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01 Dec 2015
Solving Relationship Problems With Graph Technology

Before I get started - I am not ranting about a new dating algorithm I wrote. This post is going to be about Graph databases and kickstarting work with python. Before I talk about graph databases, I’ll briefly explain how I found myself hacking with Neo4j graph database at 2 am in the morning.

I wanted to build a tool for finding opportunities. For example - I have an idea for connected shoes. I would like to find a co-founder to work with on this project. Reaching out to my network to find a suitable candidate is troublesome. The manual process includes me having to either open a Facebook chat or shoot an email to get my intentions across.

How useful would it be for me to go to a user interface and type-

Graph query

Graph Databases

Enter graph databases. They are meant to purely manage relationships and properties of actors. It’s a different data model compared to our traditional Relation Databases. Let’s try my problem mapped to the Graph data model.

As you can see - Naveen is a node and under it has properties. The part missing is relationships to other nodes.

Now we know that Naveen works at a software company specialized in JavaScript.

If a tool could suggest few people like this based on a loose criteria, it would definitely help my search.

Getting dirty with Neo4J & Python

Since the above example is advance, I am going to start work by a simple query. Find me all the people who have skills of “JavaScript” and interests on “IoT”.

I picked the most popular graph database: Neo4j for a spin. It’s made with Java/Scala and it’s fairly easy to get started. You can point a database directory and Neo4j loads up a graph database server.

Neo4j startup window

The browser console can be accessed through http://localhost:7474/browser/. First I am going to run some queries and enter data into Neo4j.

CREATE (arshad:Person { name: 'Arshad Mohideen', age: 24, interests:["startup", "entrepreneurship", "toastmaster"], skills: ["public-speaking", "people-management"]})
CREATE (naveen:Person { name: 'Naveen Dilan', age: 23, interests:["startup", "entrepreneurship", "iot"], skills: ["javascript", "event-organizing"]})
CREATE (yuda:Person { name: 'Yudhanjaya Wijeratne', age: 24, interests:["startup", "entrepreneurship", "journalism", "media"], skills: ["writing", "public-speaking"]})

In case you are wondering - this query language is called Cypher. It’s like a SQL and JSON combination. :Person is called a label in the graph world. Labels are used to categorize nodes. Properties are defined inside the JSON property bag. In the Graph world below are the main concepts:-

  1. Nodes
  2. Relationships
  3. Properties
  4. Traversal

Neo4j Docs elaborates more on these concepts.

Next I am using flask to create a quick web api. This web api return “Hello World” for now. The recommended method to access Neo4j from Python is through py2neo.

from flask import Flask, request
#we are importing the below entities from py2neo
from py2neo import Graph, Node, Relationship, authenticate
import collections
import json
app = Flask(__name__)
def query():
return Hello World
if __name__ == "__main__":
app.debug = True

How do you work with Neo4j? You need to obtain a Graph object. Before getting the graph object, I need to authenticate with the password.

authenticate("localhost:7474", "neo4j", "abc123")
graph = Graph()

After authenticating - a Cypher query needs to be crafted to match for for the problem I had earlier.

MATCH (p:Person)
WHERE ANY(interests IN p.interests WHERE interests =~ '(?i).*iot.*')
AND ANY(skills IN p.skills WHERE skills =~ '(?i).*javascript.*')

The above query can be translated to: “Find all persons with interests of ‘iot’ and skills of ‘javascript’”. Now I can use this query in python to extract the data.

statement = "MATCH (p:Person) WHERE ANY(interests IN p.interests WHERE interests =~ '(?i).* iot.*') AND ANY(skills IN p.skills WHERE skills =~ '(?i).* javascript.*') RETURN p"
person_list = []
for person in graph.cypher.stream(statement):
#convert the person_list to a JSON array
return json.dumps(person_list)

When I do a curl to the endpoint - I get a JSON response of only naveen’s node.


With workarounds and hacks - we have bended relational databases to perform what graph databases are naturally good at. In many business use cases, the relationships between objects can be used to infer greater knowledge. An example would be: Handing a baby toys coupon for a shopper who bought some napkins. To calculate this using a traditional database - it would take lot of processing power. Graph database hits the sweet spot on these types of problems. Now let me hook this up with LinkedIn API and I’ll have the ultimate stalker tool in my hands.

Till next time mate,
Dulitha at 18:22

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