part3.md

title	keywords	description
Get Started, Part 3: Services	services, replicas, scale, ports, compose, compose file, stack, networking	Learn how to define load-balanced and scalable service that runs containers.

{% include_relative nav.html selected="3" %}

Prerequisites

• Install Docker version 1.13 or higher.

• Read the orientation in Part 1.

• Learn how to create containers in Part 2.

• Make sure you have pushed the container you created to a registry, as instructed; we'll be using it here.

• Ensure your image is working by running this and visiting http://localhost/ (slotting in your info for username, repo, and tag):

  docker run -p 80:80 username/repo:tag
  

Introduction

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.

• Stack
• Services (you are here)
• Container (covered in part 2)

Understanding services

In a distributed application, different pieces of the app are called "services." For example, if you imagine a video sharing site, there will probably be 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.

A service really just means, "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.

Your first docker-compose.yml File

A docker-compose.yml file is a YAML file that defines how Docker containers should behave in production.

docker-compose.yml

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 use that info to replace username/repo:tag:

version: "3"
services:
  web:
    image: username/repository:tag
    deploy:
      replicas: 5
      resources:
        limits:
          cpus: "0.1"
          memory: 50M
      restart_policy:
        condition: on-failure
    ports:
      - "80:80"
    networks:
      - webnet
networks:
  webnet:

This docker-compose.yml file tells Docker to do the following:

• Run five instances of the image we uploaded in step 2 as a service called web, limiting each one to use, at most, 10% of the CPU (across all cores), and 50MB of RAM.
• Immediately restart containers if one fails.
• Map port 80 on the host to web's port 80.
• Instruct web's containers to share port 80 via a load-balanced network called webnet. (Internally, the containers themselves will publish to web's port 80 at an ephemeral port.)
• Define the webnet network with the default settings (which is a load-balanced overlay network).

Run your new load-balanced app

Before we can use the docker stack deploy command we'll first run

docker swarm init

Note: We'll get into the meaning of that command in part 4. If you don't run docker swarm init you'll get an error that "this node is not a swarm manager."

Now let's run it. You have to give your app a name -- here it is set to getstartedlab :

docker stack deploy -c docker-compose.yml getstartedlab

See a list of the five containers you just launched:

docker stack ps getstartedlab

You can run curl http://localhost several times in a row, or go to that URL in your browser and hit refresh a few times. Either way, you'll see the container ID change, demonstrating the load-balancing; with each request, one of the five replicas is chosen, in a round-robin fashion, to respond.

Note: At this stage, 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 will address later in the tutorial.

Scale the app

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 will do an in-place update, no need to tear the stack down first or kill any containers.

Take down the app

Take the app down with docker stack rm:

docker stack rm getstartedlab

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 will learn how to run this app on a cluster of 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.

On to "Part 4" >>{: class="button outline-btn" style="margin-bottom: 30px"}

Recap and cheat sheet (optional)

Here's a terminal recording of what was covered on this page:

<script type="text/javascript" src="https://asciinema.org/a/b5gai4rnflh7r0kie01fx6lip.js" id="asciicast-b5gai4rnflh7r0kie01fx6lip" speed="2" async></script>

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 all running applications on this Docker host
docker stack deploy -c <composefile> <appname>  # Run the specified Compose file
docker stack services <appname>       # List the services associated with an app
docker stack ps <appname>   # List the running containers associated with an app
docker stack rm <appname>                             # Tear down an application