Building microservice systems with docker and kubernetes

Building microservice systems with docker and kubernetes software#
Building microservice systems with docker and kubernetes code#

Yet again, when using Terraform, this is just a code change.

We can use elastic scaling at the cluster level to automatically grow our clusters that are nearing their resource limits. This generates substantial cost savings because, at any given moment, we only pay for the resources necessary to handle our applicationвЂ™s workload at that time. During high-demand periods, new resources are allocated to meet the increased workload. Whenever a demand is low, Kubernetes can automatically deallocate resources that arenвЂ™t needed. This is a technique where we automatically and dynamically scale our cluster to meet varying levels of demand. Moving into more advanced territory, we can now think about elastic scaling. This allows the failed instance of a microservice to restart and begin working again. By having multiple instances, there are others available to pick up the load whenever any single instance fails. Not only can we scale individual microservices for performance, we can also horizontally scale our microservices for redundancy, creating a more fault-tolerant application. Horizontally scaling a microservice with Terraform (an extract). Figure 2 shows what vertical scaling looks like for Kubernetes. When scaling microservices on a Kubernetes cluster, we can just as easily make use of either vertical or horizontal scaling. (We can monitor this through our cloud vendor or the Kubernetes dashboard.)ДЄt this point, we must increase the total amount of resources available to our cluster. As we add new microservices (or replicate existing microservices for redundancy), we will eventually max out the nodes in our cluster. Vertically Scaling the ClusterДЄs we grow our application, we might come to a point where our cluster generally doesnвЂ™t have enough compute, memory or storage to run our application. Instead, I would suggest that you create a new cluster and use blue-green deployment, or a similar deployment strategy, to buffer your users from risky changes to your infrastructure.

For this reason, you shouldnвЂ™t try to make any of these changes directly to a production cluster that your customers or staff are depending on. Scaling often requires risky configuration changes to our cluster. Elastically scaling individual microservices.Horizontally scaling individual microservices.Horizontally scaling the entire cluster.

There are also many advanced ways we could tackle performance issues, but in this post, weвЂ™ll overview a handful of relatively simple techniques for scaling our microservices using Kubernetes: For instance, we can independently fine-tune the performance of small parts of our system to eliminate bottlenecks and achieve the right mix of performance outcomes. Instead, with microservices, we have numerous options for scaling. Vertically scaling a large monolith can be an expensive proposition. Yet, we would have to vertically scale the entire monolith to fix it. This isnвЂ™t ideal, because it might only be a small part of the monolith that causes the performance problem. Horizontally scaling a monolith is much more difficult and we simply canвЂ™t independently scale any of the вЂњpartsвЂќ of a monolith. He is the CTO of Sortal, a product that automatically sorts digital assets through the magic of machine learning.

Building microservice systems with docker and kubernetes software#

Ashley is a software craftsman, entrepreneur, and author with over 20 years of experience in software development, from coding to managing teams, then to founding companies.