This project aims to offer a backend to the matchID project.

Full documentation is available at https://matchid-project.github.io.

The main objective is to process one or many datasets of civil states and identify multiple matches (at least two!) of a same person.

The backend basically offers the possibility to cook a dataset with a recipe, leading to a new dataset. The recipe can be cooked "live" or in background, offering the possibility of live-reranking (with machine learning or not) on top of an elasticsearch.

A recipe book for preparing names, birth location, fuzzy match an rescore is integrated and can be fully customized for your use-case.

It's full-api designed (no cli!) and based on Flask RESTPlus. The scalability relies on single server multiprocessing for the Pandas adn scikit-learn python part, and cloud scalability of elasticsearch for large fuzzy-match use-cases. It aims to offer capability to match two datasets with dozens millions of records in a day on a 1U server. Further developments will be still needed for full-cloud scalability.

For now the code is considered to be still in "beta" development, and still needs some steps of refactoring and documentation to reach production readiness.

This package integrates a simple page application in Vue web-app for helping developing your use-case (single user designed, so not to deserve a Lab of data-scientists), and a Docker configuration for accelerating your use-case design.

• live search and bulk-search identities in a dataset (take benefit from elasticsearch and offers the possibility of re-ranking)
• find common identities between two datasets
• deduplicate idendities into one dataset

Automatization (and thus documentation) is for now a future achievement

First clone the backend

git clone https://github.com/matchID-project/backend

matchID uses make and Docker to accelerate installation of dependencies. You'll first have to install Docker and docker-compose.

Now you just have to start matchID:

cd backend
make start

This should :

• download the frontend
• build it
• start the backend
• start elasticsearch (required)

Going to your browser to check everythings works fine.

As it starts many components your computer may encounter low memory [if <8Go]. Just go to next section to see how to still run matchID.

You can now add you own data, but we strongly to follow the tutorial and downloading the sample use case :

make download-example

We recommand now you to follow the Tutorial.

make stop

The list of the supported components is :

• backend : the api and the engine
• frontend: the single-page-application in Vue.js to develop recipes
• elasticsearch: the famous search engine used for fuzzy matching
• postgres : not needed but useful for lower memory configuration in further
• kibana: useful for elasticsearch data analysis

You can start all the components like this:

make start-all

Each component can be started or stopped alone, this will for example stop postgres:

make postgres-stop

And this will start kibana :

make kibana

For example, make start is equivalent to make backend frontend elasticsearch.

Each docker components logs its actions to log/docker-component.log. So you can easily check heath of all components like this:

tail -f log/docker-*.log

matchID use the 80 port by default. If you have another web service it may cause conflict.

Just edit docker-components/docker-compose-run-frontend.yml and change the docker ports "80:80" to "8080:80" to change the exposition port to 8080.

You will restart the frontend like this

make frontend-stop frontend

To clean everything

make clean docker-clean

If you want to contribute to the developpement, you'll be able to fork the repo on GitHub and to lauch the dev mode (you'll perhaps have to do a make docker-clean first):

make start-dev