Tag Archives: github

Bidirectional Traversals in Space


If you have never watched Firefly, then stop whatever you are doing and get to it, you can come back and read this post later. Ok good, now where were we. Firefly. The series is set a few hundred years from now, after people begin to terraform a new star system and it follows the adventures of the renegade crew of Serenity, a “Firefly-class” spaceship whose work consists of cargo runs or smuggling while failing to stay out of trouble. There is no faster than light travel in this series, so ships can’t just “warp” where ever they want. Instead they travel about from planets and moons, exchanging cargo, refueling and trying to make a living. We are going to model “The Verse” of Firefly in Neo4j, and see how we can find routes to move our illicit cargo from one place to another.
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Flight Search with the Neo4j Traversal API

Screen Shot 2015-08-30 at 2.21.07 AM

Before Cypher came along, if you wanted to describe a graph traversal in Neo4j you would use the Traversal Framework Java API. The Traversal API is one of the many hidden gems of Neo4j and today we are going to take a closer look at it. Traversing a graph is about going on a journey. All journeys have a starting point (or points) so that’s the first thing we have to do, figure out where in the graph we begin. It can be a single node, or multiple ones, but they will go on the journey following the same rules, so its easier if it’s just one node or nodes of the same “type”.
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Using the Testing Harness for Neo4j Extensions


I’ve been creating both unit tests and integration tests for Neo4j Unmanaged Extensions for far too long. The Neo4j Testing Harness was introduced in version 2.1.6 to simplify our lives and just do integration tests. Let’s try it on and see just how awesome we look. First thing we need to do is add the dependency to our project:
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Scaling Concurrent Writes in Neo4j

concurrent writes

A while ago, I showed you a way to scale Neo4j writes using RabbitMQ. Which was kinda cool, but some of you asked me for a different solution that didn’t involve adding yet another software component to the stack.

Turns out we can do this in just Neo4j using a little help from the Guava library. The solution involved a background service running that holds the writes in a queue, and every once in a while (like say every second) commits those writes in one transaction.
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It’s over 9000! Neo4j on WebSockets


In the last blog post we managed to run Neo4j at Ludicrous Speed over http using Undertow and get to about 8000 requests per second. If we needed more speed we can scale up the server or we can scale out to multiple servers by switching out the GraphDatabaseFactory and using the HighlyAvailableGraphDatabaseFactory class instead in Neo4j Enterprise Edition.

But can we go faster on a single server without new hardware? Well… yes, if we’re willing to drop http and switch to Web Sockets.

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Neo4j at Ludicrous Speed


In the last blog post we saw how we could get about 1,250 requests per second (with a 10ms latency) using an Unmanaged Extension running inside the Neo4j server… but what if we wanted to go faster?

The easy answer is to Scale Up. However, trying to add more cores to my Apple laptop doesn’t sound like a good time. Another answer is running a Neo4j Cluster and (almost) linearly scaling our read requests as we add more servers. So a 3 server cluster would give us between 3,500 and 3,750 requests per second.

But can we go faster on a single server without new hardware? Well… yes.
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The Power of Open Source Software


One of the benefits of Open Source Software is that if you want to change how something is done, you can. At Neo Technology, we have a small team of “Field Engineers” who don’t really work ON the product but rather WITH the product. We help our customers with issues of all kinds, answer questions, give suggestions and whatever we need to do to make people’s project successful. A little while back I had a support ticket for a traversal that was taking longer than they hoped it would.

Think about a social network, one of the things you may want to do is tell the user how big their friends network is. But why stop there? How about their friends of friends or even friends of friends of friends network? These are the kind of questions graph databases excel at compared to relational databases. Let’s take a look at what they were doing:
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Translating Cypher to Java


The expressive power of Cypher is already awesome and getting better with the Neo4j 2.0 release. Let’s take a step back from the bleeding edge and see Cypher in 1.9.4 and how it can be translated into Java. First a simple example where we look up a User node by an index and return a list of usernames belonging to the people who are that user’s friends:

START me = node:Users(username='maxdemarzi')
MATCH me -[:FRIENDS]-> people
RETURN people.username

The Cypher statement expresses what I want even better than my botched explanation in English. So how would we do this in the Neo4j Java API?
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Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives is a mind bending look at how no matter how individual we think we are, the people around us have a great amount of influence in our lives. One of the authors James Fowler was at GraphConnect 2012 and gave a presentation on this idea:
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Visualizing the news with Vivagraph.js


Today I want to introduce you to VivaGraphJS – a JavaScript Graph Drawing Library made by Andrei Kashcha of Yasiv. It supports rendering graphs using WebGL, SVG or CSS formats and currently supports a force directed layout. The Library provides an API which tracks graph changes and reflect changes on the rendering surface which makes it fantastic for graph exploration.

Today we will be integrating it with Neo4j and the Alchemy API.

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