Guide: How to Emulate a Raspberry Pi Cluster with Docker Compose

TL;DR

This guide discusses everything needed to build a simple, scalable, and fully binary compatible Raspberry Pi cluster using QEMU, Docker, Docker Compose, and Ansible.

Introduction

The Raspberry Pi is no longer just a low-cost platform for students to learn computing, it's now a legitimate research and development platform that's used for IoT, networking, distributed systems, and software development. It's even used administratively in production environments.

Not long after the first Raspberry Pi was released in 2012, several set out to build them into low-cost clusters, often for research and testing purposes. Interns at DataStax built a multi-datacenter, 32 nodes Cassenda fault-tolerance demo, complete with a big red button to simulate the failure of an entire datacenter. David Guill built a 40-node Raspberry Pi Cluster that was intended to be part of his MSCE thesis. Balena, built "The Beast", a 120 node Raspberry Pi cluster, for scaled testing of their online platform. And on the extreme end of the spectrum, Oracle built a 1060 node Raspberry Pi Cluster, which they introduced at Oracle OpenWorld 2019.

Innovation with the Raspberry Pi continues as they are turned into wi-fi extenders, security cameras, even bigger clusters, and more. While the main value of these clusters comes from their size and low cost, their popularity makes them an increasingly common development platform. Since the Raspberry Pi uses an ARM processor, this can make development problematic for those of us who work exclusively in the cloud. While commercial solutions exist, we will be building our own emulated cluster using a fully open source stack hosted on Google Compute Engine.

Use Cases

Other than learning from the experience, Dockerizing an emulated Raspberry Pi enables us to do three things. One, it turns into software that which would otherwise be a hardware-only device that nobody has to remember to carry around (I'm always losing the peripheral cables). Two, it enables Docker to do for the Pi what Docker does best for everything else: it makes software portable, easy to manage, and easy to replicate. And three, it takes up no physical space. If we can build one Raspberry Pi with Docker, we can build many. If we can build many, we can network them all together. While we may encounter some limitations, this build will emulate a cluster of Raspberry Pi 1s that's logically equivalent to a simple, multi-node physical cluster.

Emulated Hardware Architecture

While technically not identical, the emulation software we will be using, QEMU, provides an ARM-Versatile architecture that's roughly compatible with what is found on a Raspberry Pi 1. Some modifications to the kernel are necessary in order for it to work properly with Raspbian, but for our purposes, it's one of the more stable open source solutions available.

pi@raspberrypi:~$ cat /proc/cpuinfo 
processor       : 0
model name      : ARMv6-compatible processor rev 7 (v6l)
BogoMIPS        : 577.53
Features        : half thumb fastmult vfp edsp java tls
CPU implementer : 0x41
CPU architecture: 7
CPU variant     : 0x0
CPU part        : 0xb76
CPU revision    : 7

Hardware        : ARM-Versatile (Device Tree Support)
Revision        : 0000
Serial          : 0000000000000000

Compared to a Physical Raspberry Pi 1, they are nearly identical:

pi@raspberrypi:~ $ cat /proc/cpuinfo
processor	: 0
model name	: ARMv6-compatible processor rev 7 (v6l)
BogoMIPS	: 697.95
Features	: half thumb fastmult vfp edsp java tls 
CPU implementer	: 0x41
CPU architecture: 7
CPU variant	: 0x0
CPU part	: 0xb76
CPU revision	: 7

Hardware	: BCM2835
Revision	: 000d
Serial		: 000000003d9a54c5

Background

What is QEMU?

QEMU is a processor emulator. It supports a number of different processors, but we're only interested in something that can run Raspberry Pi images natively without a lot of difficulties. In this case, we're going to be using QEMU 4.2.0, which supports an ARM11 instruction set that's compatible with the Broadcom BCM2835 (ARM1176JZFS) chip found on the Raspberry Pi 1 and Zero. We will use ARM1176 support on QEMU, which will allow us to more or less emulate a Raspberry Pi 1. I say more or less because we will still need to use a customized Raspbian kernel in order to boot on the emulated hardware. QEMU support for the Pi is still in development, so our approach to getting it to work here is just a clever hack that will by no means be optimal or efficient in terms of CPU utilization.

