If you’ve been following along, we got Michael’s Batch Importer, compiled it, created some test data, ran it and saw millions of nodes and relationships loaded into Neo4j.
So now we’re ready for our own data. I am going to show you how to get data from a Relational Database like PostgreSQL into a format we can use. If you’re using SQL Server, MySQL, Oracle, etc, the directions will be slightly different, but you’ll get the picture.
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Data is everywhere… all around us, but sometimes the medium it is stored in can be a problem when analyzing it. Chances are you have a ton of data sitting around in a relational database in your current application… or you have begged, borrowed or scraped to get the data from somewhere and now you want to use Neo4j to find how this data is related.
Michael Hunger wrote a batch importer to load csv data quickly, but for some reason it hasn’t received a lot of love. We’re going to change that today and I’m going to walk you through getting your data out of tables and into nodes and edges.
Let’s clone the project and jump in.
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The maximum flow problem was formulated by T.E. Harris as follows:
Consider a rail network connecting two cities by way of a number of intermediate cities, where each link of the network has a number assigned to it representing its capacity. Assuming a steady state condition, a nd a maximal flow from one given city to the other.
Back in the mid 1950s the US Military had an interest in finding out how much capacity the Soviet railway network had to move cargo from the Western Soviet Union to Eastern Europe. This lead to the Maximum Flow problem and the Ford–Fulkerson algorithm to solve it.
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Wouldn’t it be cool to import Wikipedia into Neo4j?
Mirko Nasato thought so, and built graphipedia using the batch importer that does just that.
It’s written in Java, so if you’re a pure ruby guy, I’ll walk you through the steps.
Let’s clone the project and jump in.
git clone git://github.com/mirkonasato/graphipedia.git cd graphipedia
If you look in here you’ll see a pom.xml file which means you’ll need to download Maven and build the project.
sudo apt-get install maven2 mvn install
You’ll see a bunch of stuff flying by, that’s just the dependencies being downloaded. At the end you should see this:
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We’ve seen some pretty nice visualizations of nodes and their immediate neighbors, but we want to be able to visualize more. So we’re going to prepare a 200 node network, use Cypher to extract the data we want and visualize it with D3.js.
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Like I promised in my previous post, I wanted to do a little something on D3.js.
We are going to take one of their example visualizations and visualize a follows graph.
To create our graph, we will take the names of 20 people: create nodes for them, add them to an index, and randomly link them together.
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If you’re into NoSQL and Graph Databases like Neo4j, then you’ll probably tend to be working on back-end development. If you’re lucky enough to work in a team of specialists, some UX guy will come up with user requirements, hand them off to a UI gal for design, who will then pass it on to a Javascript Ninja to slice it together and they’ll just ask you provide the data and stuff it in a JSON object.
If you’re not so lucky and are working on pet projects by yourself then you’ll have to do it all. So I wanted to give you a little nudge into learning a visualization framework. Since my most popular blog post so far has been Graph Visualization and Neo4j and we’ve already seen one example that you’ll probably want to customize in your projects, we’ll stick with processing.js, and in the future I can do a little intro on D3.js, Unveil.js and maybe something a little crazier like VVVV.js.
So getting started is really easy. We’ll create an html document, add the minified processing javascript library and create a canvas element to put our visualization.
<!DOCTYPE html> <html> <head> <title>Hello World - Processing.js</title> <script src="processing-1.3.6.min.js"></script> </head> <body> <canvas data-src="helloworld.pjs"></canvas> </body> </html>
All right, let’s create the helloworld.pjs we reference as our canvas data source.
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Max De Marzi 