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Kibana Meets Kubernetes
Second Sight
The other day I faced a scenario, wherein I wanted to output logs to Elastic Stack (previously called ELK Stack) for visualization through dashboards. Elastic Stack comprises three software components: Elasticsearch, Logstash, and Kibana. When combined, they offer advanced logging, alerting, and searching [1]. The logging was needed so I could dig deeper into what was happening with an application running on a Kubernetes cluster over a prolonged period of time.
I turned to Elastic Stack because it became uber-popular as ELK Stack for good reason. It's widely adopted, helps speed up the process of analyzing massive amounts of data that's churned out by machines, and is sometimes used in enterprises as an alternative to one of the commercial market leaders, Splunk [2].
To make sure I could use my solution as hoped, I decided to create a lab before trying it out elsewhere in a more critical environment. In this article, I show the steps I took and the resulting proof-of-concept functionality.
Three Is the Magic Number
I'm going to use the excellent K3s to build my Kubernetes cluster. For instructions on how to install K3s, check out the "Teeny, Tiny" section in my article on StatusBay in this issue. However, I would definitely recommend getting some background information by visiting their site [3] or reading another of my articles [4] in which I use it for applications in the Internet of Things (IoT) space.
If, when you run the command
$ kubectl get pods --all-namespaces
you see CrashLoopBackOff errors, it's likely to do with netfilter's iptables tying itself in knots.
Kubernetes is well known for causing
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