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https://github.com/unfor19/frigga

Scrape only relevant metrics in Prometheus, according to your Grafana dashboards
https://github.com/unfor19/frigga

dashboard grafana metrics metrics-gathering prometheus

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Scrape only relevant metrics in Prometheus, according to your Grafana dashboards

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# frigga

[![testing](https://github.com/unfor19/frigga/workflows/testing/badge.svg?branch=master)](https://github.com/unfor19/frigga/actions?query=workflow%3Atesting)


frigga-logo

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](https://snapshot.raintank.io/dashboard/snapshot/p4YmuKHu4jBlA2kPmOhbuda3jo4I51bt?orgId=2). frigga generates `keep` filters on [metric_relabel_configs](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#metric_relabel_configs), and adds them to your `prometheus.yml` file

frigga is extremely useful for [Grafana Cloud](https://grafana.com/products/cloud/) customers since the pricing is per DataSeries ingestions.

## Illustration

Expand/Collapse


frigga-logo

## Requirements

Python 3.6.7+

## Installation

```bash
$ pip install frigga
```

### Docker

```bash
docker run --rm -it unfor19/frigga
```

For ease of use, add an alias in your `~/.bashrc` file

```bash
alias frigga="docker run --rm -it unfor19/frigga"
```

## Available Commands

Auto-generated by [unfor19/replacer-action](https://github.com/marketplace/actions/replacer-action), see [readme.yml](https://github.com/unfor19/frigga/blob/master/.github/workflows/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

1. Grafana - Import the dashboard [frigga - Jobs Usage](docker-compose/grafana/provisioning/dashboards/jobs-usage.json) (ID: 12537) to Grafana, and check out the number of DataSeries
1. Grafana - Generate an API Key for `Viewer`
1. frigga - Get the list of metrics that are used in your Grafana dasboards

```bash
$ 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

```json
{
"all_metrics": [
"cadvisor_version_info",
"container_cpu_usage_seconds_total",
"container_last_seen",
"container_memory_max_usage_bytes",
...
]
}
```

1. Add the following snippet to the bottom of your `prometheus.yml` file. Check the example in [docker-compose/prometheus-original.yml](docker-compose/prometheus-original.yml)

```yml
---
name: frigga
exclude_jobs: []
```

1. frigga - Use the `.metrics.json` file to apply the rules to your existing `prometheus.yml`

```bash
$ 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'
```

1. As mentioned in the previous step, reload the `prometheus.yml` to Prometheus, here are two ways of doing it
- "Kill" Prometheus
```bash
$ 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](docker-compose/docker-compose.yml)
```bash
$ frigga prometheus-reload --prom-url http://localhost:9090
```
Or with curl
```
$ curl -X POST http://localhost:9090/-/reload
```
1. Make sure the `prometheus.yml` was loaded successfully to Prometheus

```bash
$ 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
```

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

## Test it locally

### Requirements

1. [Docker](https://docs.docker.com/get-docker/)
1. [docker-compose](https://docs.docker.com/compose/install/)
1. [jq](https://stedolan.github.io/jq/download/)

### Getting Started

1. git clone this repository
1. Run Docker daemon (Docker for Desktop)
1. Make sure ports 3000,8080,9100 are not in use (state=closed)
```bash
docker run --rm -it --network=host unfor19/net-tools nmap -p 8080,3000,9100 -n localhost
```
1. Deploy locally the services: Prometheus, Grafana, node-exporter and cadvisor

```bash
$ bash docker-compose/deploy_stack.sh

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

1. 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

1. Go to [Jobs Usage](http://localhost:3000/d/U9Se3uZMz/jobs-usage?orgId=1) dashboard, you'll see that Prometheus is processing ~2800 DataSeries
1. Get all the metrics that are used in your Grafana dasboards

```bash
$ 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
```
1. Check the number of data series **BEFORE** filtering with frigga
```bash
$ frigga pg -u http://localhost:9090

# prometheus-get

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

1. Apply the rules to `prometheus.yml`, keep the defaults

```bash
$ 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'
```

1. Reload `prometheus.yml` to Prometheus

```bash
$ frigga pr -u http://localhost:9090

# prometheus-reload

>> [LOG] Successfully reloaded Prometheus - http://localhost:9090/-/reload
```
1. Check the number of data series **AFTER** filtering with frigga
```bash
$ frigga pg -u http://localhost:9090

# prometheus-get

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

1. Go to [Jobs Usage](http://localhost:3000/d/U9Se3uZMz/jobs-usage?orgId=1), 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
1. Cleanup
```bash
$ docker-compose -p frigga --file docker-compose/docker-compose.yml down
```

## Pros and Cons of this tool

### Pros

1. [Grafana-Cloud](https://grafana.com/products/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
1. Saves disk-space on the machine running Prometheus
1. Improves PromQL performance by querying less metrics; significant only when processing high volumes

### Cons

1. 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
1. The memory usage of Prometheus increases slightly, around ~30MB, not significant, but worth mentioning
1. 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](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#metric_relabel_configs)
- [Or in relabelling](https://www.robustperception.io/or-in-relabelling)
- [relabel_configs vs metrics_relabel_configs](https://www.robustperception.io/relabel_configs-vs-metric_relabel_configs)

## Contributing

Report issues/questions/feature requests on the [Issues](https://github.com/unfor19/frigga/issues) section.

Pull requests are welcome! Ideally, create a feature branch and issue for every single change you make. These are the steps:

1. Fork this repo
1. Create your feature branch from master (`git checkout -b my-new-feature`)
1. Install from source
```bash
$ 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
```
1. Add the code of your new feature
1. Test - make sure `frigga grafana-list` and `frigga prometheus-apply` commands work
1. Commit your remarkable changes (`git commit -am 'Added new feature'`)
1. Push to the branch (`git push --set-up-stream origin my-new-feature`)
1. Create a new Pull Request and tell us about your changes

## Authors

Created and maintained by [Meir Gabay](https://github.com/unfor19)

## License

This project is licensed under the MIT License - see the [LICENSE](https://github.com/unfor19/frigga/blob/master/LICENSE) file for details