opentelemetry-demo VS kubernetes

Here is a typical trace from the OpenTelemetry demo project.

# test suite based on https://github.com/open-telemetry/opentelemetry-demo/tree/main/test/tracetesting/frontend-service type: Test spec: id: frontend-view-cart name: 'Frontend: View cart' description: Simulate a user viewing the shopping cart trigger: type: http httpRequest: url: http://${var:FRONTEND_ADDR}/api/cart?userId=2491f868-88f1-4345-8836-d5d8511a9f83 method: GET headers: - key: Content-Type value: application/json specs: - name: It called the frontend with success selector: span[tracetest.span.type="general" name="Tracetest trigger"] assertions: - attr:tracetest.response.status = 200 - name: It retrieved the cart items correctly selector: span[name="oteldemo.CartService/GetCart"] assertions: - attr:rpc.grpc.status_code = 0

Contributing to the OpenTelemetry Demo is a great way to get involved and showcase your skills in the OpenTelemetry community. It's a real-world example of OpenTelemetry in action, and by actively contributing, you enhance your understanding and improve the project's quality.

Trace-based testing added to OpenTelemetry Demo

Examples and the tutorial in this blog post use the OpenTelemetry Astronomy Shop Demo to show what you can do with OpenTelemetry and New Relic. This application is built and maintained by the OpenTelemetry open-source community, and it provides a real-world example of a distributed application that’s been instrumented with OpenTelemetry. In the Deploying the OpenTelemetry Astronomy Shop demo app section, you’ll have an opportunity to get hands-on experience spinning up your own version of this application. You’ll learn how to:

Take this https://github.com/open-telemetry/opentelemetry-demo

I mean, you really just need experience instrumenting apps and tinkering with them to play with OTEL. https://github.com/open-telemetry/opentelemetry-demo is a good start. You can modify the collector to point to any backend of your choice.

I got to play around with these newer features last December, after a months-long hiatus, and it was really cool to see the evolution of the product. If you follow my work, you’ll know that I play in both the Kubernetes and Nomad worlds. Today, I’ll be taking you on a quick little guided tour of Tracetest, using Traces from the OpenTelemetry Demo App to give you a feel for how it works. The whole setup will be running on HashiCorp Nomad. \

​ By having an observability infrastructure gather information about a set of API/microservices, we can have a concise view of the operation of these services and start thinking in an observability-driven way to test your software. ​ Tracetest can help. When given an API endpoint, Tracetest checks observability traces to see if this API is behaving as intended. ​ For example, let’s try to test an OpenTelemetry Astronomy Store which has the exact same use cases that we want to check. ​ To test the "Add product to the shopping cart" task, we can create a test, define a URL and payload in the trigger section that we send to the Cart API and use the specs to define our assertions, checking if the API was called with the correct Product ID and if this product was persisted correctly. ​

In my latest Nomadification Project (TM), I got the OpenTelemetry Demo App to run on Nomad (with HashiQube, of course). To do this, I used the OpenTelemetry Demo App Helm Chart as my guide. In doing this, and other Nomadifications, I realized that I’ve never gone through the process of explaining the conversion process from Kubernetes manifests to Nomad jobspecs.

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.

Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.

If you do kubectl explain deployment than (surprise!) you'll get a description for extensions/v1beta1. Because kubectl explain works the same way, just like kubectl get:

Through the Fast Forward program, we give free services and support to open source projects and the nonprofits that support them. We support many of the world’s top programming languages (like Python, Rust, Ruby, and the wonderful Scratch), foundational technologies (cURL, the Linux kernel, Kubernetes, OpenStreetMap), and projects that make the internet better and more fun for everyone (Inkscape, Mastodon, Electronic Frontier Foundation, Terms of Service; Didn’t Read).

In this project, you will understand and get hands on experience around the entire concept around CI/CD from applications perspective. To fully gain real expertise around this idea, it is best to see it in action across different programming languages and from the platform perspective too. From the application perspective, we will be focusing on PHP here; there are more projects ahead that are based on Java, Node.js, .Net and Python. By the time you start working on Terraform, Docker and Kubernetes projects, you will get to see the platform perspective of CI/CD in action.