QEMU Features

QEMU supports many of the same features found in Docker, however, it can run full software emulation without a host kernel driver. This means that it can run inside Docker, or any other virtual machine, without host virtualization support. The QEMU feature list is extensive, and the learning curve is steep. However, the primary feature that we will be focused on for this build is host port forwarding so that data can be passed to the host.

Dockerized QEMU

One of Docker's strengths is that it doesn't handle full-fledged virtualization, but instead relies on the architecture of the host system. Since our host system will be running an Intel processor, we can't expect Docker to handle ARM operations on its own. So, we will be placing QEMU inside a Docker container. Since Docker is designed to run software at near-native performance, the operational efficiency challenge will be with QEMU itself. QEMU, on the other hand, supports the emulation of a machine's architecture completely with software.

The advantage here is that it can run inside any virtualized system or container, independent of its system architecture. If patient, we could even run a Dockerized Raspberry Pi container inside another Dockerized Raspberry Pi container. The drawback to QEMU is that it has comparatively poor performance compared to other types of virtualization. But we can benefit from the best of both worlds by leveraging QEMU's ARM emulation while depending on Docker for everything else.

Raspbian

Based on Debian, Raspbian is a popular and well-supported operating system for the Raspberry Pi. It's one of the most often recommended for the platform and the community is active and well managed.

Physical Raspberry Pi Speed Comparison

The following tests are intended as a baseline for comparing our virtualized systems. Since we will be emulating a single-core, these tests are only single-core, single thread, regardless of how many physical cores are incorporated into the architecture.

Raspberry Pi 1 2011,12

Test execution summary:
    total time:                          330.5514s
    total number of events:              10000
    total time taken by event execution: 330.5002
    per-request statistics:
         min:                                 32.92ms
         avg:                                 33.05ms
         max:                                 40.94ms
         approx.  95 percentile:              33.24ms

Threads fairness:
    events (avg/stddev):           10000.0000/0.00
    execution time (avg/stddev):   330.5002/0.00

Raspberry Pi 1 A+ V1.1 2014

Test execution summary:
    total time:                          328.7505s
    total number of events:              10000
    total time taken by event execution: 328.6931
    per-request statistics:
         min:                                 32.71ms
         avg:                                 32.87ms
         max:                                 78.93ms
         approx.  95 percentile:              33.03ms

Threads fairness:
    events (avg/stddev):           10000.0000/0.00
    execution time (avg/stddev):   328.6931/0.00

Raspberry Pi Zero W v1.1 2017

Test execution summary:
    total time:                          228.2025s
    total number of events:              10000
    total time taken by event execution: 228.1688
    per-request statistics:
         min:                                 22.76ms
         avg:                                 22.82ms
         max:                                 35.29ms
         approx.  95 percentile:              22.94ms

Threads fairness:
    events (avg/stddev):           10000.0000/0.00
    execution time (avg/stddev):   228.1688/0.00

Raspberry Pi 2 Model B v1.1 2014

Test execution summary:
    total time:                          224.9052s
    total number of events:              10000
    total time taken by event execution: 224.8738
    per-request statistics:
         min:                                 22.20ms
         avg:                                 22.49ms
         max:                                 32.85ms
         approx.  95 percentile:              22.81ms

Threads fairness:
    events (avg/stddev):           10000.0000/0.00
    execution time (avg/stddev):   224.8738/0.00

Raspberry Pi 3 Model B v1.2 2015

Test execution summary:
    total time:                          139.6140s
    total number of events:              10000
    total time taken by event execution: 139.6087
    per-request statistics:
         min:                                 13.94ms
         avg:                                 13.96ms
         max:                                 34.06ms
         approx.  95 percentile:              13.96ms

Threads fairness:
    events (avg/stddev):           10000.0000/0.00
    execution time (avg/stddev):   139.6087/0.00

