In the previous post we talked about Registrator and how, combined with a service discovery backend like Consul, it allows us to have transparent discovery for our containers while still keeping their portability. One thing we didn’t talk about though is how are we supposed to access those services registered in Consul from our consumer applications, which could be running as containers themselves.

As an example, imagine we have a service exposing a REST API. To provide horizontal scalability we decide to run 3 instances of that service, all registered in Consul. Each container will be listening on a random port assigned by Docker, so how do we know where to connect to from our consumers? We can use Consul’s own DNS capabilities, as we saw on the last post, but even though Consul offers the possibility of asking for SRV records (which include the port information as well as the IP) most client libraries in modern programming languages don’t care about this information and only use the IP address, leaving the task of specifying the port to the developer. We could always use Consul’s REST API to query for the services we are interested in and parse the IP and Port from there. But this approach seems rather complex and it would couple our consumer app to Consul’s specific API.

In this post I want to explore one possible approach to solve this problem in a portable and transparent way, both from the point of view of our services as from the point of view of our consumers. It is certainly not the only possible approach nor the best but it is something that I have seen working quite successfully in the past.


Lets think about our current problem again. We have 2 or more containers that expose a REST API and we want to consume that API from another application. We are using Consul as a service discovery mechanism and Registrator to transparently register our containers there. We know that we can get the IP of our service by using Consul’s DNS interface but we don’t know which port on that IP to use. For the purposes of this post, our service container will be the Python service that we have been using so far (available in the Docker hub as jlodiales/python-micro-service). In turn, our consumer will simply be the curl command line tool.

It would be great if there was a proxy running on a well known port that we could send requests to. That proxy would then pass the request to the correct service and transmit the response back to us. This sounds a lot like something that Nginx or HAProxy could do. But now we have just moved the problem one step further. That is, how does our proxy know which port our containers are running on? Luckily for us, the guys from HashiCorp have developed a little standalone tool to do just this: Consul Template.

Consul Template

From the project’s Github repo:

This project provides a convenient way to populate values from Consul into the filesystem using the consul-template daemon. The daemon consul-template queries a Consul instance and updates any number of specified templates on the filesystem. As an added bonus, consul-template can optionally run arbitrary commands when the update process completes.

We’ll see how this works with a simple example. First, we’ll run our Consul cluster. For simplicity we’ll run just one node but exactly the same would apply on a multi-node setup.

$ docker run -p 8400:8400 -p 8500:8500 -p 8600:53/udp \
-h consul --name consul \
progrium/consul -server -advertise $DOCKER_IP -bootstrap

Notice that we are advertising the $DOCKER_IP as Consul’s IP. The reason for that is that Registrator will always register new containers as accessible in Consul’s advertise IP. We discussed this in the previous post. Also, as a remainder, the DOCKER_IP variable is simply boot2docker’s IP (export DOCKER_IP=$(boot2docker ip 2> /dev/null)). If you are running on native Linux then that would be localhost.

Now that we have Consul running, we’ll do the same for Registrator:

$ docker run -d \
-v /var/run/docker.sock:/tmp/docker.sock \
--name registrator -h registrator \
gliderlabs/registrator:latest consul://$DOCKER_IP:8500

And finally our Python service. As we said before, lets imagine we want to run 3 instances of it:

$ docker run -d -P --name node1 -h node1 jlordiales/python-micro-service:latest
$ docker run -d -P --name node2 -h node2 jlordiales/python-micro-service:latest
$ docker run -d -P --name node3 -h node3 jlordiales/python-micro-service:latest

We can query consul to make sure that our new containers are running:

$ curl $DOCKER_IP:8500/v1/catalog/service/python-micro-service

    "Address": "",
    "Node": "node1",
    "ServiceAddress": "",
    "ServiceID": "registrator:node1:5000",
    "ServiceName": "python-micro-service",
    "ServicePort": 49162,
    "ServiceTags": null
    "Address": "",
    "Node": "node1",
    "ServiceAddress": "",
    "ServiceID": "registrator:node2:5000",
    "ServiceName": "python-micro-service",
    "ServicePort": 49163,
    "ServiceTags": null
    "Address": "",
    "Node": "node1",
    "ServiceAddress": "",
    "ServiceID": "registrator:node3:5000",
    "ServiceName": "python-micro-service",
    "ServicePort": 49164,
    "ServiceTags": null

Now for the fun part. We’ll install Consul Template and see what happens when we run it against our current setup. We can get the latest release from here for whatever architecture we are running on. In my case I’m running on a Mac so:

$ wget -O /tmp/consul-template.tar.gz
$ tar -xvzf /tmp/consul-template.tar.gz -C /tmp --strip-components=1

Next, we’ll write a simple template and run consul-template to parse it and generate the result. You can read all about the templates syntax and provided functions at the project’s documentation:

$ echo '{{range service "python-micro-service"}}\nserver {{.Address}}:{{.Port}}{{end}}' > /tmp/consul.ctmpl
$ /tmp/consul-template -consul $DOCKER_IP:8500 -template /tmp/consul.ctmpl:/tmp/consul.result -dry -once

> /tmp/consul.result


By specifying the -dry parameter we tell consul-template to send the output to stdout instead of the file specified on the command (/tmp/consul.result in this case). The -once parameter tells Consul Template to query Consul and generate the output just once. If we don’t do this then the app will keep running in the foreground polling Consul at regular intervals (which is what we would want in a typical scenario). You can see that the result includes the 3 instances of the service with their respective ports.

