too we’ll need to use HTTP instead of HTTPS. But this should be fixed if ever on production, as proper certificates will be used.


<!DOCTYPE html>
    <script src="js/shaka-player.compiled.js"></script>
    <script src="js/app.js"></script>
    <video id="video" width="1280" controls autoplay></video>

app.js (taken from here)

const manifestUri =

function initApp() {

  if (shaka.Player.isBrowserSupported()) {
  } else {
    console.error('Browser not supported.');

async function initPlayer() {
  const video = document.getElementById('video');
  const player = new shaka.Player(video);

  window.player = player;
  player.addEventListener('error', onErrorEvent);

  try {
    await player.load(manifestUri);
  } catch (e) {

function onErrorEvent(event) {

function onError(error) {
  console.error('Error code', error.code, 'object', error);

document.addEventListener('DOMContentLoaded', initApp);

And it works like a charm, it’s the best player I’ve tested so far.

Thumbs up

Bonus: We don’t need NGINX for a single viewer

If your goal is to have a single computer consuming the video stream, eg.: RPi sends webcam video to a desktop computer to be recorded. Then you don’t need a NGINX server, you can stream directly from the RPi to the desktop computer using just FFmpeg and VLC over the much simpler RTP protocol.

Some comments on that:

  • You will need to decide between playback or record, I failed to perform both on VLC without crashing.
  • The record-and-play delay is a lot smaller, around 10 seconds on my setup.
  • No encryption nor authentication.

First, let’s name the machines:

RPi -
Desktop -

Start to stream the webcam video, from the RPi:

ffmpeg \
    -f video4linux2 -framerate 25 -video_size 1280x720 -i /dev/video0 \
    -f alsa -ac 2 -i sysdefault:CARD=WEBCAM \
    -c:v libx264 -b:v 1600k -preset ultrafast \
    -x264opts keyint=50 -g 25 -pix_fmt yuv420p \
    -c:a aac -b:a 128k \
    -vf "drawtext=fontfile=/usr/share/fonts/dejavu/DejaVuSans-Bold.ttf: \
text='CLOUD DETECTION CAMERA 01 UTC-3 %{localtime\:%Y-%m-%dT%T}': fontcolor=white@0.8: fontsize=16: x=10: y=10: box=1: boxcolor=black: boxborderw=6" \
    -f rtp_mpegts "rtp://"

Note that I’m pointing to the Desktop machine even though there’s no server running there. rtp:// I’ve also chosen the port 5000, but that’s up to you.

Now, on the Desktop computer open VLC and press Ctrl + n or Cmd + n to open the network stream dialog. And on the address input enter: rtp:// Then press Enter to start watching the stream.

That’s correct, we’re entering the same IP of the Desktop computer. That’s the computer which is receiving the stream.

That’s it

Video streaming is definitely fun. I can see many projects where this will be useful. I’m also feeling happy after learning how to set up these configurations and I hope you have enjoyed it as well. Feel free to contact me if you’re facing issues or just if you want to chat.

See you!

How to use specific Ruby and Node.js legacy versions on Alpine with Dockerfile

While onboarding on a new project I ended up needing to build a Docker image for legacy versions of both Ruby and Node.js, more specifically Ruby 2.4.0 and Node.js 9.9.0. Unable to find good instructions on how to do so, I decided to write my own.

I saw two options here, one was to start from Ruby 2.4.0 official image and then install Node 9.9.0 on it; The other was the contrary, to start with Node and install Ruby. I’ve opted for the latter as I’m more familiar with the manual Ruby setup. So I’ll be starting from the image node:9.9.0-alpine.

I’ve looked both into RVM and asdf as version manager options for Ruby, really trying to ease out the Ruby installation process, but further inspection revealed that neither were built with containers in mind, it gets funky to try to set them up on a Alpine container. Not impossible, but funky. Manually compiling Ruby proved to be much easier to me, so I’m going this path.

So the steps would be:

  • Install system dependencies.
  • Use bash as the default shell (optional).
  • Download, compile and install Ruby 2.4.0 source code.

And here is it:

FROM node:9.9.0-alpine

ARG RUBY="ruby-2.4.0"

RUN apk add --no-cache git make gcc g++ libc-dev pkgconfig \
    libxml2-dev libxslt-dev postgresql-dev coreutils curl wget bash \
    gnupg tar linux-headers bison readline-dev readline zlib-dev \
    zlib yaml-dev autoconf ncurses-dev curl-dev apache2-dev \
    libx11-dev libffi-dev tcl-dev tk-dev

SHELL ["/bin/bash", "-l", "-c"]

RUN wget --no-verbose -O ruby.tar.gz ${RUBY_RELEASE} && \
    tar -xf ruby.tar.gz && \
    cd /${RUBY} && \
    ac_cv_func_isnan=yes ac_cv_func_isinf=yes \
    ./configure --disable-install-doc && \
    make && \
    make test && \
    make install

RUN cd / && \
    rm ruby.tar.gz && \
    rm -rf ${RUBY_NAME}

You can change the Ruby and Node.js versions to be the ones you need, I don’t think the process will dramatically change from version to version.

This code is on GitHub and DockerHub as zfschuindt/node_and_ruby:node-9.9.0_ruby-2.4.0.

My first WebAssembly application using Godot

It’s just a simple astrophotography application to help me fixing what I’m learning about the Godot game engine. I wish I was writing this in Rust or C++, but the Godot documentation is firendly for beginners using the GDScript, Godot’s own language.


Source code

Sunlight beam progression over a few weeks

This post was originally published on my old blog dedicated to amateur astronomy.

Recently I was working at my office when I noticed a light beam that wasn’t there on the previous days. I knew it was going to get bigger during the next days, so I decided to record it for a few weeks, plot a chart and make some calculations out of it, just for fun.

In total 14 days were recorded, but not in a 14-day time span. The first picture was taken August 20th and the last on September 12th. The missing pictures relates to days in which the weather was blocking the beam to be visible.

I used a fixed metric ruler on the wall and took a picture every day roughly at the same time (4:30pm).


It changed every day. Not only the beam size was getting bigger but the “furthest” and “nearest” point were  both moving to the left. This chart plots the evolution:


That’s the CSV data I created for this chart:


And that’s the R script I wrote to plot it: <- read.csv(file="~/sunlight_experiment.csv")$taken_at <- as.POSIXct($taken_at, format = "%Y-%m-%dT%H:%M", tz = "America/Maceio")$size <- ($ends -$starts)

ggplot(, aes(x=taken_at)) +
  labs(title = "Sunlight beam projection over a few weeks") +
  geom_line(aes(y = starts, color = "darkred")) +
  geom_line(aes(y = ends, color = "darkblue")) +
  geom_line(aes(y = size, color = "darkgreen")) +
  scale_color_discrete(name = "Labels", labels = c("Furthest Point", "Beam Size", "Nearest Point")) +
  xlab("Date") +
  ylab("Line Point (cm)")

I also wrote a super simple Elixir program to compute the growth average for these values:

defmodule SunLightExperiment do
  @moduledoc false

  alias Decimal, as: D
  require Logger

  @doc false
  def perform do
    with data <- read_data("data.csv"),
         starts_growth <- compute_growth(data, :starts),
         ends_growth <- compute_growth(data, :ends),
         size_growth <- compute_growth(data, :size),
         starts_growth_average <- average(starts_growth),
         ends_growth_average <- average(ends_growth),
         size_growth_average <- average(size_growth) do"Printing results...")

      IO.puts("\n'starts' growth: #{inspect(starts_growth)}\n")
      IO.puts("'ends' growth: #{inspect(ends_growth)}\n")
      IO.puts("'size' growth: #{inspect(size_growth)}\n")
      IO.puts("'starts' growth average: #{inspect(starts_growth_average)}\n")
      IO.puts("'ends' growth average: #{inspect(ends_growth_average)}\n")
      IO.puts("'size' growth average: #{inspect(size_growth_average)}")

  @spec read_data(String.t()) :: Enumerable.t()
  defp read_data(file) do
    |> CSV.decode()
    |> Stream.take(10)
    |> Stream.drop(1)
    |> Enum.filter(fn entry -> entry != %{} end)

  @spec compute_growth(list(map()), atom()) :: list(D.t())
  defp compute_growth(results, v) do
    Enum.reduce(results, [], fn result, acc ->
      case result ==, 0) do
        true ->

        _any ->
          acc ++ [D.sub(Map.get(result, v), tnm1(results, v, Enum.count(acc) + 1))]

  @spec average(list(D.t())) :: D.t()
  defp average(list) do
    with count <- Enum.count(list),
         count <-,
         sum <- Enum.reduce(list,, fn e, acc -> D.add(e, acc) end) do
      D.div(sum, count)

  @spec tnm1(list(map()), atom(), integer()) :: D.t()
  defp tnm1(results, variable, n) do
    |> - 1)
    |> Map.get(variable)

  @spec get_valid_row({:ok, list(String.t())} | any()) :: list(String.t())
  defp get_valid_row(result) do
    case result do
      {:ok, row} -> row
      _any -> []

  @spec to_entry(list(String.t())) :: {:ok, Entry.t()} | {:error, Error.t()}
  defp to_entry([id, filename, taken_at, starts, ends]) do
    with {:ok, taken_at, _offset} <- DateTime.from_iso8601(taken_at),
         {starts, _any} <- D.parse(starts),
         {ends, _any} <- D.parse(ends),
         entry <- do_to_entry(id, filename, taken_at, starts, ends) do

  defp to_entry(_any) do

  @spec do_to_entry(String.t(), String.t(), DateTime.t(), D.t(), D.t()) :: map()
  defp do_to_entry(id, filename, taken_at, starts, ends) do
      id: id,
      filename: filename,
      taken_at: taken_at,
      starts: starts,
      ends: ends,
      size: D.sub(ends, starts)

  @spec trim_columns(list(String.t())) :: list(String.t())
  defp trim_columns(row) do, fn
      column when is_binary(column) -> String.trim(column)
      column -> column

According to Wakatime I took 1 hour and 33 minutes to write this one:


And the results are:

  • The “nearest point” moved to the left with a average speed of 1.34cm per day.
  • The “furthest point” moved to the left with a average speed of 1.84cm per day.
  • The “beam size” grew about 0.5cm per day.

I made the calculations using only the first 8 days, as they were separated with a almost precise 24h interval. This whole thing was a proof of concept for a later iteration of this experiment. The amount of data and the lack of precision yielded funky numbers, but this was expected in some sense. I think the overall outcome of this experiment is positive, I feel ready to start processing some more serious data.

And of course, that’s the beam on the first day, August 20th:


Now it on the last day, September 12th:


Click here to see all the images.

Converting a old webcam into a planetary camera

This post was originally published on my old blog dedicated to amateur astronomy.