Raspberry Pi 4 B 2018

Test execution summary:
    total time:                          92.6405s
    total number of events:              10000
    total time taken by event execution: 92.6338
    per-request statistics:
         min:                                  9.22ms
         avg:                                  9.26ms
         max:                                 23.50ms
         approx.  95 percentile:               9.27ms

Threads fairness:
    events (avg/stddev):           10000.0000/0.00
    execution time (avg/stddev):   92.6338/0.00

Project Requirements

Single Host Specifications

Historically, QEMU has been single-threaded, emulating all cores of a system's architecture on a single CPU. While that's no longer the case, we are still going to be emulating a single core Raspberry Pi. We will do some benchmarks later to compare how different CPU limits on each node impacts performance. But for now, we will use one CPU per single-core node. Since QEMU has the potential to use a lot of CPU resources due to its inherent inefficiency, our initial three-node cluster will start with a baseline of at least one CPU per node, leaving one CPU dedicated to the host to avoid performance problems. The VM specs selected for this task are as follows.

Cloud Provider: Google Cloud Platform
Instance Type: n1-standard-4
CPUs: 4
Memory: 15GB
Disk: 100GB
Operating System: Ubuntu 18.04 LTS

Docker

Installed on the host, we're also using the default version of Docker that is available on the default apt repository for Ubuntu 18.04 LTS.

# docker -v
Docker version 18.09.7, build 2d0083d

Docker Compose

# docker-compose -v
docker-compose version 1.25.0, build 0a186604

Docker Hub Ubuntu Image

18.04, bionic-20200112, bionic, latest

QEMU

Installed inside the Docker container, we will be using the following version of QEMU for ARM:

# qemu-system-arm --version
QEMU emulator version 4.2.0
Copyright (c) 2003-2019 Fabrice Bellard and the QEMU Project developers

QEMU Customized Kernel for Raspbian

Loaded from QEMU inside Docker, we will use Dhruv Vyas's compiled kernel for Raspbian, which has been modified to be usable with QEMU.

Raspbian Lite Image

Also booted from QEMU, we will use an unmodified version of Raspbian Lite from 9/30/2019.

Expect (Tcl/Tk)

Installed on the Docker container is the following version of Expect:

# expect -v
expect version 5.45.4

ssh

sshd

sshd

ssh

Ansible

The following version of Ansible is also being used, along with its other dependencies:

# ansible --version
ansible 2.5.1
  config file = /etc/ansible/ansible.cfg
  configured module search path = [u'/root/.ansible/plugins/modules', u'/usr/share/ansible/plugins/modules']
  ansible python module location = /usr/lib/python2.7/dist-packages/ansible
  executable location = /usr/bin/ansible
  python version = 2.7.17 (default, Nov  7 2019, 10:07:09) [GCC 7.4.0]

Building the Docker Images

QEMU Build Container

We will compile QEMU 4.2.0 from source. It will need all the supporting build tools, so to keep our app container as small as possible, we will create a separate build container for the QEMU build using a minimal version of Ubuntu 18.04 from Docker Hub.

QEMU App Container

Once the QEMU is compiled from source, we will transfer it to the app container. Also from Docker Hub, we will use the same minimal version of Ubuntu 18.04 to host the QEMU binary.

Docker Configuration

The 

Dockerfile

We will be using the following Dockerfile, which may also be found updated in this guide's accompanying pidoc repository on Github. Each code snippet below makes up a segment of the Dockerfile. Thanks goes to Luke Child's for his work on dockerpi.

Build stage for 

qemu-system-arm

FROM ubuntu AS qemu-system-arm-builder
ARG QEMU_VERSION=4.2.0
ENV QEMU_TARBALL="qemu-${QEMU_VERSION}.tar.xz"
WORKDIR /qemu

RUN apt-get update && \
    apt-get -y install \
                       wget \
                       gpg \
                       pkg-config \
                       python \
                       build-essential \
                       libglib2.0-dev \
                       libpixman-1-dev \
                       libfdt-dev \
                       zlib1g-dev \
                       flex \
                       bison

Download source.

