Ben Sears

Kubernetes is the most popular management and orchestration tool today. It provides a configuration-driven framework that allows us to define and manipulate entire networks, disks, and applications in a scalable and easily managed manner.

If we haven’t finished the application containerization, then transferring the application to Kubernetes can be an intensive task. The purpose of this article is to introduce the method of integrating the application with Kubernetes.

Step 1 — Containerize your application

Containers are basic operating units that can run independently. Unlike traditional virtual machines that rely on emulated operating systems, containers utilize various kernel features to provide an environment isolated from the host.

For experienced technologists, the entire containerization process is not complicated — use Docker, define a Dockerfile containing installation steps and configurations (download packages, dependencies, etc.), and finally build an image that developers can use.

Step 2 – Adopt a multi-instance architecture

Before we can migrate the application to Kubernetes, we need to confirm the delivery method to end users.

The multi-tenant structure of traditional Web applications, where all users share a single database instance and application instance, works fine in Kubernetes, but we recommend considering a multi-instance architecture to take advantage of Kubernetes and containerized applications.

The benefits of adopting a multi-instance architecture include:

Stability — single point of failure without affecting other instances;

Scalability – with a multi-instance architecture, scaling is a matter of adding computing resources; For multi-tenant architectures, the deployment of clustered application architectures that need to be created can be cumbersome;

Security – When you use a single database, all data is together and all users are at risk in the event of a security breach, whereas with multiple data centers, only one user’s data is at risk;

Step 3 — Determine your application’s resource consumption

To be cost-effective, we need to determine the AMOUNT of CPU, memory, and storage required to run a single application instance.

We can set limits to precisely adjust how much space Kubernetes nodes need, make sure they are overloaded or unavailable, and so on.

This requires trial and error, and there are tools that do it for us.

  • After determining resource allocation, we can calculate the optimal resource size of Kubernetes node.

  • Multiply the memory or CPU required per instance by 100 (the maximum number of pods a node can hold) to get a rough estimate of how much memory and CPU your node should have;

  • Stress test the application to ensure that it runs smoothly at full node.

Step 4 — Integrate with Kubernetes

Once the Kubernetes cluster is up and running, we’ll see a lot of DevOps practices follow

Automatically scale Kubernetes nodes

When the nodes are full, more nodes usually need to be configured so that all the power runs smoothly, and automatically scaled Kubernetes nodes come in handy.

Automatically scale the application

Depending on usage, some applications need to scale. Kubernetes uses triggers that can automatically scale deployment to achieve this. Command:

kubectl autoscale deployment myapp --cpu-percent = 50 --min = 1 --max = 10
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As shown above, when the CPU percentage exceeds 50, set myApp deployment to scale to 10 containers.

Automatically configure instances as users operate

For a multi-instance architecture, the end user deploys the application in Kubernetes, and to achieve this, we should consider integrating the application with the Kubernetes API or using a third-party solution to provide an entry point to request instances.

This section describes how to define a host name

More and more end users have recently attached their domain names to applications, and Kubernetes provides tools to make the process easier, even to the point of self-service (users press a button to set the domain pointing), using systems such as Nginx Ingress.

Finally, a commercial

Kubernetes proposed a series of conceptual abstractions, very consistent with the ideal distributed scheduling system. However, a large number of difficult technical concepts also create a steep learning curve, which directly raises the bar for using Kubernetes.

The open source PaaS Rainbond package these technical concepts into a production-ready application that can be used as a Kubernetes panel that requires no special learning.

In addition, Kubernetes itself is a container orchestration tool and does not provide management processes, whereas Rainbond provides off-the-shelf management processes, including DevOps, automated operations, microservices architecture, and application marketplaces, right out of the box.

To learn more about: https://www.goodrain.com/scene/k8s-docker