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Barton: We’re going to talk about sidecars, eBPF, and the future of service mesh technology. My name is Jim Barton. I am a field engineer with solo.io here in North America. Solo is a company that’s very passionate about application networking, especially in a cloud native context. We supported a number of the largest and most influential open source projects in that space, things like Istio and Envoy proxy, as well as other projects like GraphQL and Cilium.
Parsimony (The Law of Simplicity)
I wanted to set the stage for this presentation, I want to take you back to something that happened just a few months before my graduation from engineering school a long time ago. This story goes back to a competition that was held with my IEEE student chapter, actually multiple IEEE student chapters from around our region that got together and had a contest. It was a robot car racing contest. You had a car that was to trace a line very much like what you see here in the picture. The idea was to go as fast as possible, and to beat out all the other schools. This was a high energy competition that had a lot of entrants from a lot of different engineering schools, a lot of really interesting designs. Most of them were fairly complex, some of them were a little funny. At any rate, to boil it all down, there were two designs that worked their way through the single elimination competition. One of them was this huge behemoth of a design. It had this giant central core, and these rotating legs that came off that central core, each with their own wheels. The superstructure would rotate, and would propel this vehicle down the black path there. It came from a very large, very well-respected engineering school, had a big following with a big operations team. It was a pretty impressive thing to watch.
As the competition went on, this entrant went all the way to the finals. Then there was an underdog that went through to the finals as well. It was pretty much the polar opposite of our giant behemoth. It was a little tiny entrant from a small engineering school, they sent a single delegate to this convention. He built this thing, it looked like it used the chassis of a child slot car, and had a little tiny, sweeping photodetector at the head of the car with a little battery and a little motor to drive things. It just swished its way down the track and was beating all of these much bigger, much better funded competitors. We got to the end of the competition, and there were two finalists left standing, the big behemoth with the giant rotating legs and the D cell batteries hooked into the superstructure against the little tiny one designer slot car.
I learned a lot of things my senior year of college, but that particular day taught me a lesson that I have never forgotten. That is the lesson of parsimony. Parsimony, simply being the law of simplicity. When you have a design goal in engineering, and there are multiple paths to get there, all other things being equal, the simplest path, the fewest moving parts are generally the best. As we transition into discussing the future of service mesh, the way that I see the trends in the marketplace right now, it’s all about simplicity, recognizing that we need to move toward more simple designs of our service mesh technology. There have been some really exciting developments in that space, just within the past month or two. We’re going to talk to you about that as we go forward.
Before Service Mesh
To set up that discussion, let’s go back to the “beginning.” Let’s assume you’re living in prehistoric times, let’s say 2017, and you have services that need to communicate with each other. Before there was service mesh technology, you might have two services, service A and service B. Let’s say you are a financial institution or a government entity, or in some other industry like telco that’s heavily regulated. Maybe you have a requirement like, all communication between all services need to be encrypted and use mTLS technology. Let’s say that requirement was presented to you in these prehistoric times, how might you fulfill that? You’d go out, you’d Google TLS libraries for whatever language service I’m using. Maybe you’d integrate that, and you’d test it out with the service or services it needed to communicate with. You’d deploy it and everything would be fine.
That approach is fine as far as it goes. The problem is, there aren’t many of us sitting in on this session, who live in such a small-scale environment. We often live in environments that look something more like this. I spent a number of years at Amazon, and we actually had a chart that was on a lot of walls in a lot of places that we call the Death Star. The Death Star showed, at some snapshot in time, all of the various microservice interactions that were happening within the Amazon application network. The simple approach where each application dealt with its own security infrastructure, metrics production, advanced routing capabilities, that sort of thing. It really didn’t scale very well into an environment like this. It caused us to ask a number of questions about if we want to build microservice networks at scale, how can we best answer questions like this? How do we secure communication between services in a repeatable, reliable fashion? How do we manage things like timeouts and communication failures? How do we control and route our traffic? How can we publish consistent metrics? How can we observe interactions among all of these various services?
