>> Narrator: Live from San Diego, California. It's theCube! Covering KubeCon and CloudNativeCon, brought to you by Red Hat. A cloud native computing foundation. >> Welcome back. 12 thousand here in attendance for KubeCon CloudNativeCon 2019 in San Diego. I am Stu Miniman, my cohost for this afternoon is John troyer. And happy to welcome to the program, recently out of Stealth, two gentlemen from Chronosphere, Austin. To my right is Martin Mao who is the co-founder and CEO and his co-founder Rob Skillington, who's also the CTO, we've stated on theCUBE actually, you understand where this conference is, where co-founder and CTO is like you know, the most prominent title that we've seen to get on here, because that's the type of geeks we love on the program and in this community. So first of all, congratulations on the launch >> Thank you so much >> And thank you so much for joining us. >> No worries. >> All right, when I've got the founders on, I'm going to start with the whys. How was kind of the problem statement, where you were coming from, and what led to the creation of Chronosphere. >> For sure for sure. So with Chronosphere we found a actual gap in the monitoring market, and a very crowded monitoring market, we found a gap, and the gap exists when companies with very large complex technology stacks, or large enterprises, move on to Cloud Native Technology and Kubernetes. So with this migration, what we've found was there's actually a lot more monitoring data being produced, because there's a lot more pieces now, we're moving from monoliths microservices, we're moving from like physical machines to VMs, to containers and pods. And that generates a lot more things that you need to monitor and track. And not only a lot more things, but you generally monitoring the relationship between these things. So as the number of things increases, the number of relationships exponentially increases. So yeah, that's the sort of problem we're solving, it's like monitoring all of these things at large scale, and when we couldn't find anything, and I could even store all of theses things, so that's it sort of. >> All right, so what is the background of the team that made you into position to work on this problem? >> Yeah great question. I mean me and Martin go back quite a few years. I officiated his wedding, only very very recently actually. And I, yeah we basically work together at several different companies. You know, I think both of us are entrepreneurial at heart. I'll let Martin talk a little bit more about the last few years. >> Yeah, so like you know, a few years ago we started working at Uber. And at Uber, we went through this migrations to our native communities and through that migration that's when we sort of had to solve the problem ourselves. And we solved the problem at Uber, with an open-source project called M3. That's really where this whole thing started. And Chronosphere sort of you know, building on top of M3, and now providing a product on top of the open-source platform that we created. >> Can we talk a little bit about the business? I noticed that you know, there are many ways of approaching open-source, in 2019, you know open core and but also as a service. So can you talk a little bit about how you've approached your business model. >> Yeah for sure. So we're very much in the position or in the camp of as a service, right, because you know a lot of companies do do open core, and they're sort of going into the enterprise support model, we sort of didn't want to go down that route. And also with our open-source product, it's not really an end to end solution in itself, like you use an open-source M3, but you still need to plug it together with other things yourself. So what we really wanted to do was to give customers, and end to end solution, and that was built on top of the great technology, we built with M3, but really it solves the problem sort of end to end, and we do that best as a service. >> Rob maybe you can help explain M3 a little bit for us as to how that fits in the landscape, but what it works with and the like. >> Yeah of course. Yeah it's basically at it's heart a metrics platform, that is built on, at first the lower layer in 3DB, which is a distributive time series database. And then on top of that, we have basically an aggregation platform, that is actually aggregating a lot of the samples, and metrics that we're, collecting. So we can really do some transformations on the data, as it comes in, before it's stored in the database itself. And this let's us do a lot of like smart processing, of what signals actually matter, what signals don't matter, kind of like storing them in a way that can be accessed, much faster than like, other typical systems that don't really do any aggregation before it gets stored. And then, you know we have of course like a query engine that works with this distributed set of data, and so, you know, it's really a database that was designed from day one, to be a metric store. You know, it's not built on Cassandra, it doesn't use Rocks DB, at the lower layers, it literarily every part of it, was built for this purpose. >> Can you talk a little bit about dimensionality and