MyNQL

MyNQL is a minimalistic graph database based on the Python library Networkx. Instead of replacing your relational database, it helps you to add a network with references to the data you already have.

• Nodes have the format table.id
• Connections (only) have a distance

You may already have tables like: Customers, merchants, products, places, areas, promotions, interests. Those tables used to have an id that together with the table name identify each entry.

After teaching the MyNQL network relations between two table1.id1 <-> table2.id2, you can ask the network also about all the indirect relations you like to know. A simple connect and select is all you need.

This is very simple, but also very powerful! You define a starting point, and search for the closest matches of a desired table. When you add more connections your questions will stay the same, only the results will improve. If you like to see a real live example, here is a small code for a computer store. The network can be serialized through peewee to be stored on MySQL, PostgreSQL or SQLite.

Install

MyNLQ’s source code hosted on GitHub.

git clone https://github.com/livinter/MyNQL.git
python setup.py install

or just

pip install MyNQL

Teach the Network

For example if a customer make a purchase of a product you assume a relation between customer.id and product.id, so you connect them. Optional you can specify a distance between nodes, to represent how close the nodes are related.

• connect - connect two nodes
• delete - delete a connection

Nodes are created automatically when you do the connection, and removed if they do not have any more connections. So do not worry about them.

Ask the Network

Now you can query all kinds of relations, not only the once you taught. With select you specify a starting point, like customer.id and specify the category where you like to know its closes relation.

• select - gives you the best related nodes from a specified category

The searching query takes into account all the different routes up to a radius you specify.

Example

Lets imagine we already have a table customer

Id	Name
101	jose	...
102	maria	...
103	juan	...

and you want to teach the network about recent purchases.

from MyNQL import MyNQL
mynql = MyNQL('store')

mynql.connect('customer.juan', 'product.jeans')
mynql.connect('customer.juan',  'product.socks')
mynql.connect('customer.maria', 'product.socks')

If the column Name is unique you can use it as a key, otherwise you would need column Id, and your code would look like this:

mynql.connect("customer.103', 'product.12')

Now you can ask questions from other points of view. You always specify a starting point, and the category where you want to know the best matches:

>>> mynql.select('customer.maria', 'product')
['socks', 'jeans']

Maria is more connected to socks, as she has a direct connection, but also a bit to jeans as there exist an indirect connection through Juan.

>>> mynql.select('product.jeans', 'product')
['socks']

Any combination is valid. For example you can ask about how one product is related to other.

Backend

Storage is done in memory, but if you want to use MySQL, SQLite or PostgreSQL as a backend take a look at test/pee_example.py. This will keep a copy of all updates in your database.