Description:

frigga 1.1.1

frigga




Do you have a Grafana instance? frigga makes sure you don’t scrape metrics in Prometheus, which you don’t present in Grafana dashboards.
Scrape only relevant metrics in Prometheus, according to your Grafana dashboards, see the before and after snapshot. frigga generates keep filters on metric_relabel_configs, and adds them to your prometheus.yml file
frigga is extremely useful for Grafana Cloud customers since the pricing is per DataSeries ingestions.
Illustration

Expand/Collapse




Requirements
Python 3.6.7+
Installation
$ pip install frigga

Docker
docker run --rm -it unfor19/frigga

For ease of use, add an alias in your ~/.bashrc file
alias frigga="docker run --rm -it unfor19/frigga"

Available Commands
Auto-generated by unfor19/replacer-action, see readme.yml

Usage: frigga [OPTIONS] COMMAND [ARGS]...

No confirmation prompts

Options:
-ci, --ci Use this flag to avoid confirmation prompts
--help Show this message and exit.

Commands:
client-start Alias: cs
grafana-list Alias: gl
prometheus-apply Alias: pa
prometheus-get Alias: pg
prometheus-reload Alias: pr
version Print the installed version
webserver-start Alias: ws


Getting Started


Grafana - Import the dashboard frigga - Jobs Usage (ID: 12537) to Grafana, and check out the number of DataSeries


Grafana - Generate an API Key for Viewer


frigga - Get the list of metrics that are used in your Grafana dasboards
$ frigga gl

# gl is grafana-list, or good luck :)

Grafana url [http://localhost:3000]: http://my-grafana.grafana.net
Grafana api key: (hidden)
>> [LOG] Getting the list of words to ignore when scraping from Grafana
...
>> [LOG] Found a total of 269 unique metrics to keep

.metrics.json - automatically generated in pwd
{
"all_metrics": [
"cadvisor_version_info",
"container_cpu_usage_seconds_total",
"container_last_seen",
"container_memory_max_usage_bytes",
...
]
}



Add the following snippet to the bottom of your prometheus.yml file. Check the example in docker-compose/prometheus-original.yml
---
name: frigga
exclude_jobs: []



frigga - Use the .metrics.json file to apply the rules to your existing prometheus.yml
$ frigga pa

# pa is prometheus-apply, or pam-tada-dam

Prom yaml path [docker-compose/prometheus.yml]: /etc/prometheus/prometheus.yml
Metrics json path [./.metrics.json]: /home/willywonka/.metrics.json
>> [LOG] Reading documents from docker-compose/prometheus.yml
...
>> [LOG] Done! Now reload docker-compose/prometheus.yml with 'frigga pr -u http://localhost:9090'



As mentioned in the previous step, reload the prometheus.yml to Prometheus, here are two ways of doing it

"Kill" Prometheus
$ docker exec $PROM_CONTAINER_NAME kill -HUP 1


Send a POST request to /-/reload - this requires Prometheus to be loaded with --web.enable-lifecycle, for example, see docker-compose.yml
$ frigga prometheus-reload --prom-url http://localhost:9090

Or with curl
$ curl -X POST http://localhost:9090/-/reload





Make sure the prometheus.yml was loaded successfully to Prometheus
$ docker logs --tail 10 $PROM_CONTAINER_NAME

level=info ts=2020-06-27T15:45:34.514Z caller=main.go:799 msg="Loading configuration file" filename=/etc/prometheus/prometheus.yml
level=info ts=2020-06-27T15:45:34.686Z caller=main.go:827 msg="Completed loading of configuration file" filename=/etc/prometheus/prometheus.yml



Grafana - Now check frigga - Jobs Usage dashboard, the numbers should be signifcantly lower (up to 60% or even more)


Test it locally
Requirements

Docker
docker-compose
jq

Getting Started


git clone this repository


Run Docker daemon (Docker for Desktop)


Make sure ports 3000,8080,9100 are not in use (state=closed)
docker run --rm -it --network=host unfor19/net-tools nmap -p 8080,3000,9100 -n localhost



