Juju VS prometheus

Basically juju is used to deploy microservices, and other stuff too: https://juju.is/

Juju https://juju.is/

https://juju.is/

Each app is packaged in a charm which seems to be a yaml declaring inputs, dependencies and other meta data and optional python code that can respond to certain lifecycle hooks

https://discourse.charmhub.io/t/implementing-relations/1051

   name: my-node-app

I am currently working myself through setting up our new Research GPU-Cluster where we have "sort of" managed to deploy MAAS to manage all the servers more efficiently, and on top of MAAS then use Juju to deploy the further components of the cluster. The components here are

Kubernetes Custom Resources were created to allow the Kubernetes API to be extended for domain-specific logic, by defining new resource types and controllers. OSS frameworks like operator-sdk, kubebuilder and juju were created to simplify the creation of custom resources and their controllers. Tools built with these frameworks came to be known as Operators.

Depending on what you're trying to accomplish, Juju and MAAS, if not just automate with preseed for custom desktops.

At Canonical I work on two open-source projects written in Go: Juju, a large cloud-based application deployment tool, and Pebble, a small Linux service manager. Both include CLI clients and API-based server daemons. Juju in particular is a large distributed system.

The new Portainer charm allows users of Canonical’s Charmed Kubernetes distribution to automatically install and integrate Portainer Business as part of the Kubernetes cluster deployment process, using Juju, the Charmed Operator framework.

WriteRequest::timeseries is a vector (https://github.com/prometheus/prometheus/blob/main/prompb/re...) and

Developers widely use Prometheus as a system for operational monitoring and alerting for their projects. Here is a list of tools for monitoring frontend services with Prometheus.

Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.

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.

Setting up monitoring for a system, especially one involving GRPC communication, provides crucial visibility into its operations. In this guide, we walked through the steps to instrument both a GRPC server and client with Prometheus metrics, exposed those metrics via an HTTP endpoint, and visualized them using Grafana. The Docker-Compose setup simplified the deployment of both Prometheus and Grafana, ensuring a streamlined process.

Alerting and Notification: Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like Prometheus provides robust alerting capabilities.

Prometheus

Prometheus is an open-source monitoring and alerting toolkit. It is designed to monitor highly dynamic containerized systems, making it an excellent choice for monitoring Docker containers and Kubernetes clusters.

wget https://github.com/prometheus/prometheus/releases/download/v2.46.0/prometheus-2.46.0.linux-amd64.tar.gz

Prometheus: Open-source monitoring system. Often used together with Grafana.