Willing to test EQAlign I decided to grab an old webcam I had here (Logitech C270 HD) and convert it to a so called “planetary camera”. It consists of removing the built-in lens and replacing it with a telescope.  So I removed the lens and glued a 1.25” ocular tube to the plastic case, then I could easily attach/detach it to/from telescope focusers.

Starting to disassembly:


This black dusty thing is the lens, I need to remove it:


Two screws in the back held it in place:


Sensor exposed (I forgot to photograph, but this component marked as D1 is a green LED that lights up when the camera is being used. I covered it with black electrical tape so it won’t mess with the sensor):


Reassembly without the lens and gluing the 1.25” ocular tube. It’s really important to center the sensor on the tube. (Used super glue here):


I got this tube from a crappy ocular I had here, so I used the ocular lens itself as a dust cover for the sensor:


Cut off the webcam support:


Added black tape all around it to prevent light from entering:


And just out of curiosity, you can use this on a DSLR lens:



I was able to take this picture using this lens, kinda funny:


Now it’s time to test it on a telescope:


Pointing it to the top of a far away building:


Used the notebook on Google Hangouts for a proof of concept and it worked great, just a little bit of dust on the sensor but that is easily cleaned (The low quality is due to atmospheric perturbations):


So I decided to connect my control station with Ekos / INDI:


And got it streaming wireless to my workstation, where I can also control the mount using a X Box joystick:


It was fun to do this, now I’ll manage to clean the sensor and proceed to try polar alignment using EQAlign.

Build showcase: My wireless Raspberry Pi powered 'science station' for controlling the telescope mount, DSLR camera and all future gear

This post was originally published on my old blog dedicated to amateur astronomy.

I’m really happy to announce that I finished to building/setting up what I’m calling my “science station”. It’s a plastic box with a single board computer, a wifi router and some other stuff that allows me to control physical devices like equatorial telescope mounts and DSLR cameras remotely from my notebook with no wires in between.


I believe the idea here is really similar to products like SkyFi and StellarMate but with a DIY element. Also I’m planning to later hook it up with non-astronomy related things, like SDR devices.

The main element of my build is a Raspberry Pi 4 with 4GB RAM. It’s running Manjaro ARM Linux on a 64GB SD card. I’ve also compiled and installed myself all the INDI Core libary as well as the INDI 3rd Party libraries and drivers, and installed the INDI Web Manager using Python’s pip. With that the Pi is already able to talk to dozens of astronomy devices like cameras, focusers, filter wheels, etc.

Now with the INDI Web Manager configured as a systemd service (as well with OpenSSH), I can say that the device is ready to roll. And I’ll be using it to control my Meade LX85 EQ mount and my wife’s Canon EOS 600d DSLR camera. Focus is still manual, but I’m OK with that. And either way it’s just a matter of getting a focuser later on the road.

Inside the plastic box you can also see a wifi router, and that’s only for when I’m outside home. When I’m home, the Pi is programmed to connect to my home’s wifi connection, and my notebook will do the same, so that wifi router inside the station is just ignored. With that I can connect the notebook and station together and also enjoy an internet connection on the notebook. When I’m outside however, I can just connect to the 2.4GHz session that this router is broadcasting, which will allow me to connect the Pi <–> Notebook, but no internet, since I’m outside anyway.

All other stuff is basically power adapters on a single extension (to power everything using a single outlet terminal) and a handmade cable management board. So that the cables won’t be entangling inside.

In total it has 4 cables running from/to the station.

Running in:

  • Main power cable (from the wall outlet)

Running out:

  • EQ mount power cable
  • EQ mount serial data cable
  • DSLR USB cable

All these cables can be connected close to the telescope/mount, so the station lives close to the telescope itself. But no cables on the notebook, all is connected using wifi.

On the notebook, the software I’m running to control everything is KStars with Ekos on a Manjaro Linux workstation.

The next step is to configure a wired XBox-like joystick on the notebook to control the mount and later to set up my RTL-SDR with a VHF antenna on the station itself.




Update: It’s now working controlled by a USB joystick. The joystick is connected directly on the notebook.




Linkerd service mesh CPU footprint on Kubernetes

I’ve been using Linkerd for a while on staging environments, but now I finally deployed it to production. And I went to inspect its CPU/memory footprint on my older Prometheus/Grafana setup. The Kubernetes cluster for production is 3-node-sized with 2GB RAM each and it’s running at DigitalOcean. After a whole day since the initial setup (which is really simple) the result is what follows.

CPU at node 1/3 (with annotations)

Linkerd CPU footprint graph, node 1/3 with annotations

CPU at node 1/3

Linkerd CPU footprint graph, node 1/3

CPU at node 2/3

Linkerd CPU footprint graph, node 2/3

CPU at node 3/3

Linkerd CPU footprint graph, node 2/3

My reports on memory usage

This Kubernetes cluster is basically running a dozen BEAM instances in production (we’re running a few Elixir APIs). That makes my reports on memory kinda useless due to the way that the BEAM manages its memory. At the end of the day, the memory usage of my containers is tightly related with how long they’re running.

But if you want to see them anyway, here they are:

Setting up a residential K3s single node cluster with KVM in a Manjaro host and using Cloudflare dynamic DNS


For long I’ve been playing with Kubernetes in production environments, at my job and in other projects. But everything was done using managed solutions like the one from Digital Ocean, which is great by the way. But I was willing to put a bit of my hands on it.

I happen to have a desktop computer (i5 8400 16GB RAM running Manjaro) which I’m not using that much, so I planned to setup a VM on it and run a single node Kubernetes cluster myself. But wait a second, for that we had Minikube, right? Yes, the problem being it’s designed to work inside your own computer and not to have contact with the external world, not even your local network. I was having trouble to make that work when I remembered about K3s, which is a Kubernetes distribution that’s actually simpler than Minikube, it’s great and it’s also production ready.

One of the key things here is that I wanted to take advantage of Intel’s Vt-d and KVM for virtualization. Also I want this VM to get its own IP address on my local network, so to my router it would appear as another physical machine. For that I’m going to create a bridge network interface between the host and the VM.

Before we start, mind that all the work here will be made in my Manjaro laptop, named skywitch a.k.a. “laptop”, connecting via SSH to the Manjaro bare metal desktop server, redwitch a.k.a “the host” (The SSH setup was already in place). I want to be able to connect to the K3s from skywitch and also to access its running services from the outside world. Which will need some port-forwarding at the internet router firmware. (K3s already comes with Traefik as an Ingress resource)

For the VM OS I’m going to choose Ubuntu Server 18.04 and its hostname will be named warlock.

All the servers, redwitch and warlock will have static IPv4 address on the local network. All other devices, including my laptop will be using DHCP.

Here’s how it’s going to look like after finished:
the big picture

You can ignore the HDMI KVM Switch device for the purpose of this guide.

And just to make things clear, here’s the /etc/hosts I’ve setup to my skywitch laptop:	localhost	skywitch	redwitch	warlock

::1	localhost ip6-localhost ip6-loopback
ff02::1 ip6-allnodes
ff02::2 ip6-allrouters

Another important thing is that here in Brazil is common for residential connections to lack a static external IP address, but to have a dynamic one that changes each restart. In my case my address changes depending on the route to the server, so I can be talking to Spotify as one address and to YouTube as other, you never know, but they all point to me. Plus the ports 80 and 443 are blocked to the external world, so people can not serve web pages without having to explicitly use a port like :8080 in the URL.

And that means two things:

  1. I’m going to need some sort of Dynamic DNS tool (remember no-ip?), in this case Cloudflare will be the choice, as it provides this service for private domain names with no cost.

  2. The router port-forwarding step must account for having the ports 80 and 443 blocked.

I’m going to assume you already have a server with a KVM setup and Vt-d enabled. So I’m not going to cover this process here, this can be easily found on Google. But if you’re wondering, here is one of those guides.

So the plan is:

  • Setup a bridge network interface on the host.
  • Create a Ubuntu 18.04 VM on the host with the bridged network.
  • Set up a K3s single node Kubernetes cluster into the VM.
  • Describing a Dynamic DNS cronJob into the cluster to update a domain name in Cloudflare with the external IP address.
  • Configuring the router to forward certain ports to the VM.
  • Deploying a simple application to the cluster and have it exposed to the world.

So let’s start.

Just a quick note: Many of the stuff presented here was taken from other guides on the web, which you can find all listed at the end of this post.

Setup a bridge network interface on the host

This will allow us to create a VM that will receive its own IP on our local network, pretty much as every other physical device does.

First install bridge-utils, it will come in handy later:

$ sudo pacman -S bridge-utils

Now you need to find out the name of your main network interface, which can be done with $ ip link show or $ ifconfig. In my case it was named enp0s31f6.

You’re also going to need to know the connection gateway and DNS addresses.

To get the gateway address you can just:

$ route -n | grep "^" | tr -s " " | cut -f2 -d" "

And to know the DNS:

$ cat /etc/resolv.conf

# Generated by NetworkManager
search GREATEK

In my case they’re the same, but it may differ from vendor to vendor, I’m not sure.

You should also pick up a static IP address for the host machine, I’m going to choose Plus I’m going to name the bridge interface as br1.

Now let’s create a /etc/netctl/kvm-bridge file:

$ sudo vi /etc/netctl/kvm-bridge

With the following content:

Description="Bridge Interface br10 : enp0s31f6"
set IP=no

Replace the values with the ones you want, then:

$ sudo systemctl restart NetworkManager.service

Then start and enable the kvm-bridge:

$ sudo netctl start kvm-bridge
$ sudo netctl enable kvm-bridge

And that’s it. You can use $ brctl show and $ bridge link to check bridges and see bridged interfaces respectively. But for now I think we’re good. Our host now have a new source for its static IP ( and a bridge network interface on it. Great.

Create a Ubuntu 18.04 VM on the host with the bridged network

I won’t get much into the libvirt usage, if you want here’s the virsh CLI reference page:

And the basics:

Boot a VM - virsh start <vm>
Stop a VM - virsh shutdown <vm>
Suspend a VM - virsh suspend <vm>
Delete a VM - virsh destroy <vm> and virsh undefine <vm>

We’re going to use a Shell script in order to create and configure the VM. So create a file:

$ vi ~/



if [ -z "$1" ] ;
 echo Specify a virtual-machine name.
 exit 1

sudo virt-install \
     --name $1 \
     --ram 4096 \
     --disk path=/home/fschuindt/hdd_repo/libvirt/images/$1.img,size=30 \
     --vcpus 4 \
     --os-type linux \
     --os-variant ubuntu18.04 \
     --network bridge:br1,model=virtio \
     --graphics none \
     --console pty,target_type=serial \
     --location '' \
     --extra-args 'console=ttyS0,115200n8 serial'

Here you shall stop and perform some editing in the file. A few things to look after are the RAM size, the number of CPUs and the --disk path, which mine is pointing to /home/fschuindt/hdd_repo/libvirt/images/ with a 30GB sized disk.

