Now it is time to create the timeline for our users. Most of the time, the user wants to see posts from people they could High Five in order to elicit a conversation. Sometimes, they want to see what their competition is doing and what kind of posts are getting responses… also who they can low five. I don’t think they don’t want to see messages from people who are not like them and don’t want to date them but I could be wrong.
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You ever eaten at a “Fusion Cuisine” type of restaurant? It’s a bit of a gamble. Personally I’m always up for eating just about anything… except Pho. That stuff messes me up. But back to fusion cuisine. I think my favorite is Indian and Mexican. Take your favorite Indian dish, wrap that in the warm embrace that is a burrito tortilla, heaven. Well, just about anything wrapped in a burrito is perfect. Why am I taking about Fusion and Wrapping stuff? Well, today we are going to add Auto Complete into our Dating Site, but before we can do that I need to talk to you about Neo4j’s Fusion Indexes and how they wrap the Lucene Indexes as well as our generation-aware B+tree (GB+Tree) indexes.
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In the last post, we created a User model, built the login and registration pages, hooked everything up in our front end framework Jooby and got the ball rolling. I’m no designer so I am borrowing a Application Bootstrap Theme and tweaking that as we go along (if you are a designer, pull requests are welcomed). At this stage a ton of it is just mockup, but we will replace it with real functionality. This is what we have so far:
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We started our back end service in the last blog post and created a schema and the ability to create and fetch users. We are going to flip to the front end to make use of these abilities and work on both side by side. The goal this time is to be able to register and sign in a user. If you are a regular reader you know I’m a fan of the Jooby framework, so we’re going to use that again. After creating a shell application, what I want to do is to be able to connect to the API we’re building, so we’ll be using Retrofit to turn our HTTP API into a Java interface.
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We came up with an idea for a dating site and an initial model in Part One. Next we are going to work on a back end HTTP API, because I’m old school and that’s the way I like it. We will build our HTTP API right into Neo4j using an extension which turns Neo4j from a Server into a Service. Unlike last time where we wrote a clone of Twitter, I don’t really know where I’m going with this, so let’s start with some of the obvious API endpoints and then we can design and build more as we go along. Is this Agile or am I just being an idiot? I can’t tell, so onward we go.
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Alchemy is a philosophical and protoscientific tradition practiced throughout Europe, Africa and Asia. Its aim is to purify, mature, and perfect certain objects. In popular culture we often see the case of shadowy figures trying to turn lead into gold to make themselves immensely rich or to ruin the world economy. In our case we will not be transmuting lead into gold, but documents into graphs which is just as good. In the past, we had used “Alchemy API” but they were purchased by IBM and retired. You can get similar functionality with IBM Watson, but let’s do something else instead. Let’s add Entity Extraction right into Neo4j.
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If you have started or are thinking about starting a Graph project, you ought to get in touch with me. I’ve been involved in hundreds of graph database backed projects and chances are I can point you in the right direction. It doesn’t cost anything to get on a goto meeting for an hour and talk about it. Contact me at max@neo4j.com to schedule it. If you are very serious and have a little budget allocated then I recommend you sign up for one of our bootcamps. You’ll be amazed at what we can accomplish together in a very short time. I’ll even make you a deal, if you sign up for a bootcamp and end up buying a commercial license, we’ll give you a week of professional services absolutely free.
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One of the symptoms of any fast growing company is the lack of available meeting rooms. The average office worker gets immense satisfaction to their otherwise mundane workday when they get to kick someone else out of the meeting room they booked. Of course that joy can be cut short (along with their career) once realizing some unnoticed VIP was unceremoniously kicked out. It’s not a super exciting use case, but today I’m going to show you how to use Neo4j to perform some scheduling gymnastics.
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In the movie Blade Runner, “replicants” are engineered biological copies of humans. They are implanted with memories that aren’t real (to them anyway, they are sometimes the recorded memories of other people) in order to provide a sort of replacement to their emotions. The replicants are meant to work in outer space and are illegal on earth. The ones that manage to get to earth are hunted down by Deckard and other blade runners. In order to determine who is a replicant and who is a “real person” blade runners use a “Voight-Kampff” test that measures respiration, heart rate, blushing and eye movement in response to emotionally provocative questions. Today we are going to turn Neo4j into a blade runner and use it to find and retire replicated identities in our data.
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Last year Microsoft announced “Cosmos DB”, a multi-modal database with graph support. I think multi-modal databases are like swiss army knifes, they can do everything, just not very well. I imagine you would design it to be as good as it can be at its main use case while not losing the ability to do other things. So it’s neither fully optimized for its main thing, nor very good at the other things. Maybe you can do pretty well with two things by making a few compromises, but if you try to do everything…it’s just not going to work out.
Can you imagine John Rambo stalking his enemies with an oversized swiss army knife? Here, let me help with the mental image:
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