>> Live from Barcelona, Spain. It's theCUBE! Covering KubeCon, CloudNativeCon, Europe 2019. Brought to you by Red Hat, the Cloud Native Computing Foundation, and Ecosystem Partners. >> Hi, and welcome back. This is KubeCon, CloudNativeCon 2019 here in Barcelona. 7700 in attendance according to the CNCF foundation. I'm Stu Miniman and my co-host for this week is Corey Quinn. And happy to welcome back to the program, a cube-i-lom Rob Szumski, who's the Product Manager for Red Hat OpenShift. Rob, thanks so much for joining us >> Happy to be here. >> All right, so a couple of weeks ago, we had theCUBE in Boston. You know, short drive for me, didn't have to take a flight as opposed to... I'm doing okay with the jet lag here, but Red Hat Summit was there. And it was a big crowd there, and the topic we're going to talk about with you is operators. And it was something we talked about a lot, something about the ecosystem. But let's start there. For our audience that doesn't know, What is an operator? How does it fit into this whole cloud-native space in this ecosystem? >> (Corey) And where can you hire one? >> (laughs) So there's software programs first of all. And the idea of an operator is everything it takes to orchestrate one of these complex distributor applications, databases, messaging queues, machine learning services. They all are distinct components that all need to be life-cycled. And so there's operational expertise around that, and this is something that might have been in a bash script before, you have a Wiki page. It's just in your head, and so it's putting that into software so that you can stamp out mini copies of that. So the operational expertise from the experts, so you want to go to the folks that make MongoDB for Mongo, for Reddits, for CouchBase, for TensorFlow, whatever it is. Those organizations can embed that expertise, and then take your user configuration and turn that into Kubernetes. >> Okay, and is there automation in that? When I hear the description, it reminds me a little bit of robotic process automation, or RPA, which you talk about, How can I harem them? RPA is, well there's certain jobs that are rather repetitive and we can allow software to do that, so maybe that's not where it is. But help me to put it into the >> No, I think it is. >> Okay, awesome. >> When you think about it, there's a certain amount of toil involved in operating anything and then there's just mistakes that are made by humans when you're doing this. And so you would rather just automate away that toil so you can spend you human capitol on higher level tasks. So that's what operator's all about. >> (Stu) All right. Great. >> Do you find that operator's are a decent approach to taking things that historically would not have been well-suited for autoscaling, for example, because there's manual work that has to happen whenever a no-joinser leaves a swarm. Is that something operators tend to address more effectively? Or am I thinking about this slightly in the wrong direction? >> Yeah, so you can do kind of any Kubernetes event you can hook into, so if your application cares about nodes coming and leaving, for example, this is helpful for operators that are operating the infrastructure itself, which OpenShift has under the hood. But you might care about when new name spaces are created or this pod goes away or whatever it is. You can kind of hook into everything there. >> So, effectively it becomes a story around running stateful things in what was originally designed for stateless containers. >> Yeah, that can help you because you care about nodes going away because your storage was on it, for example. Or, now I need to re-balance that. Whatever that type of thing is it's really critical for running stateful workloads. >> Okay, maybe give us a little bit of context as to the scope of operators and any customer examples you have that could help us add a little bit of concreteness to it. >> Yeah, they're designed to run almost anything. Every common workload that you can think about on an OpenShift cluster, you've got your messaging queues. We have a product that uses an operator, AMQ Streams. It's Kafka. And we've got folks that heavily use a Prometheus operator. I think there's a quote that's been shared around about one of our customer's Ticketmaster. Everybody needed some container native monitoring and everybody could figure out Prometheus on their own. Or they could use operator. So, they were running, I think 300-some instances of Prometheus and dev and staging and this team, that team, this person just screwing around with something over here. So, instead of being experts in Prometheus, they just use the operator then they can scale out very quickly. >> That's great because one of the challenges in this ecosystem, there's so many pieces of it. We always ask, how many companies need to be expert on not just Kubernetes, but any of these pieces. How does this tie into the CNCF, all the various projects that are available? >> I think you nailed it. You have to integrate all this stuff all together and that's where the value of something like OpenShift comes at the infrastructure layer. You got to pick all your networking and storage and your DNS that you're going to use and wire all that together and upgrade that. Lifecycle it. The same thing happens at a higher level, too. You've got all these components, getting your Fluentd pods down to operating things like Istio on Service Mesh's, serviceless workloads. All this stuff needs to be configured and it's all pretty complex. It's moving so fast, nobody can be an expert. The operator's actually the expert, embedded from those teams which is really awesome. >> You said something before we got started. A little bit about a certification program for operators. What is that about? >> We think of it as the super set of our community operators. We've got the TensorFlow community, for example, curates an operator. But, for companies that want to go to market jointly with Red Hat, we have a certification program that takes any of their community content, or some of their enterprise distributions and makes sure that it's well-tested on OpenShift and can be jointly supported by OpenShift in that partner. If you come to Red Hat with a problem with a MongoDB operator, for example, we can jointly solve that problem with MongoDB and ultimately keep your workload up and keep it running. We've got that times a bunch of databases and all kinds of servers like that. You can access those directly from OpenShift which is really exciting. One-click install of a production-ready Mongo cluster. You don't need to dig through a bunch of documentation for how that works. >> All right, so Rob, are all of these specific only to OpenShift, or will they work with flavors of Kubernetes? >> Most of the operators work just against the generic Kubernetes cluster. Some of them also do hook into OpenShift to use some of our specialized security primitives and things like that. That's where you get a little bit more value on OpenShift, but you're just targeting Kubernetes at the end of the day. >> What do you seeing customers doing with this specifically? I guess, what user stories are you seeing that is validating that this is the right direction to go in? >> It's a number of different buckets. The first one is seeing folks running services internally. You traditionally have a DBA team that maybe runs the shared database tier and folks are bringing that the container native world from their