opentelemetry-examples VS kubernetes

We focused on the observability ecosystem and took the time to interact with our friends from Lightstep, New Relic, Honeycomb, Dynatrace, Instana, and many more. With that in mind, keep an eye out for more integrations coming to Tracetest!

Lightstep bills itself as a platform for the reliability of cloud-native applications. The people behind Lightstep co-founded OpenTelemetry and OpenTracing, which gives them a unique perspective on the use cases of distributed tracing and the value of having a vendor-neutral tracing data format.

In the last 5 to 10 years, new Observability vendors have entered the market, including Honeycomb, Instana, Lightstep and Datadog. Similarly, traditional APM vendors such as Dynatrace, AppDynamics, and New Relic, as well as SIEM (and log management) vendors such as Splunk and Sumo Logic, have joined them in the Observability space too. Finally you also have major cloud providers such as AWS with their own observability solution. Each of them is attempting to address the observability issues that modern architecture presents by using Logs, Metrics, Traces, and Events.

I spent Day 2 at the Colony Club to attend OTel Unplugged. This event was sponsored by Lightstep, Honeycomb, New Relic, Splunk, Dynatrace, Crowdstrike, and NGINX. I came into the event not knowing what to expect. I can sometimes clamp up when I’m around folks that I don’t know, but because I was helping with the event check-in, I got to say hello to a number of the attendees, which helped break the ice. And it turns out that there were a lot of names that I recognized from my work in the OTel community, and it was nice to connect in person with folks whom I’d only previously met through Slack or Zoom.

The example in this tutorial can be found in the lightstep/opentelemetry-examples repo. We will be working with three main files:

Note how we don’t have to set a LS_ACCESS_TOKEN, since that’s already configured in the Collector’s config.yml file. Just make sure that you have a running OTel Collector instance!

Note: If you’re looking for full code listings, don’t panic! You see them in the Lightstep OTel examples repository.

In the field of software development, efficiency and agility are always sought after. In the era of cloud-native apps, traditional deployment techniques—which are frequently laborious and prone to errors—are starting to become obstacles. This is when Kubernetes and GitOps come in handy.

Are you looking for a hassle-free way to deploy Moodle™ Learning Management Systems (LMS) on Kubernetes? Look no further! Krestomatio presents the LMS Moodle Operator, an open-source Kubernetes Operator designed to simplify the deployment and management of Moodle instances on Kubernetes clusters. Let's dive into what makes this tool a great choice for Moodle administrators and developers alike.

Securing access to your Kubernetes clusters is crucial as inadequate security measures can lead to unauthorized access and potential data breaches. However, navigating the complexities of Kubernetes access security, especially when setting up strong authentication, authorization, and network policies, can be challenging.

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.