>>Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, or is modernized now. Yes, let's talk about that. And with me is Ravi, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >>Thank you so much. I'm so glad to be here with you. >>I asked you what the new requirements are around modern applications. I've seen some, you know, some of your comments, you gotta be flexible, distributed, multimodal, mobile edge. It, that those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >>Yeah, I think what has basically happened is that, uh, so far, uh, it's been a transition of sorts. And now we are come to a point where, uh, the tipping point and the tipping point has been, uh, uh, more because of COVID and there COVID has pushed us to a world where we are living, uh, in a sort of, uh, occasionally connected manner where our digital, uh, interactions, precede our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than, uh, in a digital manner, as opposed to sort of making a more specific human contact that has really been the, uh, sort of accelerant to this modernized. Now, as a team in this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. >>They're all sitting behind. Uh, they used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, uh, but they are all centralized still. Uh, but where our engagement happens with the data is, uh, at the edge, uh, at your point of convenience at your point of consumption, not where the data is actually sitting. So this has led to, uh, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? Uh, but it just basically comes down to the fact that the data needs to be where you are engaging with it. And that means if you are doing it on your mobile phone, or if you are sitting, uh, doing something in your body or traveling, or whether you are in a subway, whether you're in a plane or a ship, wherever the data needs to come to you, uh, and be available as opposed to every time you going to the data, which is centrally sitting in some place. >>And that is the fundamental shift in terms of how the modern architecture needs to think, uh, when they, when it comes to digital transformation and, uh, transitioning their old applications to, uh, the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Uh, otherwise people are basically waiting for that circle of death that we all know, uh, and blaming the networks and other pieces. The problem is actually, the data is not where you are engaging with. It has got to be fetched, you know, seven seas away. Um, and that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >>I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this, because date data by its very nature is distributed. It's always been distributed, but w w but distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, uh, of, uh, of a super rock solid database that can handle, you know, distributed data? Yes. >>So there are two issues that you're a little too over there with Forrest is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is, uh, like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data in one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, uh, when you have the data, you can first look at it to perform. >>Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five minutes. Again, this is a, there's a class of problem that we solve that same data. Now, eventually, without you ever having to, uh, sort of do a casting it to a different database, you can now do a solid, uh, acquire. These are classic sequel queries, which is our next magic. We are a no SQL database, but we have a full functional sequel. The sequel has been the language that has talked to data for 40 odd years successfully. Every other database has come and try to implement their own QL query language, but they've all failed only sequel as which stood the test of time of 40 odd years. >>Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is, uh, basically, uh, look at the data and any common tutorial, uh, any, uh, any which way you look at the data. All it will come, uh, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries, select star from where Canada stuff, because it's at an English level, it becomes easy to, so the same data, you didn't have to go move it to another database, do your, uh, sort of transformation of the data and all this stuff. Same day that you do this. >>Now, that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, but Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the ability to query the operational data in a different way. I'll talk budding. What was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. >>So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and find different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, uh, the database management system. And that's where the distributed, uh, platform that we have built enables us to get it to where you need the data to be, you know, in a classic way, we call it CDN in the data as in like content delivery networks. So far do static, uh, uh, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >>The first part of the, the answer to my question, are you saying you could do this without skiing with a no schema on, right? And then you can apply those techniques. >>Uh, fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read things. So because there is no schema, it is just a on document that is sitting inside. And Jason is the lingua franca of the web, as you very well know by now. So it just Jason that we manage, you can do key lookups of the Jason. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and the other sophisticated pieces of technology behind it. >>You can do searching on it, using the, um, the full textual analysis pipeline. You can do ad hoc wedding on the analytic side, and you can write your own custom logic on it using our eventing capabilities. So that's, that's what it allows because we keep the data in the native form of Jason. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing, uh, in the last 40 years because we developed various, uh, database systems and data processing systems of various points. In time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. >>We had queuing systems, all the systems, if you want to use any one of them, our answer has always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this is not one to fly instead, bring the logic to the data. So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this, >>As you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >>Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data casting because it required you to have it in seven schema in one sense at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably flooded, but it not really, uh, how do you say, um, keep to the promise that it actually meant to be? So that's why it was a swamp I need, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it, and you create different types of indexes to manage it. You distribute the index, you distribute the data you have, um, like we were discussing, you have acid semantics on top of, and when you, when you put all these things together, uh, it's, it's, it's a tough proposition, but they have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >>So you predicted the trend around multimodal and converged, uh, databases. Um, you kind of led Couchbase through that. I want to, I always ask this question because it's clearly a trend in the industry and it, it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, we