I could not decide if I wanted a Rhinoceros or Cthulhu as a mascot for this new project, so I went with both. The image you see above is what I ended up with. It’s absolutely outrageous and fits perfectly with our theme. That was going to be the name by the way “Outrageous DB” but it was kind of long, so I went with “Rage DB” instead. Right about now you may be thinking, wait, what are we doing, what is this new project? What is going on?
We got an odd request in User Slack the other day. A user wanted to find paths between nodes where the direction of the relationships changes up to just one time. Cypher doesn’t have a good handle on the concept of Direction beyond specifying it on a path. APOC doesn’t seem to have a method to figure this one out either… but to be fair I may have missed it since there are hundreds of awesome stored procedures now. Regardless, lets go ahead and build it the “old school” way. Using the Traversal API… and just to be slick, we’ll use the Bidirectional Traverser.
Last time we introduced the problem of single model, multiple property search queries taking a bit of time in Neo4j. We saw that using composite indexes or using “additional” labels can help us in some situations but not all. I promised you a stored procedure to build fake bitmap indexes could help, so today we’re going to see how to build one.
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If you’ve ever watched my talks about Neo4j, I tend to say we’re not very well optimized for Single Model queries where relationships aren’t considered. In fact, we’re kinda bad at it. Things got better when Composite Indexes were introduced, but we still have some limitations to deal with. Today we’re going to explore the issues and build our own faux “bitmap indexes” to get around this problem.
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Some of you may be too young to remember this, but writing your application logic in Stored Procedures was all the rage back in the day (PG devs you wouldn’t understand). That is mostly because they were typically written by consultants from IBM and Oracle that cost a fortune and told you to do it this way. Eventually people wised up and realized they (both) sucked and as all things in Software Development, the pendulum swung the other way and people started treating databases like dumb storage. A 15 year old blog post from Jeff Atwood lists some of the problems:
Neo4j 4.0 has been out for a few months now, but since the whole world is on lock down, it didn’t get a chance to make a grand entrance at Graph Connect 2020. It comes loaded with some great new features but I’m not here to tell you about all that. There are plenty of better places for it. Instead I’m going to tell you about an old feature that got a bit of an update. Unmanaged Extensions. Yup, those things have been with us since dinosaurs roamed the earth and they are still in Neo4j 4.0. Why you ask? Because they let you turn Neo4j into an HTTP API style service making it super easy to integrate into your existing infrastructure. It’s still one of my favorite ways to build Neo4j applications because once you have the documentation of the API locked down, you can crank out the endpoints quickly and the service is done before you know it.
Neo4j 4.0 has officially launched and folks are very excited about some of the new features including multiple graphs and schema based security. But you should also be excited about some of the more “quality of life” improvements in the product. One we will talk about today is the upgrade that composite indexes received.
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.
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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.
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Max De Marzi 