hypertrace VS opentelemetry-demo

Jayesh: So, like right now, we don't really work heavily on observability. But again, the AI part is more in the security context as of now. We do that, but it's in a security context and not in the observability context that much. So I won't say I contributed a lot of the AI aspect at Traceable. I'm mostly affiliated with the open-source part to do with the Hypertrace project we have. Like, we have an open-source project called Hypertrace. But it's basically a distributed tracing platform, which many of our users are using. And we are building considering the observability perspective in mind. So some of the users are contributing new features, and it is evolving in that direction. So that's what my focus has been mostly.

If you are looking for something open and more functional with enterprise features, maybe you should check out Hypertrace: https://github.com/hypertrace/hypertrace .

Until then, if your organization is using microservice architecture and exploring Observability solutions, feel free to check out Hypertrace, which is a modern API Observability platform. If you are in transition and want to learn more about Observability and instrumentation, join our slack community to interact with folks who have been through this transition or going through this transition.

Go to the GitHub Issues tab of the open source project repository you would like to contribute to. For Hypertrace, you can find open issues here

Hypertrace is a cloud-native distributed tracing based Observability platform that gives visibility into your dev and production distributed systems.

Governed by "Traceable community license".

https://github.com/hypertrace/hypertrace/blob/main/LICENSE

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.