Everybody loves benchmarks. Well let me rephrase that, everyone who publishes benchmarks loves the benchmark they publish. Nobody publishes benchmarks that make them look bad, it would be a terrible idea. But you my friend are in luck. I’m full of terrible ideas and today I’m going to publish some benchmarks that makes us look bad. Why? Because we are actively trying to improve this part of Neo4j and I want to experiment with some ideas and see what a ballpark theoretical limit should look like…and I’ll throw in some “inside baseball” about the graph database space from my point of view if you stick around until the end. Continue reading →
On Neo4j’s User Slack someone was having a bit of a hard time getting a query to return quickly. Their model had User Nodes and Transaction Nodes, and they wanted to get the last 25 transactions for a user. This particular user had millions of transactions and the query was taking forever. I’m going to show you how we can speed up the query and hopefully teach you a new modeling trick you can use in your graphs.
A few weeks ago I blogged about Activities in Neo4j, and we ended up with a way to recommend an activity to a user based on what sequence of activities they had done in the past. We also had a list of common sequences of activities, but they were a bit hard to digest. Today I’m going to show you how to visualize them so they make more sense.
The “footprints in the sand” poem is a story of a person walking on the beach with God and seeing two sets of footprints. The footprints represent the joys and sorrows of this persons life. During the worst of times the trail shows just one set of footprints. Confused as to why they would be abandoned at their time of need, God replies that “when you see only one set of footprints, it was then that I carried you”. It’s a touching poem, but a single set of footprints in the sand isn’t very graphy. Things get interesting when you superimpose the paths one over the other. We can then start to see where many paths converged and diverged, see how deep the footprints got at times, when they split where they went and where did they end up. Continue reading →
In part one, we learned to listen to our users, in part two we began learning how to talk back. Before we go any further in to the stored procedure, how about we build a little front end to show off the work we’ve done so far on this proof of concept? That will also make things easier to test out and let us get into the mindset of the user. There are a ton of options here, lots of folks like Spring and Spring Boot. Others are more hipsters and into Micronaut. I am even more of a hipster and prefer to use Jooby, but it doesn’t really matter. We’ll be using Cypher, the Neo4j Drivers and the Stored Procedures we build along the way so technically you can do this in just about any language. Continue reading →
In part one our this building a chatbot series, we figured out how to use OpenNLP to “hear” what a user is saying and figure out both their intent and any entities they may have mentioned. Today we’re going to learn how to use Neo4j to talk back… like an impudent child.
Last year eBay built a chatbot using Neo4j. Unfortunately we have grown so big I didn’t get a chance to work on that project and kinda feel left out. So I decided I’m going to build my own chatbot with Neo4j. As usual I’ve never done this before, have very little idea what I’m doing, have no team, and have barely any time to get this done. So with those disclaimers out of the way, let’s see what we can do. Continue reading →
Is not that thing above. That’s a Chart, not a Graph. But anyway…Neo4j is designed to support the property graph model natively. There are a host of other technologies that can bolt-on a “graph layer” of some kind. However it doesn’t make them a graph database. It’s like adding a rear spoiler to a van, sure it may look cool… or ridiculous, but it won’t make it a race car. Don’t fall for it. If you need fast graph queries, use a real graph database. But today we won’t talk about that. Instead we’re going to talk about the real property graph… Continue reading →
In the last blog post, we saw how we can use Neo4j to find the merchants where credit card fraud originated or was used for testing stolen data in order to prevent further fraudulent charges. It stemmed from a webinar on our amazing youtube channel with has hundreds of videos about graphs and Neo4j. We will continue diving in to the technical details by looking at how Neo4j can help you find Fraud Rings. The way this fraud works is that a large set of synthetic accounts are created and act like normal customers. Over time they request higher and higher levels of credit which they pay back on time. Then they all request the maximum credit they can get, take out the money, and disappear! Let’s find them before this happens.