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Metrics and traces correlation in Kiali

Metrics and traces correlation in Kiali

Metrics, traces, and logs might be the Three Pillars of Observability, as you’ve certainly already heard. This mantra helps us focus our mindset around observability, but it is not a religion. “There is so much more data that can help us have insight into our running systems,” said Frederic Branczyk at KubeCon last year.

These three kind of signals do have their specificities, but they also have common denominators that we can generalize. They could all appear on a virtual timeline and they all originate from a workload, so they are timed and sourced, which is a good start for enabling correlation. If there’s anything as important as knowing the signals that a system can emit, it’s knowing the relationships between those signals and being able to correlate one with another, even when they’re not strictly of the same nature. Ultimately, we can postulate that any sort of signal that is timed and sourced is a good candidate for correlation as well, even if we don’t have hard links between them.

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Installing Kubeflow v0.7 on OpenShift 4.2

Installing Kubeflow v0.7 on OpenShift 4.2

As part of the Open Data Hub project, we see potential and value in the Kubeflow project, so we dedicated our efforts to enable Kubeflow on Red Hat OpenShift. We decided to use Kubeflow 0.7 as that was the latest released version at the time this work began. The work included adding new installation scripts that provide all of the necessary changes such as permissions for service accounts to run on OpenShift.

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DevNation Live Bengaluru: Sail into cloud — An introduction to Istio

DevNation Live Bengaluru: Sail into cloud — An introduction to Istio

Our first DevNation Live regional event was held in Bengaluru, India in July. This free technology event focused on open source innovations, with sessions presented by elite Red Hat technologists.

In this session, Kamesh Sampath provides an overview of Envoy and Istio, two open source projects that will change the way you write cloud-native Java applications on Kubernetes. We’ll show how to download and set up Istio on your local laptop, then deploy Java microservices as part of the Istio service mesh with Istio sidecar proxy.

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Looking up a hash table library for caching in the 3scale Istio adapter

Looking up a hash table library for caching in the 3scale Istio adapter

You have probably already heard about the service mesh concept and one of its leading implementations, Istio. In the 3scale engineering team at Red Hat, we are working on a component to extend the functionality of Istio (and Red Hat’s distribution, Maistra) by integrating some API Management features via the 3scale platform. In this article, I’ll describe this work and some of the decisions made along the way.

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Manage your APIs deployed with Istio service mesh

Manage your APIs deployed with Istio service mesh

With the rise of microservices architectures, companies are looking for a way to connect, secure, control, and observe their microservices. Currently, a service mesh such as Istio is the best option to reach this goal.

  • Connect: Istio can intelligently control the flow of traffic between services, conduct a range of tests and upgrade gradually with blue/green deployments.
  • Secure: Automatically secure your services through managed authentication, authorization, and encryption of communication between services.
  • Control: Apply policies and ensure that they are enforced and that resources are fairly distributed among consumers.
  • Observe: See what’s happening with rich automatic tracing, monitoring, logging of all your services.

And, as explained in “Distributed microservices architecture: Istio, managed API gateways and, enterprise integration”, a service mesh does not relieve the need for an API management solution. A service mesh manages services and the connections between them, whereas an API management solution manages APIs and their consumers. In this article, I’ll describe how to manage APIs using the Red Hat Integration adapter for Istio.

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Guru Night at Red Hat Summit: Hands-on experience with serverless computing

Guru Night at Red Hat Summit: Hands-on experience with serverless computing

Millions of developers worldwide want to learn more about serverless computing. If you’re one of the lucky thousands attending Red Hat Summit in Boston May 7-9, you can gain hands-on experience with the help of Burr Sutter and the Red Hat Developer team.

Guru Night is a BYOL (bring your own laptop) event taking place Wednesday, May 8 from 5:00 p.m. to 8:00 p.m. at the Boston Convention and Event Center in ML2 East-258AB. (Doubtless there will be a map to show you where or what ML2 East etc. is; we have no idea.) Head to the signup page and fill out your details now.

TL;DR: Beer and pizza will be served.

We felt compelled to point that out. But read on.

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Distributed microservices architecture: Istio, managed API gateways and, enterprise integration

Distributed microservices architecture: Istio, managed API gateways and, enterprise integration

The rise of microservices architectures drastically changed the software development landscape. In the past few years, we have seen a shift from centralized monoliths to distributed computing that benefits from cloud infrastructure. With distributed deployments, the adoption of microservices, and system scaling to cloud levels, new problems emerged, as well as new components that tried to solve the problems.

By now, you most likely have heard that the service mesh or Istio is here to save the day. However, you might be wondering how it fits with your current enterprise integration investments and API management initiatives. That is what I discuss in this article.

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Solving the challenges of debugging microservices on a container platform

Solving the challenges of debugging microservices on a container platform

Microservices have become mainstream in the enterprise. This proliferation of microservices applications generates new problems, which requires a new approach to managing problems. A microservice is a small, independently deployable, and independently scalable software service that is designed to encapsulate a specific semantic function in the larger applicationl. This article explores several approaches to deploying tools to debug microservices applications on a Kubernetes platform like Red Hat OpenShift, including OpenTracing,  Squash, Telepresence, and creating a Squash Operator in Red Hat Ansible Automation.

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Observe what your Istio microservices mesh is doing with Kiali

Observe what your Istio microservices mesh is doing with Kiali

The Istio service mesh is a powerful tool for building a service mesh. If you don’t know about Istio yet, have a look at the Introduction to Istio series of articles or download the ebook Introducing Istio Service Mesh for Microservices.

The power of Istio comes with the cost of some complexity at configuration and runtime. To help this, the Kiali project provides observability of the mesh and the services in the mesh. Kiali visualizes the mesh with its services and workloads. It indicates the health of the mesh and shows hints about applied configuration options. You can then drill in on individual services or settings to view details.

This post describes how to use Kiali to observe what the microservices in your Istio service mesh are doing, validate the Istio configuration, and see any issues.

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Why Kubernetes is The New Application Server

Why Kubernetes is The New Application Server

Have you ever wondered why you are deploying your multi-platform applications using containers? Is it just a matter of “following the hype”? In this article, I’m going to ask some provocative questions to make my case for Why Kubernetes is the new application server.

You might have noticed that the majority of languages are interpreted and use “runtimes” to execute your source code. In theory, most Node.js, Python, and Ruby code can be easily moved from one platform (Windows, Mac, Linux) to another platform. Java applications go even further by having the compiled Java class turned into a bytecode, capable of running anywhere that has a JVM (Java Virtual Machine).

The Java ecosystem provides a standard format to distribute all Java classes that are part of the same application. You can package these classes as a JAR (Java Archive), WAR (Web Archive), and EAR (Enterprise Archive) that contains the front end, back end, and libraries embedded. So I ask you: Why do you use containers to distribute your Java application? Isn’t it already supposed to be easily portable between environments?

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