Goldilocks Recommendation Scraper

Acknowledgement

This would be an utterly useless tool without Goldilocks.

Why does this exist?

While Goldilocks does have a CLI that can output JSON, it doesn't clean up after itself, and is scoped to a single namespace.

Couldn't you have just run that in a for loop with kubectl get namespaces?

Go away.

Limitations

This does not change your k8s resource limits or requests, nor does it edit files for you. Honestly, that's probably a good thing. If you want to extend this to calling kubectl apply, be my guest, and be sure to post your prod disaster story later.

Prerequisites

• Python 3.6+ for Typing and f-strings
• Goldilocks installed in at least one cluster
• A .kubeconfig file or some other method of authenticating with the cluster.
• Packages from requirements.txt
  • Note that you may have to change the kubernetes library version depending on your cluster's version

Usage

usage: goldilocks_getter.py [-h] -d DOMAIN -f FILE [-m] \
       [-l {min,max,mean,median}] [-r {min,max,mean,median}] [-t]

This script scrapes recommendations from Goldilocks for all SOAs
in all namespaces, in all clusters available in the user's .kubeconfig.

optional arguments:
  -h, --help            show this help message and exit
  -d DOMAIN, --domain DOMAIN
                        domain where goldilocks is installed, 
                        e.g. foo.net
  -f FILE, --file FILE  Output file to store recommendations in
  -m, --mib             Use MiB instead of GiB for memory units
  -l {min,max,mean,median}, --limit {min,max,mean,median}
                        Stat to use for limits - defaults to max
  -r {min,max,mean,median}, --request {min,max,mean,median}
                        Stat to use for requests - defaults to median
  -t, --test            Only collect information from the first 
                        cluster to speed up testing

Example output

soa_1:
  resources:
    limits:
      cpu: 151.0m
      memory: 2.13Gi
    requests:
      cpu: 62.0m
      memory: 2.01Gi
soa_2:
  resources:
    limits:
      cpu: 25.0m
      memory: 0.24Gi
    requests:
      cpu: 25.0m
      memory: 0.24Gi