 Hi, this is Sandeep Bhatian. We are here at DockerCon in San Francisco. Today we have with us Rajesh from SignalFX. Can you tell us a bit about SignalFX? What does the company do? Sure, so SignalFX is a metrics-based monitoring system. We think of ourselves as an operational intelligence platform for people to understand their cloud infrastructure and cloud deployment. So this can be, you know, whether it's the infrastructure metrics themselves, which might be coming from, you know, public cloud infrastructure like AWS or GCP or Azure. But it's also their applications, the services that are deployed on those cloud environments and also perhaps even lambdas and, you know, serverless infrastructure. So we have a standard way of getting all these metrics and helping people understand how their environments are working. You have touched upon a lot of things already, you know. So what is the need for monitoring in this kind of setup where you are doing everything by just clicking a button? So deployment is, of course, one aspect of it. So Docker is really good at, you know, providing you a standardized way to deploy your applications. But it's quite another thing to understand if your applications are working the way that you would expect them to. At the very least, you'd need to know whether your infrastructure is performing the way it is. That might be as simple as looking at CPU usage and memory usage. But if your application is more complicated, if it's performing some sort of workload or using third-party open source systems, you need to actually instrument what's interesting or important to you. You cannot really understand or you cannot really improve things that you don't measure. So the quantities that actually represent the performance or the working of your application are things that you want to kind of pay a close eye to. So you want to get those metrics, send it to a system like SignalFX and we'll help you visualize them, perform analytics on them, do anomaly detection, let you know if things are working good or not. So that's kind of the value that SignalFX brings. You focus only on Docker containers or you're talking about cloud native words in general? So we are basically focused on the entire spectrum of cloud native infrastructure. Docker is, of course, one of the platforms that we do support. But it can even be serverless like lambdas. There can even be applications that you're running. So we are basically happy to take metrics from anywhere. So when you do talk about serverless, that kind of changes the equation because you're talking about the function which are triggered by certain events. So you don't have the same level of either access or control that you have in other space. So how does the whole equation changes of monitoring in the serverless space versus? Sure, so there are like two kind of, you can decompose the problem into two parts. So one is like, how do you grab the metrics? How do you actually instrument what you need to do to get the information that you need? That is what primarily changes in the serverless world because you don't have a node that you can deploy a heavy weight agent to get these metrics from. So you need some APIs and you need some infrastructure to help you to get those metrics. So we of course provide some help for people to do that. But once the metrics actually get to us, from SignalFX's perspective, we are happy to get metrics and time series from anywhere. And we are actually agnostic about where these metrics come from, how they are measured and what they represent. So we provide a platform for you to then perform analytics on it as a general purpose thing. So we help on both sides of it, but to us, the main solution as a platform, we are agnostic about where those metrics come from and Lambda is just yet another form factor that we support. What are the concerns in the serverless space? So I think the concerns are, so one is that there are some infrastructure kind of concerns with Lambda's, which is you want to know things like cold starts, like how many cold starts are happening in Lambda's. And this is something that Amazon does not actually give you a lot of insight about. And then you want to know on each Lambda invocation, like what's the performance on, and in single call, like how long does that take? So that's something that we can give you almost like out of the bag without you having to do very much to instrument your own application. But in addition, you might go, want to like change the code in your application and instrument the things that are important to you. Like how's it taking, you know, what are you doing in this specific action? You might be doing two or three different things, maybe looking up a cache, putting something here or there. And we want to instrument those metrics as well, so that when you look at it in aggregate across all your Lambda invocations, not only do you want to know how many cold starts there are, what's the average time for each Lambda invocation. But on the operation of the Lambda invocation itself, like did you throw any errors? You know, what kind of work did you do? What kind of metrics did you gather? And so we kind of like gather both of that and let you visualize them. Again, you can do a nominal detection on this. And the other thing about Lambda's is that people are very sensitive about the latency of how quickly you can make these measurements and how quickly you can provide intelligence or monitoring on this. And that's something that SignalFX is very good at. Like we are known for real-time streaming analytics. So within a couple of seconds of something happening, you immediately see that in a dashboard or you can have anomalies detected. When Lambda's can last for only a few seconds sometimes. And so for something to be known like a few minutes after it happened, like does not provide enough value. So that's one of the key strengths of SignalFX is to provide real-time intelligence. And since you are already doing with customers who are, I mean, you are in the next phase, you know, you're talking or monitoring. So what kind of