This directory on my host is on a 2TB HDD mount, it’s important you have it mapped to a physical device or to a place where you have enough space to install the VM. I just allocated 30GB, but that’s arbitrary. Just mind that this space will be actually occupied during the VM creation.

Also the --network bridge:br1,model=virtio part shall point to the br1 bridge interface we’ve created earlier in the guide.

Save and:

$ chmod +x

$ ./ warlock

Where warlock is the name of the VM.

Now the S.O. installation will begin, the Ubuntu setup is pretty straight forward so you must complete it with no problem. Just mind tho, at the “Software selection” phase to pick up the OpenSSH server.

After finished the installation, it will reboot and end the process.

Let’s connect via SSH to the freshly installed VM, for that, find the VM IP using:

sudo nmap -sP

It will have the port 22 (OpenSSH) opened. In my case it was

So, from my laptop:

$ ssh fschuindt@

Edit with your username (fschuindt for me) and it will ask you for the password you’ve set during installation. A good thing to do now is to set a static IP to the VM.

Edit the file /etc/netplan/01-netcfg.yml with:

# warlock VM

# This file describes the network interfaces available on your system
# For more information, see netplan(5).
  version: 2
  renderer: networkd
     dhcp4: no
     addresses: []
       addresses: [,]

I’m using my local values for gateway, I’m setting Cloudflare DNS as my DNS servers ( and and the VM local static IP to

To apply just:

$ sudo netplan apply

You will get disconnected from the SSH session, but that’s ok, just connect again with the new IP. Better yet, add it to your /etc/hosts file with your VM name, as I showed in the beginning.

Another important thing to do is to upload your SSH public key to the VM and to disable password logins. I won’t be covering it here, for that just go to:

Now we have a virtual Ubuntu 18.04 running with a static IP on our local network. Let’s do some work in it.

Set up a K3s single node Kubernetes cluster into the VM

There’s nothing much to say in this section really, K3s installation and usage is really simple, I recommend checking their homepage and the docs for a quick start. But here’s the basic:

This is inside the VM.

To install:

$ curl -sfL | sh -

This will also configure systemd, so K3s will start after every reboot.

To get the admin .yaml file:

$ sudo cat /etc/rancher/k3s/k3s.yaml

I’m going to save this on my laptop’s ~/.kube/. Saving it named as config connects your kubectl with the new cluster.

Now our single node K3s cluster is up and running, you can already start playing with kubectl. The next steps are really for exposing it to the world, so if you don’t want that you can declare work done =]. Otherwise we still have some more stuff to do.

Describing a Dynamic DNS cronJob into the cluster to update a domain name in Cloudflare with the external IP address

So I plan to expose services running on the K3s cluster to the outside world. If I had a static external IP address that would be great, but as you may already know it’s not the case. But it’s still possible to have a domain name pointed to the server as a Dynamic DNS (DDNS) using one DDNS provider like and DynDNS. That requires a DDNS client running on my system checking changes on the external IP and updating it against the provider.

The provider will be Cloudflare, as it’s not only a DNS provider but also supports DDNS. I own the domain name on Cloudflare and I’m going to use the subdomain to point to the cluster.

The client will be ddclient, a well known DDNS client written in Perl. I’ve setup a ddclient public Docker image that you can configure and use for that same matter. For using it I’m going to set up a Kubernetes cronJob that every 5 minutes will spawn a container using that image and run a DDNS check/update command, then exits and waits to the next execution, and so on.

It’s not perfect but it’s enough, that shall keep the subdomain name updated.

So if you will, create a ddclient-job.yml file:

apiVersion: v1
kind: ConfigMap
  name: ddclient-config-map
    owner: ddclient
  LOGIN: ""
  PASSWORD: "your-cloudflare-global-api-key"
apiVersion: batch/v1beta1
kind: CronJob
  name: ddclient-job
    owner: ddclient
  concurrencyPolicy: Forbid
  failedJobsHistoryLimit: 5
  successfulJobsHistoryLimit: 5
  startingDeadlineSeconds: 60
  # At every 5th minute.
  schedule: "*/5 * * * *"
      name: ddclient-job
        owner: ddclient
      activeDeadlineSeconds: 240
      backoffLimit: 3
          name: ddclient-job-pod
            owner: ddclient
          - name: ddclient-job-container
            image: zfschuindt/ddclient:latest
            command: ["bash", "-c", "/ddclient/"]
              - configMapRef:
                  name: ddclient-config-map
          restartPolicy: OnFailure

And edit it to fit your needs. Especially the ConfigMap section, where you want to provide your Cloudflare credentials with the domain/subdomain names. I’ve created space for up to 10 subdomains, but if you want more you can easily edit the file of the image.

Here’s the ddclient image GitHub repository if you want:

Now we can just spawn the job into K3s:

$ kubectl apply -f ddclient-job.yml

Now someone is working to keep my external IP updated on the domain name. Wonderful!

Configuring the router to forward certain ports to the VM

This part changes for everyone. It’s really dependent on which internet router vendor/model you have, but in general the concept is the same: Let the router to know which static IP address on the local network is to forward incoming connections on given ports.

If skipped, the outside world won’t be able to connect to the services on the cluster, as the router won’t know what to do with those connections, it must deliver it to some device on the network, but without knowing which it drops it.

For configuring this you need to access your router firmware interface, for me it’s on for any wire-connected device on the network. Then you must provide credentials and look for any “port forward” option.

You can find the default user/password combination for your router as well as instructions for port forwarding (if it supports) on the PDF manual for your router model (every model has one, just check online).

For me I added two entries on the port-forwarding rules list. One forwarding every external connection on the port 7222 to the port 80 on the (the warlock VM) and other forwarding every external connection on the port 7223 to the port 443 on the same server, the (warlock VM).

It’s looking like this:


And that will let the router know to which device to send the incoming connections. One more thing to do, let’s deploy a service to the K3s and test the whole thing.

Deploying a simple application to the cluster and have it exposed to the world

This will be done at the skywitch laptop, connected to cluster using kubectl.

Right now we have only one DNS pointing to the cluster, which is I’m going to deploy a service to operate on this address, more precisely a Ingress resource.

The service we’re going to deploy is a simple HTTP “ping/pong” echo. It serves only one route GET /ping, which will reply 200 OK, "pong". I wrote this service using Elixir and it’s on GitHub here:

It already comes with its own Dockerfile and its image is publicly available at DockerHub. Plus if you check the /k8s folder on the repository you will find a group of Kubernetes resources for deploying it into Kubernetes. This will make everything easier.

The resources are:

  • config_map.yml
  • service.yml
  • ingress.yml
  • deployment.yml

Important: The files in the repository are just examples and may differ a bit from the ones presented here.

And we’re going to create them in this order, so the ConfigMap first:


apiVersion: v1
kind: ConfigMap
  name: echo-config-map
  namespace: default
    owner: echo
  MIX_ENV: "prod"
$ kubectl create -f config_map.yml

Then the Service:


apiVersion: v1
kind: Service
  name: echo-service
    app: echo
    owner: echo
  type: NodePort
    app: echo
    tier: web
  - port: 4080
    targetPort: 4080
$ kubectl create -f config_map.yml

The Ingress:


apiVersion: extensions/v1beta1
kind: Ingress
  name: echo-ingress
  - host:
      - backend:
          serviceName: echo-service
          servicePort: 4080
  - host:
      - backend:
          serviceName: echo-service
          servicePort: 4080

Notice here I’m also setting the domain name to be used within the local network, I’m going to add this hostname on my laptop’s /etc/hosts as well.

$ kubectl apply -f ingress

And finally the deployment:


apiVersion: extensions/v1beta1
kind: Deployment
 name: echo-deployment
 namespace: default
    owner: echo
    app: echo
    tier: web
  revisionHistoryLimit: 5
  replicas: 1
    type: RollingUpdate
      maxSurge: 1
      maxUnavailable: 0
        owner: echo
        app: echo
        tier: web
      terminationGracePeriodSeconds: 60
        - name: echo-container
          image: zfschuindt/http_echo:latest
          imagePullPolicy: Always
            - containerPort: 4080
          args: ["bash", "-c", "./app/"]
            - configMapRef:
                name: echo-config-map
              path: /ping
              port: 4080
$ kubectl create -f deployment.yml

And that shall make the available and serving the HTTP echo service to the world. :)

It may be already offline by the time you’re reading this, but believe me, it worked.

And that’s how my Kubernetes Dashboard looks like:

kubernetes dashboard

By the way, if you want to install this dashboard on your cluster, check how to do it here.

And with that we shall have our residential K3s cluster running and serving to the outside world on top of a fast and optimized virtualization method. I hope this guide have served you well, setting up this environment brought me new ideas for residential server setups and was a lot of fun!

Thank you for staying with me.

See you soon. :)


Cover picture: “Gutenberg Publishes the World’s First Printed Book (Illustration) Civil Rights Medieval Times Famous Historical Events Visual Arts”

Philosophical Perceiving Of Bits As The Fundamental Particles Of Our Universe

Part of: Les Tres Riches Heures du Duc de Berry


UPDATE on 08-08-2020: I no longer believe this is accurate. Instead I see many flaws in it, the bigger being the misunderstanding of the Heisenberg’s Microscope thought experiment.

Modern physics describe our Universe as built of Fundamental Particles. In 1814, the French mathematician, physicist and astronomer Pierre-Simon Laplace stated:

We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes.

This philosophical perception became known as the Laplace’s Demon.

Knowing each one of the fundamental particles of our Universe, where they are and how they interact with each other is enough to compute the whole past, present and future of everything in it.

Standard Model of Elementary Particles

Thinking about it raises me questions about the capability of creating a digital/simulated Universe.

You may say we already live in a simulation just like Matrix (which satisfies the Simulation Hypothesis), or we live in a real world. But none of those conditions remove us the possibility of creating ourselves a Universe simulation inside this one.

Once you know the nature of everything in a system, you can compute it. Incomputable programs only exists when some information is missing. That means everything in the Universe is computable, we just don’t have the information.

But from here we all must agree that consciousness (human thinking, emotions, imagination) is also computable. It fits inside a (x, y, z) dimensioned brain, which is inside this universe, no need to wonder.

No matter what you believe in, if we aren’t in agreement with each other, it’s due to some kind of religion/spiritual thing.

If you assume:


  • You assume every Fundamental Particle can be described in terms of 0 and 1.

So the real Fundamental Particles of the Universe can be perceived as 0 and 1.
The minimal components to describe all the other particles(?).

That’s a big question.

Now I see myself in the obligation to quote this part of the Yin and yang Wikipedia page:

In Chinese philosophy, yin and yang (陰陽 yīnyáng, lit. “dark-bright”, “negative-positive”) describe how seemingly opposite or contrary forces may actually be complementary, interconnected, and interdependent in the natural world, and how they may give rise to each other as they interrelate to one another.