Service Mesh Separates Concerns
We began to realize collectively as a community that a lot of these concerns that we talk about here are actually cross-cutting. They apply to all of these services. Wouldn’t it be good rather than building this undifferentiated heavy lifting logic into each and every application, what if we could externalize that in some way and handle these cross-cutting concerns in some piece of infrastructure? The first architecture that came out of just about every service mesh platform in that era was through something called sidecar injection. You see that depicted here where there would be a sidecar injected that lived next to the application code in question, and would actually manage these kinds of connect, secure, observe concerns on behalf of the application. There you can see some of the very common capabilities that evolved or that were design goals for this first round of service mesh technology. The key pillars here being connect, secure, observe what’s going on inside your complex application network.
How Istio Works Today – Data Plane, Sidecar Proxies
Whether we’re talking about the Istio service mesh, or a variety of other service meshes that are out there, a lot of them operated along this same basic architectural line. That is, there are two components, there’s a data plane component and a control plane component. The data plane component is actually responsible for the processing of the request. What would happen is, at some point, an Envoy proxy would be injected into each one of those services as a sidecar, and the sidecar handling all of these cross-cutting, connect, secure, observe concerns. When an application needed to talk to another application, say service A needs to talk to service B, then those kinds of issues, metrics, publication, and failover, and timeout, could all be handled by these Envoy proxies that would intercept all of the requests going in and out of various service components. Let’s say you have a security mandate. You have a security mandate, and by simply injecting these sidecars into your applications and applying some very simple policies, then you could guarantee that your network of microservices was using proper security practices to manage traffic inside the mesh. That’s the data plane.
How Istio Works Today – Control Plane
The data plane also depends on a control plane. The control plane is the brains of the operation. There is some outside stimulus that comes in, maybe it’s a kubectl command, or it’s an Argo controller that’s applying configuration, something like that, that’s actually specifying what kinds of policies we want to apply within our service mesh network. The control plane then is responsible for taking those policy specifications, translating those into configuration for the various Envoy proxies deployed in all of the services around our network, and making sure that those Envoy configurations are distributed properly, out to the various services so that those policies can be enforced in the best and proper place. That’s been the general state of the service mesh technology for a while now.
Istio Sidecar Model Challenges
Let’s be clear, there are a lot of people who have been and who are very successful using this architectural approach, because it does solve a lot of problems. There are a lot of cross-cutting concerns that are able to be absorbed into the mesh components so that that work no longer has to be done in the application code itself, which is a very good thing. There are operational challenges that have arisen as we gained experience over the intervening, say, four years since Istio went 1.0 GA release in 2018. Over the intervening years, we’ve learned a lot. This is a great quote from a very senior engineer at T-Mobile named Joe Searcy, and what he said, the biggest enemy of service mesh adoption has always been complexity of the resource and operational overhead to manage the service mesh for a large enterprise, makes adoption cumbersome, even as projects like Istio have worked to decrease complexity. We’re talking here about this particular approach. While it does remove a lot of cross-cutting concerns from the underlying application, it can be fairly complex to administer, especially at scale.
Let’s talk about briefly just a couple of those issues. Then we’ll talk about some of the innovations that are going on right now to address that, from an operational complexity standpoint. This shows you a typical Istio deployment, obviously simplified quite a bit. You can see we have three nodes that are deployed presumably in our Kubernetes cluster. There are a number of apps that are deployed, some of them with multiple instances. Because we’re using Istio, we have an Envoy proxy sidecar that’s injected into each one of those application pods. There are issues that can happen, let’s say, because these components, the application workload and the proxy, live in the same pod, there can be issues when you’re trying to take down a service, maybe to apply an update to the underlying workload software, maybe to apply an update to Envoy itself, or to the Istio data plane. There can be issues like, for example, let’s say the application comes up first, app 1, and the sidecar, there’s a race condition there. The sidecar hasn’t quite made it yet. The application starts to try to do its thing to fulfill its mission. It sends out requests, but there’s no sidecar to intercept that request and apply whatever policies are relevant. As a result, it may spew a number of failures as it’s in this race condition, startup phase. That’s not a good thing.
