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Building an observability stack for automated performance tests on Kubernetes and OpenShift (part 2)

Building an observability stack for automated performance tests on Kubernetes and OpenShift (part 2)

This is the second of a series of three articles based on a session I held at Red Hat Tech Exchange in EMEA. In the first article, I presented the rationale and approach for leveraging Red Hat OpenShift or Kubernetes for automated performance testing, and I gave an overview of the setup.

In this article, we will look at building an observability stack. In production, the observability stack can help verify that the system is working correctly and performing well. It can also be leveraged during performance tests to provide insight into how the application performs under load.

An example of what is described in this article is available in my GitHub repository.

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Leveraging Kubernetes and OpenShift for automated performance tests (part 1)

Leveraging Kubernetes and OpenShift for automated performance tests (part 1)

This is the first article in a series of three articles based on a session I hold at Red Hat Tech Exchange EMEA. In this first article, I present the rationale and approach for leveraging Red Hat OpenShift or Kubernetes for automated performance testing, give an overview of the setup, and discuss points that are worth considering when executing and analyzing performance tests. I will also say a few words about performance tuning.

In the second article, we will look at building an observability stack, which—beyond the support it provides in production—can be leveraged during performance tests. Open sources projects like Prometheus, Jaeger, Elasticsearch, and Grafana will be used for that purpose. The third article will present the details for building an environment for performance testing and automating the execution with JMeter and Jenkins.

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Structured application logs in OpenShift

Structured application logs in OpenShift

Logs are like gold dust. Taken alone they may not be worth much, but put together and worked by a skillful goldsmith they may become very valuable. OpenShift comes with The EFK stack: Elasticsearch, Fluentd, and Kibana. Applications running on OpenShift get their logs automatically aggregated to provide valuable information on their state and health during tests and in production.

The only requirement is that the application sends its logs to the standard output. OpenShift does the rest. Simple enough!

In this blog I am covering a few points that may help you with bringing your logs from raw material to a more valuable product.

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How to Install Elastic Stack (ELK) on Red Hat Enterprise Linux (RHEL)

Sometimes, software just goes together. Linux, the Apache Web server, MySQL, and PHP, the four ingredients of the LAMP stack, which revolutionized data centers and made open source a big deal two decades ago, are probably the most famous example. But there are lots of others.

Here’s another open source software stack you should know about in our present age of cloud and big data: the Elastic Stack, or ELK. Based on Elasticsearch, Logstash and Kibana, ELK is a fully open source solution for searching, analyzing and visualizing data in any format, at any scale.

Since ELK has multiple parts, and some of them have other dependencies, setting up ELK is not as simple as installing other stacks, which sometimes require a simple one-line yum installation command. But fear not. ELK is still easy enough to install if you follow the proper steps.

Below, we’ll walk through configuring a Red Hat Enterprise Linux (RHEL) server for ELK, installing each of the requisite components and configuring them to work with one another. (RHEL is now free for development use — download it here.)

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