Nature seems to replicate itself in different scales, from galaxies, moons, particles. And we see many fundamental dualities present in everything. I’m not pushing to spirituality (far from it), but it’s a remarkable pattern in our lives.

Clarifying the Uncertainty Principle

Some people think that in Quantum Mechanics particles (and/or information) are created from nothing or just to please the observer. This is wrong. The Uncertainty Principle exists because we don’t posses a device to proceed those measurements in subatomic level without interacting with the system.

To look to a particle using something like (let’s say) an optic microscope, you will need to fire light to the particle so it reflects its image back to you. But the photon will interact with the particle and will change its state. So the measurement is uncertain.


In order of relevance.


  • A part of Les Tres Riches Heures du Duc de Berry from the Books of Hours.
  • Standard Model of Elementary Particles from Wikipedia.

Concurrent Calculation Of Fibonacci In Elixir

There’s a known formula to evaluate a n Fibonacci term position without iteration or recursion. Formula

This formula give us a precise approximation:

  • Term n(26) = 121393.00000000009
  • Term n(1200) = 2.7269884455407366e250

Which according with this list, is fine:

  • Term n(26) = 121393
  • Term n(1200) = 272.698844554059974143456202000 × 10²⁷

The actual 1200th term position is 251 digit long, so I’ve posted a equivalent notation here.

A cool (and unrelated) fact is that it uses the Golden Ratio in the calculation (1 + √5) / 2, and what seems a reverse form of it (1 - √5) / 2.

So, the formula I’ve described as:

defmodule FibonacciCalculus do
  @golden_n :math.sqrt(5)

  def of(n) do
    (x_of(n) - y_of(n)) / @golden_n

  def x_of(n) do
    :math.pow((1 + @golden_n) / 2, n)

  def y_of(n) do
    :math.pow((1 - @golden_n) / 2, n)

By the way, if you haven’t already wondered, after the term 1474 it crashes:
Crash output

That’s because Erlang’s math module operates only with numbers that can be represented with floating points, and such a big number can’t.

But anyway, my goal here isn’t to calculate Fibonacci at all.
I’m just wanting to see Elixir’s power doing hundreds of those big number calculations at the same time.

I want to give Elixir a sequential integer list, in this case from 1 to 1474, which is the apparent limit. Then ask it to spawn a Erlang process for each one of those elements. Each process should receive the element and return the result of its FibonacciCalculus.of(n), being n the element.

I should end up with a unordered result, as reflect of the concurrent computation.

So, I’ve described the module:

defmodule ConcurrentFibonacci do
  def start do
    concurrent_map(1..1474, fn(x) ->
      "Fibonacci of #{x} is: #{FibonacciCalculus.of(x)}"

  def concurrent_map(list, func) do
    list |> e -> spawn(fn ->
      IO.puts func.(e)
    end) end)

And the result is beautiful:
Demonstration 1

A simple code and it executes more than 1400 concurrent processes in less than a blink of an eye.

Also the unordered result is evident:

Fibonacci of 1456 is: 8.640108610267577e303
Fibonacci of 1455 is: 5.3398807876359814e303
Fibonacci of 1457 is: 1.397998939790356e304
Fibonacci of 1458 is: 2.262009800817114e304
Fibonacci of 1460 is: 5.922018541424584e304
Fibonacci of 1465 is: 6.567619203443404e305
Fibonacci of 1470 is: 7.28360130920199e306

In an attempt to go deeper, instead of using the 1..1474 list, I’m using the following function:

def list do
  Enum.take(Stream.cycle(1..1474), 10_000)

This will repeat the 1..1474 list approximately 7x, to result in a list containing 10.000 elements.

Let’s see how it goes:
Demonstration 2

You can see it takes more time, obviously.
But let’s think about it. It’s ten thousand concurrent executions, ten thousand processes.

I have even tested with 100.000 processes, in my 4GB RAM i5@1.7GHz notebook it takes 5.47 seconds. One hundred thousand processes in 5 seconds.

I have to say I’m impressed.

I’m about one year playing with Elixir in my spare time, it’s a really fun language, especially if you never programmed functional before.

Ruby Gem To Verify Firebase Id Token Signatures

Article screenshot

This is a off blog’s topic, but I just released a new Ruby gem.

The gem firebase_id_token was developed to easily verify Firebase ID Token signatures in Ruby back-end environments. It uses Redis to store Google’s x509 certificates, which helps other processes in your application to access it really fast.

The Firebase ID Token is really a JWT. What the gem does is to check if the token was made for your application and if it’s valid, both in it’s parameters and in it’s RSA signature.

Check out the gem’s Github for more info. :)

Structure And Interpretation Of Computer Programs

Book cover illustration

Also referred as SICP, it’s a well known MIT book published in the late 80’s and used in the Computer Science and Electrical Engineering courses. Written by Harold Abelson, Gerald Jay Sussman and Julie Sussman. It’s the definitive functional programming book.

It’s available for free in many formats such as HTML and PDF. This book insipred me to create this blog. It’s just wonderful, it’s the great grimoire of computer wizardry. But not only that, the MIT OpenCourseWare has the whole video lectures from 1986 taught by Harold Abelson and Gerald Jay Sussman themselves, and with no cost.

These twenty video lectures by Hal Abelson and Gerald Jay Sussman are a complete presentation of the course, given in July 1986 for Hewlett-Packard employees, and professionally produced by Hewlett-Packard Television. These videos are also available here under a Creative Commons license compatible with commercial use.

Note: These lectures follow the first edition (1985) of Structure and Interpretation of Computer Programs. Many of the programs discussed were rewritten for the second edition (1996) of the book, and new material was added. These video lectures will still be useful for students using the second edition, since the overall themes of the course and order of presentation are unchanged.

These videos are courtesy of Hal Abelson and Gerald Jay Sussman, and are used with permission.

The book uses the language Scheme which is a Lisp dialect, one of the oldest languages (1959) to teach pure functional concepts from the very basics to the more complex cases. It’s like learning to program again from a pure functional perspective.

This course introduces students to the principles of computation. Upon completion of 6.001, students should be able to explain and apply the basic methods from programming languages to analyze computational systems, and to generate computational solutions to abstract problems. Substantial weekly programming assignments are an integral part of the course. This course is worth 4 Engineering Design Points.

And here’s the first lecture, just in case:

Course Citation and License

Eric Grimson, Peter Szolovits, and Trevor Darrell. 6.001 Structure and Interpretation of Computer Programs. Spring 2005. Massachusetts Institute of Technology: MIT OpenCourseWare, License: Creative Commons BY-NC-SA.

My Elixir Study Notes

Article illustration

While studying the Elixir Getting Started Guide I decided to begin resuming it out as a way to get it fixed. I ended up with a very useful document for those who already read the guide and want to quickly review any topic. It’s a one page document resuming the whole guide, which is just great to use with the Find command.

I basically copied, slightly altered and omitted great part of the content to focus on things I thought more important (to me). But provides a quick explanation of everything. Also includes some notes I made.

As this post still serve as a study resource, it’s constantly updated.


  • Get some help:
iex> i 'hello'
Data type
Raw representation
  [104, 101, 108, 108, 111]
Reference modules
  • When “counting” the number of elements in a data structure, Elixir also abides by a simple rule: the function is named size if the operation is in constant time (i.e. the value is pre-calculated) or length if the operation is linear (i.e. calculating the length gets slower as the input grows).
  • String concatenation is done with: <>.
  • Operators or, and, not can only accept boolean values. Besides these boolean operators, Elixir also provides ||, && and ! which accept arguments of any type. For these operators, all values except false and nil will evaluate to true.
  • The variable _ is special in that it can never be read from. Trying to read from it gives an unbound variable error.
  • Guard Clauses are neat:
# Anonymous functions can have guard clauses:
# They also apply to the 'case' statement, 'when'.
iex> f = fn
...>   x, y when x > 0 -> x + y
...>   x, y -> x * y
...> end
#Function<12.71889879/2 in :erl_eval.expr/5>
iex> f.(1, 3)
iex> f.(-1, 3)

Basic Types


All good, just true and false. Nothing special.


Pretty much like Lisp’s Atoms, A.K.A. Symbols in Ruby.

Anonymous Functions

Anonymous Functions (function literal, lambda abstraction) is delimited between fn and end.

# first class citizens (can be passed as arguments)
iex> add = fn a, b -> a + b end
iex> add.(3, 2)

Anonymous functions are closures and as such they can access variables that are in scope when the function is defined.


Describes itself.

# Add or subtract using ++ or --
iex> [2, 23, 42, 11, true]
iex> list = [1, 2, 3]
# Get head and tail.
iex> hd(list)
[2, 3]
  • When Elixir sees a list of printable ASCII numbers, Elixir will print that as a char list (literally a list of characters).
  • Single-quotes are char lists, double-quotes are strings.


Similar to lists, but stored in memory, all data is availible with no recursion needed.

iex> {:ok, "hello"}
{:ok, "hello"}
iex> tuple_size {:ok, "hello"}

List or Tuples?

Accessing the length of a list is a linear operation: we need to traverse the whole list in order to figure out its size. Updating a list is fast as long as we are prepending elements.

Tuples, on the other hand, are stored contiguously in memory. This means getting the tuple size or accessing an element by index is fast. However, updating or adding elements to tuples is expensive because it requires copying the whole tuple in memory.

When “counting” the number of elements in a data structure, Elixir also abides by a simple rule: the function is named size if the operation is in constant time (i.e. the value is pre-calculated) or length if the operation is linear (i.e. calculating the length gets slower as the input grows).