There’s converse sorts of problems that can happen when services are trying to shut down, and so forth. This race condition, that’s an artifact of the fact that we’ve injected this proxy into the pod. It’s something that we’d like to avoid if possible. Although our goal is for applications to not be aware of the mesh, in fact, sometimes it does need to happen. For example, here’s a case where we have a pod that spins up a job, and so this is a job that’s going to run for some period of time. It completes its mission, and then it shuts down. We have operational issues like when that job shuts down, you can have conditions where perhaps because the sidecar is still running, that pod is not cleaned up properly. Those resources are not cleaned up in a proper timely fashion, in order to use those resources as efficiently as possible. That’s something we’d like to address as well.
There’s also a very popular topic of latency. What happens here with our sidecars is those are injected into the application workload. Those sidecars have, obviously certain latency that’s associated with them. These are fully configured Envoy proxies that live as sidecars in Istio. They have a full layer 7 TCP stack that has to be traversed, every time traffic goes in or out. You can see, you get some latencies that build up going from the application to its sidecar into another sidecar. Then to the serving application, that can be a few milliseconds of processing time. That’s something we’d like to improve as well.
Then, finally, from a cost standpoint, all of these Envoy sidecars that we are provisioning, they cost money, they cost memory, and CPU, and actual money. One of the things that we see in our business as we talk to people is that often, because you don’t want these sidecars to be under-provisioned, because that creates issues. What happens is often people tend to overprovision the sidecars, give them more capacity than they need. That actually results in an unnecessary spending of money, which is something we would like to avoid as well. Those are some of the challenges.
Introducing Istio Ambient Mesh
Again, this model has worked very well for a number of years, but there are some challenges and we’d like to be able to address that. The folks at Google and Solo have been talking about this problem for roughly a year, maybe a little longer now, and have just contributed in the September timeframe to the Istio project, a new sidecarless data plane option that allows service meshes to operate completely, if that’s what you want, without sidecars. That has a number of really important implications that we’ll be talking about. Things like cost reduction, and simplifying operations, and improving performance. We will talk about all of those.
Including Applications in Ambient
One of the things from an operational standpoint that you see is it’s much easier to get started with Istio Ambient Mesh due to the simplified onboarding process. When you were working in a sidecar mode, obviously, every workload in a namespace that you wanted to include into the mesh, you have to go inject sidecars into each one of those. It’s a decently heavyweight process. Whereas with ambient mode, it’s simply a matter of labeling your namespace as being, I want this namespace to be something that participates in the mesh using ambient mode. What happens is the appropriate infrastructure will get spun up to make that a reality, but without any change to the underlying workload itself. You’ll see that in the demonstration in just a few minutes. Similarly, if you want to take things out of the mesh, it’s a very simple process, where you’re just simply taking away that infrastructure, the Istio CNI will change some routing rules. Basically, the underlying workload itself remains completely untouched. Pretty good stuff.
How this works, is there are two layers to the Istio ambient mode, two new layers that you can use. These modes can interoperate with each other as well. If you have existing workloads that operate in a sidecar fashion, and you want to spin up, let’s say, new services that operate in ambient mode, those two can interact with each other. There are no issues there at all. Going forward, we anticipate a lot of the new adoption in Istio once ambient is really, fully production ready. We see a lot of new adoption going in this direction. Ambient mode is available in Istio 1.15 on an experimental, basically on a beta basis. It’s not considered ready for production use yet, but we are moving up the experience curve very quickly and expect it to be a predominant deployment model going forward. When we add ambient mode to a namespace, what we actually get are potentially two different sets of components. There is a secure transport layer that operates on a per node basis, it handles just level 4 issues. Its primary purpose is to manage securing traffic between workloads. We’ll show you a picture of that in just a moment.
