Category Archives: Kubernetes

Implementing ChatOps With Jenkins X

Jenkins X main logic is based on applying GitOps principles. Every change must be recorded in Git, and only Git is allowed to initiate events that result in changes in our clusters. That logic is the cornerstone of Jenkins X, and it served us well so far. However, there are actions we might need to perform that do not result in changes to the source code or configurations. Hence the emergence of ChatOps.
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Going Serverless With Jenkins X: Exploring Prow, Jenkins X Pipeline Operator, And Tekton

The serverless flavor of Jenkins X or, as some call it, Jenkins X Next Generation, is an attempt to redefine how we do continuous delivery and GitOps inside Kubernetes clusters. It does that by combining quite a few tools into a single easy-to-use bundle. As a result, most people will not have a need to understand intricacies of how the pieces work independently, nor how they are all integrated. Instead, many will merely push a change to Git and let the system do the rest. But, there are always those who would like to know what’s happening behind the hood. To satisfy those craving for insight, we’ll explore the processes and the components involved in the serverless Jenkins X platform. Understanding the flow of an event initiated by a Git webhook will give us insight into how the solution works and help us later on when we go deeper into each of the new components.
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Improving And Simplifying Software Development With Jenkins X

Software development is hard. It takes years to become a proficient developer, and the tech and the processes change every so often. What was effective yesterday, is not necessarily effective today. The number of languages we code in is increasing. While in the past, most developers would work in the same language throughout their whole carrier, today it is not uncommon for a developer to work on multiple projects written in different languages. We might, for example, work on a new project and code in Go, while we still need to maintain some other project written in Java. For us to be efficient, we need to install compilers, helper libraries, and quite a few other things.
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Creating Custom Jenkins X Build Packs

I stand by my claim that “you do not need to understand Kubernetes to use Jenkins X.” To be more precise, those who do not want to know Kubernetes and its ecosystem in detail can benefit from Jenkins X ability to simplify the processes around software development lifecycle. That’s the promise or, at least, one of the driving ideas behind the project. Nevertheless, for that goal to reach as wide of an audience as possible, we need a variety of build packs. The more we have, the more use cases can be covered with a single jx import or jx quickstart command. The problem is that there is an infinite number of types of applications and combinations we might have. Not all can be covered with community-based packs. No matter how much effort the community puts into creating build packs, they will always be a fraction of what we might need. That’s where you come in.
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Is Your Cluster Ready For Jenkins X?

If you’re reading this, the chances are that you do not want to use jx cluster create to create a new cluster that will host Jenkins X. That is OK, or even welcome. That likely means that you are already experienced with Kubernetes and that you already have applications running in Kubernetes. That’s a sign of maturity and your desire to add Jenkins X to the mix of whichever applications you are already running there. After all, it would be silly to create a new cluster for each set of applications.

However, using an existing Kubernetes cluster is risky. Many people think that they are so smart that they will assemble their Kubernetes cluster from scratch. “We’re so awesome that we don’t need tools like Rancher to create a cluster for us.” “We’ll do it with kubeadm.” Then, after a lot of sweat, we announce that the cluster is operational, only to discover that there is no StorageClass or that networking does not work. So, if you assembled your own cluster and you want to use Jenkins X inside it, you need to ask yourself whether that cluster is set up correctly. Does it have everything we need? Does it comply with standards, or did you tweak it to meet your corporate restrictions? Did you choose to remove StorageClass because all your applications are stateless? Were you forced by your security department to restrict communication between Namespaces? Is the Kubernetes version too old? We can answer those and many other questions by running compliance tests.
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“The DevOps 2.6 Toolkit: Jenkins X” is born

When I finished the last book (The DevOps 2.5 Toolkit: Monitoring, Logging, and Auto-Scaling Kubernetes), I wanted to take a break from writing for a month or two. I thought that would clear my mind and help me decide which subject to tackle next. Those days were horrible. I could not make up my mind. So many cool and useful tech is emerging and being adopted. I was never as undecided as those weeks. Which should be my next step?