Pattern Matching

The match operator is not only used to match against simple values, but it is also useful for destructuring more complex data types. For example, we can pattern match on tuples:

iex> {a, b, c} = {:hello, "world", 42}
{:hello, "world", 42}
iex> a
iex> b

A list also supports matching on its own head and tail:

iex> [head | tail] = [1, 2, 3]
[1, 2, 3]
iex> head
iex> tail
[2, 3]

The pin operator ^ should be used when you want to pattern match against an existing variable’s value rather than rebinding the variable.

iex> x = 1
iex> ^x = 2
** (MatchError) no match of right hand side value: 2
iex> {y, ^x} = {2, 1}
{2, 1}
iex> y
iex> {y, ^x} = {2, 2}
** (MatchError) no match of right hand side value: {2, 2}

case, cond and if


Behavious pretty much as the classic case statement.

iex> case {1, 2, 3} do
...>   {4, 5, 6} ->
...>     "This clause won't match"
...>   {1, x, 3} ->
...>     "This clause will match and bind x to 2 in this clause"
...>   _ ->
...>     "This clause would match any value"
...> end

If you want to pattern match against an existing variable, you need to use the ^ operator:

iex> x = 1
iex> case 10 do
...>   ^x -> "Won't match"
...>   _  -> "Will match"
...> end

Another cool example, now with clauses conditions:

iex> case {1, 2, 3} do
...>   {1, x, 3} when x > 0 ->
...>     "Will match"
...>   _ ->
...>     "Would match, if guard condition were not satisfied"
...> end
  • If none of the clauses match, an error is raised.


case is useful when you need to match against different values. However, in many circumstances, we want to check different conditions and find the first one that evaluates to true. In such cases, one may use cond.

iex> cond do
...>   2 + 2 == 5 ->
...>     "This will not be true"
...>   2 * 2 == 3 ->
...>     "Nor this"
...>   1 + 1 == 2 ->
...>     "But this will"
...> end
  • This is equivalent to else and if clauses in many imperative languages.
  • If none of the conditions return true, an error is raised. For this reason, it may be necessary to add a final condition, equal to true, which will always match.

if and unless

Are useful when you need to check for just one condition, also pro provides a else statement.

iex> if true do
...>   "This works!"
...> end
"This works!"
iex> unless true do
...>   "This will never be seen"
...> end

do / end blocks

Equivalent to { / }, it’s also possible things like:

iex> if false, do: :this, else: :that
# Expressions like:
iex> is_number if true do
...>  1 + 2
...> end
** (CompileError) undefined function: is_number/2
# Should be:
iex> is_number(if true do
...>  1 + 2
...> end)

Binaries, strings and char lists

Binaries and bitstrings

You can define a binary using <<>>. It’s just a sequence of bytes. The string concatenation operation is actually a binary concatenation operator <>.

A common trick in Elixir is to concatenate the null byte <<0>> to a string to see its inner binary representation:

iex> "hełło" <> <<0>>
<<104, 101, 197, 130, 197, 130, 111, 0>>

A binary is a bitstring where the number of bits is divisible by 8. Smaller bit are just bitstrings!

A string is a UTF-8 encoded binary, and a binary is a bitstring where the number of bits is divisible by 8.

Char lists

A char list is nothing more than a list of characters.

Char list contains the code points of the characters between single-quotes (note that IEx will only output code points if any of the chars is outside the ASCII range). So while double-quotes represent a string (i.e. a binary), single-quotes represents a char list (i.e. a list).

Keywords and maps

Keyword list

It’s a associative data structure. In Elixir, when we have a list of tuples and the first item of the tuple (i.e. the key) is an atom, we call it a keyword list:

iex> list = [{:a, 1}, {:b, 2}]
[a: 1, b: 2]
iex> list == [a: 1, b: 2]
iex> list[:a]
  • It’s the default mechanism for passing options to functions in Elixir.
  • Only allows Atoms as keys.
  • Ordered as specified by the developer.
  • Remember, though, keyword lists are simply lists, and as such they provide the same linear performance characteristics as lists. The longer the list, the longer it takes to read from. For bigger data use maps instead.


Whenever you need a key-value store, maps are the “go to” data structure in Elixir:

iex> map = %{:a => 1, 2 => :b}
%{2 => :b, :a => 1}
iex> map[:a]
iex> map[2]
iex> map[:c]
  • Allows any value as key.
  • Maps’ keys do not follow any ordering.

Interacts great with pattern matching:

iex> %{} = %{:a => 1, 2 => :b}
%{:a => 1, 2 => :b}
iex> %{:a => a} = %{:a => 1, 2 => :b}
%{:a => 1, 2 => :b}
iex> a
iex> %{:c => c} = %{:a => 1, 2 => :b}
** (MatchError) no match of right hand side value: %{2 => :b, :a => 1}

Better syntax when all keys are atoms:

iex> map = %{a: 1, b: 2}

Another interesting property of maps is that they provide their own syntax for updating and accessing atom keys:

iex> map = %{:a => 1, 2 => :b}
%{:a => 1, 2 => :b}
iex> map.a
iex> map.c
** (KeyError) key :c not found in: %{2 => :b, :a => 1}
iex> %{map | :a => 2}
%{:a => 2, 2 => :b}
iex> %{map | :c => 3}
** (KeyError) key :c not found in: %{2 => :b, :a => 1}

Nested data structures (put_in/2 and update_in/2)

iex> users = [
  john: %{name: "John", age: 27, languages: ["Erlang", "Ruby", "Elixir"]},
  mary: %{name: "Mary", age: 29, languages: ["Elixir", "F#", "Clojure"]}

We have a keyword list of users where each value is a map containing the name, age and a list of programming languages each user likes. If we wanted to access the age for john, we could write:

iex> users[:john].age

It happens we can also use this same syntax for updating the value:

iex> users = put_in users[:john].age, 31
[john: %{age: 31, languages: ["Erlang", "Ruby", "Elixir"], name: "John"},
 mary: %{age: 29, languages: ["Elixir", "F#", "Clojure"], name: "Mary"}]

The update_in/2 macro is similar but allows us to pass a function that controls how the value changes. For example, let’s remove “Clojure” from Mary’s list of languages:

iex> users = update_in users[:mary].languages, &List.delete(&1, "Clojure")
[john: %{age: 31, languages: ["Erlang", "Ruby", "Elixir"], name: "John"},
mary: %{age: 29, languages: ["Elixir", "F#"], name: "Mary"}]

There is more to learn about put_in/2 and update_in/2, including the get_and_update_in/2 that allows us to extract a value and update the data structure at once. There are also put_in/3, update_in/3 and get_and_update_in/3 which allow dynamic access into the data structure. Check their respective documentation in the Kernel module for more information.


In Elixir we group several functions into modules.

iex> defmodule Math do
...>   def sum(a, b) do
...>     a + b
...>   end
...> end

iex> Math.sum(1, 2)


Given a file math.ex:

defmodule Math do
  def sum(a, b) do
    a + b

This file can be compiled using elixirc:

$ elixirc math.ex

This will generate a file named Elixir.Math.beam containing the bytecode for the defined module. If we start iex again, our module definition will be available (provided that iex is started in the same directory the bytecode file is in).

Elixir projects are usually organized into three directories:

  • ebin - contains the compiled bytecode
  • lib - contains elixir code (usually .ex files)
  • test - contains tests (usually .exs files)

When working on actual projects, the build tool called mix will be responsible for compiling and setting up the proper paths for you.

Scripted mode

  • .ex - files to be compiled
  • .exs - files to run in scripted mode (Learning purposes)


$ elixir math.exs

Named functions

  • def/2 - defines a function
  • defp/2 - defines a private function

Function declarations also support guards and multiple clauses. If a function has several clauses, Elixir will try each clause until it finds one that matches.

defmodule Math do
  def zero?(0) do

  def zero?(x) when is_integer(x) do

IO.puts         #=> true
IO.puts         #=> false
IO.puts[1, 2, 3]) #=> ** (FunctionClauseError)
IO.puts       #=> ** (FunctionClauseError)

Similar to constructs like if, named functions support both do: and do/end block syntax, as we learned do/end is just a convenient syntax for the keyword list format. For example, we can edit math.exs to look like this:

defmodule Math do
  def zero?(0), do: true
  def zero?(x) when is_integer(x), do: false

Function capturing

Can actually be used to retrieve a named function as a function type. (Given the file)

iex> fun = &
iex> is_function(fun)
iex> fun.(0)

Local or imported functions, like is_function/1, can be captured without the module:

iex> &is_function/1
iex> (&is_function/1).(fun)

Note the capture syntax can also be used as a shortcut for creating functions:

iex> fun = &(&1 + 1)
#Function<6.71889879/1 in :erl_eval.expr/5>
iex> fun.(1)

The &1 represents the first argument passed into the function.
The above is the same as fn x -> x + 1 end. It’s useful for short function definitions.

If you want to capture a function from a module, you can do &Module.function():

iex> fun = &List.flatten(&1, &2)
iex> fun.([1, [[2], 3]], [4, 5])
[1, 2, 3, 4, 5]

&List.flatten(&1, &2) is the same as writing fn(list, tail) -> List.flatten(list, tail) end which in this case is equivalent to &List.flatten/2. You can read more about the capture operator & in the Kernel.SpecialForms documentation.

Default arguments

Named functions default arguments:

defmodule Concat do
  def join(a, b, sep \\ " ") do
    a <> sep <> b

IO.puts Concat.join("Hello", "world")      #=> Hello world
IO.puts Concat.join("Hello", "world", "_") #=> Hello_world

If a function with default values has multiple clauses, it is required to create a function head (without an actual body) for declaring defaults:

defmodule Concat do
  def join(a, b \\ nil, sep \\ " ")

  def join(a, b, _sep) when is_nil(b) do

  def join(a, b, sep) do
    a <> sep <> b

IO.puts Concat.join("Hello", "world")      #=> Hello world
IO.puts Concat.join("Hello", "world", "_") #=> Hello_world
IO.puts Concat.join("Hello")               #=> Hello
  • Default values won’t be evaluated during the function definition
  • When using default values, one must be careful to avoid overlapping function definitions


Loops through recursion

Beautifully without mutating:

defmodule Recursion do
  def print_multiple_times(msg, n) when n <= 1 do
    IO.puts msg

  def print_multiple_times(msg, n) do
    IO.puts msg
    print_multiple_times(msg, n - 1)

Recursion.print_multiple_times("Hello!", 3)
# Hello!
# Hello!
# Hello!

Reduce and map algorithms

Let’s now see how we can use the power of recursion to sum a list of numbers:

defmodule Math do
  def sum_list([head | tail], accumulator) do
    sum_list(tail, head + accumulator)

  def sum_list([], accumulator) do

IO.puts Math.sum_list([1, 2, 3], 0) #=> 6

The process of taking a list and reducing it down to one value is known as a reduce algorithm and is central to functional programming.

What if we instead want to double all of the values in our list?

defmodule Math do
  def double_each([head | tail]) do
    [head * 2 | double_each(tail)]

  def double_each([]) do
$ iex math.exs
iex> Math.double_each([1, 2, 3]) #=> [2, 4, 6]

Here we have used recursion to traverse a list, doubling each element and returning a new list. The process of taking a list and mapping over it is known as a map algorithm.

Recursion and tail call optimization are an important part of Elixir. However, when programming in Elixir you will rarely use recursion as above. The Enum module, (next chapter), already provides many conveniences for working with lists. For instance, the examples above could be written as:

iex> Enum.reduce([1, 2, 3], 0, fn(x, acc) -> x + acc end)
iex>[1, 2, 3], fn(x) -> x * 2 end)
[2, 4, 6]

Or, using the capture syntax:

iex> Enum.reduce([1, 2, 3], 0, &+/2)
iex>[1, 2, 3], &(&1 * 2))
[2, 4, 6]

Enumerables and Streams

The Enum module provides a huge range of functions to transform, sort, group, filter and retrieve items from enumerables. It is one of the modules developers use frequently in their Elixir code.

For specific operations, like inserting and updating particular elements, you may need to reach for modules specific to the data type. For example, if you want to insert an element at a given position in a list, you should use the List.insert_at/3 function from the List module.

