Author Archives: Viktor Farcic

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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Kubernetes’ Cluster Autoscaler Compared in GKE, EKS, and AKS

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Kubernetes’ Cluster Autoscaler is a prime example of the differences between different managed Kubernetes offerings. We’ll use it to compare the three major Kubernetes-as-a-Service providers.

I’ll limit the comparison between the vendors only to the topics related to Cluster Autoscaling.
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Setting Up Cluster Autoscaler In EKS

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Unlike GKE, EKS does not come with Cluster Autoscaler. We’ll have to configure it ourselves. We’ll need to add a few tags to the Autoscaling Group dedicated to worker nodes, to put additional permissions to the Role we’re using, and to install Cluster Autoscaler.
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To Replicas Or Not To Replicas In Kubernetes Deployments And StatefulSets?

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Knowing that HorizontalPodAutoscaler (HPA) manages auto-scaling of our applications, the question might arise regarding replicas. Should we define them in our Deployments and StatefulSets, or should we rely solely on HPA to manage them? Instead of answering that question directly, we’ll explore different combinations and, based on results, define the strategy.

First, let’s see how many Pods we have in our cluster right now.

You might not be able to use the same commands since they assume that go-demo-5 application is already running, that the cluster has HPA enabled, that you cloned the code, and a few other things. I presented the outputs so that you can follow the logic without running the same commands.

kubectl -n go-demo-5 get pods

The output is as follows.

NAME    READY STATUS  RESTARTS AGE
api-... 1/1   Running 0        27m
api-... 1/1   Running 2        31m
db-0    2/2   Running 0        20m
db-1    2/2   Running 0        20m
db-2    2/2   Running 0        21m

We can see that there are two replicas of the api Deployment, and three replicas of the db StatefulSets.
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Four Phases Of Kubernetes Adoption

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Kubernetes is probably the biggest project we know. It is vast, and yet many think that after a few weeks or months of reading and practice they know all there is to know about it. It’s much bigger than that, and it is growing faster than most of us can follow. How far did you get in Kubernetes adoption?

From my experience, there are four main phases in Kubernetes adoption.

In the first phase, we create a cluster and learn intricacies of Kube API and different types of resources (e.g., Pods, Ingress, Deployments, StatefulSets, and so on). Once we are comfortable with the way Kubernetes works, we start deploying and managing our applications. By the end of this phase, we can shout “look at me, I have things running in my production Kubernetes cluster, and nothing blew up!” I explained most of this phase in The DevOps 2.3 Toolkit: Kubernetes.
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Rumblings Of An Old Man

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Continuous Deployment is about making a decision to do things right. It is a clear goal and a proof that the changes across all levels were successful. The primary obstacle is thinking that we can get there without drastic changes in the application’s architecture, processes, and culture. Tools are the least of our problems.

I spend a significant chunk of my time helping companies improve their systems. The most challenging part of my job is going back home after an engagement knowing that the next time I visit the same company, I will discover that there was no significant improvement. I cannot say that is not partly my fault. It certainly is. I might not be very good at what I do. Or maybe I am not good at conveying the right message. Maybe my advice was wrong. There can be many reasons for those failures, and I do admit that they are probably mostly my fault. Still, I cannot shake the feeling that my failures are caused by something else. I think that the root cause is in false expectations.
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High-Level Overview Of The Continuous Delivery Pipeline

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Let’s paint a high-level picture of the continuous delivery pipeline. To be more precise, we’ll draw a diagram instead of painting anything. But, before we dive into a continuous delivery diagram, we’ll refresh our memory with the one we used before for describing continuous deployment.

The continuous deployment pipeline contains all the steps from pushing a commit to deploying and testing a release in production.

Continuous delivery removes one of the stages from the continuous deployment pipeline. We do NOT want to deploy a new release automatically. Instead, we want humans to decide whether a release should be upgraded in production. If it should, we need to decide when will that happen. Those (human) decisions are, in our case, happening as Git operations. We’ll comment on them soon. For now, the important note is that the deploy stage is now removed from pipelines residing in application repositories.
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