Get Docker Compose. On Docker Desktop for Mac and Docker Desktop for Windows it’s pre-installed, so you’re good-to-go. On Linux systems you need to install it directly. On pre Windows 10 systems without Hyper-V, use Docker Toolbox.
Read the orientation in Part 1.
Learn how to create containers in Part 2.
Make sure you have published the
friendlyhello image you created by pushing it to a registry. We use that shared image here.
Be sure your image works as a deployed container. Run this command, slotting in your info for
docker run -p 4000:80 username/repo:tag, then visit
In part 3, we scale our application and enable load-balancing. To do this, we must go one level up in the hierarchy of a distributed application: the service.
In a distributed application, different pieces of the app are called “services”. For example, if you imagine a video sharing site, it probably includes a service for storing application data in a database, a service for video transcoding in the background after a user uploads something, a service for the front-end, and so on.
Services are really just “containers in production.” A service only runs one image, but it codifies the way that image runs—what ports it should use, how many replicas of the container should run so the service has the capacity it needs, and so on. Scaling a service changes the number of container instances running that piece of software, assigning more computing resources to the service in the process.
Luckily it’s very easy to define, run, and scale services with the Docker platform -- just write a
docker-compose.yml file is a YAML file that defines how Docker containers should behave in production.
Save this file as
docker-compose.yml wherever you want. Be sure you have pushed the image you created in Part 2 to a registry, and update this
.yml by replacing
username/repo:tag with your image details.
version: "3" services: web: # replace username/repo:tag with your name and image details image: username/repo:tag deploy: replicas: 5 resources: limits: cpus: "0.1" memory: 50M restart_policy: condition: on-failure ports: - "4000:80" networks: - webnet networks: webnet:
docker-compose.yml file tells Docker to do the following:
Pull the image we uploaded in step 2 from the registry.
Run 5 instances of that image as a service called
web, limiting each one to use, at most, 10% of a single core of CPU time (this could also be e.g. “1.5” to mean 1 and half core for each), and 50MB of RAM.
Immediately restart containers if one fails.
Map port 4000 on the host to
web’s port 80.
web’s containers to share port 80 via a load-balanced network called
webnet. (Internally, the containers themselves publish to
web’s port 80 at an ephemeral port.)
webnet network with the default settings (which is a load-balanced overlay network).
Before we can use the
docker stack deploy command we first run:
docker swarm init
Note: We get into the meaning of that command in part 4. If you don’t run
docker swarm inityou get an error that “this node is not a swarm manager.”
Now let’s run it. You need to give your app a name. Here, it is set to
docker stack deploy -c docker-compose.yml getstartedlab
Our single service stack is running 5 container instances of our deployed image on one host. Let’s investigate.
Get the service ID for the one service in our application:
docker service ls
Look for output for the
web service, prepended with your app name. If you named it the same as shown in this example, the name is
getstartedlab_web. The service ID is listed as well, along with the number of replicas, image name, and exposed ports.
Alternatively, you can run
docker stack services, followed by the name of your stack. The following example command lets you view all services associated with the
docker stack services getstartedlab ID NAME MODE REPLICAS IMAGE PORTS bqpve1djnk0x getstartedlab_web replicated 5/5 username/repo:tag *:4000->80/tcp
A single container running in a service is called a task. Tasks are given unique IDs that numerically increment, up to the number of
replicas you defined in
docker-compose.yml. List the tasks for your service:
docker service ps getstartedlab_web
Tasks also show up if you just list all the containers on your system, though that is not filtered by service:
docker container ls -q
You can run
curl -4 http://localhost:4000 several times in a row, or go to that URL in your browser and hit refresh a few times.
Either way, the container ID changes, demonstrating the load-balancing; with each request, one of the 5 tasks is chosen, in a round-robin fashion, to respond. The container IDs match your output from the previous command (
docker container ls -q).
To view all tasks of a stack, you can run
docker stack ps followed by your app name, as shown in the following example:
docker stack ps getstartedlab ID NAME IMAGE NODE DESIRED STATE CURRENT STATE ERROR PORTS uwiaw67sc0eh getstartedlab_web.1 username/repo:tag docker-desktop Running Running 9 minutes ago sk50xbhmcae7 getstartedlab_web.2 username/repo:tag docker-desktop Running Running 9 minutes ago c4uuw5i6h02j getstartedlab_web.3 username/repo:tag docker-desktop Running Running 9 minutes ago 0dyb70ixu25s getstartedlab_web.4 username/repo:tag docker-desktop Running Running 9 minutes ago aocrb88ap8b0 getstartedlab_web.5 username/repo:tag docker-desktop Running Running 9 minutes ago
Running Windows 10?
Slow response times?
Depending on your environment’s networking configuration, it may take up to 30 seconds for the containers to respond to HTTP requests. This is not indicative of Docker or swarm performance, but rather an unmet Redis dependency that we address later in the tutorial. For now, the visitor counter isn’t working for the same reason; we haven’t yet added a service to persist data.
You can scale the app by changing the
replicas value in
docker-compose.yml, saving the change, and re-running the
docker stack deploy command:
docker stack deploy -c docker-compose.yml getstartedlab
Docker performs an in-place update, no need to tear the stack down first or kill any containers.
docker container ls -q to see the deployed instances reconfigured. If you scaled up the replicas, more tasks, and hence, more containers, are started.
Take the app down with
docker stack rm:
docker stack rm getstartedlab
Take down the swarm.
docker swarm leave --force
It’s as easy as that to stand up and scale your app with Docker. You’ve taken a huge step towards learning how to run containers in production. Up next, you learn how to run this app as a bonafide swarm on a cluster of Docker machines.
Note: Compose files like this are used to define applications with Docker, and can be uploaded to cloud providers using Docker Cloud, or on any hardware or cloud provider you choose with Docker Enterprise Edition.
To recap, while typing
docker run is simple enough, the true implementation of a container in production is running it as a service. Services codify a container’s behavior in a Compose file, and this file can be used to scale, limit, and redeploy our app. Changes to the service can be applied in place, as it runs, using the same command that launched the service:
docker stack deploy.
Some commands to explore at this stage:
docker stack ls # List stacks or apps docker stack deploy -c # Run the specified Compose file docker service ls # List running services associated with an app docker service ps # List tasks associated with an app docker inspect # Inspect task or container docker container ls -q # List container IDs docker stack rm # Tear down an application docker swarm leave --force # Take down a single node swarm from the manager