RUN wget "https://download.qemu.org/${QEMU_TARBALL}"

RUN # Verify signatures...
RUN wget "https://download.qemu.org/${QEMU_TARBALL}.sig"
RUN gpg --keyserver keyserver.ubuntu.com --recv-keys CEACC9E15534EBABB82D3FA03353C9CEF108B584
RUN gpg --verify "${QEMU_TARBALL}.sig" "${QEMU_TARBALL}"

Extract tarball.

RUN tar xvf "${QEMU_TARBALL}"

Build source.

RUN "qemu-${QEMU_VERSION}/configure" --static --target-list=arm-softmmu
RUN make -j$(nproc)
RUN strip "arm-softmmu/qemu-system-arm"

Build the intermediary

pidoc

FROM ubuntu as pidoc-vm
ARG RPI_KERNEL_URL="https://github.com/dhruvvyas90/qemu-rpi-kernel/archive/afe411f2c9b04730bcc6b2168cdc9adca224227c.zip"
ARG RPI_KERNEL_CHECKSUM="295a22f1cd49ab51b9e7192103ee7c917624b063cc5ca2e11434164638aad5f4"

Transfer binary from build container to app container.

COPY --from=qemu-system-arm-builder /qemu/arm-softmmu/qemu-system-arm /usr/local/bin/qemu-system-arm

Download modified kernel and install.

ADD $RPI_KERNEL_URL /tmp/qemu-rpi-kernel.zip

RUN apt-get update && \
    apt-get -y install \
                        unzip \
                        expect
RUN cd /tmp && \
    echo "$RPI_KERNEL_CHECKSUM  qemu-rpi-kernel.zip" | sha256sum -c && \
    unzip qemu-rpi-kernel.zip && \
    mkdir -p /root/qemu-rpi-kernel && \
    cp qemu-rpi-kernel-*/kernel-qemu-4.19.50-buster /root/qemu-rpi-kernel/ && \
    cp qemu-rpi-kernel-*/versatile-pb.dtb /root/qemu-rpi-kernel/ && \
    rm -rf /tmp/*

VOLUME /sdcard

Then we copy the entry point script from the host's main directory.

ADD ./entrypoint.sh /entrypoint.sh
ENTRYPOINT ["./entrypoint.sh"]

Build the final app

pidoc

FROM pidoc-vm as pidoc
ARG FILESYSTEM_IMAGE_URL="http://downloads.raspberrypi.org/raspbian_lite/images/raspbian_lite-2019-09-30/2019-09-26-raspbian-buster-lite.zip"
ARG FILESYSTEM_IMAGE_CHECKSUM="a50237c2f718bd8d806b96df5b9d2174ce8b789eda1f03434ed2213bbca6c6ff"

ADD $FILESYSTEM_IMAGE_URL /filesystem.zip
ADD pi_ssh_enable.exp /pi_ssh_enable.exp

RUN echo "$FILESYSTEM_IMAGE_CHECKSUM  /filesystem.zip" | sha256sum -c

The 

entrypoint.sh

First, the script determines if the filesystem has been downloaded or not, and if not, it downloads and decompresses it.

#!/bin/sh

raspi_fs_init() {

  image_path="/sdcard/filesystem.img"
  zip_path="/filesystem.zip"
  
  if [ ! -e $image_path ]; then
    echo "No filesystem detected at ${image_path}!"
    if [ -e $zip_path ]; then
        echo "Extracting fresh filesystem..."
        unzip $zip_path
        mv *.img $image_path
        rm $zip_path
    else
      exit 1
    fi
  fi
}

The script then checks for an empty 

raspi-init

if [ ! -e /raspi-init ]; then
  touch /raspi-init
  raspi_fs_init
  echo "Initiating Expect..."
  /usr/bin/expect /pi_ssh_enable.exp `hostname -I`
  echo "Expect Ended..."