We say the functions in the Enum module are polymorphic because they can work with diverse data types. In particular, the functions in the Enum module can work with any data type that implements the Enumerable protocol.

BTW, Elixir also provides ranges:

iex>, fn x -> x * 2 end)
[2, 4, 6]
iex> Enum.reduce(1..3, 0, &+/2)

Eager vs Lazy

All the functions in the Enum module are eager. Many functions expect an enumerable and return a list back. This means that when performing multiple operations with Enum, each operation is going to generate an intermediate list until we reach the result:

iex> 1..100_000 |> * 3)) |> Enum.filter(odd?) |> Enum.sum

The pipe operator

The |> symbol used in the snippet above is the pipe operator: it simply takes the output from the expression on its left side and passes it as the first argument to the function call on its right side. It’s similar to the Unix | operator.


As an alternative to Enum, Elixir provides the Stream module which supports lazy operations:

iex> 1..100_000 |> * 3)) |> Stream.filter(odd?) |> Enum.sum

In the example above, 1..100_000 |> * 3)) returns a data type, an actual stream, that represents the map computation over the range 1..100_000. Furthermore, they are composable because we can pipe many stream operations:

iex> 1..100_000 |> * 3)) |> Stream.filter(odd?)
#Stream<[enum: 1..100000, funs: [...]]>

Instead of generating intermediate lists, streams build a series of computations that are invoked only when we pass the underlying stream to the Enum module. Streams are useful when working with large, possibly infinite, collections.

It also provides functions for creating streams. For example, Stream.cycle/1 can be used to create a stream that cycles a given enumerable infinitely:

iex> stream = Stream.cycle([1, 2, 3])
#Function<15.16982430/2 in Stream.cycle/1>
iex> Enum.take(stream, 10)
[1, 2, 3, 1, 2, 3, 1, 2, 3, 1]

Another interesting function is Stream.resource/3 which can be used to wrap around resources, guaranteeing they are opened right before enumeration and closed afterwards, even in the case of failures. For example, we can use it to stream a file:

iex> stream =!("path/to/file")
#Function<18.16982430/2 in Stream.resource/3>
iex> Enum.take(stream, 10)

The example above will fetch the first 10 lines of the file you have selected. This means streams can be very useful for handling large files or even slow resources like network resources.


In Elixir, all code runs inside processes. Processes are isolated from each other, run concurrent to one another and communicate via message passing.


The basic mechanism for spawning new processes is with the auto-imported spawn/1 function:

iex> pid = spawn fn -> 1 + 2 end
iex> Process.alive?(pid)

We can retrieve the PID of the current process by calling self/0.

send and receive

We can send messages to a process with send/2 and receive them with receive/1:

iex> send self(), {:hello, "world"}
{:hello, "world"}
iex> receive do
...>   {:hello, msg} -> msg
...>   {:world, msg} -> "won't match"
...> end

When a message is sent to a process, the message is stored in the process mailbox. The receive/1 block goes through the current process mailbox searching for a message that matches any of the given patterns. receive/1 supports guards and many clauses, such as case/2.

If there is no message in the mailbox matching any of the patterns, the current process will wait until a matching message arrives. A timeout can also be specified (A timeout of 0 can be given when you already expect the message to be in the mailbox):

iex> receive do
...>   {:hello, msg}  -> msg
...> after
...>   1_000 -> "nothing after 1s"
...> end
"nothing after 1s"

Let’s put it all together and send messages between processes:

iex> parent = self()
iex> spawn fn -> send(parent, {:hello, self()}) end
iex> receive do
...>   {:hello, pid} -> "Got hello from #{inspect pid}"
...> end
"Got hello from #PID<0.48.0>

While in the shell, you may find the helper flush/0 quite useful. It flushes and prints all the messages in the mailbox.

iex> send self(), :hello
iex> flush()

The most common form of spawning in Elixir is actually via spawn_link/1. Before we show an example with spawn_link/1, let’s try to see what happens when a process fails:

iex> spawn fn -> raise "oops" end

[error] Process #PID<0.58.00> raised an exception
** (RuntimeError) oops

It merely logged an error but the spawning process is still running. That’s because processes are isolated. If we want the failure in one process to propagate to another one, we should link them. This can be done with spawn_link/1:

iex> spawn_link fn -> raise "oops" end

** (EXIT from #PID<0.41.0>) an exception was raised:
    ** (RuntimeError) oops

In Elixir applications, we often link our processes to supervisors which will detect when a process dies and start a new process in its place. This is only possible because processes are isolated and don’t share anything by default. And since processes are isolated, there is no way a failure in a process will crash or corrupt the state of another.

While other languages would require us to catch/handle exceptions, in Elixir we are actually fine with letting processes fail because we expect supervisors to properly restart our systems. “Failing fast” is a common philosophy when writing Elixir software!

spawn/1 and spawn_link/1 are the basic primitives for creating processes in Elixir. Although we have used them exclusively so far, most of the time we are going to use abstractions that build on top of them. Let’s see the most common one, called tasks.


Tasks build on top of the spawn functions to provide better error reports and introspection:

iex(1)> Task.start fn -> raise "oops" end
{:ok, #PID<0.55.0>}

15:22:33.046 [error] Task #PID<0.55.0> started from #PID<0.53.0> terminating
** (RuntimeError) oops
    (elixir) lib/task/supervised.ex:74: Task.Supervised.do_apply/2
    (stdlib) proc_lib.erl:239: :proc_lib.init_p_do_apply/3
Function: #Function<20.90072148/0 in :erl_eval.expr/5>
    Args: []

Instead of spawn/1 and spawn_link/1, we use Task.start/1 and Task.start_link/1 to return {:ok, pid} rather than just the PID. This is what enables Tasks to be used in supervision trees. Furthermore, Task provides convenience functions, like Task.async/1 and Task.await/1, and functionality to ease distribution.


We haven’t talked about state so far in this guide. If you are building an application that requires state, for example, to keep your application configuration, or you need to parse a file and keep it in memory, where would you store it?

Processes are the most common answer to this question. We can write processes that loop infinitely, maintain state, and send and receive messages. As an example, let’s write a module that starts new processes that work as a key-value store in a file named kv.exs:

defmodule KV do
  def start_link do
    Task.start_link(fn -> loop(%{}) end)

  defp loop(map) do
    receive do
      {:get, key, caller} ->
        send caller, Map.get(map, key)
      {:put, key, value} ->
        loop(Map.put(map, key, value))

Let’s give it a try by running $ iex kv.exs:

iex> {:ok, pid} = KV.start_link
iex> send pid, {:get, :hello, self()}
{:get, :hello, #PID<0.41.0>}
iex> flush

At first, the process map has no keys, so sending a :get message and then flushing the current process inbox returns nil. Let’s send a :put message and try it again:

iex> send pid, {:put, :hello, :world}
{:put, :hello, :world}
iex> send pid, {:get, :hello, self()}
{:get, :hello, #PID<0.41.0>}
iex> flush

Notice how the process is keeping a state and we can get and update this state by sending the process messages. In fact, any process that knows the pid above will be able to send it messages and manipulate the state.

It is also possible to register the pid, giving it a name, and allowing everyone that knows the name to send it messages:

iex> Process.register(pid, :kv)
iex> send :kv, {:get, :hello, self()}
{:get, :hello, #PID<0.41.0>}
iex> flush

Using processes around state and name registering are very common patterns in Elixir applications. However, most of the time, we won’t implement those patterns manually as above, but by using one of the many abstractions that ship with Elixir. For example, Elixir provides agents, which are simple abstractions around state:

iex> {:ok, pid} = Agent.start_link(fn -> %{} end)
{:ok, #PID<0.72.0>}
iex> Agent.update(pid, fn map -> Map.put(map, :hello, :world) end)
iex> Agent.get(pid, fn map -> Map.get(map, :hello) end)

A :name option could also be given to Agent.start_link/2 and it would be automatically registered. Besides agents, Elixir provides an API for building generic servers (called GenServer), tasks and more, all powered by processes underneath. Those, along with supervision trees, will be explored with more detail in the Mix and OTP guide which will build a complete Elixir application from start to finish.

IO and the file system

The IO module is the main mechanism in Elixir for reading and writing to standard input/output (:stdio), standard error (:stderr), files and other IO devices. Usage of the module is pretty straightforward:

iex> IO.puts "hello world"
hello world
iex> IO.gets "yes or no? "
yes or no? yes

By default, functions in the IO module read from the standard input and write to the standard output. We can change that by passing, for example, :stderr as an argument (in order to write to the standard error device):

iex> IO.puts :stderr, "hello world"
hello world

The File module

The File module contains functions that allow us to open files as IO devices. By default, files are opened in binary mode, which requires developers to use the specific IO.binread/2 and IO.binwrite/2 functions from the IO module:

iex> {:ok, file} = "hello", [:write]
{:ok, #PID<0.47.0>}
iex> IO.binwrite file, "world"
iex> File.close file
iex> "hello"
{:ok, "world"}

A file can also be opened with :utf8 encoding, which tells the File module to interpret the bytes read from the file as UTF-8-encoded bytes.

It also provides Unix like functions: File.rm/1, File.mkdir/1, File.mkdir_p/1, etc. (Checkout the module documentation) Notice the variations with a trailing bang !.

iex> "hello"
{:ok, "world"}
iex>! "hello"
iex> "unknown"
{:error, :enoent}
iex>! "unknown"
** (File.Error) could not read file unknown: no such file or directory

Notice that when the file does not exist, the version with ! raises an error. The version without ! is preferred when you want to handle different outcomes using pattern matching:

case do
  {:ok, body}      -> # do something with the `body`
  {:error, reason} -> # handle the error caused by `reason`

However, if you expect the file to be there, the bang variation is more useful as it raises a meaningful error message. Avoid writing: {:ok, body} = as, in case of an error, will return {:error, reason} and the pattern matching will fail. You will still get the desired result (a raised error), but the message will be about the pattern which doesn’t match (thus being cryptic in respect to what the error actually is about).

Therefore, if you don’t want to handle the error outcomes, prefer using!/1.

The Path module

The majority of the functions in the File module expect paths as arguments. Most commonly, those paths will be regular binaries. The Path module provides facilities for working with such paths:

iex> Path.join("foo", "bar")
iex> Path.expand("~/hello")

Processes and group leaders

By modelling IO devices with processes, the Erlang VM allows different nodes in the same network to exchange file processes in order to read/write files in between nodes. Of all IO devices, there is one that is special to each process: the group leader.

