Disclaimer: all code snippets below are working only with Docker 1.13+

TL;DR

Docker 1.13 simplifies deployment of composed application to a swarm (mode) cluster. And you can do it without creating a new dab (Distribution Application Bundle) file, but just using familiar and well-known docker-compose.yml syntax (with some additions) and --compose-file option.

Swarm cluster

Docker Engine 1.12 introduced a new swarm mode for natively managing a cluster of Docker Engines called a swarm. Docker swarm mode implements Raft Consensus Algorithm and does not require using external key value store anymore, such as Consul or etcd.

If you want to run a swarm cluster on a developer’s machine, there are several options.

The first option and most widely known, is to use a docker-machine tool with some virtual driver (Virtualbox, Parallels or other).

But, in this post I will use another approach: using docker-in-docker Docker image with Docker for Mac, see more details in my Docker Swarm cluster with docker-in-docker on MacOS post.

Docker Registry mirror

When you deploy a new service on local swarm cluster, I recommend to setup local Docker registry mirror and run all swarm nodes with --registry-mirror option, pointing to local Docker registry. By running a local Docker registry mirror, you can keep most of the redundant image fetch traffic on your local network and speedup service deployment.

Docker Swarm cluster bootstrap script

I’ve prepared a shell script to bootstrap 4 nodes swarm cluster with Docker registry mirror and very nice swarm visualizer application.

The script initialize docker engine as a swarm master, then starts 3 new docker-in-docker containers and joins them to the swarm cluster as worker nodes. All worker nodes run with --registry-mirror option.

#!/bin/bash

# vars[ -z "$NUM_WORKERS" ] && NUM_WORKERS=3

# init swarm (need for service command); if not createddocker node ls 2> /dev/null | grep "Leader"if [ $? -ne 0 ]; thendocker swarm init > /dev/null 2>&1fi

# get join tokenSWARM_TOKEN=$(docker swarm join-token -q worker)

# get Swarm master IP (Docker for Mac xhyve VM IP)SWARM_MASTER=$(docker info --format "{{.Swarm.NodeAddr}}")echo "Swarm master IP: ${SWARM_MASTER}"sleep 10

# start Docker registry mirrordocker run -d --restart=always -p 4000:5000 --name v2_mirror \-v $PWD/rdata:/var/lib/registry \-e REGISTRY_PROXY_REMOTEURL=https://registry-1.docker.io \registry:2.5

# run NUM_WORKERS workers with SWARM_TOKENfor i in $(seq "${NUM_WORKERS}"); do

remove node from cluster if exists

docker node rm --force \$(docker node ls --filter "name=worker-${i}" -q) \> /dev/null 2>&1

remove worker container with same name if exists

docker rm --force \$(docker ps -q --filter "name=worker-${i}") > /dev/null 2>&1

run new worker container

docker run -d --privileged --name worker-${i} \--hostname=worker-${i} \-p ${i}2375:2375 \-p ${i}5000:5000 \-p ${i}5001:5001 \-p ${i}5601:5601 \docker:1.13-rc-dind \--registry-mirror http://${SWARM_MASTER}:4000

add worker container to the cluster

docker --host=localhost:${i}2375 swarm join \--token ${SWARM_TOKEN} ${SWARM_MASTER}:2377done

# show swarm clusterprintf "\nLocal Swarm Cluster\n===================\n"

docker node ls

# echo swarm visualizerprintf "\nLocal Swarm Visualizer\n===================\n"docker run -it -d --name swarm_visualizer \-p 8000:8080 -e HOST=localhost \-v /var/run/docker.sock:/var/run/docker.sock \manomarks/visualizer:beta

Deploy multi-container application — the “old” way

The Docker compose is a tool (and deployment specification format) for defining and running composed multi-container Docker applications. Before Docker 1.12, you could use docker-compose tool to deploy such applications to a swarm cluster. With 1.12 release, it’s not possible anymore: docker-compose can deploy your application only on single Docker host.

