Tag Archives: java

Bidirectional Traversals in Space

firefly

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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Benchmarks and Superchargers

Interceptor

For the most part, I hate competitive benchmarks. The vendor who publishes them always seems to come out on top regardless. The numbers are always amazing, but once you start digging in a little bit you start to see faults in what is actually being measured and it never applies to real world workloads. For example you have Cassandra claiming 1 Million writes per second on 300 servers. Then Aerospike claiming 1 Million writes per second on 50 servers. MongoDB claiming almost 32k writes per second on a single server, but claiming Cassandra can only do 6k w/s and Couch can only do 1.2k w/s on a single server… Then ScyllaDB has almost 2 Million writes per second on 3 servers blowing everybody away.
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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

harness

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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Triggers in Neo4j

al-capones-gun

One of the often overlooked features in Neo4j is the “TransactionEventHandler” capabilities… better known in the database world as “Triggers“. When a transaction occurs, we can analyze that event and decide to take some action. To accomplish this, we’ll write a “Kernel Extension” ( a little different from the Unmanaged Extensions we’ve seen on this blog ) to tie in our trigger.

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One Direction Relationships in Neo4j

onedirectionchop

In the Neo4j Property Graph model, every single Relationship must be Typed and Directed. This means they must have a specific name (FRIENDS, LIKES, FOLLOWS, etc) and have a Start Node and an End Node to show direction. What’s neat is that when you write your queries you can choose to ignore that. The following queries are all valid:

// Get all the people I follow 
MATCH (u1:Person)-[:FOLLOWS]->(u2:Person)
WHERE u1.username = "maxdemarzi"
RETURN u2.username

// Get all the people that I follow or follow me
MATCH (u1:Person)-[:FOLLOWS]-(u2:Person)
WHERE u1.username = "maxdemarzi"
RETURN u2.username

// Get all the people related to me 
MATCH (u1:Person)--(u2:Person)
WHERE u1.username = "maxdemarzi"
RETURN u2.username

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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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Kickstarting a Neo4j Video Series

Learn how to build high performance @neo4j applications with this video training course.

I’m on Kickstarter to ask for your help in order to create a set of videos to teach you how to build high performance Neo4j applications. I am going to capture the lessons I’ve learned over the past 4 years working with graph databases and share them with you.

These videos will teach you everything you need to know about building high performance applications using Neo4j.
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Translating Cypher To Neo4j Java API 2.0

cypher-translate-2.0ish600x293

About 6 months ago we looked at how to translate a few lines of Cypher in to way too much Java code in version 1.9.x. Since then Cypher has changed and I suck a little less at Java, so I wanted to share a few different ways to translate one into the other just in case you stuck in a mid-eighties time warp and are paid by the number of lines of code you write per hour.

But first, lemme take a #Selfie let’s make some data. Michael Hunger has a series of blog posts on getting and creating data in Neo4j, we’ll steal borrow his ideas. Let’s create 100k nodes:

WITH ["Jennifer","Michelle","Tanya","Julie","Christie","Sophie","Amanda","Khloe","Sarah","Kaylee"] AS names 
FOREACH (r IN range(0,100000) | CREATE (:User {username:names[r % size(names)]+r}))

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Neo4j all the way Down!

ext_inside_server_inside_embedded

Just because you can, doesn’t mean you should. However sometimes it can be comforting to know how. I am going to show you how to run Neo4j Embedded and Neo4j Server at the same time…and an Unmanaged Extension inside that Neo4j Server. There aren’t any real good reasons why you’d want to do this, but it’s April Fools, so here we go.

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