If Expect has already been previously enabled, then we only need to launch QEMU, without Expect. Note that we are forwarding port 

22

2222

else
  /usr/local/bin/qemu-system-arm \
        --machine versatilepb \
        --cpu arm1176 \
        --m 256M \
        --hda /sdcard/filesystem.img \
        --net nic \
        --net user,hostfwd=tcp:`hostname -I`:2222-:22 \
        --dtb /root/qemu-rpi-kernel/versatile-pb.dtb \
        --kernel /root/qemu-rpi-kernel/kernel-qemu-4.19.50-buster \
        --append "root=/dev/sda2 panic=1" \
        --no-reboot \
        --display none \
        --serial mon:stdio
fi

Enable SSHD on Raspbian (Expect Tcl/Tk Method)

QEMU doesn't have a straightforward method for running configuration scripts on boot. And because Raspbian doesn't come with SSH enabled by default, we will have to turn it on ourselves. Our options are to do it manually or to use some sort of scripting tool that can interact with 

stdio

Another option is to customize the Raspbian image before installation. This would have to be done on the host, however, as Docker restricts the mounting of new filesystems. In any case, to make this build the most portable and host independent, the most straightforward for our purposes will be to use an Expect script, and have it copied into our Docker image on build.

The 

pi_ssh_enable.exp

Since an unmodified Raspbian image has no accessible ports by default, we will use Expect to interface with 

stdio

sshd

#!/usr/bin/expect -f
set ipaddr [lindex $argv 0]
set timeout -1
spawn /usr/local/bin/qemu-system-arm \
  --machine versatilepb \
  --cpu arm1176 \
  --m 256M \
  --hda /sdcard/filesystem.img \
  --net nic \
  --net user,hostfwd=tcp:$ipaddr:2222-:22 \
  --dtb /root/qemu-rpi-kernel/versatile-pb.dtb \
  --kernel /root/qemu-rpi-kernel/kernel-qemu-4.19.50-buster \
  --append "root=/dev/sda2 panic=1" \
  --no-reboot \
  --display none \
  --serial mon:stdio
expect "raspberrypi login:"
send -- "pi\r"
expect "Password:"
send -- "raspberry\r"
expect "pi@raspberrypi:"
send -- "sudo systemctl enable ssh\r"
expect "pi@raspberrypi:"
send -- "sudo systemctl start ssh\r"
expect "pi@raspberrypi:"
expect eof

Build Image

In the folder with the 

Dockerfile

docker build -t pidoc .

Network Forwarding and Troubleshooting

Once the build is complete, bring it up detached and follow the logs.

docker run -itd --name testnode pidoc
docker logs testnode -f

Raspbian will download and decompressed automatically, and QEMU should begin booting from the image.

Once Raspbian is fully booted, Expect should automatically enable 

sshd

2222

# docker exec -it testnode bash
root@d4abc2f655e6:/# hostname -I
172.17.0.3
root@d4abc2f655e6:/# cat < /dev/tcp/172.17.0.3/2222
SSH-2.0-OpenSSH_7.9p1 Raspbian-10

Cancel out and kill the container and remove the volume.

root@d4abc2f655e6:/# exit
exit
# docker kill testnode
testnode
# docker container rm testnode
testnode

Testing the Docker Container

Start/Test Container

We will need to start the container for testing. This is primarily to gain some understanding of the performance of QEMU so that we can better make design decisions regarding our cluster. The system should come up clean with maybe a few benign warnings related to differences between the somewhat more generalized emulated hardware and the expected physical raspberry Pi hardware.

I found it necessary to make sure port forwarding was working properly between QEMU and the Docker image so that I could further verify that port forwarding between the Docker image and host was working properly. Our first goal is to double forward SSH so that QEMU is accessible directly from the host.

docker run -itd -p 127.0.0.1:2222:2222 --name testnode pidoc
docker logs testnode -f

Once the system again comes online, test for 

sshd

2222

ssh

# ssh pi@localhost -p 2222
The authenticity of host '[localhost]:2222 ([127.0.0.1]:2222)' can't be established.
ECDSA key fingerprint is SHA256:N0oRF23lpDOFjlgYAbml+4v2xnYdyrTmBgaNUjpxnFM.
Are you sure you want to continue connecting (yes/no)? yes
Warning: Permanently added '[localhost]:2222' (ECDSA) to the list of known hosts.
pi@localhost's password:
Linux raspberrypi 4.19.50+ #1 Tue Nov 26 01:49:16 CET 2019 armv6l

The programs included with the Debian GNU/Linux system are free software;
the exact distribution terms for each program are described in the
individual files in /usr/share/doc/*/copyright.

