title	keywords	description
Get Started, Part 5: Stacks	stack, data, persist, dependencies, redis, storage, volume, port	Learn how to create a multi-container application that uses all the machines in a cluster.

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Prerequisites

Install Docker version 1.13 or higher.
Get Docker Compose as described in Part 3 prerequisites.
Get Docker Machine as described in Part 4 prerequisites.
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'll use that shared image here.
Be sure your image works as a deployed container. Run this command, slotting in your info for username, repo, and tag: docker run -p 80:80 username/repo:tag, then visit http://localhost/.
Have a copy of your docker-compose.yml from Part 3 handy.
Make sure that the machines you set up in part 4 are running and ready. Run docker-machine ls to verify this. If the machines are stopped, run docker-machine start myvm1 to boot the manager, followed by docker-machine start myvm2 to boot the worker.
Have the swarm you created in part 4 running and ready. Run docker-machine ssh myvm1 "docker node ls" to verify this. If the swarm is up, both nodes will report a ready status. If not, reinitialze the swarm and join the worker as described in Set up your swarm.

Introduction

In part 4, you learned how to set up a swarm, which is a cluster of machines running Docker, and deployed an application to it, with containers running in concert on multiple machines.

Here in part 5, you'll reach the top of the hierarchy of distributed applications: the stack. A stack is a group of interrelated services that share dependencies, and can be orchestrated and scaled together. A single stack is capable of defining and coordinating the functionality of an entire application (though very complex applications may want to use multiple stacks).

Some good news is, you have technically been working with stacks since part 3, when you created a Compose file and used docker stack deploy. But that was a single service stack running on a single host, which is not usually what takes place in production. Here, you will take what you've learned, make multiple services relate to each other, and run them on multiple machines.

You're doing great, this is the home stretch!

Add a new service and redeploy

It's easy to add services to our docker-compose.yml file. First, let's add a free visualizer service that lets us look at how our swarm is scheduling containers.

Open up docker-compose.yml in an editor and replace its contents with the following. Be sure to replace 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
      restart_policy:
        condition: on-failure
      resources:
        limits:
          cpus: "0.1"
          memory: 50M
    ports:
      - "80:80"
    networks:
      - webnet
  visualizer:
    image: dockersamples/visualizer:stable
    ports:
      - "8080:8080"
    volumes:
      - "/var/run/docker.sock:/var/run/docker.sock"
    deploy:
      placement:
        constraints: [node.role == manager]
    networks:
      - webnet
networks:
  webnet:

The only thing new here is the peer service to web, named visualizer. You'll see two new things here: a volumes key, giving the visualizer access to the host's socket file for Docker, and a placement key, ensuring that this service only ever runs on a swarm manager -- never a worker. That's because this container, built from an open source project created by Docker, displays Docker services running on a swarm in a diagram.

We'll talk more about placement constraints and volumes in a moment.

Make sure your shell is configured to talk to myvm1 (full examples are here).
- Run docker-machine ls to list machines and make sure you are connected to myvm1, as indicated by an asterisk next it.
- If needed, re-run docker-machine env myvm1, then run the given command to configure the shell.
  
  On Mac or Linux the command is:
```
eval $(docker-machine env myvm1)
```
  On Windows the command is:
```
& "C:\Program Files\Docker\Docker\Resources\bin\docker-machine.exe" env myvm1 | Invoke-Expression
```

Re-run the docker stack deploy command on the manager, and whatever services need updating will be updated:

$ docker stack deploy -c docker-compose.yml getstartedlab
Updating service getstartedlab_web (id: angi1bf5e4to03qu9f93trnxm)
Updating service getstartedlab_visualizer (id: l9mnwkeq2jiononb5ihz9u7a4)

Take a look at the visualizer.

You saw in the Compose file that visualizer runs on port 8080. Get the IP address of one of your nodes by running docker-machine ls. Go to either IP address at port 8080 and you will see the visualizer running:

The single copy of visualizer is running on the manager as you expect, and the 5 instances of web are spread out across the swarm. You can corroborate this visualization by running docker stack ps <stack>:
```
docker stack ps getstartedlab
```
The visualizer is a standalone service that can run in any app that includes it in the stack. It doesn't depend on anything else. Now let's create a service that does have a dependency: the Redis service that will provide a visitor counter.

