prometheus

MicroProfile 3.3 now available on Open Liberty 20.0.0.4, brings updated features, yum/apt-get support, pattern tracking

MicroProfile 3.3 now available on Open Liberty 20.0.0.4, brings updated features, yum/apt-get support, pattern tracking

yasmin-aumeeruddy
By Yasmin.Aumeeruddy

Open Liberty 20.0.0.4 provides support for MicroProfile 3.3 which includes updates to MicroProfile Rest Client, Fault Tolerance, Metrics, Health, and Config. Improved developer experience is also achieved with support for yum/apt-get installs and the ability to track use patterns with JAX-RS 2.1.

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What’s new in the OpenShift 4.3 console developer experience

What’s new in the OpenShift 4.3 console developer experience

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By Steve Speicher and Serena Chechile Nichols

The developer experience is significantly improved in the Red Hat OpenShift 4.3 web console. If you have used the Developer perspective, which was introduced in OpenShift 4.2 Console, you are probably familiar with our streamlined user flows for deploying applications, the new Topology view, and the enhanced experience around OpenShift Pipelines powered by Tekton and OpenShift Serverless powered by Knative. This release continues to improve upon the features that were introduced in 4.2 and introduces new flows and features for the developer.

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Domain-specific Prometheus metrics with Red Hat Process Automation Manager and Decision Manager

Domain-specific Prometheus metrics with Red Hat Process Automation Manager and Decision Manager

duncandoyle
By Duncan Doyle

Since version 7.4, Red Hat Process Automation Manager (RHPAM) and Red Hat Decision Manager (RHDM) provide support for monitoring various metrics through Prometheus. The platform’s Execution Server (also known as KIE-Server) provides a Prometheus extension which, if enabled, exposes metrics like the number of containers deployed, the response time of rule and decision evaluations, etc.

In this example, we will use the RHDM 7 Insurance Pricing Demo as our base project. This demo contains a simple Decision Model and Notation (DMN) model that calculates the insurance price based on the age of the customer and whether the customer had previous incidents. The GitHub repository for this demo can be found here and contains detailed instructions on how to set up and run the demo.

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Understanding Red Hat OpenShift’s Application Monitoring Operator

Understanding Red Hat OpenShift’s Application Monitoring Operator

jmonteir
By Jose Silva

Monitoring systems are usually composed of three layers: a database layer that hosts metrics data, a layer to display the stored metric data graphically in dashboards, and an alerting layer to send out notifications via methods such as email, on-call notification systems, and chat platforms. This article presents an overview of the components used in Red Hat OpenShift‘s Application Monitoring Operator, how to install them using the Operator, and an example of the Operator in action.

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Build a monitoring infrastructure for your Jaeger installation

Build a monitoring infrastructure for your Jaeger installation

jpkroehling
By Juraci Paixão Kröhling

When you deploy Jaeger in a production configuration, it makes sense to keep an eye on your Jaeger instance to see if it is performing as expected. After all, an outage in Jaeger means that tracing data is being lost, making it challenging to understand possible problems happening within your production applications.

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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)

fgiloux
By Frédéric Giloux

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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Monitoring Node.js Applications on OpenShift with Prometheus

Monitoring Node.js Applications on OpenShift with Prometheus

lballredhat-com
By Lance Ball

Observability is Key

One of the great things about Node.js is how well it performs in a container. Its fast start up time, and relatively small size make it a favorite for microservice applications on OpenShift. But with this shift to containerized deployments comes some complexity. As a result, monitoring Node.js applications can be difficult. At times it seems as though the performance and behavior of our applications become opaque to us. So what can we do to find and address issues in our services before they become a problem? We need to enhance observability by monitoring the state of our services.

Instrumentation

Instrumentation of our applications is one way to increase observability. Therefore, in this article, I will demonstrate the instrumentation of a Node.js application using Prometheus.

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

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

fgiloux
By Frédéric Giloux

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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A Beginner’s Guide to Kubernetes (PodCTL Podcast #38)

A Beginner’s Guide to Kubernetes (PodCTL Podcast #38)

rctkv
By Rob Terzi

If you aren’t following the OpenShift Blog, you might not be aware of the PodCTL podcast. It’s a free weekly tech podcast covering containers, kubernetes, and OpenShift hosted by Red Hat’s Brian Gracely (@bgracely) and Tyler Britten (@vmtyler). I’m reposting this episode here on the Red Hat Developer Blog because I think their realization is spot on—while early adopters might be deep into Kubernetes, many are just starting and could benefit from some insights.

Original Introduction from blog.openshift.com:

The Kubernetes community now has 10 releases (2.5 yrs) of software and experience. We just finished KubeCon Copenhagen, OpenShift Commons Gathering, and Red Hat Summit and we heard lots of companies talk about their deployments and journeys. But many of them took a while (12–18) months to get to where they are today. This feels like the “early adopters” and we’re beginning to get to the “crossing the chasm” part of the market. So thought we’d discuss some of the basics, lessons learned, and other things people could use to “fast-track” what they need to be successful with Kubernetes.

The podcast will always be available on the Red Hat OpenShift blog (search: #PodCTL), as well as on RSS Feeds, iTunes, Google Play, Stitcher, TuneIn, and all your favorite podcast players.

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