cardinality? Because as I look at this observability monitoring space, I see a lot of current discussion about that and frankly a little bit of fighting, and I'm not always, I can kind of see it, why it's important, but what are some of the reasons and what do people do where you know by having it, and what is it actually, let's start with that. >> Yeah for sure. So you know, with this hot topic of like high cardinality and high dimensionality is, what I was talking about earlier, where as you move into cloud native world, you're now monitoring things at like a pod level. So it's like instead of tracking things on like a per host level, you're now tracking things on like a per pod level now, and that is at >> (interjects) You're tracking more things per pod. >> More things per pod and like every pod unit, these are ephemeral pods now, so they don't live for very long. So you end up having more pieces of data and they're kept around for shorter period of time. And now you need a system that can store all of these pieces of data, because you want to see them uniquely. So you want to monitor each individual pod to see exactly what is running at the finest levels. Right, so you actually need technology that can store a lot more data than you could before. >> And I you know, adding to that, there's a lot more people running with like mobile applications, they use you know that are running in markets all round the world, using different cell providers, and different backend services. You may deploy your backend services multiple times, a week or even a day, and if you want to tag you know, the meta data on and slice and dice by that metadata, with your business and with your applications and your system, that requires you know, adding yet another dimension on your data, which adds to that cardinality. Every time you add a dimension, you know that just multiplies the cardinality of your existing data set of monitoring data. >> And it quickly adds up a lot right, so. >> All right Martin, maybe, since you're just out of Stealth, give us some of the speeds and feeds you know, the product GA, is it globally available? Series A funding, who's behind that? >> Yeah so we just kind of still two weeks ago, we closed up Series A a few months ago actually. It was led by Great Luck, we raised 11 million dollars, and our partner at Great Luck is Gary, and we like him very much. And you know the state of the companies that we are currently in private beta right now. So with our hosted platform, we are onboarding to customers into a private offering right now. And early next year, we'll sort of open that up for more public beta. Yeah. >> And the way folks would use this. You'll be using Prometheus or Graphite or something, and you'd be, so you'd have tracing, you'd have logs, you'd have other things and you would be plugging all of them into, into your services. >> Yeah it's a great question. So you mentioned two of the technologies. So if you're Prometheus or Graphite like to try find metrics, both of those can be pushed into the M3 system for sure. We actually just announced a trace integration, this week a KubeCon actually, Rob David spoke about that integration earlier this week at KubeCon. We haven't moved into the logs yet because the way we look at the problem is not from like a sort of like providing a one-stop shop for all observability solutions, we actually look at it from a use case perspective. So the use case we're looking at is like, realtime monitoring and remediation. So tracing is a part of that stroy, it's a critical part of that story, and now to add additional context, when you get to load it based on your metrics, but, we haven't quite moved into logging yet. >> Yeah, and we don't really want to solve any of these problems without knowing it'll work at scale, you know like a fundamental reason we even built the open-source project in the first place, was we were dealing with cardinality in the tens of billions of unique time series, and so, we don't want to just kind of like roll into any, every single feature under the sun, we really want to solve it once correctly and be able to systematically roll that out to enterprises at scale. >> Without, I mean without talking too much about Uber and any Uber secrets, I mean it seems like the game has changed with that kind of a scale of, you could not have done, you can't run Uber if you're tracking all those cars like literarily without some sort of a tracing like high cardinality sort of a system right? Because you're literarily tracking cars all over the world people all over the world, routes all over the world. >> Exactly, well uniquely positioned, we had the requirements to solve it at such a scale, and that's why we had to build this technology to solve it for that unique situation, because you know technologies ahead of time, did not really have this use case to solve. So that's why we had to sort of, we couldn't find anything out in the market because to solve it at that scale, that's why we sort of had to build our own, to uniquely solve it for this use case. >> And yeah, I would add to that, that typically engineers you know, at larger organizations, tend to want to