Then, in cases where you also need layer 7 policies, things that require HTTP headers, maybe retries and fault injection, and advanced load balancing, those sorts of things that require layer 7 HTTP access, those policies will be delegated off to a separate set of layer 7 proxies that we’re calling waypoint proxies. Two layers here, one is ztunnel that handles primarily level 4 security mTLS kinds of concerns. Then a second waypoint proxy layer that operates at layer 7 and handles the higher-level set of concerns typically associated with HTTP. In terms of the control plane architecture, as you can see there at the bottom, that’s unchanged. Same APIs as before. The control plane is the same old istiod control plane that you may have used if you’ve used Istio in the past. It will program the ztunnels and the waypoint proxies exactly the way it’s always programmed sidecars, and will continue to program sidecars in the future. Those are the two layers of the ambient architecture.
What you see with a request, so let’s say we just care about establishing mTLS through our network, and that’s a common adoption pattern that we see for Istio. People often don’t want to bite off on the full connect, secure, observe, all the features of Istio initially. A lot of times they come to us with a mandate, like for example, federal agencies, public sector clients come to us with a mandate, say from the Biden administration that we have to use mTLS security among all the applications in our mesh. We do that here with this ztunnel component. What’s going to happen is, if app A wants to talk to app B, the request goes to the ztunnel. The ztunnel then directs that to the ztunnel, where the target application is, manage this communication, the request and the response back and forth between the two, and life is good.
It’s very possible then to do this incremental adoption of Istio using ambient mesh, where if all you care about is mTLS, you can get by with just these very simple per node deployments that are not injected as sidecars into your applications. If layer 7 policies are needed, and they often are, then we’re going to introduce the notion of a waypoint proxy where you can provision these proxies as you want. They don’t have to be on any particular node. You can provision using standard Istio service deployments. They’re going to handle all of the layer 7 kinds of policies that need to be applied to a particular request. You don’t have to worry about the details of how these proxies get programmed, istiod control plane is going to take care of that for you. You simply specify via the API, what you want done, and the control plane is going to make sure that the proper components are programmed as they need to be. Those are the two layers.
Why Istio Ambient Mesh?
From a benefit standpoint, we see right off, reduced cost. That one’s a pretty obvious one. Instead of having, in this case, we got 12 workloads across 3 nodes, each with their own layer 7 proxy, we can now eliminate those proxies in favor of an approach that uses, again, if we’re doing just security, we can do a proxy per node as opposed to a sidecar proxy per pod, so really reduces the set of infrastructure that is required there. In doing that, we see some pretty dramatic cost reductions. You can actually go visit this blog, if you’d like to explore this in more detail, bit.ly/ambient-cost, will take you there. Just to give you the conclusions of this, comparing the sidecar approach to the ambient approach, especially for cases where you’re doing incremental adoption of just the security features of Istio, you can see pretty dramatic, in some cases, like 75% reduction of the amount of CPU and memory required to accomplish the same objective. Going back to our original theme of simplicity, we’re trying to make this new model as simple as possible.
Another thing, and this is perhaps the most significant benefit of the ambient architecture, from what people are telling us is the potential for simplifying applications. We’ve talked about some of the issues around upgrades, and around adding new applications or taking things out of the mesh, this becomes a much easier process now. If we’re doing a rolling upgrade, say there’s an Envoy CVE at layer 7, and we need to roll that out throughout our service mesh network, we don’t have to go take down every single workload that’s in the mesh, in order to make that happen, we can simply do an update at the proxy layer. Immediately those changes will be visible to the entire mesh without the actual applications being impacted. That is a huge benefit here. We can see this graphically here where this is our initial pre-ambient setup. If we just need the security components, we could go to something like this where we have, rather than one sidecar per proxy, we have one per node where workloads are deployed. Those are not injected as sidecars. They are their own standalone ztunnel deployments. Then if we need more sophisticated layer 7 processing, we can add that as well as a separate set of deployments. We can scale those up independently of one another based on how much traffic we anticipate to be going to the service account in question.
