dataclay-class

This repository contains the file containing the data model and the docker files to deploy dataClay with the model.

Python dependencies

The Python used is 3.6.9. The following python packages must be installed as well:

python3 -m pip install geolib dataclay requests pygeohash

Install docker-compose

sudo curl -L https://github.com/docker/compose/releases/download/1.21.2/docker-compose-`uname -s`-`uname -m` -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
docker-compose --version

Build

To build the dockers containing the data model defined below, use the script located in ./build/build_dockers.sh, which generates the dataclay dockers.

Model

detected_object attribute in Event class is needed for dataClay federation purposes. objects_refs attribute in EventSnapshot class is provided for PyWren to retrieve objects remotely. objects attribute in EventSnapshot is used to store all objects in one single make_persistent call to avoid extra visits to dataClay.

Deployment

First the exposed dataclay IP should be changed to the IP of your cloud resource, that is, updating the ./deploy/dataclay/docker-compose.yml file replacing the content of EXPOSED_IP_FOR_CLIENT=${IP} by the actual IP.

Then, go to the ./deploy/ folder and execute the script that deploys the dockers:

./launch_dockers.sh

In order to populate the dataClay database with information, a simulator code is provided in the app/ folder to fill it with actual data. The following dependencies are required:

pip3 install pandas

And to run it, execute the following command:

cd app/
python3 run_demo.py