Articles

How to Achieve Effective Kubernetes Cost Optimization

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Kubernetes is ruling the container market. According to a CNCF survey, the use of Kubernetes in production in 2020 was 93%, up from 78% in 2019. Moreover, the survey reveals that the use of containers in production in 2020 was 92%. This figure is up 300% from CNCF’s first survey in 2016. 

Due to the adoption of Kubernetes by DevOps teams and the open source community’s encouragement, this figure could grow more. And if it stays at present prices, this market share still is a significant portion. This means that even though Kubernetes makes a lot of things easier, challenges will always appear, as the survey confirms. Namely, the problems listed include networking, storage, tracking, surveillance, a lack of preparation, and, of course, cost management.

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Top 7 Benefits of GitOps: How to Reinforce Your Software Productivity and Accelerate Business Success

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Today’s world regards time as of paramount importance. This said, the disruptive tech-enabled innovations are foremost meant to save both business owners’ and users’ time. The industry of software development is based upon the same postulates – the sooner the better, provided that timely product deliverables are coupled with efficiency and convenience.

The Continuous Integration (CI) and Continuous Delivery (CD) practices are among popular software engineering industry trends that help cut costs, time, and risks associated with delivering changes. Yet another trend to optimize the industry lies in applying DevOps methods, oriented to increase the efficiency of development and operation processes, and to speed up product delivery.

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Scale to Zero With Kubernetes

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In the era of web-scale, every organization is looking to scale its applications on-demand, while minimizing infrastructure expenditure. Cloud-native applications, such as microservices are designed and implemented with scale in mind and Kubernetes provides the platform capabilities for dynamic deployment, scaling, and management. 

Autoscaling and scale to zero is a critical functional requirement for all serverless platforms as well as platform-as-a-service (PaaS) solution providers because it helps to minimize infrastructure costs.

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API Security Weekly: Issue #120

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This week, we take a look at the security issues in cheap video doorbells and security cameras, as well as tutorials and webinars on protecting APIs running in Kubernetes, JSON web tokens (JWT), and web and API authentication and authorization.

Oh, and we also have a link to DZone community awards where you can vote for this newsletter!

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Kubernetes Autoscaling with Custom Metrics (Updated)

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In the previous blog post about Kubernetes autoscaling, we looked at different concepts and terminologies related to autoscaling such as HPA, cluster auto-scaler, etc. In this post, we’ll do a walkthrough of how Kubernetes autoscaling can be implemented for custom metrics generated by the application.

Why Custom Metrics?

The CPU or RAM consumption of an application may not indicate the right metric for scaling always. For example, if you have a message queue consumer that can handle 500 messages per second without crashing. Once a single instance of this consumer is handling close to 500 messages per second, you may want to scale the application to two instances so that load is distributed across two instances. Measuring CPU or RAM is a fundamentally flawed approach for scaling such an application and you would have to look at a metric that relates more closely to the application’s nature. The number of messages that an instance is processing at a given point in time is a better indicator of the actual load on that application. Similarly, there might be applications where other metrics make more sense and these can be defined using custom metrics in Kubernetes.

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Alexa and Kubernetes: Deploying the Alexa Skill on Google Kubernetes Engine (IX)

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Now, we have everything prepared and ready to go to a Kubernetes Cluster in a cloud provider. It is a fact that creating a cluster in any cloud provider manually is a difficult task. Moreover, if we want to automate this deployment, we need something that helps us in this tedious task. In this article, we will see how to create a Kubernetes Cluster and all of its required objects, deploying our Alexa Skill with Terraform using Google Kubernetes Engine.

Pre-Requisites

Here, you have the technologies used in this project:

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Kubernetes Multi-Cluster Management and Governance

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Due to the performance nature of modern cloud-native applications, Kubernetes environments need to be highly distributed. Proper multi-cluster management and governance ensure consistent, secure operations across all environments. In this Refcard, we further explore Kubernetes multi-cluster management and governance, why it’s important, and core practices for success.
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Hands-On GitOps With OneDev and Kubernetes

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Set Up Kubernetes Cluster

First, we need a Kubernetes cluster. You may use an existing one or set up a new one. For this tutorial, we chose to use GKE (Google Kubernetes Engine).

Just follow the quick start to create a cluster. To save your money, the default pool with only one node is sufficient for our testing. For node image type, use the default Container-Optimized OS; for machine type, please select one with at least 8G mem. After creating the cluster, go ahead to configure kubectl to connect to the cluster following this guide.

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Exploring Kubernetes With Gigi Sayfan

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You have probably read about Kubernetes, and maybe even dipped your toes in and used it in a side project or even at work. But understanding what Kubernetes is all about, how to use it effectively, and what the best practices are requires much more effort. Kubernetes is a big open-source project and ecosystem with a lot of code and a lot of functionality. Kubernetes came out of Google, but joined the Cloud Native Computing Foundation (CNCF) and became the clear leader in the space of container-based applications.

Let’s hear from Gigi Sayfan, author of the bestseller Mastering Kubernetes, Third Edition, about his methodologies and the approach he followed to create a powerful resource to acquaint learners all over the globe with the fundamentals and more advanced concepts of Kubernetes.

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Answers to 5 Kubernetes CKAD Practice Questions

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While doing some work with Kubernetes (K8s) and studying for the CKAD exam, I came across a page on Matthew Palmer’s website entitled “Practice Exam for Certified Kubernetes Application Developer (CKAD) Certification” and which contains five practice questions, which I’ll go over here. If you see a problem with anything I’ve done below, including inefficient solutions, please let me know in the comments.

We will focus on not just showing a possible solution to each problem but also verifying our work.

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