I could explore serverless. That’s definitely useful, and it might be considered the next big thing. Or I could dive into Istio. It is probably the biggest and the most important project sitting on top of Kubernetes. Or I could tackle some smaller subjects. Kaniko is the missing link in continuous delivery. Building containers might be the only thing we still do on the host level, and Kaniko allows us to move that process inside containers. How about security scanning? It is one of the things that are mandatory in most organizations, and yet I did not include it in “The DevOps 2.4 Toolkit: Continuous Deployment To Kubernetes”. Then there is skaffold, prow, KNative, and quite a few other tools that are becoming stable and very useful.
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“The DevOps 2.5 Toolkit: Monitoring, Logging, and Auto-Scaling Kubernetes” is available!

The DevOps 2.5 Toolkit: Monitoring, Logging, and Auto-Scaling Kubernetes is finally finished!!!

What do we do in Kubernetes after we master deployments and automate all the processes? We dive into monitoring, logging, auto-scaling, and other topics aimed at making our cluster resilient, self-sufficient, and self-adaptive.
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What Should We Expect From Centralized Logging?

There are quite a few candidates for your need for centralized logging. Which one should you choose? Will it be Papertrail, Elasticsearch-Fluentd-Kibana stack (EFK), AWS CloudWatch, GCP Stackdriver, Azure Log Analytics, or something else?

When possible and practical, I prefer a centralized logging solution provided as a service, instead of running it inside my clusters. Many things are easier when others are making sure that everything works. If we use Helm to install EFK, it might seem like an easy setup. However, maintenance is far from trivial. Elasticsearch requires a lot of resources. For smaller clusters, compute required to run Elasticsearch alone is likely higher than the price of Papertrail or similar solutions. If I can get a service managed by others for the same price as running the alternative inside my own cluster, service wins most of the time. But, there are a few exceptions.
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Visualizing Kubernetes Metrics And Alerts

Dashboards are useless! They are a waste or time. Get Netflix if you want to watch something. It’s cheaper than any other option.

I repeated those words on many public occasions. I think that companies exaggerate the need for dashboards. They spend a lot of effort creating a bunch of graphs and put a lot of people in charge of staring at them. As if that’s going to help anyone. The main advantage of dashboards is that they are colorful and full of lines, boxes, and labels. Those properties are always an easy sell to decision makers like CTOs and heads of departments. When a software vendor comes to a meeting with decision makers with authority to write checks, he knows that there is no sale without “pretty colors”. It does not matter what that software does, but how it looks like. That’s why every software company focuses on dashboards.

Think about it. What good is a dashboard for? Are we going to look at graphs until a bar reaches a red line indicating that a critical threshold is reached? If that’s the case, why not create an alert that will trigger under the same conditions and stop wasting time staring at screens and waiting until something happens. Instead, we can be doing something more useful (like staring Netflix).
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A Quick Introduction To Prometheus And Alertmanager

Kubernetes HorizontalPodAutoscaler (HPA) and Cluster Autoscaler (CA) provide essential, yet very rudimentary mechanisms to scale our Pods and clusters. While they do scaling decently well, they do not solve our need to be alerted when there’s something wrong, nor do they provide enough information required to find the cause of an issue. We’ll need to expand our setup with additional tools that will allow us to store and query metrics as well as to receive notifications when there is an issue.

If we focus on tools that we can install and manage ourselves, there is very little doubt about what to use. If we look at the list of Cloud Native Computing Foundation (CNCF) projects, only two graduated so far (October 2018). Those are Kubernetes and Prometheus. Given that we are looking for a tool that will allow us to store and query metrics and that Prometheus fulfills that need, the choice is straightforward. That is not to say that there are no other similar tools worth considering. There are, but they are all service based. We might explore them later but, for now, we’re focused on those that we can run inside our cluster. So, we’ll add Prometheus to the mix and try to answer a simple question. What is Prometheus?
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