thanos VS ent

Monitoring = netdata on each RPi https://www.netdata.cloud/ binded to the vpn interface being scraped into a prometeus thaons https://thanos.io/ setup with grafana to give management the Green all is good screens (very important).

Sounds like you want something like Thanos

Yes, but also no. The Prometheus ecosystem already has two FOSS time-series databases that are complementary to Prometheus itself. Thanos and Mimir. Not to mention M3db, developed at Uber, and Cortex, then ancestor of Mimir. There's a bunch of others I won't mention as it would take too long.

Long term storage all depends on your needs and sophistication. I use Thanos for our system since it has an extremely flexible scaling system. But there is also Grafana Mimir. They're both similar in that they use Prometheus TSDB format as part of the underlying storage. One nice Thanos advantage is that it does do downsampling in addition to being able to store raw metric data for a long time. It will auto-select downsampled data to make requests faster.

You can aggregate all your clusters Prometheus metrics together with a wonderful tool called Thanos. This will allow you to use just a single Grafana instance against Thanos and using a label select which cluster you wish to see metrics from. The downside of this, is that none of the Grafana dashboards from the internet will work as-is. You'll need to customize all of them for Thanos support. The other downside is, you have a single point of failure, and (see next item) you can't customize who can access what in regards to your dev vs production data/metrics/access.

Depending on how you want to set things up, you can use Thanos or Mimir to create the single-pane-of-glass view of your data.

You could look at something like Thanos and store your data in S3: https://thanos.io/

In this article, we'll delve into the world of concurrency control in Go, specifically focusing on the optimistic locking approach. We'll explore its implementation using Ent ORM to illustrate how to manage data consistency when multiple users interact with the same resource. Keep in mind that this example serves as a simplified illustration, and real-world booking systems involve a many of additional complexities. However, the core concepts presented here provide a solid foundation for understanding optimistic locking in Go applications. Feel free to explore the complete source code in my GitHub repository for a more in-depth look at the implementation.

ent is a fairly recent ORM that uses a code-first approach where you define your schema in Go code. Ent is popular thanks to its ability to handle complex data models and relationships elegantly. It's statically typed, which can help catch errors at compile time. However, the learning curve might be steeper compared to more straightforward ORMs like GORM. It's a good fit for applications where complex data models and type safety are priorities.

If you found this article, than you are probably similar to how I was a few months ago. I started a project in Go that required a SQL backend and I wanted to use any tool that would help me build this backend quickly. I stumbled upon entgo (an ORM for Go) and decided to give it a try.

The ent orm for golang actually does some useful work for you. https://github.com/ent/ent

Backend Database interaction with entgo

You can use entgo.io for ORM stuff, it also has entgql extension that integrated with GQLGen. See more at the document: https://entgo.io/docs/tutorial-todo-gql