Debian GNU/Linux comes with ABSOLUTELY NO WARRANTY, to the extent
permitted by applicable law.
Last login: Tue Jan 21 12:24:59 2020

SSH is enabled and the default password for the 'pi' user has not been changed.
This is a security risk - please login as the 'pi' user and type 'passwd' to set a new password.

pi@raspberrypi:~ $

Testing Fractional CPU Utilization

To run this cluster, we're using a GCP n1-standard-4 instance (a 4x15) running Ubuntu 18.04 LTS. But we now notice how inefficient QEMU is once Raspbian begins doing anything. Multiple Raspberry Pi instances might stack fine if their idle, but if we want to keep the system viable, we will need to restrict CPU utilization on each instance, or else the system could be rendered unusable once more than a few nodes are put under load. Fortunately, Docker can handle this for us.

We have 15GB of ram on this instance, so let's see what happens if we are slightly more ambitious and squeezed 6 Raspberry Pi containers onto our VM. We will have a whole core left for the host to manage other tasks without much risk of a failure. We can scale this at some point later with Docker Compose.

We will run two test containers at 50% and 100% for benchmark testing.

docker run -itd --cpus="0.50" -p 127.0.0.1:2250:2222 --name pidoc_50_test pidoc
docker run -itd --cpus="1.00" -p 127.0.0.1:2200:2222 --name pidoc_00_test pidoc

At this point, we technically already have a cluster. We just don't have a method to manage them, except by hand.

Performance

While a full core allocation performs at near Physical Raspberry Pi speeds, an instance running at 50% runs rightly at half that speed. This might be manageable under certain circumstances, but it's not the most desirable. The overall efficiency of the cluster may increase, depending on the task at hand. But for now, we will continue with our original full core allocation of 3 nodes, and then later it tests with 6 nodes.

Single Thread Benchmarks

Testing can be done by using the following simple benchmark tests.

CPU Prime Test

sysbench --test=cpu --cpu-max-prime=9999 run

CPU Integer Test

time $(i=0; while ((i<9999999)); do ((i++)); done)

HDD Read Test

dd bs=16K count=102400 iflag=direct if=test_data of=/dev/null

HDD Write Test

dd bs=16k count=102400 oflag=direct if=/dev/zero of=test_data

Results (Single Thread)

For this guide, we will only focus on the CPU Prime Test using 

sysbench

Host

General statistics:
    total time:                          10.0009s
    total number of events:              9417

Latency (ms):
         min:                                  1.04
         avg:                                  1.06
         max:                                  1.63
         95th percentile:                      1.10
         sum:                               9992.36

Threads fairness:
    events (avg/stddev):           9417.0000/0.00
    execution time (avg/stddev):   9.9924/0.00

Virtual Raspberry Pi - Limit: 100%

Test execution summary:
    total time:                          397.8781s
    total number of events:              10000
    total time taken by event execution: 397.4056
    per-request statistics:
         min:                                 38.61ms
         avg:                                 39.74ms
         max:                                 57.15ms
         approx.  95 percentile:              40.92ms

Threads fairness:
    events (avg/stddev):           10000.0000/0.00
    execution time (avg/stddev):   397.4056/0.00

Virtual Raspberry Pi - Limit: 50%

Test execution summary:
    total time:                          823.8272s
    total number of events:              10000
    total time taken by event execution: 822.9329
    per-request statistics:
         min:                                 38.68ms
         avg:                                 82.29ms
         max:                                184.02ms
         approx.  95 percentile:              94.65ms

Threads fairness:
    events (avg/stddev):           10000.0000/0.00
    execution time (avg/stddev):   822.9329/0.00

