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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

What does standalone Docker Swarm look like with service discovery?


Now that we have a better understanding of the requirements and the reasons behind the usage of service discovery, we can define the (real) flow of a request to a Docker Swarm manager.

Please note that we are still exploring how the old (standalone) Swarm is working:

  1. A user sends a request with the desired state to one of the Swarm managers.
  2. The Swarm manager gets the cluster information from the service registry, creates a set of tasks, and dispatches them to Swarm workers.
  3. Swarm workers translate the tasks into commands and send them to the local Docker Engine which, in turn, runs or stops containers.
  4. Swarm workers continuously monitor Docker events and update the service registry.

That way, information about the whole cluster is always up-to-date. The exception is when one of the managers or workers fails. Since managers are monitoring each other, the failure of a manager or a worker is considered a failure of the whole node...

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