To see what happens when we change the information registered in Consul, we are going to run consul-template again but this time we won’t specify the -once parameter in order to leave the daemon running:

$ /tmp/consul-template -consul $DOCKER_IP:8500 -template /tmp/consul.ctmpl:/tmp/consul.result -dry

With that running, we’ll go to a new terminal and stop one of the running python containers:

$ docker stop node3

You should almost instantly see the refreshed output in the terminal running consul-template that now only shows 2 entries. Conversely, if we run a new container:

$ docker run -d -P --name node4 -h node4 jlordiales/python-micro-service:latest

The consul-template output gets updated again with the new service.

Combining Consul Template and a reverse proxy

So we saw that we can use Consul Template to parse a template file and produce a new file with the information read from Consul. How can we use this from our consumer applications in order to have transparent service location and load balance? Well, one option is to front our services with Nginx or HAProxy, creating the config files for these with Consul Template. We’ll how this would work for Nginx. All the files that I’ll describe in the following section can be found in this repo if you just want to clone from it.

I’ll first show the Dockerfile that we’ll use for the Nginx image and then explain each section of it:

FROM nginx:latest

ENTRYPOINT ["/bin/"]
VOLUME /templates
ENV CONSUL_URL consul:8500

ADD /bin/
RUN rm -v /etc/nginx/conf.d/\*.conf

ADD /usr/bin/
RUN tar -C /usr/local/bin --strip-components 1 -zxf /usr/bin/consul-template_0.7.0_linux_amd64.tar.gz

We are basing our image from the official Nginx image, available here. This gives us a ready to use, default Nginx installation. Then we say that the entrypoint will be the /bin/ (will see that one in a bit) and that our container will expose port 80, where Nginx will be listening for new connections. Next we define a volume /templates, which is where we will mount our template files from the host. This way we can reuse the same image for different services and templates. In the following step we define and environment variable with the location of our Consul cluster. By default, it will try to resolve to consul:8500 which would be the behavior if we have Consul running as a container in the same host and we link it to this Nginx container (with the alias consul, of course). But this environment variable can also be overridden when we run the container if we want to point somewhere else. We then add the start up script (which is used as the entrypoint to our containers) and remove all default configurations from Nginx. On the last 2 lines we download the latest version of Consul Template (0.7.0 at the time of writing this) and we put it on /usr/local/bin.

The script is very simple:

service nginx start
consul-template -consul=$CONSUL_URL -template="/templates/service.ctmpl:/etc/nginx/conf.d/service.conf:service nginx reload"

We just start the nginx service and then leave consul-template running on the foreground. Here we use the CONSUL_URL environment variable that we defined before. Consul template expects to find a service.ctmpl file in /templates. This is the template that we would mount as a volume from our host. The result is then placed in /etc/nginx/conf.d/service.conf where Nginx will be able to read from. Finally, every time the template is re-rendered we do a service nginx reload in order to read the new configuration.

Time to see this in action. If you still have the Consul, Registrator and Python containers running from the first part of this post then you only need to run the Nginx container (otherwise start them again).

The only thing you’ll need is a template file like the following, save it as /tmp/service.ctmpl for convenience:

upstream python-service {
  {{range service "python-micro-service"}}server {{.Address}}:{{.Port}} max_fails=3 fail_timeout=60 weight=1;
  {{else}}server; # force a 502{{end}}

server {
  listen 80 default_server;

  charset utf-8;

  location ~ ^/python-micro-service/(.\*)$ {
    proxy_pass http://python-service/$1$is_args$args;
    proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    proxy_set_header Host $host;
    proxy_set_header X-Real-IP $remote_addr;

Now run the nginx container with:

$ docker run -p 8080:80 -d --name nginx --volume /tmp/service.ctmpl:/templates/service.ctmpl --link consul:consul jlordiales/nginx-consul

We can curl the service multiple times:

$ curl $DOCKER_IP:8080/python-micro-service/
$ curl $DOCKER_IP:8080/python-micro-service/
$ curl $DOCKER_IP:8080/python-micro-service/
$ curl $DOCKER_IP:8080/python-micro-service/

You should see a “Hello World from nodeX” where X alternates between 1, 2 and 3. We are effectively load balancing between the 3 nodes. But there’s something even cooler that you can try. Leave this running on a terminal:

$ while true; do curl $DOCKER_IP:8080/python-micro-service/; echo -----; sleep 1; done;

That will keep calling nginx every second, which in turn will send the request to one of the 3 nodes. Now from another terminal, stop node1 with:

$ docker stop node1

If you check the terminal running the while loop you’ll notice that the requests are now going to node2 and node3 only. You can play around with this (starting and stopping nodes) to see the configuration updated almost instantaneous and nginx adjusting which nodes it sends requests to. And, more importantly, all of this while keeping our service containers and our nginx container completely ignorant about the fact that we are using Consul as a service discovery mechanism!


This post completes the subject of transparent service discovery in Docker. We saw how we can use a reverse proxy sitting in front of our containers, accessible through a well known port. The proxy, in turn is kept updated with the information available in our Consul cluster thanks to a small and handy tool called Consul Template.

Combined with Registrator and Consul this gives us extreme flexibility and portability. Of course, as with almost everything else, there are other alternatives and approaches. I would be glad to hear other people’s experiences around this area.

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