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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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}))
Sometimes you’ll find yourself looking at a traversal and thinking… “I’m going to be doing this one thing over and over again.” That sounds kind of wasteful and years of recycling have taught us not to be wasteful. Let’s take a look at an example from our past. Look back at the Neo Love application, the one with the picture of Marilyn Monroe and Groucho Marx. Let’s see what a Neo4j 2.0 version of that query would look like:
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.
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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I really like this saying by Corey Lanum:
Finding the relationships that should not be there is a great use case for Neo4j, and today I want to highlight an example of why. When you purchase something online, the merchant hands off your information to the payment gateway which processes your actual payment. Before they accept the transaction, they run it via series of risk management tests to validate that it is a real transaction and protect themselves from fraud. One of the hardest things for SQL based systems to do is cross check the incoming payment information against existing data looking for relationships that shouldn’t be there.
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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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Rock climbing is a physically and mentally demanding sport, it test the limits of one’s strength, endurance, agility, balance and concentration. Sasha DiGiulian is one of the best rock climbers in the world. I can’t get past 15 feet without starting to panic and freak out. Maybe it’s because I’m afraid of heights…and overweight, but I’m just not right for that kind of challenge.
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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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Most of the applications using Neo4j are read heavy and scale by getting more powerful servers or adding additional instances to the HA cluster. Writes however can be a little bit tricker. Before embarking on any of the following strategies it is best that the server is tuned. See the Linux Performance Guide for details. One strategy we’ve seen already is splitting the reads and writes to the cluster, so the writes only go to the Master. The brave can even change the push factor to zero and set a pull interval only in neo4j/conf/neo4j.properties:
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Max De Marzi 