VM's that they're used to. Using operators to help with that and so now it's self-service. You have a dedicated cluster infrastructure team that runs clusters and gives out quota. Then, you're just eating into that quota to run whatever workloads that you want in an operator format. That's kind of one bucket of it. Then, you see folks that are building operators for internal operation. They've got deep expertise on one team, but if you're running any enterprise today especially like a large scale Ecommerce shop, there's a number of different services. You've got caching tier, and load balancing tiers. You've got front-ends, you've got back-ends, you've got queues. You can build operators around each one of those, so that those teams even when they're sharing internally, you know, hey where's the latest version of your stack? Here's the operator, go to town. Run it in staging QA, all that type of stuff. Then, lastly, you see these open source communities building operators which is really cool. Something like TensorFlow, that community curates an operator to get you one consistent install, so everyone's not innovating on 30 different ways to install it and you're actually using it. You're using high level stuff with TensorFlow. >> It's interesting to lay it out. Some of these okay, well, a company is doing that because it's behind something. Others you're saying it's a community. Remind me, just Red Hat's long history of helping to give if you will, adult supervision for all of these changes that are happening in the world out there. >> It's a fast moving landscape and some tools that we have are our operator SDK are helping to tame some of that. So, you can get quickly up and running, building an operator whether you are one of those communities, you are a commercial vendor, you're one of our partners, you're one of our customers. We've got tools for everybody. >> Anything specific in the database world that's something we're seeing, that Cambrian explosion in the database world? >> Yeah, I think that folks are finally wrapping their heads around that Kubernetes is for all workloads. And, to make people feel really good about that, you need something like an operator that's got this extremely well-tested code path for what happens when these databases do fail, how do I fail it over? It wasn't just some person that went in and made this. It's the expert, the folks that are committing to MongoDB, to CouchBase, to MySQL, to Postgres. That's the really exciting thing. You're getting that expertise kind of as extension of your operations team. >> For people here at the show, are there sessions about operators? What's the general discussion here at the show for your team? >> There's a ton. Even too many to mention. There's from a bunch of different partners and communities that are curating operators, talking about best practices for managing upgrades of them. Users, all that kind of stuff. I'm going to be giving a keynote, kind of an update about some of stuff we've been talking about here later on this evening. It's all over the place. >> What do you think right now in the ecosystem is being most misunderstood about operators, if anything? >> I think that nothing is quite misunderstood, it's just wrapping your head around what it means to operate applications in this manner. Just like Kubernetes components, there's this desired state loop that's in there and you need to wrap your head around exactly what needs to be in that. You're declarative state is just the Kubernetes API, so you can look at desired and actual and make that happen, just like all the Kub components. So, just looking at a different way of thinking. We had a panel yesterday at the OpenShift Commons about operators and one of the questions that had some really interesting answers was, What did you understand about your software by building an operator? Cause sometimes you need to tease apart some of these things. Oh, I had hard coded configuration here, one group shared that their leader election was not actually working correctly in every single incidences and their operator forced them to dig into that and figure out why. So, I think it's a give and take that's pretty interesting when you're building one of these things. >> Do you find that customers are starting to rely on operators to effectively run their own? For example, MongoDB inside of their Kubernetes clusters, rather than depending upon a managed service offering provided by their public cloud vendor, for example. Are you starting to see people effectively reducing public cloud to baseline primitives at a place to run containers, rather than the higher level services that are starting to move up the stack? >> A number of different reasons for that too. You see this for services if you find a bug in that service, for example, you're just out of luck. You can't go introspect the versions, you can't see how those components are interacting. With an operator you have an open source stack, it's running on your cluster in your infrastructure. You can go introspect exactly what's going on. The operator has that expertise built in, so it's not like you can screw around with everything. But, you have much more insight into what's going on. Another thing you can't get with a cloud service is you can't run it locally. So, if you've got developers that are doing development on an airplane, or just want to have something local so it's running fast, you can put your whole operator stack right on your laptop. Not something you can do with a hosted service which is really cool. Most of these are opens source too, so you can go see exactly how the operator's built. It's very transparent, especially if you're going to trust this for a core part of the infrastructure. You really want to know what's going on under the hood. >> Just to double check, all this can run on OpenShift? It is agnostic to where it lives, whether public cloud or data center? >> Exactly. These are truly hybrid services, so if you're migrating your database to here, for example, over now you have a truly hybrid just targeting Kubernetes environment. You can move that in any infrastructure that you like. This is one of the things that we see OpenShift customers do. Some of them want to be cloud-to-cloud, cloud-to-on-prem, different environments on prem only, because you've got database workloads that might not be leaving or a mainframe you need to tie into, a lot of our FSI customers. Operators can help you there where you can't move some of those workloads. >> Cloud-on-prem makes a fair bit of sense to me. One thing I'm not seeing as much of in the ecosystem is cloud-to-cloud. What are you seeing that's driving that? >> I think everybody has their own cloud that they prefer for whatever reasons. I think it's typically not even cost. It's tooling and cultural change. And, so you kind of invest in one of those. I think people are investing in technologies that might allow them to leave in the future, and operators and Kubernetes being one of those important things. But, that doesn't meant that they're not perfectly happy running on one cloud versus the other, running Kubernetes on top of that. >> Rob, really appreciate all the updates on operators. Thanks so much for joining us again. >> Absolutely. It's been fun. >> Good luck on the keynote. >> Thank you. >> For Corey Quinn, I'm Stu Miniman, back with more coverage two days live from wall to wall here at KubeCon CloudNativeCon 2019 in Barcelona, Spain. Thanks for watching.