Dashboard that aggregates relevant metrics for Open Source projects.
JavaScript Python CSS Shell
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Dashboard providing an easy way to track a GitHub repo's evolution. There's a timeline to quickly bring you up to speed, graphs to view aggregated statistics and useful insights so that you never miss an issue again.

You can view an example dashboard here.

##Setting it up

###Config file

First, you need to set up your config file:

cp example_config.json config.json

Add the repository(ies) that interest you. The syntax is the following:

    "owner": "facebook",
    "repository": "react",
    "interval": 3600,
    "authentication": {
        "username": "SOMEUSER",
        "password": "SOMEPASS"

Note that the authentication part is optional (helps with the API rate limit and private repositories).

###Deploy with docker

  1. Download and run the docker image:

    sudo docker run -v `pwd`:/home/elasticboard -p 8080:80 -p 5000:5000 -i mihneadb/elasticboard /bin/start.sh

  2. There is no step 2. You now have elasticboard running at http://localhost:8080.

###Manual deploy

Make sure you have an elasticsearch instance accessible.

You also have to install the necessary Python dependencies:

# probably in a virtualenv
pip install -r data_processor/requirements-pip

Install the GitHub river. (link has instructions, currently you need a version >= 1.6.3)

Run the init_rivers.py file to load the rivers inside elasticsearch (make sure you installed the python dependencies):

python init_rivers.py

Fire up the API server:

gunicorn -k eventlet -w 4 -b data_processor.api:app

Serve the dashboard:

Start a webserver in dashboard:

cd dashboard; python -m SimpleHTTPServer

Point your browser to http://localhost:8000 (or whatever URL you are using) and you are good to go!

##Current status

Working prototype. We have the timeline and some graphs.

We are working on implementing queries and insights in order to provide relevant information.

##How you can help

Help us implement queries and insights! We are using the friendly elasticutils library for this. The existing code and issue descriptions should help you get started. If you need more info, please post a comment on the respective issue.

If you have an idea of a metric that is not covered in that list, please submit it on the issue tracker.


The dashboard takes care of the visualization aspect.

The data_processor is where all querying happens. It accesses data stored in elasticsearch using elasticutils and exposes the results through a simple API server that the dashboard calls.

Data flows directly into elasticsearch through the GitHub river.