When you write to :stdio, you are actually sending a message to the group leader, which writes to the standard-output file descriptor:

iex> IO.puts :stdio, "hello"
iex> IO.puts Process.group_leader, "hello"

The group leader can be configured per process and is used in different situations. For example, when executing code in a remote terminal, it guarantees messages in a remote node are redirected and printed in the terminal that triggered the request.

alias, require and import

# Alias the module so it can be called as Bar instead of Foo.Bar
alias Foo.Bar, as: Bar

# Ensure the module is compiled and available (usually for macros)
require Foo

# Import functions from Foo so they can be called without the `Foo.` prefix
import Foo

# Invokes the custom code defined in Foo as an extension point
use Foo

We are going to explore them in detail now. Keep in mind the first three are called directives because they have lexical scope , while use is a common extension point.


defmodule Math do
  alias Math.List, as: List

From now on, any reference to List will automatically expand to Math.List. In case one wants to access the original List, it can be done by prefixing the module name with Elixir.:

List.flatten             #=> uses Math.List.flatten
Elixir.List.flatten      #=> uses List.flatten
Elixir.Math.List.flatten #=> uses Math.List.flatten

Note: All modules defined in Elixir are defined inside a main Elixir namespace. However, for convenience, you can omit “Elixir.” when referencing them.

Note that alias is lexically scoped, which allows you to set aliases inside specific functions:

defmodule Math do
  def plus(a, b) do
    alias Math.List
    # ...

  def minus(a, b) do
    # ...


In order to use a macro, we need to guarantee its module and implementation are available during compilation. This is done with the require directive:

iex> Integer.is_odd(3)
** (CompileError) iex:1: you must require Integer before invoking the macro Integer.is_odd/1
iex> require Integer
iex> Integer.is_odd(3)

Note that like the alias directive, require is also lexically scoped.


We use import whenever we want to easily access functions or macros from other modules without using the fully-qualified name. For instance, if we want to use the duplicate/2 function from the List module several times, we can import it:

iex> import List, only: [duplicate: 2]
iex> duplicate :ok, 3
[:ok, :ok, :ok]

:except could also be given as an option. import also supports :macros and :functions to be given to :only. For example, to import all macros, one could write: import Integer, only: :macros.

Note that import is lexically scoped too. This means that we can import specific macros or functions inside function definitions:

defmodule Math do
  def some_function do
    import List, only: [duplicate: 2]
    duplicate(:ok, 10)

Note that importing a module automatically requires it.


Although not a directive, use is a macro tightly related to require that allows you to use a module in the current context. The use macro is frequently used by developers to bring external functionality into the current lexical scope, often modules.

For example, in order to write tests using the ExUnit framework, a developer should use the ExUnit.Case module:

defmodule AssertionTest do
  use ExUnit.Case, async: true

  test "always pass" do
    assert true

Module attributes

  • They serve to annotate the module, often with information to be used by the user or the VM.
  • They work as constants.
  • They work as a temporary module storage to be used during compilation.

As annotations

Elixir has a handful of reserved attributes. Here are a few of them, the most commonly used ones:

  • @moduledoc - provides documentation for the current module.
  • @doc - provides documentation for the function or macro that follows the attribute.
  • @behaviour - (notice the British spelling) used for specifying an OTP or user-defined behaviour.
  • @before_compile - provides a hook that will be invoked before the module is compiled. This makes it possible to inject functions inside the module exactly before compilation.

As constants

defmodule MyServer do
  @initial_state %{host: "", port: 3456}
  IO.inspect @initial_state

Note: Unlike Erlang, user defined attributes are not stored in the module by default. The value exists only during compilation time. A developer can configure an attribute to behave closer to Erlang by calling Module.register_attribute/3.

Attributes can also be read inside functions:

defmodule MyServer do
  @my_data 14
  def first_data, do: @my_data
  @my_data 13
  def second_data, do: @my_data

MyServer.first_data #=> 14
MyServer.second_data #=> 13

Every time an attribute is read inside a function, a snapshot of its current value is taken. In other words, the value is read at compilation time and not at runtime.

As temporary storage

Attributes can be used to do so. The ExUnit framework which uses module attributes as annotation and storage:

defmodule MyTest do
  use ExUnit.Case

  @tag :external
  test "contacts external service" do
    # ...


Structs are extensions built on top of maps that provide compile-time checks and default values.

iex> defmodule User do
...>   defstruct name: "John", age: 27
...> end

Structs take the name of the module they’re defined in. In the example above, we defined a struct named User. Let’s create one so:

iex> %User{}
%User{age: 27, name: "John"}
iex> %User{name: "Meg"}
%User{age: 27, name: "Meg"}

To access and update:

iex> john = %User{}
%User{age: 27, name: "John"}
iex> meg = %{john | name: "Meg"}
%User{age: 27, name: "Meg"}
iex> %{meg | oops: :field}
** (KeyError) key :oops not found in: %User{age: 27, name: "Meg"}

When using the update syntax (|), the VM is aware that no new keys will be added to the struct, allowing the maps underneath to share their structure in memory.

Structs can also be used in pattern matching, both for matching on the value of specific keys as well as for ensuring that the matching value is a struct of the same type as the matched value.

iex> %User{name: name} = john
%User{age: 27, name: "John"}
iex> name
iex> %User{} = %{}
** (MatchError) no match of right hand side value: %{}

Note: Structs are bare maps underneath. As maps, structs store a “special” field named __struct__ that holds the name of the struct. We referred to structs as bare maps because none of the protocols implemented for maps are available for structs. For example, you can neither enumerate nor access a struct. However, since structs are just maps, they work with the functions from the Map module.

Structs alongside protocols provide one of the most important features for Elixir developers: data polymorphism.


Protocols are a mechanism to achieve polymorphism in Elixir. Dispatching on a protocol is available to any data type as long as it implements the protocol.

Let’s implement that to specify a blank? protocol that returns a boolean for other data types that should be considered blank.

defprotocol Blank do
  @doc "Returns true if data is considered blank/empty"
  def blank?(data)

The protocol expects a function called blank? that receives one argument to be implemented. We can implement this protocol for different Elixir data types as follows:

# Integers are never blank
defimpl Blank, for: Integer do
  def blank?(_), do: false

# Just empty list is blank
defimpl Blank, for: List do
  def blank?([]), do: true
  def blank?(_),  do: false

# Just empty map is blank
defimpl Blank, for: Map do
  # Keep in mind we could not pattern match on %{} because
  # it matches on all maps. We can however check if the size
  # is zero (and size is a fast operation).
  def blank?(map), do: map_size(map) == 0

# Just the atoms false and nil are blank
defimpl Blank, for: Atom do
  def blank?(false), do: true
  def blank?(nil),   do: true
  def blank?(_),     do: false
iex> Blank.blank?(0)
iex> Blank.blank?([])
iex> Blank.blank?([1, 2, 3])

And we would do so for all native data types. The types available are:

  • Atom
  • BitString
  • Float
  • Function
  • Integer
  • List
  • Map
  • PID
  • Port
  • Reference
  • Tuple

Manually implementing protocols for all types can quickly become repetitive and tedious. In such cases, Elixir provides two options: we can explicitly derive the protocol implementation for our types or automatically implement the protocol for all types. In both cases, we need to implement the protocol for Any.


defimpl Blank, for: Any do
  def blank?(_), do: false
defmodule DeriveUser do
  @derive Blank
  defstruct name: "john", age: 27

Fallback to Any

Another alternative to @derive is to explicitly tell the protocol to fallback to Any when an implementation cannot be found. This can be achieved by setting @fallback_to_any to true in the protocol definition:

defprotocol Blank do
  @fallback_to_any true
  def blank?(data)


Declared by for:

# Map a list of integers into their squared values.
iex> for n <- [1, 2, 3, 4], do: n * n
[1, 4, 9, 16]

A comprehension is made of three parts: generators, filters and collectables.

Generators and filters

In the expression above, n <- [1, 2, 3, 4] is the generator. It is literally generating values to be used in the comprehension. Any enumerable can be passed in the right-hand side of the generator expression:

iex> for n <- 1..4, do: n * n
[1, 4, 9, 16]

It also supports pattern matching:

iex> values = [good: 1, good: 2, bad: 3, good: 4]
iex> for {:good, n} <- values, do: n * n
[1, 4, 16]

Alternatively to pattern matching, filters can be used to select some particular elements:

iex> multiple_of_3? = fn(n) -> rem(n, 3) == 0 end
iex> for n <- 0..5, multiple_of_3?.(n), do: n * n
[0, 9]

Comprehensions also allow multiple generators and filters to be given. Here is an example that receives a list of directories and gets the size of each file in those directories:

for dir  <- dirs,
    file <-!(dir),
    path = Path.join(dir, file),
    File.regular?(path) do

Calculating the cartesian product of two lists:

iex> for i <- [:a, :b, :c], j <- [1, 2], do:  {i, j}
[a: 1, a: 2, b: 1, b: 2, c: 1, c: 2]

A more advanced example of multiple generators and filters is Pythagorean triples. A Pythagorean triple is a set of positive integers such that a*a + b*b = c*c, let’s write a comprehension in a file named triple.exs:

defmodule Triple do
  def pythagorean(n) when n > 0 do
    for a <- 1..n,
        b <- 1..n,
        c <- 1..n,
        a + b + c <= n,
        a*a + b*b == c*c,
        do: {a, b, c}


$ iex triple.exs
iex> Triple.pythagorean(5)
iex> Triple.pythagorean(12)
[{3, 4, 5}, {4, 3, 5}]
iex> Triple.pythagorean(48)
[{3, 4, 5}, {4, 3, 5}, {5, 12, 13}, {6, 8, 10}, {8, 6, 10}, {8, 15, 17},
 {9, 12, 15}, {12, 5, 13}, {12, 9, 15}, {12, 16, 20}, {15, 8, 17}, {16, 12, 20}]

Take a closer look on how it performs without filters:

 defmodule Triple do
  def pythagorean(n) when n > 0 do
    for a <- 1..n,
        b <- 1..n,
        c <- 1..n,
        do: IO.puts "#{a} #{b} #{c}"



$ elixir triple.exs
1 1 1
1 1 2
1 1 3
1 1 4
1 1 5
1 1 6
1 1 7
1 1 8
1 1 9
1 1 10
1 2 1
1 2 2


Is one of the mechanisms provided by the language for working with textual representations (also allowing extensibility). Sigils start with the tilde (~) character which is followed by a letter (which identifies the sigil) and then a delimiter; optionally, modifiers can be added after the final delimiter.