From a performance standpoint, just a very simple analysis here that I think nevertheless illustrates what’s going on. We have these ztunnel proxies that are configured with Envoy and layer 4. Because they’re just dealing with the bottom level of the TCP stack, they’re much more efficient than if you have to go through the full layer 7 stack. We anticipate the proxy overhead of these layer 4 proxies, about a half millisecond back of the envelope on average. Layer 7, so the waypoint proxy is typically 2 milliseconds of latency added. You can see no mesh is obviously the baseline case, there’s no proxy overhead there. In the traditional model with L4 plus L7, and you’re guaranteed to have to traverse two of these proxies on each request-response, that’s about 4 milliseconds of added latency. Whereas if you’re just doing layer 4, again, secure communication, that can be as low as 1 millisecond of overhead, or with the full layer 7 policies taken into account, even there, there’s typically a significant reduction, say from 4 to 3 milliseconds. We think this is something especially applied at scale where people are going to see pretty dramatic differences in performance cost and operational overhead.
Ambient Mesh Demo
We’re going to switch over at this point and show you a demo of ambient mesh in action. We’re going to start by installing Istio. If you come here and look at our cluster, we don’t have Istio system installed. We have a handful of applications, some very simple sample applications that we’ll use to demonstrate this capability. The sleep service here, if we exec into this, we can curl helloworld, and get a response from one of the services. In this case, I see it load balancing between v1 and v2. I can even curl a specific version if I want. I can curl the other version as well. What we want to do is we want to install Istio. We want to implement service mesh capabilities without having to deploy a sidecar. Let’s come over here, and we will run Istio install. We’ll run it using istioctl. We will set the profile to ambient mode. Let’s take a moment. We’ll install some familiar components like the istiod control plane. It installs ingress gateways as well. Then we’ll come to some of the new components like the ztunnel that we’ve been talking about. If I come back here, and we look to the bottom pane, we see that Istio system has been installed as a namespace. If I click into that namespace, we see some components that run as a CNI plugin on the node. We see the istiod control plane, and we see the ztunnel agents that are deployed in a daemon set, one per node.
The next thing we’re going to do is we’re going to add our applications in the default namespace to this ambient mesh. What we’re going to do is label the default namespace with ambient mode, or data point mode, which is equivalent to ambient. Now we’ll automatically add the workloads that are in the default namespace, these services are going to go into the ambient mesh. If you notice, there are no sidecars. In fact, these applications have been running for a while, so we didn’t have to restart them or inject anything into them. We didn’t have to change their descriptors or anything. If we come in to the sleep application, which we see is running on the ambient worker node, and we exec into that, and we come back down here to this pane, we find that the ztunnel that’s running on the ambient worker node, and let’s say we watch its logs. Let’s do that full screen. If we now do our curl to helloworld, then what we should see is traffic that’s now going through the ztunnel. If you look a little bit closer at the logs, we can see things like while it’s trying to do some mutual TLS, or it’s invoking SPIFFE and establishing mutual TLS. Let’s take a closer look when we make those calls through the ztunnel. What does that actually look like? We’re going to do a kubectl debug in the Istio system namespace around the ztunnel, and we will use netshoot to help us understand what’s going on here. Let’s take a look at this.
Let’s come back here, and let’s make a couple of curls actually just to capture some traffic between sleep and helloworld. We’ll stop now, we probably have enough traffic. Let’s come back a little further up here. We should see that when the traffic is going through, like here we see traffic getting to the ztunnel. Then when it gets there, we have TLS encryption. The traffic going over the ztunnels, we get mutual TLS between the sleep and the helloworld service. As a reminder, we didn’t inject any sidecars into these workloads. We didn’t have to restart those workloads. All we’ve done in ambient mode is we’ve been able to include those workloads and the communication between them as part of the service mesh. A big part of the service mesh is also enabling layer 7 capabilities. That’s where the waypoint proxy or the layer 7 capabilities of the ambient mesh comes into the picture. Instead of the traffic passing between these tunnels, we can actually pass them and hand them off to a layer 7 Envoy proxy, which then implements certain capabilities like retries, fault injection, header-based manipulations, and so forth.
