 Live from San Francisco, it's theCUBE. Covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. Hello everyone, welcome back to theCUBE's live coverage here. We're in San Francisco for Google Cloud's major conference next 2018. I'm John Furrier, we're here for three days. Wall-to-wall coverage one again, day one. We've got two great guests from Signal FX, Kartik Rao, founder and CEO, and Rajesh Rahman, who's the chief architect, a hot startup in the area, way ahead of its time, but now as the world gets more advanced, their solution is front and center as the value proposition of cloud moves into the mainstream DevOps going to a world at large scale, not just networking monitoring applications, you got service meshes, booming, great topic. Kartik, great to see you, Rajesh, thanks for joining us. Thank you. John, great to be on. So first of all, let's just get out of the way. You guys have some fresh funding in May. Just quickly give an update on the company. You guys raised a series? A series D. A series D, give us how much? Yeah, so we raised 45 million from General Catalyst, leading the round back in May. Been building a ton of momentum as a company, close to a couple hundred people today. We're using a lot of that to expand internationally. We've got a team in Europe now, just opened up a team in Australia, so things have been going great. Congratulations, we've had a chat before, always been impressed with you guys have a great stable of awesome engineers and talent in the company, doing some great work. But it begs the question, I always like to get into the what-if. What if I could have large scale application development environments with programmable infrastructure? How does that change things? So Kartik, how does that what-if change? Because now that is what's happening. You're starting to see the cloud at scale for the common masses of enterprises where old ways of doing things are kind of moving away. Like horse and buggy versus having a car for the first time, jobs are changing, but the value doesn't necessarily change. You still go from point A to point B. You still got an engine, people who care about fixing cars, so you just want to drive the clouds, people want to get under the hood. Whole new architecture. What's the what-if if I could have all these resources? What's the challenges and what do you guys solve? Well, I think there are a couple of challenges in this new environment. One is the number of components are just orders of magnitude more than they used to be in a cloud environment, right? We went from having physical machines that lived for three years in a data center, divided up into VMs 10 years ago, now divided up into containers for every process. And not only that, but these containers get spun up and spun down every few minutes or every few hours. And so it's just the number of components in the churn is just massive. So that in and of itself requires a far more analytics-based approach to understand patterns rather than what's happening on an individual component. The second thing that's changed is the operating model is fundamentally different because now you're building and running web services. And when you're running web services, the people who build the software are the ones who technically are responsible for operating it. And so you have more updates, you've got more people involved, you've got lots of different components that all need to interact with one another. And so having a communication framework across all of these disparate teams becomes really, really, really critical. So those are the two fundamental changes as you move from for operating these modern, massively distributed applications. And I would just add just some observation data that we've been seeing in theCUBE is those same folks building aren't necessarily operating. So they don't want to be in and out fast. They don't want to be running and operating all the time. They want to push some code. Melody McFessel here at Google ran a survey with developers and said, you know, what makes you happy? And it was two things that bothered developers. Technical debt and speed for deployments, commits. And the commit number was around minutes. If you can't get something done in minutes, then they're onto something else of the mind-share attention of developers and technical. So this is a challenge at scale when you have technical debt, which we've seen companies start to come out of the river. Oh yeah, I'm going to automate something. I'm going to throw some compute at it with the cloud. We have the best monitoring package on the planet and look how great it is. But all they did was just code some instrumentation and that's it. They weren't dealing with a lot of moving parts. Now as more things come in, this is a challenge that a lot of companies face. You guys kind of solved this problem. Yeah, absolutely. So maybe Rajesh was a part of the team at Facebook that built the Facebook monitoring system and that's actually what gave us a lot of the vision to start SignalFX five and a half years ago. So maybe- Tell about the architecture and the vision and what you guys are doing. Yeah, so CI CD, you know, it's kind of like underlies a lot of this vision of like moving fast. You mentioned that there were people who want to like, you know, push out their code in a few minutes. The thing that makes that possible is for you to have observability into what's happening while that push happens. Because it's one thing to push very fast. It's another thing to recognize that you might have pushed something bad to be able to revert it very quickly too. And so you really need like, you know, good observability into all the things that matter that characterize the health of your system, to be able to quickly recognize patterns, to be able to quickly recognize anomalies and to be able to maybe push forward or even roll back very quickly. So I think like observability is like a very