opentelemetry-go VS opentelemetry-demo

Our first approach was to implement a separate SDK for each independent technology stack. We decided to use OpenTelemetry which is widely adopted and covers most of our needs.

Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.

OpenTelemetry

When choosing distributed tracing tools, considerations include your technology stack, business requirements, and monitoring complexity. Zipkin, SkyWalking, and OpenTelemetry are popular distributed tracing solutions, each with its unique features.

You can follow this process with any large token AI system like Claude by identifying tracing data relevant to the code you are working on, using it as context to prompt OpenAI or other LLMs. Generally, you’d generate tracing data by implementing OpenTelemetry (aka OTEL) libraries into your application, adding spans to your functions with Jaeger, or using commercial SaaS tools like Honeycomb and Datadog.

While many programming languages provide robust support for Open Telemetry, this instance focuses on Golang. It's important to note that, in the current context, the logs SDK for Golang is not implemented. For future reference consult the list of supported languages and explore the Open Telemetry repositories. Always prioritize the main repository and its contrib repository, housing extensions and instrumentation libraries crucial to the Open Telemetry framework. Stay updated with the latest developments to ensure seamless integration and enhanced functionality.

OneUptime (https://github.com/oneuptime/oneuptime) is the open-source alternative to DataDog. It's 100% free and you can self-host it on your VM / server / cloud or you can use SaaS at https://oneuptime.com

NEW UPDATES (since we last posted to HN): We now support OpenTelemetry (https://opentelemetry.io/) natively which will help you to monitor, observe and debug any app, service, database or stack.

OpenTelemetry is the fastest growing Cloud Native Computing Foundation (CNCF) project. It standardizes the instrumentation and collection of traces, metrics, and logs from applications, and is supported by all the major observability projects, languages, and tools. One standard to rule them all!

OpenTelemetry is a collection of tools, APIs, and SDKs. Use it to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) to help you analyze your software’s performance and behavior. https://opentelemetry.io/

Tracetest uses your existing OpenTelemetry traces to power trace-based testing with assertions against your trace data at every point of the request transaction. You only need to point Tracetest to your existing trace data source, or send traces to Tracetest directly!

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.