Nagios VS Riemann

I've used this opportunity to look at some recent init files.

Here's one of the first I stumbled upon.

https://github.com/NagiosEnterprises/nagioscore/blob/master/...

It's 288 lines long; the LSB dependency nonsense is 8 lines of that.

Then I looked up one for Postgres;

https://wiki.postgresql.org/wiki/Lsb_conforming_init_script

This one is a whooping 356 lines long, LSB is again about 10 lines long, depending whether you count the header or not.

I don't think the "LSB dependency" argument holds water.

wget -O nagioscore.tar.gz https://github.com/NagiosEnterprises/nagioscore/archive/nagios-4.4.12.tar.gz

Might be this bug? https://github.com/NagiosEnterprises/nagioscore/issues/861

As far as i know there is no option to log all executed service checks except the debug log. Probably you are looking for log_service_retries=1 ?

This is fine- we do something similar using riemann.

"It can't go into infinite loop" is utterly irrelevant. Over last maybe 15 years I've used a bunch of apps that just used their own programming language (from simple DSL to "just write exactly how the app is supposed to handle data") and literally not a single time has that become a problem.

Metrics are measurements of something about your system. They are numeric values, over an interval of time, usually with associated metadata (e.g., timestamp, name). They can be raw, calculated, or aggregated over a period of time. They can come from a variety of sources like servers or APIs. Metrics are structured by default and can be stored in open source systems like Prometheus and Riemann or in off-the-shelf solutions like Amazon CloudWatch and Azure Monitor. These optimized storage systems allow you to perform queries, create alerts, and store them for long periods of time.

Reason #1 - Riemann https://riemann.io/

Riemann is a tool for distributed system monitoring. It aggregates events from user servers and applications, combines them into a stream and transmits them for further processing or storage. Greater flexibility and fault-tolerance make Riemann different from other similar systems. Moreover, it’s written in Clojure almost completely. The code is available on GitHub and is distributed under Eclipse Public License 1.0.

I did a new release today of Mirabelle, a stream procesing tool heavily inspired by Riemann. I also spent a lot of time on the documentation website if you want to try it, and also wrote an article today about an use case.

Consider dabbling in a project to get your feet wet first. You have a neat problem you want solved? Give it a shot. There an interesting open source project, fork it and tinker with the code. This will be tremendously educational both vocationally and will help you get a feel for if you'd like to work in clojure all the time. There are a lot of projects, but I chose https://github.com/riemann/riemann to read and try better to understand real world clojure.