key aspect of this entire CI CD story. That's great. And then it's great to know that you were at Facebook because obviously Facebook built at scale from the ground up. A lot of open source, obviously they contribute a lot to open source, but it's interesting as they matured and you start to see their philosophy change. It used to be move fast, break stuff. To move fast, be reliable. This is now the norm that's the table stakes in cloud. You have to move fast. You've got to push code, but you've got to maintain an operational integrity. This is like not like an option. This is like standard. How do you guys help solve that problem? So I think there are a few different aspects to it. So the first is to, you know, people need to ensure that they have observability into their application. So this is ensuring that you have the right kinds of instrumentation in place. Thankfully, this is kind of becoming commoditized right now in getting metrics from your system. The second part and the more key part is then being able to process this data in a real time way. You know, have high resolution, very low latency, and then to be able to do real time streaming analytics on this data. In highly elastic environments when things come and go very quickly, the identity of any individual component is less important than the aggregate system behavior. And so you really need the analytics capability to kind of like go across this data, do various kinds of aggregations, compare it against past data, do predictive analytics, that sort of thing. So analytics becomes the very key concept of how you operate these environments. It sounds so easy. Yeah, well, one thing I'll add to that. So, you know, to your point, a lot of big companies sometimes are scared by this. You know, how do we, you know, we can't move quickly and break things. And everything that they've designed is around having process and structure to check and make sure everything is clean before they push changes out. And now you're in this world where, you know, an intern or a developer can push a change directly out on production. How do you manage that? The key thing in this modern world, when you're trying to release software quickly, Rajesh, hit on this earlier. You need the magic undo button. That is the key to this entire process. You need to design your software. You need to design your process. And you need to design your tools so that if you introduce something bad, you catch it immediately and you can roll it back. So, lots of DevOps practices are oriented around this, right? The idea of a Canary release. I'm going to roll out an update to 1% of my systems and users, test it out, observe all the metrics, make sure everything is clean before I roll out to everyone else. And the ability to roll back quickly is also important. But in order to do all of this, you need the visibility, you need the metrics, and you need to be able to do analytics on it quickly to identify the patterns as they emerge. That's a great point. I'd love to just double down on that and get your thoughts, because I'm talking to the Google Cloud people who are operating at the scale. I've put some on this whole service-centric architecture because there's services, we don't want services. Managing sets of services, having analytics, observation space, the reverting back, and the undo button, the magic button do over, whatever you want to call it. But the interesting thing is clean, having a clean service, whether it's an API, message queue, or an event, this stuff's happening all over the place in the new services world. How do you guys help there? Is that where you guys get involved? Do you see up in that layer? How far up are you guys looking at some of the instrumentation and the insights? You want to take that? Yeah, sure. So the one thing that we really like about SignalFX and we were very keen on when we built the platform is that we're very agnostic about metrics. We're happy to accept metrics from anywhere. So we'll take instrumentation from cloud environments, we'll take instrumentation metrics from open source systems and from your applications. So some of these systems are already kind of built in to get metrics from. We can talk to the Kafka's and Cassandra's of the world, for example. We can also talk to GCP and AWS and grab metrics from their system. I think the interesting question is like when people really take in the DevOps philosophy of like, so how do you instrument your own application? What questions do you want to ask from your environment that answer the critical questions that you kind of have? And so that's the one, that's the next step in the hierarchy of needs is for people to ask the right kinds of questions and instrument their applications properly. But like having done that, we can go up and down the stack in terms of like insight into whether all the way from your cloud environments through open sources. You guys will take data from anyone, just stream it in, normal mechanisms there. What's the value added? Where's the secret sauce on signal effects? So I think it's all about analytics. We are all about analytics. So we are able to look at patterns of the data. We can go up and down the stack and correlate across different layers of software, look at interactions across components in your microservice, for example. One very interesting thing that's happening is you might be aware of the whole service mesh aspect of it which gives us insight into interactions between components in the microservices architecture. So we are able to get all that data and give you insight into how your whole system is working. So you guys, you can see in the microservices layer. Absolutely. Yeah. And the key point is monitoring really has become an analytics problem. That's what we keep saying, right? Because what's happening on an individual component is no longer as interesting as what's happening across the entire service. So you