Service Mesh

Bringing Coolstore Microservices to the Service Mesh: Part 1 – Exploring Auto-injection

Bringing Coolstore Microservices to the Service Mesh: Part 1 – Exploring Auto-injection

As the industry heads toward the Trough of Disillusionment with cloud-native microservices, finally understanding that distributed architectures introduce more complexity (weird, right?), services meshes can help soften the landing and shift some of that complexity out of our applications and place it where it belongs, in the application operational layer.

At Red Hat we are committed to (and actively involved in) the upstream Istio project and working to integrate it into Kubernetes and Red Hat OpenShift to bring the benefits of a service mesh to our customers and the wider communities involved. If you want to play with Istio, check out the Service Mesh Tutorials on learn.Openshift.com. If you want to install it, follow the Istio Kubernetes quickstart instructions and install it on Red Hat OpenShift 3.7 or later (or 3.9 if you want to use auto-injection).

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Istio Tracing & Monitoring: Where Are You and How Fast Are You Going?

Istio Tracing & Monitoring: Where Are You and How Fast Are You Going?

The Heisenberg Uncertainty Principle states that you cannot measure an object’s position and velocity at the same time. If it’s moving, it’s not in a location. If it’s in a location, then it has no velocity.

Thanks to some awesome open-source software, our microservices running in Red Hat OpenShift (using Kubernetes) can report both their performance and their health. Granted, they can’t violate the Uncertainty Principle, but they can help bring certainty to your cloud-native applications. Istio brings tracing and monitoring to your system with very little effort, helping you keep things humming.

[This is part five of my ten-week Introduction to Istio Service Mesh series.  My previous article was Part 4: Istio Circuit Breaker: When Failure Is an Option.]

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Istio Circuit Breaker: When Failure Is an Option

Istio Circuit Breaker: When Failure Is an Option

The phrase “Failure is not an option” is tossed about with much bravado, as though one could make something work by just their strength of will. But the fact remains, things eventually fail. Everything. How then, do you handle the inevitable failure of your microservices? Well, by combining containers, Kubernetes, Red Hat OpenShift, and Istio, we can skip over-the-top displays of swagger, let the system handle things, and get some sleep at night.

[This is part four of my ten-week Introduction to Istio Service Mesh series.  My previous article was Part 3: Istio Circuit Breaker: How to Handle (Pool) Ejection.]

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Istio Route Rules: Telling Service Requests Where to Go

Istio Route Rules: Telling Service Requests Where to Go

OpenShift and Kubernetes do a great job of working to make sure calls to your microservice are routed to the correct pods. After all, that’s one of the raison d’être for Kubernetes: routing and load balancing. What if, however, you want to customize the routing? What if you want to run two versions at the same time? How do Istio Route Rules handle this?

[This is part two of my ten-week Introduction to Istio Service Mesh series.  My previous article was Part 1: Introduction to Istio; It Makes a Mesh of Things. Want to see this in a video? Check out the video edition here.]

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The CoolStore Microservices Example: DevOps and OpenShift

The CoolStore Microservices Example: DevOps and OpenShift

An introduction to microservices through a complete example

Today I want to talk about the demo we presented @ OpenShift Container Platform Roadshow in Milan & Rome last week.

The demo was based on JBoss team’s great work available on this repo:
https://github.com/jbossdemocentral/coolstore-microservice

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Micro-rules on OpenShift: The CoolStore just became even cooler!

Micro-rules on OpenShift: The CoolStore just became even cooler!

One of our  most popular Red Hat JBoss BRMS demo’s, and one that has been available for quite some time, is the CoolStore demo. The CoolStore demo shows how business rules can be used to calculate values like promotional and shipping discounts in a shopping-cart. It furthermore illustrates concepts like ruleflow-groups and dynamic rule updates using KieScanner.

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