# kumameshon Twitter to chat about Kuma.
Welcome to the official documentation for Kuma, theUniversal Control Plane.
Here you will find all you need to know about the product. While Kuma is ideal for Service Mesh and Microservices, you will soon realize that it can be used to modernize any architecture.
The word “Kuma” means “bear” in Japanese (ク マ
What is Kuma?
Kuma is a universal open-source control plane for Service Mesh and Microservices. It can run and be operated natively across both Kubernetes and VM environments, making it easy to adopt by every team in the organization.
Built on top ofEnvoy, Kuma can instrument any L4 / L7 traffic to secure, observe, route and enhance connectivity between any service or database. It can be used natively in Kubernetes via CRDs or via a RESTful API across other environments, and it doesn’t require a change to your application’s code in order to be used.
While being simple to use for most use-cases, Kuma also provides policies to configure the underlying Envoy data-planes in a more fine-grained manner. The result caters to both first-time users of Service Mesh, as well as the most experienced ones.
Kong built Kuma with feedback from 150 enterprise organizations running Service Mesh in production. Kuma implements a pragmatic approach that is very different from the first-generation control planes:
- it runs with low operational overhead across all the organization
- it supports every platform
- It’s easy to use while relying on a solid networking foundation delivered by Envoy.
Built by Envoy contributors at Kong 🦍.
Need help?Don’t forget to check theCommunitysection!
When building any software architecture, we will inevitably introduce services that will communicate with each other by making requests on the network.
For example, think of any application that communicates w ith a database to store or retrieve data, or think of a more complex microservice-oriented application that makes many requests across different services to execute its operations:
Every time our services interconnect via a network request, we put the end-user experience at risk. As we all know the connectivity between different services can be slow and unpredictable. It can be insecure, hard to trace, and pose many other problems (eg, routing, versioning, canary deployments).
Usually, at this point, developers take one of the following actions to remedy the situation:
Write more code: The developers build aSmartclient that every service will have to utilize in the form of a library. Usually, this approach introduces a few problems:
- it creates more technical debt
- it is typically language-specific; therefore, it prevents innovation
- multiple implementations of the library exist, which creates fragmentation in the long run.
Sidecar proxy: The services delegate all the connectivity and observability concerns to an out-of-process runtime, that will be on the execution path of every request. It will proxy all the outgoing connections and accept all the incoming ones. By using this approach, developers don’t worry about connectivity and only focus on delivering business value from their services.
Sidecar Proxy: It’s calledsidecarproxy because it’s another process running alongside our service process on the same host, like a motorcycle sidecar. There is going to be one instance of a sidecar proxy for each running instance of our services, and because all the incoming and outgoing requests – and their data – always go through the sidecar proxy, it is also called a data-plane (DP) .
The sidecar proxy modelrequiresa control plane that allows a team to configure the behavior of the data-planes and to keep track of the state of its services. Teams that adopt the sidecar proxy model will either build a control plane from scratch or use existing general-purpose control planes available on the market, such as Kuma.Compare Kuma with other CPs.
Unlike a data-plane (DP), the control-plane (CP) is never on the execution path of the requests that the services exchange with each other, and it’s being used to configure the data-planes and retrieve data from them (like observability information).
Service Mesh: An architecture made of sidecar proxies deployed next to our services (the data-planes, or DPs), and a control plane (CP) controlling those DPs, is called Service Mesh. Usually, Service Mesh appears in the context of Kubernetes, but anybody can build Service Meshes on any platform (including VMs and Bare Metal).
With Kuma, our main goal is to reduce the code that has to be written and maintained to build reliable architectures. Therefore, Kuma embraces the sidecar proxy model by leveraging Envoy as its sidecar data-plane technology.
By outsourcing all the connectivity, security, and routing concerns to a sidecar proxy, we benefit from our enhanced ability to:
- build applications faster
- focus on the core functionality of our services to drive more business
- build a more secure and standardized architecture by reducing fragmentation
By reducing the code that our teams create and maintain, we can modernize our applications piece by piece without ever needing to bite more than we can chew.
Learn moreabout how Kuma enables modernization within our existing architectures.
Kuma vs XYZ
When Service Mesh first became mainstream around 2017, a few control planes were released by small and large organizations in other to support the first implementations of this new architectural pattern.
These control planes captured a lot of enthusiasm in the early days, but they all lacked pragmatism into creating a viable journey to Service Mesh adoption within existing organizations. These 1st generation solutions are:
- Greenfield-only: Hyper-focused on new greenfield applications, without providing a journey to modernize existing workloads running on VM and Bare Metal platforms where the current business runs today, in addition to Kubernetes.
- Complicated to use: Service Mesh doesn’t have to be complicated, but early implementations were hard to use; they had poor documentation and no clear upgrade path to mitigate breaking changes.
- Hard to deploy: Many moving parts, which need to be running optimally at the same time, makes it harder to run and scale a Service Mesh due to the side-effect of higher operational costs.
- For hobbyists, not organizations: Lack of understanding of the challenges enterprise organizations face today, with
Kuma exists today to provide a pragmatic journey to implementing Service Mesh for the entire organization and for every team: for those running on modern Kubernetes environments and for those running on more traditional platforms like Virtual Machines and Bare Metal.
- Universal and Kubernetes-Native: Platform-agnos tic, can run and operate anywhere.
- Easy to use: Via automation and a gradual learning curve to Service Mesh policies.
- Simple to deploy: In one step, across both Kubernetes and other platforms.
- Enterprise-Ready: Pragmatic platform for the Enterprise that delivers business value today.
Until now, Service Mesh has been considered to be the last step of architecture modernization after transitioning to containers and perhaps to Kubernetes. This approach is entirely backwards. It makes the adoption and the business value of Service Mesh available only after implementing other massive transformations that — in the meanwhile — can go wrong.
In reality, we want Service Mesh to be availablebeforewe implement other transitions so that we can keep the network both secure and observable in the process. With Kuma, Service Mesh is indeed thefirst steptowards modernization.
Unlike other control planes, Kuma natively runs across any platform, and it’s not limited in scope (ie, Kubernetes only). Kuma works on both existing brownfield applications (those apps that deliver business value today), as well as new and modern greenfield applications that will be the future of our journey.
Unlike other control planes, Kuma is easy to use. Anybody – from any team – can implement Kuma inthree simple stepsacross both traditional monolithic applications and modern microservices.
Finally, by leveraging out-of -the-box policies and Kuma’s powerful tagging selectors, we can implement a variety of behaviors in a variety of topologies, similar to multi-cloud and multi-region architectures.