Beats dashboards

This repository contains sample Kibana4 dashboards for visualizing the data gathered by the Elastic Beats.

Installation

To load the dashboards, execute the script pointing to the Elasticsearch HTTP URL:

    # Unix
    ./load.sh -url "http://localhost:9200"

    # Windows
    .\load.ps1 -url "http://localhost:9200"

If you want to use HTTP authentication for Elasticsearch, you can specify the credentials as a second parameter:

    # Unix
    ./load.sh -url "http://localhost:9200" -user "admin:secret"

    # Windows
    .\load.ps1 -url "http://localhost:9200" -user "admin:secret"

Technical details

The dashboards folder contains the JSON files as exported from Kibana, by using the simple python tool from the save directory. The loader is a simple shell script so that you don't need python installed when loading the dashboards.

Create a new dashboard

If you added support for a new protocol in Packetbeat or a module in Metricbeat, it would be nice to create a dedicated Kibana dashboard to visualize your data. The Kibana dashboards are saved in a special index in Elasticsearch. By default it's .kibana, but it can be configured to anything else.

The first step in creating your own Kibana dashboard is to get a fresh installation of the Kibana dashboards/visualizations/searches/index patterns, that you can use as a starting point for your own dashboard. You can use the load.sh script on Unix and load.ps1 on Windows for loading the sample dashboards/visualizations/searches/index patterns in Kibana. The usage of this script is described above.

Note: Make sure you are using the latest Kibana version to create and download the dashboards.

Then, you can create the dashboard together with the necessary visualizations and searches in Kibana. After the dashboard is ready, you can download all the dashboards using the save/kibana_dump.py script.

Before executing the save/kibana_dump.py script, make sure you have python and virtualenv installed:

    # Prepare the environment
    virtualenv env
    . env/bin/activate
    pip install -r requirements.txt

    # go to save directory
    cd save

    # Download all Kibana dashboards to your host
    python kibana_dump.py --url 'http://localhost:9200' --dir output

where url points to the Elasticsearch URL, and dir is the directory where you want to save the Kibana dashboards.

Finally, copy the related dashboards, visualizations, searches and eventually index patterns to the dashboards directory, and send us a pull request.

Screenshots