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DIVE Backend

The Data Integration and Visualization Engine (DIVE) is a platform for semi-automatically generating web-based, interactive visualizations of structured data sets. Data visualization is a useful method for understanding complex phenomena, communicating information, and informing inquiry. However, available tools for data visualization are difficult to learn and use, require a priori knowledge of what visualizations to create. See dive.media.mit.edu for more information.

Development setup involves the following steps:

  1. Installing system dependencies
  2. Setting up postgres
  3. Setting up rabbitMQ
  4. Starting and entering virtual environment
  5. Installing python dependencies
  6. Migrating database
  7. Starting celery worker
  8. Starting server

Install System Dependencies (Linux / apt)

$ sudo apt-get update && sudo apt-get install -y postgresql git python2.7 python-pip build-essential python-dev libpq-dev libssl-dev libffi-dev liblapack-dev gfortran libxml2-dev libxslt1-dev rabbitmq-server

Install System Dependencies (Mac / brew)

Install Homebrew if you don't already have it. Then, run the following code:

$ brew install postgres
$ brew install rabbitmq

OR Install postgres.app

Install postgres.app by following the instructions here: (http://postgresapp.com/).

Download and open the app to start postgres.

Setup postgres

Make sure that you have a postgres server instance running:

postgres -D /usr/local/pgsql/data >logfile 2>&1 &
sudo -u postgres -i

Create the dive database by running:

$ createuser admin -P
$ createdb dive -O admin

Start RabbitMQ AMQP Server

  1. Add rabbitmq-server executable to path (add PATH=$PATH:/usr/local/sbin to ~/.bash_profile or ~/.profile)

  2. Run the server as a background process sudo rabbitmq-server -detached

  3. Create a RabbitMQ user and virtual host:

$ sudo rabbitmqctl add_user admin password
$ sudo rabbitmqctl add_vhost dive
$ sudo rabbitmqctl set_permissions -p dive admin ".*" ".*" ".*"

Install and Enter Virtual Python Environment

  1. Installation: See this fine tutorial.
  2. Starting virtual env: source venv/bin/activate.

Install Python Dependencies

Within a virtual environment, install dependencies in requirements.txt. But due to a dependency issue in numexpr, we need to install numpy first.

$ pip install -U numpy && pip install -r requirements.txt

Start Celery Worker

  1. Start celery worker: ./run_worker.sh
  2. Start celery monitor (flower): celery -A base.core flower

Database Migrations

Follow the docs. The first time, run the migration script.

python migrate.py db init

Then, review and edit the migration script. Finally, each time models are changed, run the following:

$ python migrate.py db migrate
$ python migrate.py db upgrade

Run API

  1. To run development Flask server, run python run_server.py.
  2. To run production Gunicorn server, run ./run_server.sh.

Deployment

  1. Set environment variable before running any command:
$ source production_env

Building Docker Images

conda env export > environment.yml
conda env create -f environment.yml

conda list -e > conda-requirements.txt
conda create --name dive --file conda-requirements.txt