In this demo, we’re going to deploy a waypoint proxy, and we’re going to inject a layer 7 policy fault injection. In this case, we’ll observe how we can implement layer 7 capabilities in the service mesh in ambient mode without sidecars. To do this, let’s get out of termshark. Now we’re going to apply a waypoint proxy. Let’s take a look at that, the layer 7 proxy for the helloworld services. One thing you’ll notice is we don’t try to share layer 7 components between different identities, we want to keep those separate and try and avoid noisy neighbor problems. We can create this waypoint proxy by applying this. Then we should see a new proxy come up, that would enable us to write layer 7 policies about traffic going into the helloworld service.
Now, if I come back, we can take a look at a virtual service, which is a familiar API in Istio that specifies traffic rules, when a client tries to talk to helloworld, and in this case, we’ll do some layer 7 matching, and then we’ll do some fault injection. A hundred percent of the time, we will delay the calls by 5 seconds. Let’s apply this virtual service, and then we’ll go back to our client. Now when we curl the helloworld service, we should see a delay of 1, 2, 3, 4, 5 seconds. We should see that potentially load balance across both v1 and v2. As you can see, we get layer 7 capabilities, we get layer 4 capabilities and mutual TLS. We can write policies about this communication between these services, and we have not touched the applications. We can include them and even exclude them dynamically. If we’re done with this service, and we don’t want it anymore, then all we have to do is just uninstall it. Then we’ll remove the system namespace and come back to our applications. You see that they’re not touched. Go into one of the applications, we make a curl. Then without ambient in place, the services continue to work, just as we expected.
Summarizing Istio Ambient Mesh
To summarize what we’ve seen so far with Istio Ambient Mesh, both from a presentation and a demonstration standpoint, three core benefits remain. We want to reduce cost. We want to perhaps, most importantly, simplify operations. We want to make the service mesh truly transparent to applications. Decouple the proxy from the applications and just make it part of the core infrastructure, and so simplifying doing things like application updates. We want to improve performance as well by taking advantage of this new proxy configuration that allows us to just bite off on the functionality that we need for our particular use cases.
Responses to Istio Ambient
What are the responses that we’ve seen to ambient mesh so far? I think the general responses have been very positive. Occasionally, there’s been a wait and see. Yes, this is interesting, but let’s see an introduction before we get too excited. This is one that I think is really interesting. This is from Matt Klein. Matt Klein is at Lyft, and was the founder of Envoy proxy built back in the mid-2010s. He says, “This is the right path forward. Sidecars have been an unfortunate implementation detail in the past. Now we’re going to see mesh features that get absorbed into the underlying infrastructure, excited to see how this evolves.” Going back to our quote from the beginning of this talk from Joe Searcy at T-Mobile. He says the opportunities that ambient Mesh provides are extremely exciting, better transparency for applications, fewer moving parts, simpler invocation, huge potential in savings of compute resources and engineering hours. Those are all really good things.
There’s at least one episode of unintentional humor. This is from the CEO of a company that leads the Linkerd community which has doubled down on its sidecar-based architecture. He actually put out a joke tweet in response to all of the chatter around ambient mesh, and said, “By the way, Linkerd, we’re actually going to switch to having a sidecarless option as well,” put some things in there to try to provide hints that he was joking. You can see a day or two later, he realized people weren’t getting the joke, so he had to come back and say, “Yes, this is a joke. We really like sidecars with Linkerd.” That was some fun that came out of this whole announcement process.
What’s Next in Service Mesh?
What’s next beyond this in service mesh? There are two capabilities. I want to talk about both of them on the data plane. That’s where we see a lot of the innovation happening these days, is in the data plane itself. One of the things we want to be able to do is to improve the performance of these new components. What we did for the experimental implementation that’s out there now is with the ztunnels. Those are implemented as Envoy proxies that are just configured to operate at level 4. There are some additional opportunities for increasing performance of that even more. What we see is being able to perhaps replace the Envoy proxy there with an even lighter-weight ztunnel implementation, even using technologies like eBPF. eBPF allows us to do things like packet filtering in the kernel of the operating system, and so it’s extremely efficient. It’s not something that’ll be a revolution in terms of what’s going on here. The architecture is going to remain the same, but just providing a more efficient implementation based on eBPF to actually make those ztunnels run even faster than they do today.
