monogon VS Grafana

It's somewhere in my git stack :).

Until I get to publishing it, the proto/gRPC definitions for node management are a good enough start: https://github.com/monogon-dev/monogon/blob/main/metropolis/...

And the top level API to actually deploy workloads is plain Kubernetes.

Monogon, a fully remote, self-funded and engineer-led technology company, is hiring software engineers to work on Metropolis, an open source [1], secure, distributed cluster operating system based on Linux and Kubernetes.

Metropolis runs on a fleet of bare metal or cloud machines and provides users with a hardened, production ready Kubernetes - without the overhead of traditional Linux distributions or configuration management systems. It does away with the scripting/YAML duct tape and configuration drift inherent to traditional deployments, and instead provides a stable, API-driven, secure and vendor-lock-in-free platform for companies to build their products upon.

We're looking for senior candidates who can design, implement and verify complex systems that will make up part of Metropolis. We offer a kind and honest work environment in which we prioritize quality over quantity. You'll be the fourth member of a team working on an ambitious, industry-challenging product.

Our ideal candidate is a generalist with deeper knowledge in one or more of the following areas:

- Distributed systems;

- Software engineering of systems built to last;

- Security engineering, especially experience with secure boot chains;

- Low-level programming and debugging (C, Linux Kernel, …);

- Kubernetes, especially practical experience of running bare-metal production deployments;

- Platform development, ie. running a 'Company A' style infrastructure/DevOps team [2].

Our codebase is mostly Go (including pid1!), so knowledge of the language is a plus, but not a requirement (given the seniority of the position, we expect any candidate to be able to ramp up on Go within a few weeks).

To get in touch, email me at at nexantic.com.

[1] - https://github.com/monogon-dev/monogon

[2] - https://rachelbythebay.com/w/2020/05/19/abc/

- Platform development, ie. running a 'Company A' style infrastructure/DevOps team [2].

Our codebase is mostly Go (including pid1!), so knowledge of the language is a plus, but not a requirement (given the seniority of the position, we expect any candidate to be able to ramp up on Go within a few weeks).

To get in touch, email me at at nexantic.com.

[1] - https://github.com/monogon-dev/monogon

Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.

To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.

Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.

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

Grafana

Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.

So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:

In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:

Robin switched to the Grafana dashboard tab, and sure enough, the 5xx volume on web service was rising. It had not hit the critical alert thresholds yet, but customers had already started noticing.