have to aggregate the information and look at the trend across the entire service. But the second thing that's really important is you need to be able to do it quickly. And this is where our streaming real-time system really matters. And people might ask, why does it matter to do something real-time, like seconds versus minutes? Can a human actually process something in seconds versus minutes? Perhaps not. But everyone's moving towards automation, right? So if you want to move to a system where you have closed loop, you have automation, and guess what? All of these modern systems, all the stuff that Google's talking about here is all about automation. And in that world, seconds versus minutes means a tremendous amount of difference, right? Where if you can find signals that will tell you there's an emerging problem within seconds, and then you can revert a bad code push, or you can autoscale a cluster, or you can change your load balancing algorithms all within seconds, that is what enables you to deliver four nines, five nines type of availability. And the consequences of not having that is outages, performance, performance degradations, unhappy customers. I mean, the cost to a brand now of having any kind of a performance issue is enormous, right? It's your people are on Twitter before your team knows about it. So you guys have a lot of things you're solving. What's the core problem that you solve? What's the value purpose? If you narrow it down, the high order bit for signal effects, what's the core problem you solve? Well, we are solving the monitoring and observation problem for people operating cloud applications. So what happens is when you use signal effects, you have the confidence to move quickly, right? It gives you the safety net to be able to deploy changes on a daily basis, to have the shared context across a distributed team. So if you've got hundreds of two pizza box teams working together, we give you that framework, the communication framework and the proactive intelligence to find issues as they emerge and proactively address them. And bottom line, what that means is you can move as quickly as a Google or a Facebook or a Netflix. Even if you're a traditional Fortune 500 company that's regulated and you think you may not be able to do it, but you really can. You give them the turbo charge, basically for the analytics. All right, here's a question for you. What are the core guiding principles for the company? And you guys also have a lot going on, so you got a core tech team, I mentioned it earlier. What are some of the guiding principles as you guys hire, build product, talk to customers? What's the key DNA of signal effects? Yeah, I would say we are a very impact-driven company. So I'm very, very proud of all the people that we have on the team. We've got a lot of entrepreneurs who are focused on solving big problems, solving problems that customers may not necessarily know they need at the time, but as the market evolves, we're there to solve it for them. So we're a very customer-centric company. We have a fantastic, we invest aggressively in technology. So it's not just about wrapping a pretty UI around bolt-on tech. We have real differentiated technology that solves real problems for people. And I think we've, in general, just tried to skate to where the puck is and understand where the market's headed. And as a customer... What are some of the customer feedback that you're getting? What folks that don't know signal effects? What are some of the things that you're hearing from customers? Why are you winning? What are some of the examples? Can you share some color commentary? I'll give one example. It's Fortune 500 company that has been very aggressively investing in cloud the past four or five years, built an entire digital team. And their entire initiative is, like a lot of people in the Fortune 500 now, is to have a direct-to-consumer type of relationship. And one of the things that they struggled with early was how do they move quickly, support product launches that might have massive load, and have the visibility to know that they can do that and catch issues as they emerge. And they didn't have a solution that could give that visibility to them until they leverage signal effects. And so now, if you talk to people there, they'll say that they've essentially gone from defense every time they did one of these product launches to being on offense and really understanding what it takes to successfully launch a product. And they're doing way more of these. So... Moving the needle on time to market. Moving their business forward, and digital transformation just by having signal effects that say core enabler. It's the cloud version of putting out fires, so to speak, when you do product launches, right? I got to ask you guys a question. You guys are both industry veterans. Obviously Facebook has a storied history. We know all the gradients that happened on the infrastructure side. Karthik, you've been at VMware. You've seen the movie before where VMware made the market, changed IT for the better. Still talking about VMware's now. Now, as we see cloud taking that next transformational push, describe the waiver on right now because it's kind of an interesting time in tech history where the talent that's coming in is pretty amazing. The young gun's coming in with open source, the way it's flourishing is pretty phenomenal. Some of the smartest computer science and or engineering talent is really solving what was old school B2B problems that really no one really wanted to solve. I mean, people were buying IT. Now you're talking about building operating systems. So the computer science kind of mojo in the enterprise is up to bit. What's this wave about? How would you describe the wave of this time in history of the tech industry? Do you want to, I'll add my take on it and you go first. I think the thing that I find striking is just like, you know, when