rook VS prometheus

I have some experience with Ceph, both for work, and with homelab-y stuff.

First, bear in mind that Ceph is a distributed storage system - so the idea is that you will have multiple nodes.

For learning, you can definitely virtualise it all on a single box - but you'll have a better time with discrete physical machines.

Also, Ceph does prefer physical access to disks (similar to ZFS).

And you do need decent networking connectivity - I think that's the main thing people think of, when they think of high hardware requirements for Ceph. Ideally 10Gbe at the minimum - although more if you want higher performance - there can be a lot of network traffic, particularly with things like backfill. (25Gbps if you can find that gear cheap for homelab - 50Gbps is a technological dead-end. 100Gbps works well).

But honestly, for a homelab, a cheap mini PC or NUC with 10Gbe will work fine, and you should get acceptable performance, and it'll be good for learning.

You can install Ceph directly on bare-metal, or if you want to do the homelab k8s route, you can use Rook (https://rook.io/).

Hope this helps, and good luck! Let me know if you have any other questions.

Another option is to leverage a Kubernetes-native distributed storage solution such as Rook Ceph as the storage backend for stateful components running on Kubernetes. This has the benefit of simplifying application configuration while addressing business requirements for data backup and recovery such as the ability to take volume snapshots at a regular interval and perform application-level data recovery in case of a disaster.

This is beyond your question but might help someone else: I switch from docker-compose to kubernetes for my home lab a while ago. The storage solution I've settled on is Rook. It was a bit of up-front work learning how to get it up but now that it's done my storage is automatically managed by Ceph. I can swap out drives and Ceph basically takes care of everything itself.

The stumbling point I am at is I want to use rook.io(Ceph) as my storage solution for the cluster. The Ceph prerequisites are one of the following:

Have you looked at Rook?

I've briefly run ceph in an external mode, you can actually use a rook deployment to manage it (sort of). Here is the documentation for doing that. For me it didn't pass my testing phase because I need better networking equipment before I can try that.

This article explains the data corruption issue happened in Rook in 2021. The root cause lies in an unexpected place and can also occurs in all Ceph environment. It's interesting that Rook had started to encounter this problem recently even though this problem has existed for a long time. It's due to a series of coincidences. I wrote this article because the word "Atari" used in a non-historical context in 2021.

Rook (this is a nice article for Rook NFS)

Storage: Favor any distributed storage you know to start with for Persistent Volumes: Ceph maybe via rook.io, Longhorn if you go rancher etc

I've dealt with a lot of issues that are very close to just unplugging a node. Unfortunately on node lost, my stateful workloads using rook-ceph block storage won't migrate over to another node automatically due to an issue with rook. Stateless apps (ingress nginx, etc..) not using rook-ceph block failover to another node just fine. I've kind of accepted this for now and I know Longhorn has a feature that makes this work but I find rook-ceph to be more stable for my workloads.

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.