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Nebulis Coding Analytics Platform

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Nebulis provides realtime analytics and reporting for coding projects. With Nebulis you can embed an endpoint into your coding units so that you can gather code changes to a project fork. Nebulis is set up to be close to zero configuration, so as soon as the endpoint is launched on a fork, that user will be instantly registered into the system and attached to the associated project. Requires Nebulis-Endpoint.

Installation


Install nenv and build nodejs:

nenv install 6.9.1
nenv local v6.9.1

Clone project:

git clone https://github.com/NebulisAnalytics/nebulis-server.git
cd nebulis-server

Install prerequisites:

npm install

Build and run development server:

npm run dev

Build and run production server:

npm run prod

Environmental Variables

GIT_HOST=<your ip>:7000
NODE_ENV=production

Client-side tests

Run client-side tests:

npm run client:test

Run client-side tests continuously:

npm run client:test:watch

Server-side tests

Run server-side tests:

npm run server:test:watch

Run server-side tests continuously:

npm run server:test:watch

Integration test

Endpoint Integration test

Performs complete end to end test of the client connecting to this server.

npm run test:endpoint

Run integration test:

npm run ghoulies

Run integration test continuously:

npm run ghoulies:watch

Docker Container


Install docker and virtualbox (these commands are for MacOS):

brew cask install virtualbox
brew install docker
curl -L https://github.com/docker/machine/releases/download/v0.9.0/docker-machine-`uname -s`-`uname -m` >/usr/local/bin/docker-machine && chmod +x /usr/local/bin/docker-machine
curl -L "https://github.com/docker/compose/releases/download/1.10.0/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose && chmod +x /usr/local/bin/docker-compose
docker-machine create --driver=virtualbox default
eval $(docker-machine env default)

Find the virtual machine ip address:

docker-machine ls

Run the docker container:

docker-compose up

Open browser to the virtual machine ip address:

http://192.168.99.100:1337