The most common sigil in Elixir is ~r, which is used to create regular expressions:

# A regular expression that matches strings which contain "foo" or "bar":
iex> regex = ~r/foo|bar/
iex> "foo" =~ regex
iex> "bat" =~ regex

So far, all examples have used / to delimit a regular expression. However sigils support 8 different delimiters:



The ~s sigil is used to generate strings, like double quotes are. The ~s sigil is useful, for example, when a string contains both double and single quotes:

iex> ~s(this is a string with "double" quotes, not 'single' ones)
"this is a string with \"double\" quotes, not 'single' ones"

Char lists

iex> ~c(this is a char list containing 'single quotes')
'this is a char list containing \'single quotes\''

Word lists (words are just regular strings)

iex> ~w(foo bar bat)
["foo", "bar", "bat"]

The ~w sigil also accepts the c, s and a modifiers (for char lists, strings and atoms, respectively), which specify the data type of the elements of the resulting list:

iex> ~w(foo bar bat)a
[:foo, :bar, :bat]

Interpolation and escaping in sigils

Besides lowercase sigils, Elixir supports uppercase sigils to deal with escaping characters and interpolation.

iex> ~s(String with escape codes \x26 #{"inter" <> "polation"})
"String with escape codes & interpolation"
iex> ~S(String without escape codes \x26 without #{interpolation})
"String without escape codes \\x26 without \#{interpolation}"

The following escape codes can be used in strings and char lists:

  • \" – double quote
  • \' – single quote
  • \\ – single backslash
  • \a– bell/alert
  • \b – backspace
  • \d - delete
  • \e - escape
  • \f - form feed
  • \n – newline
  • \r – carriage return
  • \s – space
  • \t – tab
  • \v – vertical tab
  • \0 - null byte
  • \xDD - represents a single byte in hexadecimal (such as \x13)
  • \uDDDD and \u{D...} - represents a Unicode codepoint in hexadecimal (such as \u{1F600})

Also supports herecods:

iex> ~s"""
...> this is
...> a heredoc string
...> """

Writing escape characters in documentation would soon become error prone because of the need to double-escape some characters. By using ~S, this problem can be avoided altogether:

@doc ~S"""
Converts double-quotes to single-quotes.

## Examples

    iex> convert("\"foo\"")

def convert(...)

Custom sigils

We can also provide our own sigils by implementing functions that follow the sigil_{identifier} pattern. For example, let’s implement the ~i sigil that returns an integer (with the optional n modifier to make it negative):

iex> defmodule MySigils do
...>   def sigil_i(string, []), do: String.to_integer(string)
...>   def sigil_i(string, [?n]), do: -String.to_integer(string)
...> end
iex> import MySigils
iex> ~i(13)
iex> ~i(42)n

Sigils can also be used to do compile-time work with the help of macros. For example, regular expressions in Elixir are compiled into an efficient representation during compilation of the source code, therefore skipping this step at runtime. If you’re interested in the subject, we recommend you learn more about macros and check out how sigils are implemented in the Kernel module (where the sigil_* functions are defined).

try, catch and rescue


Errors (or exceptions) are used when exceptional things happen in the code. A sample error can be retrieved by trying to add a number into an atom:

iex> :foo + 1
** (ArithmeticError) bad argument in arithmetic expression
     :erlang.+(:foo, 1)

A runtime error can be raised any time by using raise/1:

iex> raise "oops"
** (RuntimeError) oops

Other errors can be raised with raise/2 passing the error name and a list of keyword arguments:

iex> raise ArgumentError, message: "invalid argument foo"
** (ArgumentError) invalid argument fo

You can also define your own errors by creating a module and using the defexception construct inside it; this way, you’ll create an error with the same name as the module it’s defined in. The most common case is to define a custom exception with a message field:

iex> defmodule MyError do
iex>   defexception message: "default message"
iex> end
iex> raise MyError
** (MyError) default message
iex> raise MyError, message: "custom message"
** (MyError) custom message

Errors can be rescued using the try/rescue construct:

iex> try do
...>   raise "oops"
...> rescue
...>   e in RuntimeError -> e
...> end
%RuntimeError{message: "oops"}

If you don’t have any use for the error, you don’t have to provide it:

iex> try do
...>   raise "oops"
...> rescue
...>   RuntimeError -> "Error!"
...> end

In Elixir, we avoid using try/rescue because we don’t use errors for control flow. We take errors literally: they are reserved for unexpected and/or exceptional situations. In case you actually need flow control constructs, throws should be used. That’s what we are going to see next.


In Elixir, a value can be thrown and later be caught. throw and catch are reserved for situations where it is not possible to retrieve a value unless by using throw and catch.

Those situations are quite uncommon in practice except when interfacing with libraries that do not provide a proper API. For example, let’s imagine the Enum module did not provide any API for finding a value and that we needed to find the first multiple of 13 in a list of numbers:

iex> try do
...>   Enum.each -50..50, fn(x) ->
...>     if rem(x, 13) == 0, do: throw(x)
...>   end
...>   "Got nothing"
...> catch
...>   x -> "Got #{x}"
...> end
"Got -39"

Since Enum does provide a proper API, in practice Enum.find/2 is the way to go:

iex> Enum.find -50..50, &(rem(&1, 13) == 0)


All Elixir code runs inside processes that communicate with each other. When a process dies of “natural causes” (e.g., unhandled exceptions), it sends an exit signal. A process can also die by explicitly sending an exit signal:

iex> spawn_link fn -> exit(1) end
** (EXIT from #PID<0.56.0>) 1

exit can also be “caught” using try/catch:

iex> try do
...>   exit "I am exiting"
...> catch
...>   :exit, _ -> "not really"
...> end
"not really"

Using try/catch is already uncommon and using it to catch exits is even more rare.


Sometimes it’s necessary to ensure that a resource is cleaned up after some action that could potentially raise an error. The try/after construct allows you to do that. For example, we can open a file and use an after clause to close it–even if something goes wrong:

iex> {:ok, file} = "sample", [:utf8, :write]
iex> try do
...>   IO.write file, "olá"
...>   raise "oops, something went wrong"
...> after
...>   File.close(file)
...> end
** (RuntimeError) oops, something went wrong

Sometimes you may want to wrap the entire body of a function in a try construct, often to guarantee some code will be executed afterwards. In such cases, Elixir allows you to omit the try line:

iex> defmodule RunAfter do
...>   def without_even_trying do
...>     raise "oops"
...>   after
...>     IO.puts "cleaning up!"
...>   end
...> end
iex> RunAfter.without_even_trying
cleaning up!
** (RuntimeError) oops

Typespecs and behaviours

Types and specs

It’s used for:

  • declaring custom data types;
  • declaring typed function signatures (specifications).

Function specifications

@spec round(number) :: integer
def round(number), do: # implementation...

Defining custom types

defmodule LousyCalculator do
  @typedoc """
  Just a number followed by a string.
  @type number_with_remark :: {number, String.t}

  @spec add(number, number) :: number_with_remark
  def add(x, y), do: {x + y, "You need a calculator to do that?"}

  @spec multiply(number, number) :: number_with_remark
  def multiply(x, y), do: {x * y, "It is like addition on steroids."}

Custom types defined through @type are exported and available outside the module they’re defined in:

defmodule QuietCalculator do
  @spec add(number, number) :: number
  def add(x, y), do: make_quiet(LousyCalculator.add(x, y))

  @spec make_quiet(LousyCalculator.number_with_remark) :: number
  defp make_quiet({num, _remark}), do: num

If you want to keep a custom type private, you can use the @typep directive instead of @type.

Static code analysis

Typespecs are not only useful to developers and as additional documentation. The Erlang tool Dialyzer, for example, uses typespecs in order to perform static analysis of code. That’s why, in the QuietCalculator example, we wrote a spec for the make_quiet/1 function even if it was defined as a private function.


Behaviours provide a way to:

  • Define a set of functions that have to be implemented by a module;
  • Ensure that a module implements all the functions in that set.

Defining behaviours

defmodule Parser do
  @callback parse(String.t) :: any
  @callback extensions() :: [String.t]

Modules adopting the Parser behaviour will have to implement all the functions defined with the @callback directive. As you can see, @callback expects a function name but also a function specification like the ones used with the @spec directive we saw above.

Adopting a behaviour is straightforward:

defmodule JSONParser do
  @behaviour Parser

  def parse(str), do: # ... parse JSON
  def extensions, do: ["json"]
defmodule YAMLParser do
  @behaviour Parser

  def parse(str), do: # ... parse YAML
  def extensions, do: ["yml"]

Erlang libraries

As you grow more proficient in Elixir, you may want to explore the Erlang STDLIB Reference Manual in more detail. Check below some of the most widely used libraries.

The binary module

The built-in Elixir String module handles binaries that are UTF-8 encoded. The binary module is useful when you are dealing with binary data that is not necessarily UTF-8 encoded.

Formatted text output

Elixir does not contain a function similar to printf found in C and other languages. Luckily, the Erlang standard library functions :io.format/2 and :io_lib.format/2 may be used. The first formats to terminal output, while the second formats to an iolist. The format specifiers differ from printf, refer to the Erlang documentation for details.

The crypto module

The crypto module contains hashing functions, digital signatures, encryption and more:

iex> Base.encode16(:crypto.hash(:sha256, "Elixir"))

The :crypto module is not part of the Erlang standard library, but is included with the Erlang distribution. This means you must list :crypto in your project’s applications list whenever you use it. To do this, edit your mix.exs file to include.

The digraph module

The digraph module (as well as digraph_utils) contains functions for dealing with directed graphs built of vertices and edges. After constructing the graph, the algorithms in there will help finding for instance the shortest path between two vertices, or loops in the graph.

Erlang Term Storage

The modules ets and dets handle storage of large data structures in memory or on disk respectively.

ETS lets you create a table containing tuples. By default, ETS tables are protected, which means only the owner process may write to the table but any other process can read. ETS has some functionality to be used as a simple database, a key-value store or as a cache mechanism.

The functions in the ets module will modify the state of the table as a side-effect.

iex> table =, [])
# Store as tuples with {name, population}
iex> :ets.insert(table, {"China", 1_374_000_000})
iex> :ets.insert(table, {"India", 1_284_000_000})
iex> :ets.insert(table, {"USA", 322_000_000})
iex> :ets.i(table)
<1   > {"USA", 322000000}
<2   > {"China", 1_374_000_000}
<3   > {"India", 1_284_000_000}

The math module

The math module contains common mathematical operations covering trigonometry, exponential and logarithmic functions.

The queue module

The queue is a data structure that implements (double-ended) FIFO (first-in first-out) queues efficiently:

iex> q =
iex> q ="A", q)
iex> q ="B", q)
iex> {value, q} = :queue.out(q)
iex> value
{:value, "A"}
iex> {value, q} = :queue.out(q)
iex> value
{:value, "B"}
iex> {value, q} = :queue.out(q)
iex> value

The rand module

rand has functions for returning random values and setting the random seed.

iex> :rand.uniform()
iex> _ = :rand.seed(:exs1024, {123, 123534, 345345})
iex> :rand.uniform()
iex> :rand.uniform(6)

The zip and zlib modules

The zip module lets you read and write zip files to and from disk or memory, as well as extracting file information.


  • Cover picture: The Banquet in the Pine Forest (1482/3) is the third painting in Sandro Botticelli’s series The Story of Nastagio degli Onesti, which illustrates events from the Eighth Story of the Fifth Day. (Public Domain)