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The DevOps 2.1 Toolkit: Docker Swarm

You're reading from   The DevOps 2.1 Toolkit: Docker Swarm The next level of building reliable and scalable software unleashed

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787289703
Length 436 pages
Edition 1st Edition
Tools
Concepts
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Author (1):
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Viktor Farcic Viktor Farcic
Author Profile Icon Viktor Farcic
Viktor Farcic
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Table of Contents (17) Chapters Close

Preface 1. Continuous Integration with Docker Containers FREE CHAPTER 2. Setting Up and Operating a Swarm Cluster 3. Docker Swarm Networking and Reverse Proxy 4. Service Discovery inside a Swarm Cluster 5. Continuous Delivery and Deployment with Docker Containers 6. Automating Continuous Deployment Flow with Jenkins 7. Exploring Docker Remote API 8. Using Docker Stack and Compose YAML Files to Deploy Swarm Services 9. Defining Logging Strategy 10. Collecting Metrics and Monitoring the Cluster 11. Embracing Destruction: Pets versus Cattle 12. Creating and Managing a Docker Swarm Cluster in Amazon Web Services 13. Creating and Managing a Docker Swarm Cluster in DigitalOcean 14. Creating and Managing Stateful Services in a Swarm Cluster 15. Managing Secrets in Docker Swarm Clusters 16. Monitor Your GitHub Repos with Docker and Prometheus

Jenkins failover


Let's stop the service and observe Swarm in action. To do that, we need to find out the node it is running in, point our Docker client to it, and remove the container:

NODE=$(docker service ps \
    -f desired-state=running jenkins \
    | tail -n +2 | awk '{print $4}')

eval $(docker-machine env $NODE)

docker rm -f $(docker ps -qa \
    -f label=com.docker.swarm.service.name=jenkins)

 

We listed Jenkins processes and applied the filter that will return only the one with the desired state running docker service ps -f desired-state=running jenkins. The output was piped to the tail command that removed the header tail -n +2 and, later on, piped again to the awk command that limited the output to the fourth column awk '{print $4}' that contains the node the process is running in. The final result was stored in the NODE variable.

Later on, we used the eval command to create environment variables that will be used by our Docker client to operate the remote engine. Finally, we retrieved...

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