This post shows how to create a multi-architecture docker swarm with deployment constraints on different architectures.
For the purpose of this post, we will be using simple images with both ARM and X86 variants.
First step is to have a raspberry that is able to run docker.
Pre Requisites
I will not be getting into details on how to install HypriotOS on rpi, or raspbian. If you want to install HypriotOS follow their instructions at (https://blog.hypriot.com/), HypriotOS is a very straightfoward and simple way to get docker running in your RPI in minutes.
1
2
* RPI 3 with HypriotOS installed
* Docker-machine installed (tested with version 0.14.0)
What we will actually build
A temperature monitoring system for our RPI’s :D, with connection to ElasticSearch for data storage and Kibana for data visualization, using python+flask in our RPI
Setting RPI to be used with docker-machine
First we need to trick the docker-machine provisioning system into accepting the RPI OS.
So let’s change the os-release of RPI
1
ssh USER@RPI-IP-ADDRESS
With any terminal editor (example Nano)
1
sudo nano /etc/os-release
Change the line
1
ID=raspbian
To
1
ID=debian
If you don’t have ssh via key, its time to copy your public key to rpi
In your computer
1
ssh-copy-id -i ~/.ssh/id_rsa.pub USER@RPI-IP-ADDRESS
If you don’t use a Linux based OS, please refer to Putty or other means you use to ssh into your RPI
Now we are set up to create a docker-machine
From a terminal run
1
2
3
4
5
6
docker-machine create --driver generic \
--generic-ip-address RPI-IP-ADDRESS \
--generic-ssh-key ~/.ssh/id_rsa.pub \
--generic-ssh-user PI-USER \
--engine-storage-driver overlay \
rpi
And you should now have a new docker-machine named rpi
To test it.
1
docker-machine ls
Should give you a list of machines, in this case one.
1
rpi
Creating Docker Swarm
Now let’s create a swarm ( your computer as the Manager and RPI as a worker)
You can increase this to how many RPI’s you wish as long as you follow the process above.
For simplicity you should have two terminals tabs open as we will have different environments on each on this step
To init the swarm is as easy as:
1
docker swarm init --advertise-addr $GIVE-IP-OF-INTERFACE
The --advertise-addr is required in most situations for multiple IP’s available, either from eth0 and wlo1 , etc..
If all goes well the command should return something like
1
docker swarm join --token SWMTKN-1-2ip14zs1g7tjwd6afbd2y6gg8d0oi6zgjxnp819uhg7u4g0da9-cbr0jcaz4tf5yqe550gdi4acg 192.168.1.1:2377
You will need this token to allow other nodes to join the swarm as workers.
If you lose the token, you may get it again by doing
1
docker swarm join-token worker
Or if you wish to have a node join as a manager
1
docker swarm join-token manager
So now, let’s copy this line
On another terminal window, let’s set the environment to make use of docker environment at the RPI.
1
eval $(docker machine env rpi)
This will allow you to run docker commands as if you in a shell in RPI
So let’s join the swarm. Just copy the line and the docker-machine currently in user rpi will join the swarm.
To check, then just switch to the terminal running your local docker environment, now the Manager Node
And do
1
docker node ls
This will list your nodes.
To have more information on nodes, you can always inspect them, just like inspecting a docker container.
1
docker node inspect rpi
One important part of this inspection is that it allows you to check on how you may use the constraints directive under docker-compose service declaration.
For the example in this post. We will be interested in the following part of the output
1
2
3
4
5
6
7
8
9
10
"Description": {
"Hostname": "rpi",
"Platform": {
"Architecture": "armv7l",
"OS": "linux"
},
"Resources": {
"NanoCPUs": 4000000000,
"MemoryBytes": 1024184320
},
In particular the Platform : Architecture
So now let’s use docker-compose to create a deployable stack.
In fact we will be using a base docker-compose with our stack declaration, and one for our deploy strategy that will add/override a deploy declaration.
More on that later.
Creating Our RPI operating Temperature Monitor
(A previous version of what will build here is available https://github.com/Ilhicas/docker-swarm-demo)
Create the following structure and files
1
2
3
4
5
6
7
8
demo\
app\
app.py
db.py
requirements.txt
Dockerfile
docker-compose.yml
deploy.yml
The contents of each file are below
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
from db import db
import psutil
from flask import Flask, make_response
import psutil
import time
import socket
app = Flask(__name__)
def temperature_thread():
logged = {}
logged['hostname'] = socket.gethostname()
logged['cpu_perc'] = psutil.cpu_percent()
logged['mem_perc'] = psutil.virtual_memory().percent
logged['@timestamp'] = time.time()
logged['container_id'] = socket.gethostname()
with open("/sys/class/thermal/thermal_zone0/temp") as temp:
logged['temperature'] = float(temp.read().strip())/1000
db.index(index="ilhicas.com", doc_type='log', body=logged)
threading.Timer(30, temperature_thread).start()
@app.route("/")
def index():
return make_response("Container ID: {}".format(socket.gethostname()), 200)
if __name__ == "__main__":
threading.Timer(1, temperature_thread).start()
app.run("0.0.0.0", port=5000, threaded=True)
db.py
1
2
3
from elasticsearch import Elasticsearch
db = Elasticsearch(["elasticsearch"], port=9200)
requirements.txt
1
2
3
flask
psutil
elasticsearch==5.4.0
Dockerfile
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
FROM armhf/alpine:3.5
RUN apk add --update alpine-sdk python python-dev py-pip linux-headers
RUN rm -rf /var/cache/apk/*
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt
COPY /app/. /app
WORKDIR /app
EXPOSE 5000
ENTRYPOINT ["python"]
CMD ["app.py"]
docker-compose.yml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
version: "3.2"
services:
demo:
image: swarm_demo_v1
ports:
- mode: host
target: 5000
published: 80
kibana:
image: kibana
environment:
- ELASTICSEARCH_URL=http://elasticsearch:9200
ports:
- 5601:5601
elasticsearch:
image: elasticsearch:alpine
volumes:
- ek:/usr/share/elasticsearch/data
environment:
ES_JAVA_OPTS: '-Xms2048m -Xmx2048m'
volumes:
ek:
deploy.yml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
version: '3.2'
services:
elasticsearch:
deploy:
replicas: 1
constraints:
- node.platform.arch == x86_64
kibana:
deploy:
replicas: 1
constraints:
- node.platform.arch == x86_64
demo:
deploy:
replicas: 1
placement:
constraints:
- node.platform.arch == armv7l
Ok, sorry for the long paste of files, but its nice to have all in one place, and as well to learn other things along the way besides swarm.
We are actually using BI tool to analyse our RPI operating temperature.
Nice overkill :P
But first things first.
Before deploying we need to build the arm image in RPI
So switch to the terminal containing the RPI Environment and
1
docker-compose build demo
When you are done, all you have to do is deploy the Service stack
Switch back to the Manager Node terminal (your local computer)
1
docker stack deploy -c docker-compose-yml -c deploy.yml temperature-monitor
Now if everything went well you should be able to visit both IP’s and get the same info
1
2
3
http://127.0.0.1.5601
http://RPI-IP:5601
Should both return the Kibana Dashboard
Just go to management and add the index, after Elastic Search has started up The index should be named
1
ilhicas.com
Or other name if you changed the python code at app.py
You may also visit
1
http://127.0.0.1:5000
And get the container ID of the container running under the RPI
That’s it, hope you found it helpful.