One other thing I want to talk about here is a different level of innovation. We’ve talked a lot about the architecture of the data path. I also want to talk about, there’s a lot of innovation happening with service mesh at the edge of the mesh as well. One really good example of that, that I’m excited about is GraphQL innovation. Just very quickly, with GraphQL, today, what we typically see in enterprises who deploy it is you have an architecture that looks roughly like what’s on the right here. You have an API gateway, something that’s like you’ve been using before that serves up your REST interfaces in your maybe gRPC, whatever APIs you have out there. Then you end up having to build this separate server component that knows just about GraphQL, how to assemble GraphQL responses from underlying services and that sort of thing. We’re not sure that’s the best approach. What if you could have GraphQL APIs that don’t require dedicated servers? What if you could reuse existing API gateways to serve GraphQL? It’s like they already serve OpenAPI, gRPC, other kinds of interfaces, why can’t they serve GraphQL as well? Why do we need to stand up a separate server component or set of server components in order to enable GraphQL? That doesn’t make a lot of sense. What if you could do that in a way that would allow things like declarative configuration, as well as leveraging your existing API contracts?
Solo has done a lot of work in this space, specifically in a service mesh context. Basically, the takeaway from the story is, what if you could actually absorb the GraphQL server capability into a filter in your Envoy proxy at the edge of your network? Basically, inspect existing applications and their interface contracts, derive GraphQL schema from that, be able to stitch those together to create a super graph that spans multiple services, and deploy those in an API gateway. We think that’s a powerful innovation growing in popularity API standard in GraphQL.
If you would like to learn more about any of the things we’ve talked about, especially ambient mesh, I definitely recommend this resource. There’s a Bitly link that will take you there, bit.ly/ambient-book. This a completely free book from O’Reilly, written by a couple of people at Solo, and basically goes through in a fair amount of detail, Istio Ambient Mesh, and will help you understand exactly what the objectives and implementations are there. There’s also a hands-on workshop, if you’re like me, and you like to get your hands on the keyboard to understand this stuff better. Encourage you to follow that Bitly link there, bit.ly/ambient-workshop. It will take you through a hands-on exercise for getting started with ambient mesh.
Did Parsimony Win the Day?
Back to our original story. What happened? We got to the finals. We had the big behemoth design with the committee and so forth, and the little tiny car with the single designer. Who won the day? The behemoth designers, they got in there at the starting line, two people carry it up to the starting gate, they twist up all the potentiometers to make it run as fast as possible. They spun it up a little too much, and the thing actually goes careening off the track. The little car just shuffles its way around the track, and it wins the day, much to the delight of all the college students who were there watching the finals of this competition. In my view, there was a happy ending, David beat Goliath. Parsimony and simplicity won the day. That’s what we think is going to happen in the service mesh community as well is that simplicity and ambient mesh are going to be ruling the day very soon.
Questions and Answers
Betts: You mentioned that the three pillars were secure, connect, and observe. The ambient mesh really seemed to focus on reducing the complexity and it helps for those connections. Then security doesn’t have to be configured as many places. Does it help with the observability as well?
Barton: Yes, absolutely. I’m not sure that ambient per se really addresses so much the observability concerns. There’s a ton of metrics that already get published out of Istio and Envoy proxy. If you’ve ever done anything with Envoy proxy, whether in a service mesh context or not, you’ll know that there’s just a raft of metrics that gets published there. I don’t think ambient really is going to enhance the observability per se, but it does just allow that to be delivered in a more cost-efficient fashion.
Betts: Do you see any service mesh deployments that aren’t using Kubernetes?