Deploy locally the services: Prometheus, Grafana, node-exporter and cadvisor
$ bash docker-compose/deploy_stack.sh

Creating network "frigga_net1" with the default driver
...
>> Grafana - Generating API Key - for Viewer
eyJrIjoiT29hNGxGZjAwT2hZcU1BSmpPRXhndXVwUUE4ZVNFcGQiLCJuIjoibG9jYWwiLCJpZCI6MX0=
# Save this key ^^^



Open your browser, navigate to http://localhost:3000

Username and password are admin:admin
You'll be prompted to update your password, so just keep using admin or hit Skip



Go to Jobs Usage dashboard, you'll see that Prometheus is processing ~2800 DataSeries


Get all the metrics that are used in your Grafana dasboards
$ export GRAFANA_API_KEY=the-key-that-was-generated-in-the-deploy-locally-step
$ frigga gl -gurl http://localhost:3000 -gkey $GRAFANA_API_KEY

>> [LOG] Getting the list of words to ignore when scraping from Grafana
...
>> [LOG] Found a total of 269 unique metrics to keep
# Generated .metrics.json in pwd



Check the number of data series BEFORE filtering with frigga
$ frigga pg -u http://localhost:9090

# prometheus-get

>> [LOG] Total number of data-series: 1863



Apply the rules to prometheus.yml, keep the defaults
$ frigga pa

# prometheus-apply

Prom yaml path [docker-compose/prometheus.yml]:
Metrics json path [./.metrics.json]:
...
>> [LOG] Done! Now reload docker-compose/prometheus.yml with 'docker exec $PROM_CONTAINER_NAME kill -HUP 1'



Reload prometheus.yml to Prometheus
$ frigga pr -u http://localhost:9090

# prometheus-reload

>> [LOG] Successfully reloaded Prometheus - http://localhost:9090/-/reload



Check the number of data series AFTER filtering with frigga
$ frigga pg -u http://localhost:9090

# prometheus-get

>> [LOG] Total number of data-series: 898
# Decreased from 1863 to 898, decreased 51% !



Go to Jobs Usage, you'll see that Prometheus is processing only ~898 DataSeries (previously ~1863)

In case you don't see the change, don't forget to hit the refersh button



Cleanup
$ docker-compose -p frigga --file docker-compose/docker-compose.yml down



Pros and Cons of this tool
Pros

Grafana-Cloud - As a Grafana Cloud customer, the main reason for writing this tool was lowering the costs. This goal was achieved by sending only the relevant DataSeries to Grafana Cloud
Saves disk-space on the machine running Prometheus
Improves PromQL performance by querying less metrics; significant only when processing high volumes

Cons

After applying the rules in prometheus.yml, it makes the file less readable. Due to the fact it's not a file that you play with on a daily basis, it's okayish
The memory usage of Prometheus increases slightly, around ~30MB, not significant, but worth mentioning
If you intend to use more metrics, for example, you've added a new dashboard which uses more metrics, you'll need to do the same process again; frigga gl and frigga pa

References

metric_relabel_configs
Or in relabelling
relabel_configs vs metrics_relabel_configs

Contributing
Report issues/questions/feature requests on the Issues section.
Pull requests are welcome! Ideally, create a feature branch and issue for every single change you make. These are the steps:

Fork this repo
Create your feature branch from master (git checkout -b my-new-feature)
Install from source
$ git clone https://github.com/${GITHUB_OWNER}/frigga.git && cd frigga
...
$ pip install --upgrade pip
...
$ python -m venv ./ENV
$ . ./ENV/bin/activate
...
$ (ENV) pip install --editable .
...
# Done! Now when you run 'frigga' it will get automatically updated when you modify the code


Add the code of your new feature
Test - make sure frigga grafana-list and frigga prometheus-apply commands work
Commit your remarkable changes (git commit -am 'Added new feature')
Push to the branch (git push --set-up-stream origin my-new-feature)
Create a new Pull Request and tell us about your changes

Authors
Created and maintained by Meir Gabay
License
This project is licensed under the MIT License - see the LICENSE file for details

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

Share

• Share on Facebook
• Share on Twitter