Compose the Cluster

Create 

docker-compose.yml

We will use Docker Compose for cluster creation. Initially, we will keep this at three nodes to keep it easy to manage. Once we have a proof of concept cluster, we can then scale it out. The most straightforward way to handle this is to map separate ports to localhost for each container. We can specify a range of ports to be used in the 

docker-compose.yml

version: '3'

services:
  node:
    image: pidoc
    ports:
      - "2201-2203:2222"

Bring Up Cluster

To bring up three nodes with 

docker-compose

--scale

docker-compose up --scale node=3

Ansible Configuration

Now that we have all the infrastructure in place for a cluster, we need to manage it. We could use Docker to double attach to the QEMU monitor, but ssh is much more robust. Since we are using 

ssh

update

upgrade

reboot

shutdown

hosts

Please note of the ports we specified in the 

docker-compose.yml

hosts

[all:vars]
ansible_user=pi
ansible_ssh_pass=raspberry
ansible_ssh_extra_args='-o StrictHostKeyChecking=no'

[pidoc-cluster]
node_1.localhost:2201
node_2.localhost:2202
node_3.localhost:2203

For a more comprehensive walkthrough of Ansible, please read How to Install and Configure Ansible on Ubuntu.

update.yml

---
- name: Apt update Pi...
  hosts: pidoc-cluster
  tasks:
    - name: Update apt cache...
      become: yes
      apt:
        update_cache=yes

Usage: 

ansible-playbook playbooks/update.yml -i hosts

upgrade.yml

---
- name: Upgrade Pi...
  hosts: pidoc-cluster
  gather_facts: no
  tasks:
    - name: Update and upgrade apt packages...
      become: true
      apt:
        upgrade: yes
        update_cache: yes
        cache_valid_time: 86400

Usage: 

ansible-playbook playbooks/upgrade.yml -i hosts

reboot.yml

---
- name: Reboot Pi...
  hosts: pidoc-cluster
  gather_facts: no
  tasks:
    - name: Reboot Pi...
      shell: shutdown -r now
      async: 0
      poll: 0
      ignore_errors: true
      become: true

    - name: Wait for reboot...
      local_action: wait_for host={{ ansible_host }}state=started delay=10
      become: false

Usage: 

ansible-playbook playbooks/reboot.yml -i hosts

shutdown.yml

---
- name: Shutdown Pi...
  hosts: pidoc-cluster
  gather_facts: no
  tasks:
    - name: 'Shutdown Pi'
      shell: shutdown -h now
      async: 0
      poll: 0
      ignore_errors: true
      become: true

    - name: "Wait for shutdown..."
      local_action: wait_for host={{ ansible_host }} state=stopped
      become: false

Usage: 

ansible-playbook playbooks/shutdown.yml -i hosts

Scaling Up

Docker Compose makes scaling Raspberry Pi containers on the same host near trivial. By using Ansible for cluster management, it also becomes incredibly easy to scale horizontally to other hosts by changing the port binding from localhost to an IP address that's routable. Here is our example with 6 nodes instead of 3.

docker-compose.yml

version: '3'

services:
  node:
    image: pidoc
    ports:
      - "2201-2212:2222"
    deploy:
      resources:
        limits:
          cpus: "0.5"

Bring Up Cluster

We should stop containers from our previous cluster, and prune all volumes before scaling up our revised cluster. To bring up all 6 nodes with 

docker-compose

--scale

docker-compose up --scale node=5

Future Work

Raspberry Pi emulation is still under development for QEMU. While the configuration for this project is relatively stable, there's a lot of room for improvement. Attempting a migration to Raspberry Pi 3 emulation would be an ambitious next step. Docker Compose, though designed for single-host builds, is already easy enough to replicate to other hosts manually or through Ansible. But it could just as easily be scaled out with Swarm or k8s, enabling us to build an emulated Raspberry Pi cluster of any size.

Additionally, with one or more port redirects, other systems of control can be put into place, including various node endpoints, depending on purpose and application.

This article was originally published on https://appfleet.com/blog/raspberry-pi-cluster-emulation-with-docker-compose/.