In order to deploy it to a swarm cluster, you need to create a special deployment specification file (also knows as Distribution Application Bundle) in dab format (see more here).

The way to create this file, is to run the docker-compose bundle command. The output of this command is a JSON file, that describes multi-container composed application with Docker images referenced by @sha256 instead of tags. Currently dab file format does not support multiple settings from docker-compose.yml and does not allow to use supported options from docker service create command.

Such a pity story: the dab bundle format looks promising, but currently is totally useless (at least in Docker 1.12).

Deploy multi-container application — the “new” way

With Docker 1.13, the “new” way to deploy a multi-container composed application is to use docker-compose.yml again (hurrah!). Kudos to Docker team!

*Note: And you do not need the docker-compose tool, only yaml file in docker-compose format (version: "3")

$ docker deploy --compose-file docker-compose.yml

Docker compose v3 (version: "3")

So, what’s new in docker compose version 3?

First, I suggest you take a deeper look at docker-compose schema. It is an extension of well-known docker-compose format.

Note: docker-compose tool (ver. 1.9.0) does not support docker-compose.yaml version: "3" yet.

The most visible change is around swarm service deployment. Now you can specify all options supported by docker service create/update commands:

• number of service replicas (or global service)
• service labels
• hard and soft limits for service (container) CPU and memory
• service restart policy
• service rolling update policy
• deployment placement constraints link

Docker compose v3 example

I’ve created a “new” compose file (v3) for classic “Cats vs. Dogs” example. This example application contains 5 services with following deployment configurations:

1. voting-app - a Python webapp which lets you vote between two options; requires redis
2. redis - Redis queue which collects new votes; deployed on swarm manager node
3. db - Postgres database backed by a Docker volume; deployed on swarm manager node
4. result-app - Node.js webapp which shows the results of the voting in real time; 2 replicas, deployed on swarm worker nodes
5. worker .NET worker which consumes votes and stores them in db;

• # of replicas: 2 replicas
• hard limit: max 25% CPU and 512MB memory
• soft limit: max 25% CPU and 256MB memory
• placement: on swarm worker nodes only
• restart policy: restart on-failure, with 5 seconds delay, up to 3 attempts
• update policy: one by one, with 10 seconds delay and 0.3 failure rate to tolerate during the update

version: "3"

services:

redis:image: redis:3.2-alpineports:- "6379"networks:- voteappdeploy:placement:constraints: [node.role == manager]

db:image: postgres:9.4volumes:- db-data:/var/lib/postgresql/datanetworks:- voteappdeploy:placement:constraints: [node.role == manager]

voting-app:image: gaiadocker/example-voting-app-vote:goodports:- 5000:80networks:- voteappdepends_on:- redisdeploy:mode: replicatedreplicas: 2labels: [APP=VOTING]placement:constraints: [node.role == worker]

result-app:image: gaiadocker/example-voting-app-result:latestports:- 5001:80networks:- voteappdepends_on:- db

worker:image: gaiadocker/example-voting-app-worker:latestnetworks:voteapp:aliases:- workersdepends_on:- db- redis# service deploymentdeploy:mode: replicatedreplicas: 2labels: [APP=VOTING]# service resource managementresources:# Hard limit - Docker does not allow to allocate morelimits:cpus: '0.25'memory: 512M# Soft limit - Docker makes best effort to return to itreservations:cpus: '0.25'memory: 256M# service restart policyrestart_policy:condition: on-failuredelay: 5smax_attempts: 3window: 120s# service update configurationupdate_config:parallelism: 1delay: 10sfailure_action: continuemonitor: 60smax_failure_ratio: 0.3# placement constraint - in this case on 'worker' nodes onlyplacement:constraints: [node.role == worker]

networks:voteapp:

volumes:db-data:

Run the docker deploy — compose-file docker-compose.yml VOTE command to deploy my version of “Cats vs. Dogs” application on a swarm cluster.

Cats vs. Dogs on Swarm cluster

Hope you find this post useful. I look forward to your comments and any questions you have.

Originally published at Codefresh Blog.