nfs-ganesha-server-and-external-provisioner VS opentelemetry-go

Now, for the purposes of this article, in case you don't have an NFS server available, we will use a simple NFS Server Provisioner, which we'll use only for example purposes. As mentioned before, using a managed solution from a cloud provider or a properly configured HA NFS server in your infrastructure is highly recommended. We'll install not the most up-to-date solution, but it should work for example purposes. We will follow the Quickstart found in the repo, mixed with this repo which does some small tweaks to make it work with K3d, which is summarized in the following commands run from the helm folder:

Some people just setup an NFS share. There's one that uses existing NFS and another that also provides NFS. This becomes a single point of failure though.

Conceptually to attach your storage to your pod, you have to go through 2 objects, the PVC that attaches to the PV, which itself must have a physical support, so the nfs mount on your nodes in hostpath, which is globally disgusting, it is better to inform the NFS server in your PV. Maybe I'm wrong but it seems clear to me. However, if you ask this kind of questions, you might be missing two or three things about K8. I advise you to read the documentation about PV, PVC, SC etc... Also NFS is not POSIX and by nature slow, which can cause inconsistencies in your data, but this is an extreme case. In a logic of automation you can use this: https://github.com/kubernetes-sigs/nfs-ganesha-server-and-external-provisioner Help yourself with this . https://www.linuxtechi.com/configure-nfs-persistent-volume-kubernetes/

I found something similar that seems to be a continuation of the nfs-server-provisioner- https://github.com/kubernetes-sigs/nfs-ganesha-server-and-external-provisioner

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!