Monthly Archives: October 2018

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

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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