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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

Axis scaling


Axis scaling can be best represented through three dimensions of a cube; X-Axis, Y-Axis, and Z-Axis. Each of those dimensions describes a type of scaling:

  • X-Axis: Horizontal duplication
  • Y-Axis: Functional decomposition
  • Z-Axis: Data partitioning

  Figure 2-1: Scale cube 

Let's go through the Axes, one at a time.

X-axis scaling

In a nutshell, x-axis scaling is accomplished by running multiple instances of an application or a service. In most cases, there is a load balancer on top that makes sure that the traffic is shared among all those instances. The biggest advantage of x-axis scaling is simplicity. All we have to do is deploy the same application on multiple servers. For that reason, this is the most commonly used type of scaling. However, it comes with its set of disadvantages when applied to monolithic applications.

Having a large application usually requires a big cache that demands heavy usage of memory. When such an application is multiplied, everything is multiplied with it...

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