Quickly run queries on prometheus and view results on csv and pdf reports with plots.

Usage

Configuration

A configuration file is required with a list of Prometheus metrics to be collected.

By default, metrics are listed in file: config/metrics.txt

If you want to use a custom metrics file, place it in config directory: e.g. config/metrics_haproxy.txt

Example of a metrics config file:

rate(go_gc_duration_seconds[5m])
scrape_duration_seconds
prometheus_http_request_duration_seconds_bucket{handler="/api/v1/query_range", instance="localhost:9090", job="prometheus", le="0.1"}
sum(rate(http_server_requests_seconds_count{instance="application:8080", status!~"5.*"}[5m]))

How to use

You need a prometheus instance running.

File docker-compose in root folder can be used to run a prometheus instance on port 9090 and node exporter:

docker-compose up -d 

Generate csv files from prometheus metrics

This component is written in python v3, and you need some python libs to be installed on your local machine:

• python modules: jproperties, requests, pandas, pillow, plotly, kaleido

you can install those with pip3:

pip3 install jproperties requests pandas pillow plotly kaleido

Generate csv files from prometheus metrics

python3 export_csv.py <prometheus_url> <dateStart RFC 3339 | unix_timestamp> <dateEnd RFC 3339 | unix_timestamp> <custom_metrics_file_name>

Example of usage:

python3 export_csv.py http://localhost:9090 2022-12-14T10:00:00Z 2022-12-14T11:30:00Z metrics.txt

A new directory named csv/metrics_%Y-%m-%d-%H:%M:%S with the csv files will be generated.

Create a pdf report with plots from csv files

python3 plot.py <csv_directory>

Example of usage:

python3 plot.py csv/metrics_2022-12-14_11:20:20

A new pdf file report.pdf will be generated in directory csv/metrics_%Y-%m-%d-%H:%M:%S

Teardown

docker-compose down --volumes