gotests VS thanos

A huge time-saver for me when generating table-driven test boilerplate in Go has been using gotests[0] to generate the template.

If you use VSCode with the Go extension it's already available there as a command "Go: Generate Unit Tests for Function/Package".

[0] https://github.com/cweill/gotests

I believe vscode does it using gotests (https://github.com/cweill/gotests), so people can probably use it outside of vscode too.

What can do this plugin? - Modify struct tags. - Run go get, go mod & go generate commands inside of nvim. - Implement interface by impl. - Generate tests by gotests. - Install required tools for plugin working(by go install).

I was just thinking about some smart solutions. For example generating test plan and fake data by inspecting current changes on the code and db schema. Just like this https://github.com/cweill/gotests but for performance test plans.

Did you encounter gotests when you were doing your research? It's a table test generation tool that generates test code using t.Run(). I do wish it could generate tests using testify, because I frequently end up rewriting the code in the inner loop, but I still use it because it's better than I am about ensuring there's a test for all functions I write.

I use https://github.com/cweill/gotests to generate the test scaffolding.

You can save yourself a minute and generate the test case boilerplate for your with https://github.com/cweill/gotests :)

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/