people used to talk about big data, you know, a few years ago and now that is like, that's just normal. Yeah. And like the amount of compute and the amount of storage that people are able to, you know, bring to command at on any problem is just incredible. And that's just going to, I think like continue to grow, right? That's going to be an amazing thing to watch. I think, you know, what this means, it also has interesting implications for, you know, companies like SignalFX and who are trying to be in the monitoring space because the mojo used to be like, oh, you had to have all this complicated software to do the instrumentation. Well, the instrumentation spot is easy, but now all the value that's going to come about monitoring is in what you do with all that data, how you analyze it and look for like, you know, so the whole AI ops and all that is going to be the key of the whole monitoring problem going forward, you know, five, 10 years from now. But we already see that analytics is such a key aspect of the whole thing, so. Yeah, I'm very, I think we're at the beginning, still at the beginning of a massive 30 to 40 year cycle. And this hasn't happened since the PC revolution in the 1970s, right? So the smartphone comes out 2007, opens, massively opens up the market for software-based services to several billion people who are connected all the time now, drives a massive refresh of the back-end infrastructure, drives the adoption of open source. And so we're at this magical point now where the market for software-based services is just exploding and every enterprise, you know, is becoming a software company. And, you know, I think the volume of data that we're accumulating is just growing exponentially and what you can do with AI at this point is just we're just beginning to see the benefit of it. So I think this is a really, really exciting time and I think we're just at the beginning. Most of the enterprises and even the tech companies are just beginning to capitalize on what is in store for us. I find it to be intoxicating, fun, and just great people coming in to your point about the beginning of a 40 year run. Also, the nature of software development is being modernized at an extremely accelerated pace. So as people in the enterprise start reimagining how they do software, because if they're a software company, they've never had product managers. I mean, so the notion of what is a product? How do you launch a product? Is all kind of first generation problems an opportunity? So I think to me it's really the enablement. And this is really what I think people are looking for. Who can take the burden off my shoulders, help me move faster, more gas, less break, go faster, drive value, and then ultimately compete because competitive advantage with technology. What does that mean to you guys? Because how do you react to that? Because what you essentially are doing is creating instrumentation for enabling companies to create new value faster with technology and software. In some cases at a level that they've never seen before. What do you, how do you react to that? Well, I think that that's exactly what we do, right? I mean, every company, I think most companies realize that they had to invest in software and focus on all of these new opportunities at the early part of this decade. First thing they had to do is figure out who's going to build all the software. So most of them had to go hire engineers or build digital teams. They had to decide where they're going to run. Cloud wars of the early part of this decade. Do we build a private cloud? Do we use public cloud? I think both of those things have happened and people are now comfortable with those decisions. The third leg, which is what we're the squarely in the space that we're in, which is how do you operationalize this new model? And I think people are working through that now. As they get through that in the next few years, the company's like signal effects helping every company operationalize it very quickly. I think that's when the true promise of this new digital era will be realized, where you'll start to see all of these fantastic applications, mobile apps, web service apps, direct to consumer, streamlined supply chains. We're just beginning to see the benefit of that. And we'll see when that happens, then the volume of data that they're collecting will increase exponentially. And then the promise of machine learning and AI will take an altogether another step. You got to know what to automate before you can automate it, basically. What's next? Final question for you guys. What's going on with signal effects? What are you guys going to conquer? What's the next major milestones for you guys? What are you looking to do? Yeah, well, we're continuing to focus on driving value for our customers. So we're expanding our geographic presence. So we're doing a lot of international expansion at this point. We're hiring a lot of engineers. If anyone is interested in a development job, reach out to us. What kind of engineers are you looking to hire? Rajesh, do you want to take that? Sorry? What kind of engineers? What kind of engineers are you looking to hire? I mean, all kinds of engineers. We are, especially in our distributed systems engineers, front end engineers, full stack engineers. Like, we'll take all the good engineers we can get. Awesome. A lot of product development. There's a lot of interesting things happening in this space. And so we're continuing to invest very closely. Large scale, distributed systems. You got decentralized right around the corner. So you got a lot of stuff happening. Great job to have you come on. Thanks for coming on. Great. Great to be here. Thank you so much. SignalFX here in the cloud of Google, here at Next is theCUBE. theCUBE Cloud, CUBE Data. We're bringing it all to you. I'm John Furrier. Thanks for watching. More coverage. Stay with us. We'll be back after this short break.