Barton: Obviously, no one, at the scale that I expect the people on this session are managing, you’re not dealing with Greenfields. Definitely, Istio has a lot of best practices established around non-service mesh deployments. We blog about it quite a bit at Solo, because these are the kinds of real-world issues that we encounter. I would not recommend trying to deploy Istio without any Kubernetes context at all. In other words, if Kubernetes is not part of the strategic direction of your organization, then I wouldn’t recommend going there. Definitely, if you’re in a mode, like a lot of the organizations we deal with where this is our objective is to move toward Kubernetes, and cloud native, and Istio service mesh deployments, then definitely can accommodate services that are deployed in VMs, or EC2s, or wherever.
Betts: If you’re doing the migration from a monolith architecture, and breaking it up into microservices, this is pricing that you’re thinking about, should it be one of the very first things you do, or should you figure out and learn Kubernetes first, and then layer on the Istio?
Barton: Anytime you’re contemplating a major organizational change, and this is independent of Kubernetes, or service mesh, start small, ideally. Start with something where you can wrap your mind around it and understand it, and that’s non-critical, and that you can manage the situation. Then, over time, layer in additional complexity and outside elements and that sort of thing. Just as a general rule, I think that’s a good idea.
Betts: eBPF, the Extended Berkeley Packet Filters, like even saying the acronym seems hard to me. It seems like one of those really low-level things that somebody’s really excited about, but me as an application developer, an architect, I don’t want to have to learn that. Is that something that you see eventually becoming something that’s sold as a package. Like, just get this add-in and it adds eBPF. It’s the thing that you had before, but now it’s lower level so it’s faster.
Barton: I think you hit it exactly right. It’s like I drive a car, I know very little about the internals, how an internal combustion engine operates. It’s something that just exists below the level of abstraction I care about. For the majority of people who are consumers of service mesh technology, I’d say the same would be true of eBPF. It provides a critical service. It’s going to make things run faster. It’s going to be below the level that most people are consciously dealing with. There are exceptions. In fact, if you go to academy.solo.io, you’ll see courses on eBPF, so you can learn more about it. There are people who clearly need to be able to understand it. Generally speaking, we think that will be commoditized, just baked into the service mesh layers. I know that Istio is clearly moving in that direction. I think some of the other alternatives that are out there, I know Kuma is moving in that direction, and probably some others as well.
Betts: Istio seems to be synchronous in terms of communication, does that come with scaling concerns? For async, can consumers control the rate of request consumption? Is that possible out of the box? Does he even have it in the right context, it’s about messaging and queuing solution?
Barton: You can definitely use Istio with asynchronous sorts of models. There’s no question about that. I view those two independently. I’ve done a lot of work building enterprise systems over the years and frequently will move into an asynchronous mode for applications that require extreme degrees of scalability and where the use case fits that. Definitely, Istio can be used in context of both synchronous and asynchronous services. The whole idea with Istio and with any service mesh, is to be as transparent as possible to the underlying applications. In the ideal world, it’s like an official at a sporting event. Ideally, I want to be there to enjoy the sporting event. I don’t want to watch guys in striped shirts blow whistles. That’s not my goal.
Betts: It should get out of the way, and like you said, a good abstraction. You don’t have to know about the details of how it’s doing it, but it shouldn’t also raise up to the level of your application has to know about all the Istio plumbing that’s there to be properly implemented.
You started to tease at the end, there’s future of GraphQL changes. Where’s that in the pipeline? Is that in the works, is it a year out?
Barton: No, definitely not a year out. I can speak with some authority to what Solo is doing in that space. Solo has a couple of products in this space. One is a standalone application gateway, it integrates with Istio, but does not require Istio. In that environment, there’s a full GraphQL solution that’s already there that basically allows you to absorb GraphQL server capabilities up into the Envoy proxy. There’s essentially a beta version of that that’s available in Solo’s commercial service mesh product that’s based on Istio as well that’ll be full implementation within a couple of months. Yes, it’s really exciting stuff. We see massive migrations of people toward GraphQL as an API standard, and we think a lot of the architectural decisions that are coming along with that are a little unfortunate. They don’t make a lot of sense to me. Why would we want a separate [inaudible 00:54:03] kind type of API server instance to serve up this API, when we already have something that serves up like REST, and OpenAPI, and gRPC, and so forth. Why can’t we bring those all together?
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