organize everything very nicely, and split it up, and really control how they're monitoring that data, but we've noticed actually, definitely over the last few years, more and more people are open to letting people just start collecting you know, random data, that is relevant to the systems that they're building as they're rolling it out, even as they're experimenting with it, and you know systems today that are built from scratch, to deal with, to be as efficient as possible, with very unstructured data is becoming wildly popular because that's how developers want to develop software. You know, they don't want to have to have to like slice and dice it neatly and package it up and pass it on to others to run. They want to basically slice and dice however they want to, and dynamically , and as they scale up. >> I've always enjoyed every sequel skimmer I've had two, or change oh, yeah. (laughter) >> All right, how have you found the show? How's the reception been? Give us a little bit of the vibe of the show and how it's been going for you. >> Yeah it's been fantastic for us actually. So we just came in at silk so like the name is still quite new, but yeah, we've had a bunch of folks set up with the whole day, we've been giving a demo on the product, so a lot of companies are getting excited about it. I think a we're solving at a scale and that really resonates with, you know, a lot of the people here at the show, we're still solving at a scope, we're solving at a scale that's also in a cost efficient way as well. So that's really been our, we sleep quite well so far. >> Yeah Rob, you gave some sessions. What kind of feedback are you getting from people? Is the problem statement that we talked about at the beginning you know resonating with people that you talk to. >> I mean, I was really, yeah pleased to hear that after my session today, that a lot of people came up to me and said you know, I've never really seen metrics been linked to tracers, the way that we're doing it, in fact that's the first time they'd ever seen a demo, that can do, what we're kind of trying to upstream, we're actually you know, up-streaming a lot of those changes in the open-source well, as well at the same time. And so, you know we've found especially in a lot of the companies today that are pushing everything forward with development wise and how they are running operations is that they using a lot of pages in open source, and then those pages are battle tested in open-source, generally it becomes abstracted, to the point where we're actually a very large amount of people, but then when they need to scale it up, that's when it becomes difficult. So, no I think that you know, a lot of people have been very positive with basically us being able to also push forward the feature on >> Back upstream into the M3 project. >> And also into Prometheus. So I, you know I'm an open metrics, contributor and that's essentially, an exposition format that's built on the Prometheus, exposition format. So it's kind of become a standard way of exchanging metrics, from one system to another. And that's kind of like, basically commoditized and democratize the exchange of metrics to make a lot more systems, interoperable with each another. Which we fundamentally believe in as well, of course we're developing in open-source, and we believe that this systems need to play nicely together. So we can build you know, have building blocks that large companies and organizations can all share and build better things on top of. >> All right, so looking to go to public beta early 2020s, what we said, when we come back in 2020, what kind of the, some of the key KPIs and metrics that you'll be looking at to be successfull in your first year out of Stealth. >> Yeah it's a great question. So you know, since some of the KPIs you guys were looking at doing is coming at the public beta, making it available to a large range of companies, because right now we're sort of onboarding companies sort of one or two at a time, so yeah it's seeing how many companies adopt the product and also, we're again adding more features over time, for that particular use case of like you know, monitoring your technology just like in your business in real time. So it'll be a lot more features coming down the pipeline, and a lot more customer adoption along with that. >> And I would also say you know, our hosted platform is really about offering like deep isolation, between our tenants as well, so basically when we you know, in the next few months to come, we want to make sure that it works basically like clockwork, and everyone can, we can roll out and scale that highly isolated platform for you know tens and hundreds of organizations, and thousands eventually. And so, and doing that at scale is hard. So I think yeah, we'll see how we're doing with that. >> Yeah for sure. >> All right. Rob, Martin congratulations on coming out of Stealth, look forward to hearing more and thank you so much for joining us. >> Glad, thank you so much. >> All right, for John Troyer I'm Stu Miniman, we'll be back getting towards the end of three days, want to walk over here KubeCon, CloudNativeCon thanks for watching. (upbeat music)