[AriyosenaS]

Summary of the discussion

First trial at using known open source solutions to increase software observability.

The basics of software-observability is made up of reading signals that are outputted by the underlying software. There are five kinds of signals that we can read as telemetry, but at the moment only three most important ones are considered, which are Logs, Metrics and Traces.

Example literature for reference:
https://www.ibm.com/think/insights/observability-pillars
https://opentelemetry.io/docs/specs/otel/glossary/#signals
https://opentelemetry.io/docs/concepts/signals/

This PR contains the very default podman setup for:
-Opentelemetry (Zero-code, collector)
-Prometheus (Metrics)
-Jager (Traces)
-Grafana (Visualization)

Opentelemetry zero-code instrumentation is the tool used it could however be configured much further, as it is also available as a Python package for more fine grained instrumentation.

Instrumentation provides the Logs, Metrics, and Traces that make up Observability.

At the moment for metrics are collected in Prometheus and traces are collected in Jager. Logs are also possible to be collected but not yet tried or further configured.

All of them feed into Grafana for visualization.

The configuration here is the minimal default setup required just to have it running via podman compose.

Type of change (CHANGELOG.md)

Features

• Add a new functionality
  (#)

Changes

-requirements.txt
-prometheus.yml
-docker-compose-telemetry.yaml

Bugs

• Fixed a problem with
  (#)

Removed

• Remove a broken link
  (#)

Documentation updates

• Updated documentation for
  (#)

Workflow checklist

Automation

Closes #

PR-Assignee

• 🐙 Follow the workflow in
  CONTRIBUTING.md
• 📝 Update the
  CHANGELOG.md
• 📙 Update the documentation on
  mkdocs

Reviewer

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• 🐙 Provided feedback and show sufficient appreciation for the work done