Monday 26 February 2018

Podcast Interview with Jonathan Schmidt, Waykonect

Here's another cool user story for you. I had a great chat with Jonathan Schmidt, founder and CTO of a great French startup called Waykonect that offers intelligent vehicle management based on Neo4j. They have been doing some really smart stuff and the use case seems like such a great fit for a graph - it's like a hand in glove. Listen to his story - it's very cool.

Here's the transcript of our conversation:
RVB: 00:00:01.221 Good morning, everyone. My name is Rik. Rik Van Bruggen from Neo4j. And here I am recording another Graphistania Neo4j podcast. And this morning I have got, well, someone not too far away from me on the other side of this call, and that's Jonathan Schmidt from WayKonect. Hello, Jonathan. 
JS: 00:00:23.314 Hello, Rik. 
RVB: 00:00:23.952 Hi. Thank you for joining me. 
JS: 00:00:26.779 Welcome. It's a pleasure to be here.
RVB: 00:00:28.886 Fantastic. Jonathan, we've been emailing back and forth, and you've been talking to me about your project with Neo4j, but most people probably don't know you, yet. So if you could perhaps introduce yourself a little bit. Who are you, and what do you do, and what's your relationship to the wonderful world of graphs? 
JS: 00:00:48.982 All right. Well, I'm Jonathan Schmidt. I'm CTO and cofounder of WayKonect. We are a fleet management company. Telematics fleet management. Our focus is data analysis, actionable intelligence from your vehicles, and we take a very driver-oriented approach to the field. Our belief is that you have to engage drivers if you want any kind of savings, any kind of actions to be successful on your fleet. So that's what we do. Analysing data and engaging drivers. 
RVB: 00:01:29.005 When you say fleet management that means lease cars or it means-- what is the fleet for you? Is it any kind of rolling material, or what is it? 
JS: 00:01:38.584 We mostly focus on small, light vehicles. So cars, mostly, small trucks, that kind of thing. 
RVB: 00:01:52.184 Okay. Very good. And then how does it work, and how does that work with the graph as well, potentially? Could you explain that to us? 
JS: 00:01:59.999 Sure. Well, we have telematics dongle that actually give back a lot of flow data about vehicles. And we use graphs to map the relationship between dongle, the vehicle, the account that manages the vehicle, the driver that drives the vehicle, the trips that are recorded, events that might happen on that trip, the maintenance of the vehicle. Basically, we use Neo4j as our metre graph for information. Everything that we collect from the data is stored and recorded in Neo4j in a graph style, which actually allows us to analyse it very quickly, very efficiently because we can link multiple things together, multiple items together, and get very interesting intelligence from it. And it also gives us flexibility to innovate, to improve over time because it's a NoSQL model. So when we need another kind of item to track down, when we need another kind of-- or should I say-- 
RVB: 00:03:16.511 A property or a relationship or whatever. 
JS: 00:03:17.789 A property, relationship, real-world object, a new feature that we want to track, we started checking and keeping track of maintenance for our vehicles. And that was just a new level, new relationships, and that's it. And from maintenance, came appointments, came markers on the map to-- so when was the appointment, where's located, came reviews on these repair shops, came-- and all of this just flows out naturally in the graph. And it gives us flexibility to improve, and the flexibility to analyse very efficiently. 
RVB: 00:04:02.049 Wow. That's really cool. So your model has been evolving a little bit as you had more requirements, agile development, those types of things? Is that what I'm hearing? 
JS: 00:04:11.271 Indeed. Indeed. It's evolving constantly and basically every month or every two months we add one, two new labels to the graph, new relationships and new way to actually get insight from the data. 
RVB: 00:04:24.217 Wow. Cool. And are there any other data components to your application that you're using? Analytical components or other database stores, or is it mostly Neo4j? 
JS: 00:04:36.157 Neo4j is our metadata store, so everything we get out from the data is new. For the raw data itself, we actually use InfluxDB as a time series repository. And we are also using Kafka as a messaging backbone for the whole infrastructure and so that all of our services can analyse the data as it comes in. 
RVB: 00:05:04.584 Yeah. That's sounds like a great idea. Great architecture. So can you talk to us a little bit about how you got to Neo4j and why you started using a graph for this? What's the main advantage to it for you? 
