A simple Flask API for storing users and groups of users. Optimized for read-performance over write performance based on the client-side specifications.

• Framework: This is a single Flask app using the Flask-RESTful module to implement a RESTful API.
• Data/Persistence: Currently uses an in-memory data layer, but this can be swapped easily based on performance/scaling requirements with changes to usergroups/db.py and minimal changes to usergroups/models/base.py.
• Minimal API = No dependencies on outside services

Any version of python2.7 with setuptools and pip, Recommended: virtualenv

1. Checkout the repo
2. Set up virtualenv from root directory
3. Install requirements with pip
4. Run the server
   1. Option 1. No options (default port 5000, minimal logging): python runserver.py
   2. Option 2. Customize
      1. set env for development configs: export USERGROUPS_ENV=DEVELOPMENT
      2. AND/OR Run on custom port python runserver.py --port XXXX

git clone git@github.com:bri-bri/user-groups.git && cd user-groups
virtualenv env && . env/bin/activate
pip install -r requirements.txt
export USERGROUPS_ENV=DEVELOPMENT
python runserver.py --port 5000

Simply run the following command and verify the output manually: sh ./integration-test.sh

TODO... real unit tests

1. Without request parameters: curl -i http://127.0.0.1:5000/users/<userid>
2. With request parameters: curl -i -X POST http://127.0.0.1:5000/users/ -d '{"userid":"user1", "first_name":"John", "last_name": "Doe", "groups":["gentlemen", "scholars"]}' -H "Content-type: application/json"

• Unit Tests for controllers and models (partially implemented but not presentable)
• Commented code
• Persistent log storage (w/ improved logging and logger class)
• process manager config (e.g. supervisord)
• Datastore options need to be considered based on further requirements and prototype performance

• custom RequestParser
• custom ExceptionHandler
• custom ResponseHandler routing all responses
• metric/performance tracking
• misc. work to get it running in a distributed remote environment

All work - including research and testing - took about 7.5 hours.

2015-07-09 PM - 2.5 hours setting up skeleton

2015-07-10 NOON - 0.5 hours researching

2015-07-10 PM - 2.5 hours finishing core functionality

2015-07-11 AM - 1.0 hour attempting unit tests

2015-07-11 NOON - 1.0 hour doing integration test instead & writing setup instructions

Grand total: 7 hours