Persist the data

Let's go through the same workflow once more to add a Redis database for storing app data.

Save this new docker-compose.yml file, which finally adds a Redis service. Be sure to replace 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
      restart_policy:
        condition: on-failure
      resources:
        limits:
          cpus: "0.1"
          memory: 50M
    ports:
      - "80:80"
    networks:
      - webnet
  visualizer:
    image: dockersamples/visualizer:stable
    ports:
      - "8080:8080"
    volumes:
      - "/var/run/docker.sock:/var/run/docker.sock"
    deploy:
      placement:
        constraints: [node.role == manager]
    networks:
      - webnet
  redis:
    image: redis
    ports:
      - "6379:6379"
    volumes:
      - /home/docker/data:/data
    deploy:
      placement:
        constraints: [node.role == manager]
    command: redis-server --appendonly yes
    networks:
      - webnet
networks:
  webnet:
```
Redis has an official image in the Docker library and has been granted the short image name of just redis, so no username/repo notation here. The Redis port, 6379, has been pre-configured by Redis to be exposed from the container to the host, and here in our Compose file we expose it from the host to the world, so you can actually enter the IP for any of your nodes into Redis Desktop Manager and manage this Redis instance, if you so choose.

Most importantly, there are a couple of things in the redis specification that make data persist between deployments of this stack:
- redis always runs on the manager, so it's always using the same filesystem.
- redis accesses an arbitrary directory in the host's file system as /data inside the container, which is where Redis stores data.
Together, this is creating a "source of truth" in your host's physical filesystem for the Redis data. Without this, Redis would store its data in /data inside the container's filesystem, which would get wiped out if that container were ever redeployed.

This source of truth has two components:
- The placement constraint you put on the Redis service, ensuring that it always uses the same host.
- The volume you created that lets the container access ./data (on the host) as /data (inside the Redis container). While containers come and go, the files stored on ./data on the specified host will persist, enabling continuity.
You are ready to deploy your new Redis-using stack.
Create a ./data directory on the manager:
```
docker-machine ssh myvm1 "mkdir ./data"
```
Make sure your shell is configured to talk to myvm1 (full examples are here).
- Run docker-machine ls to list machines and make sure you are connected to myvm1, as indicated by an asterisk next it.
- If needed, re-run docker-machine env myvm1, then run the given command to configure the shell.
  
  On Mac or Linux the command is:
```
eval $(docker-machine env myvm1)
```
  On Windows the command is:
```
& "C:\Program Files\Docker\Docker\Resources\bin\docker-machine.exe" env myvm1 | Invoke-Expression
```

Run docker stack deploy one more time.

$ docker stack deploy -c docker-compose.yml getstartedlab

Run docker service ls to verify that the three services are running as expected.

$ docker service ls
ID                  NAME                       MODE                REPLICAS            IMAGE                             PORTS
x7uij6xb4foj        getstartedlab_redis        replicated          1/1                 redis:latest                      *:6379->6379/tcp
n5rvhm52ykq7        getstartedlab_visualizer   replicated          1/1                 dockersamples/visualizer:stable   *:8080->8080/tcp
mifd433bti1d        getstartedlab_web          replicated          5/5                 orangesnap/getstarted:latest    *:80->80/tcp

Check the web page at one of your nodes (e.g. http://192.168.99.101) and you'll see the results of the visitor counter, which is now live and storing information on Redis.

Also, check the visualizer at port 8080 on either node's IP address, and you'll see the redis service running along with the web and visualizer services.

On to Part 6 >>{: class="button outline-btn"}

Recap (optional)

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

You learned that stacks are inter-related services all running in concert, and that -- surprise! -- you've been using stacks since part three of this tutorial. You learned that to add more services to your stack, you insert them in your Compose file. Finally, you learned that by using a combination of placement constraints and volumes you can create a permanent home for persisting data, so that your app's data survives when the container is torn down and redeployed.

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