JS: 00:05:18.219 Well, our first proof of concept was obviously [laughter] built on SQL. That panned out good for a few vehicles, for the 100 or so vehicles we had in the test, and but my belief was that it was untenable at scale, storing telemetries, storing raw data, and the metadata in SQL was going to be a nightmare whenever joints would be involved. And I mean, whenever you want a trip, you have to join the accounts that the vehicle belongs to, the vehicle itself, the trip, some events that might be off-scoring but may be related to it, and it's three, four, five-part joints on every request. And that would quickly become problematic. And when I started devising our data model, I had an engineer with me who said, "Actually, this makes me think of graphs and maybe we should check out the field of graph databases." And it basically started this way. We checked a few graph databases, settled on Neo, because it seemed to be the best fit for architecture and in terms of maturity, in terms of functionality. And we did a first proof of concept with the new project client in C# because our whole architecture's on .NET and it was just so easy. You create a class, you put in properties, and you can [inaudible] in one message, and relationships comes at almost no additional costs. 
RVB: 00:07:19.950 That's fantastic to hear. I mean, it sounds like a great [crosstalk]. 
JS: 00:07:23.237 Yeah. It was just so easy, so fitting, it stuck. And it's a choice I've never ever regretted making. Our transition to Neo4j was, I believe today, one of the best decision we ever made. 
RVB: 00:07:43.778 Wow. There must be something wrong with it [laughter]. 
JS: 00:07:47.867 Well, we had a few, of course, there was a few problems along the way, but mostly it came from our data model. We had a few issues with transactions and HAProxy because we are on an enterprise high-availability cluster. So routing transaction correctly from slave to master or keeping a transaction on the same server is a bit of challenge sometimes. But other than that most of our troubles was because we didn't understand technology at first and we actually built it as we learned. And we've made a few mistakes in model along the way and it was actually incredibly easy to smooth them out at a later time to refactor the graph. And that was also something that clenched it for me. I mean, you identify and isolate the problem, you refactor in one or two codes and a few change in your code and that's it, problem solved. That's just so easy. 
RVB: 00:09:07.273 Excellent. Well, that was kind of the past, right? So what does the future look like? How do you plan to use it in the future? How do you plan to evolve your application and also maybe any perspectives on what the industry is going to be doing on this? 
JS: 00:09:28.361 Well, as for us, my next step is [inaudible] clustering and I'm just a few steps away from having it work correctly [laughter]. Currently having a bit of dependency problems with dot net. But [laughter] that's something that should be easily solved and [crosstalk]. 
RVB: 00:09:50.075 I hope so. Otherwise, you know where to find us, right [laughter]?
JS: 00:09:52.654 Exactly. And I think [inaudible] clustering will be the next big step and what will take us from really scalable to infinitely scalable [laughter], I guess. As far as the industry, well, it's a bit-- as much as you-- when you start using graph databases, you start seeing application for them everywhere. Even up to research in ancient languages. You can probably index and cross-reference hundreds of texts in minutes or hours just because you can actually cross-reference words and phrase from it. And the language doesn't matter as long as you have a [unique alphabet?] for it. And this is so powerful and it's just one application off the top of my head because I double in that on the side. But-- 
RVB: 00:11:00.404 Graphs are everywhere, right? 
JS: 00:11:01.883 Graphs are everywhere.
RVB: 00:11:02.063 Once [laughter] you start getting into the mindset you start to see them everywhere and that's so fascinating. 
JS: 00:11:10.033 And you start to see also what good they could do. You have so many people building complicated software just to solve graph-related question that could be solved in a few Cypher queries and at the time port, so. 
RVB: 00:11:28.278 Couldn't agree more, Jonathan. I think we're on the same page there. Absolutely. Thank you for sharing your experience with us. I think that was super nice and super useful for lots of people. We'll put some links to your company and your experience in the transcription of the podcast. But for now, this is I guess where we're going to be wrapping up. Thank you so much for coming online, really appreciate it. And I look forward to meeting you soon sometime. 
JS: 00:11:58.764 You're welcome. It was a pleasure. And please, feel free to drop by just give me a holler one point. And, yeah, we can meet definitely. 
RVB: 00:12:07.894 Fantastic. Thank you, Jonathan. Have a nice day. 
JS: 00:12:10.741 Have a nice day. And thank you, Rik. 
RVB: 00:12:12.138 Bye.
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