smi-spec VS gRPC

The Service Mesh Interface (SMI) specification was created to solve this portability issue.

> I suspect if a Service Mesh is ultimately shown to have broad value, one will make it's way into the K8S core

I'm not so sure. I suspect it'll follow the same roadmap as Gateway API, which it already kind of is with the Service Mesh Interface (https://smi-spec.io/)

It is very common that a service mesh deploys a control plane and a data plane. The control plane does what you might expect; it controls the service mesh and gives you the ability to interact with it. Many service meshes implement the Service Mesh Interface (SMI) which is an API specification to standardize the way cluster operators interact with and implement features.

Before we start, let's review the SMI Access Control Specification. There are two forms of traffic policies in osm-edge: Permissive Mode and Traffic Policy Mode. The former allows services in the mesh to access each other, while the latter requires the provision of the appropriate traffic policy to be accessible.

osm-edge is a simple, complete, and standalone service mesh and ships out-of-the-box with all the necessary components to deploy a complete service mesh. As a lightweight and SMI-compatible Service Mesh, osm-edge is designed to be intuitive and scalable.

I started on Monday, attending ServiceMeshCon2019. My guesstimate is that about 1000 people attended it. I believe Service Mesh is playing such a crucial role in scaling cloud native technologies that large scale cloud-native deployments may not be possible without service mesh. Just like you cannot really succeed in deploying a microservices based application without a microservices orchestration engine, like Kubernetes, you cannot scale the size and capacity of a microservices-based application without service mesh. That’s what makes it so compelling to see all the service mesh creators — Istio, Linkerd, Consul, Kuma — and listen to them. There was also a lot of discussion of SMI (Service Mesh Interface) — a common interface among all services mesh. The panel at the end of the day included all the major service mesh players, and some very thought provoking questions were asked and answered by the panel.

Do you need a Service Mesh?

In the episode with Annie, she gave a great overview of the CNCF and a handful of projects that she's excited about. Those include Helm, Linkerd, Kudo, Keda and Artifact Hub. I gave a bonus example of the Service Mesh Interface project.

SMI official website: https://smi-spec.io

gRPC, built on HTTP/2, inherently supports flow control. The server can push updates, but it must also respect flow control signals from the client, ensuring that it doesn't send data faster than what the client can handle.

Yes, grpc_cli tool uses essentially the same mechanism except implemented as a grpc service rather than as a stubby service. The basic principle of both is implementing the C++ proto library's DescriptorDatabase interface with cached recursive queries of (usually) the server's compiled in FileDescriptorProtos.

See also https://github.com/grpc/grpc/blob/master/doc/server-reflecti...

The primary difference between what grpc does and what stubby does is that grpc uses a stream to ensure that the reflection requests all go to the same server to avoid incompatible version skew and duplicate proto transmissions. With that said, in practice version skew is rarely a problem for grpc_cli style "issue a single RPC" usecases: even if requests do go to two or more different versions of a binary that might have incompatible proto graphs, it is very common for the request and response and RPC to all be in the same proto file so you only need to make one RPC in the first place unless you're using an extension mechanism like proto2 extensions or google.protobuf.Any.

While gRPC and Apache Thrift have served the microservice architecture well, CloudWeGo's advanced features and performance metrics set it apart as a promising open source solution for the future.

The loadBalancingConfig is what we use in order to decide which policy to go for (round_robin in this case). This JSON representation is based on a protobuf message, then why does the name resolver returns it in the JSON format? The main reason is that loadBalancingConfig is a oneof field inside the proto message and so it can not contain values unknown to the gRPC if used in the proto format. The JSON representation does not have this requirement so we can use a custom loadBalancingConfig .

The Dart implementation of gRPC which puts mobile and HTTP/2 first. It's built and maintained by the Dart team. gRPC is a high-performance RPC (remote procedure call) framework that is optimized for efficient data transfer.

gRPC is a high-performance, open-source RPC (Remote Procedure Call) framework initially developed by Google. It uses Protocol Buffers for serialization and supports bidirectional streaming.

In general, tunneling through HTTP2 turns out to be a great choice. There is a RPC protocol built on top of HTTP2: gRPC[1].

This is because HTTP2 is great at exploiting a TCP connection to transmit and receive multiple data structures concurrently - multiplexing.

There may not be a reason to use HTTP3 however, as QUIC already provides multiplexing.

I expect that in the future most communications will be over encrypted HTTP2 and QUIC simply because middleware creators can not resist to discriminate.

[1] <https://grpc.io>

Even in the https://grpc.io blog says this

gRPC