have a little teeny scissors and a knife. That's not that sharp. How do you respond to that? Uh, >>A great one. Um, my answer is always, I use another analogy to tackle that, but is that, have you ever accused a smartphone of being a Swiss army knife? No. No. Nobody does that because it's actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Like as in a moment, it could be a Tom, Tom telling you all the directions, the next one, it's your PDA. >>Third one, it's a fantastic phone. Uh, four, it's a beautiful camera, which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment is a video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just taught that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, they missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app is the economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get the alert saying that today you got to leave home at eight 15 for your nine o'clock meeting. >>And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's gone there's notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place without that, you couldn't even do this simple function, uh, in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build, because half the time you're running sideline to sideline, just, you know, um, integrating data from one system to the other. >>So I love the analogy with the smartphone. I w I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? And so, so, but, but is there, is that a fair, where, in other words, those specialized databases, they say there still is a place for them, but they're getting >>Absolutely, absolutely great analogy and a great extension to the question. That's, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of the music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they haven't, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. >>Yes, it's 90% there or 80% there. It depends on your audio file mess of your, uh, I mean, you don't experience the super specialized ones do not go away. You know, there are, there are places where, uh, the specialized use cases will demand a separate system to exist, but even there that has got to be very closed. Um, how do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that, oh, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car and walk into my living room, that's same songs should continue and play in my living room speakers. Then it's a world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >>I love, I love that example too. When I was a kid, we used to go to Twitter, et cetera. And we'd to play around with, we take off the big four foot speakers. Those stores are out of business too. Absolutely. Um, now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi. >>I believe so. Uh, because I think, uh, what had happened was the relational systems. Uh, I've been where the norm, they rule the roost, if you will, for the last 40 odd years, and then gain this no sequel movement, which was almost as though a rebellion from the relational world, we all inhibited, uh, uh, because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee, they required your DBA and your data architect. And you have to call them just to add one column and stuff like that. And the world had moved on. This was the world of blogs and tweets and, uh, you know, um, mashups and, um, uh, uh, a different generation of digital behavior, digital, native people now, um, who are operating in these and the, the applications, the, the consumer facing applications. >>We are living in this world. And yet the enterprise ones were still living in the, um, in the other, the other side of the divide. So all came this solution to say that we don't need SQL. Actually, the problem was never sequel. No sequel was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations, and the inability for these, the system to scale, the relational systems were built like, uh, airplanes, which is that if, uh, San Francisco Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set in from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the alarm to somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. >>These are called vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world, uh, is make the system how it is only scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guests. I'll add one more coach to it, one more car to it. And the better part of the way we have done this year is that, and we have super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have ID only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. >>You can attach the kind of coaches we call this multi-dimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it quite. So that's the beauty of this architecture. Now, why is that important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because you would say that I cannot run this analytical Barre because then my operational workload will suffer. Then my friend, then we'll slow down millions of customers that impacted that problem. We will solve the same data in which you can do analytical buddy, an operational query because they're separated by these cars, right? As in like we, we fence the, the, the resources, so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or equity. >>And then yet you can run this analytical body, which will take a couple of minutes to run one, not impeding the other. So that's in one sense, sort of the, part of the, um, uh, problems that we have solved here is that relational versus, uh, uh, the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same quality language on top. Y it's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, uh, the internal combustion engine, uh, I think gas, uh, you says, these are the issues we really wanted to solve. Um, so solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, or that are for your shifters. >>Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So, uh, even when you feed people the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blue harder to go fast and lean back for, for it to, you know, uh, to apply a break that's, that's how we seem to define, uh, design software. Instead, we should be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, uh, and the gas bottle and the, um, and the gear shifter is by putting cul back on underneath the surface, we have completely solved, uh, the relational, uh, uh, limitations of schema, as well as scalability. >>So in, in, in that way, and by bringing back the classic acid capabilities, which is what relational systems, uh, we accounted on and being able to do that with the sequel programming language, we call it like multi-state SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up the salt in the modern times, but rather than get, um, sort of pedantic about whether it's, we have no SQL or sequel or new sequel, or, uh, you know, any of that sort of, uh, jargon, oriented debate, uh, this, these are the debates of computer science that they are actually, uh, and they were the solve and they have solved them with, uh, the latest release of $7, which we released a few months ago. >>Right, right. Last July, Ravi, we got to leave it there. I, I love the examples and the analogies. I can't wait to be face to face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >>Fantastic. Thanks for the time. And the Aboriginal Dan was, I mean, very insightful questions really appreciate it. Thank you. >>Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.