adoption is there for serverless already because very, very new buzzword. Yeah, yes, we are actually seeing pretty good adoption. I think it depends more on the company and where they are on their cloud native journey. So we think of people having, you know, starting from a somewhat an experimental phase where the company as a whole is using somewhat legacy IT but they might have a few labs or trying to experiment and see what should their next generation architecture look like. And then there's kind of like companies that are in the middle phase that we call like somewhat decentralized chaos where you have different parts of the company. They each want to find a tool chain that works for them. So each team might be doing something different. And then finally, you have the teams that are thinking very strategically about deploying what kinds of architectures, do they need, what kind of tooling, what kind of monitoring. And so those guys, we provide what we call organized enablement. And so we help companies in each of these three stages of their kind of growth and their life cycle. And different companies are thinking in slightly different ways about how they want to deploy these architectures. But we are definitely seeing the companies who have gone through this journey and are seeing the value of kind of like lambdas, lambdas of course not applicable to every single workload, but there are a broad kind of class of workloads where lambdas provide a lot of value. And we are seeing pretty strong adoption for those kinds of workloads. So we have like, you know, jump from one topic to the topic because so much is already happening. But one thing that you did mention was matrix. And I did feel that, you know, you do want to talk about, but can you explain, you know, elaborate, you know, what is the... Sure. So there are a few different pillars to the whole monitoring and observability space. You know, there's like kind of like logs. Now traces is becoming a little bit hard. Metrics, we believe plays an important role. And then there are some structured event type solutions. So metrics is something that is actually going to play an increasing role in this entire monitoring landscape because as we see, as these platforms kind of like change, your deployment models change, the one thing that's common across all of them is like the concept of having to make measurements and do kind of like real-time streaming analytics on it. And these measurements, like, there may be infrastructure type measurements. Like if you have a more traditional deployment, but as you move to serverless, these metrics are more like workload measurements of what your application is actually doing. And so there can be very high level things like how many transactions are you processing. Or, you know, if you're looking up a cache, how many cache hits are you seeing, how many cache misses are you seeing, how many exceptions are you seeing. So these are the things that for a developer coming from a DevOps mindset, really characterize the health of your application. It's not whether CPU hire is low because that may be normal that it's a little bit high. Maybe you're utilizing your resources very well. But for people to think strategically about what are the quantities that I should measure, that really characterize whether my application or my infrastructure is working well or not. And you get more and more into this, what we call custom metrics ecosystem where people are measuring things that kind of bridge between operational and business metrics that really tell you, okay, is your service doing what it's supposed to be doing. So we talked about technology. We talked about monitoring and all those things. Let's talk about Hugo a bit. So when you're not doing all these tech stuff, what do you do in your free time? So I, of course, have two kids. I have a dog that keeps me busy. My hobby is playing guitar, actually. Really? Yeah. So, okay, that's interesting. So when do you pick the hobby of playing guitar? Oh, I've played it for a really long time. I should be awesome, but I don't practice as much. So did you learn guitar to impress somebody or just for yourself? Because usually people learn guitar for a certain reason. Yeah, I have a theory that people continue to play guitar for their own reasons, but they all start playing guitar for the same reason. Uh-huh. So I started playing guitar in high school, you can imagine. Okay, and you still play, right? I still play, yes. So how much you have progressed? You know, how have you progressed? Where, you know, next year there's a keynote and they need a band. Can you be on the stage? Well, actually I recently switched to classical guitar, which is a little bit different, but I'm really enjoying it. It's very challenging. It's much more theoretical. It's much more, you know, you really have to pick up on your technique. So it's a very different ball game to me. I used to play a lot more rock before, and that's kind of like more fun, more gig kind of music, but it's different now and I don't have enough time to dedicate to playing in a band, but I do practice by myself. So if I'm not wrong, you know, as a technologist you also travel a lot, right? Sometimes, yeah. Okay, so how do you keep up with your guitar practices and the tech? To be honest, I don't keep up very well. With what tech or guitar? With guitar. Okay. So do you wish there was something portable which you can just carry with you all the time? Yeah, they do have some traveling guitar type things, but it's still, you know, some amount of infrastructure that you have to lug around. So, all right, all right. Anything else beyond guitar, kids and dogs? No, I think that's where most of my time goes. It's with spending time with the family, working, and yeah, just keeping afloat, I suppose. Awesome, awesome. In the next video, maybe we'll try to have, you know, a demo of your guitar practice. Sure thing. Thank you. Why not? And here, Rajesh, thanks for talking to me today. Thank you so much. And hopefully we'll catch up with you again in the next event. That'll be great. That'll be great.