exporter | Clubhouse data exporter | Continuous Backup library

I am new to Grafana and Prometheus. I have read a lot of documentation and now I"m trying to work backwards by reviewing some existing queries and making sure I understand them

I have downloaded the Node Exporter Full dashboard (https://grafana.com/grafana/dashboards/1860). I have been reviewing the CPU Busy query and I"m a bit confused. I am quoting it below, spaced out so we can see the nested sections better:

In this query, job is node-exporter while instance is the IP and port of the server. This is my base understanding of the query: node_cpu_seconds_total is a counter of the number of seconds the CPU took at a given sample.

1. Line 5: Get cpu seconds at a given instant, broken down by the individual CPU cores
2. Line 4: Add up all CPU seconds across all cores
3. Line 3: Why is there an additional count()? Does it do anything?
4. Line 12: Rate vector - get cpu seconds of when the cpu was idle over the given rate period
5. Line 11: Take a rate to transfer that into the rate of change of cpu seconds (and return an instant vector)
6. Line 10: Sum up all rates, broken down by CPU modes
7. Line 9: Take the single average rate across all CPU mode rates
8. Line 8: Subtract the average rate of change (Line 9) from total CPU seconds (Line 3)
9. Line 16: Multiple by 100 to convert minutes to seconds 10: Line 18-20: Divide Line 19 by the count of the count of all CPU seconds across all CPUs

My questions are as follows:

• I would have thought that CPU usage would simply be (all non idle cpu usage) / (total cpu usage). I therefore don't understand why take into account rate at all (#6 and #8)
• The numerator here seems to be trying to get all non-idle usage and does so by getting the full sum and subtracting the idle time. But why does one use count and the other sum?
• If we grab cpu seconds by filtering by mode=idle, then does adding the by (mode) add anything? There is only one mode anyways? My understanding of by (something) is more relevant when there are multiple values and we group the values by that category (as we do by cpu in this query)
• Lastly, as mentioned in bold above, what is with the double count(), in the numerator and denominator?

I hoped I could do something like this:

p.pl :

I know this is well trodden territory but I have a specific question... I promise.

Having spent very little time in the statically typed, object oriented world, I recently came across this design pattern while reading Crafting Interpreters. While I understand this pattern allows for extensible behavior (methods) on a set of well defined existing types (classes), I don't quite get the characterization of it as a solution to the double dispatch problem, at least not without some additional assumptions. I see it more as making a tradeoff to the expression problem, where you trade closed types for open methods.

In most of the examples I've seen, you end up with something like this (shamelessly stolen from the awesome Clojure Design Patterns)

We have setup a GKE cluster using Terraform with private and shared networking:

Network configuration:

I'm new to monitoring the k8s cluster with prometheus, node exporter and so on.

I want to know that what the metrics exactly mean for though the name of metrics are self descriptive.

I already checked the github of node exporter, but I got not useful information.

Where can I get the descriptions of node exporter metrics?

Thanks

I am using nbconvert programmatically to export a jupyter notebook file to pdf:

I am writing a lambda function that takes a list of CW Log Groups and runs an "export to s3" task on each of them.

I am writing automated tests using pytest and I'm using moto.mock_logs (among others), but create_export_tasks() is not yet implemented (NotImplementedError).

To continue using moto.mock_logs for all other methods, I am trying to patch just that single create_export_task() method using mock.patch, but it's unable to find the correct object to patch (ImportError).

I successfully used mock.Mock() to provide me just the functionality that I need, but I'm wondering if I can do the same with mock.patch()?

Working Code: lambda.py

I am playing around with a existing perl module lets call it Obj. I have added some new features (subroutines / methods) to Obj but store them in another .pm call it Foo. However I dont want Obj to inherit every sub from Foo.

Now I have been reading perl documentation for a few hours and am confused. https://perldoc.perl.org/Exporter#Selecting-What-to-Export Just says 'Do not export method names!'

Here is some example code, I'd like to not see sub _not_exported from Obj.pm:

Is there any way we can run pod based on the alert fired from Prometheus? We have a scenario where we need to execute a pod based on the disk pressure threshold. I am able to create alert but I need to execute a pod. How can I achieve that?