smi-spec VS zipkin

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

When choosing distributed tracing tools, considerations include your technology stack, business requirements, and monitoring complexity. Zipkin, SkyWalking, and OpenTelemetry are popular distributed tracing solutions, each with its unique features.

From the perspective of the realization of GraphQL infrastructure, the interesting direction is "Finding". How to find the problem? How to find the bottleneck of the system? Distributed Tracing System (DTS) will help answer this question. Distributed tracing is a method of observing requests as they propagate through distributed environments. In our scenario, we have dozens of subgraphs, gateway, and transport layer through which the request goes. We have several tools that can be used to detect the whole lifecycle of the request through the system, e.g. Jaeger, Zipkin or solutions that provided DTS as a part of the solution NewRelic.

Zipkin is a distributed tracing system used for tracking and analyzing how requests move through complex systems, especially in setups with many interconnected services, known as microservices.

Distributed Tracing: Middleware for distributed tracing like Jaeger and Zipkin helps monitor and trace requests as they flow through multiple microservices, aiding in debugging, performance optimization, and understanding the system's behavior.

For microservice tracing, you might want to look at Zipkin [0], or OpenTelemetry [1]

[0] https://zipkin.io/

I’ve not used a self-hosted solution before, but here’s one I found. https://zipkin.io/

> IMO the reason these vendors can and do charge so much is not because telemetry software is hard.

I always saw it as "they are charging for their polished UI/experience"

The UI of https://zipkin.io/ versus DataDog is kind of... not really in the same ballpark?

There are the zipkin https://zipkin.io/ and jaeger https://www.jaegertracing.io/ packages/components you can use both have quickstarts if you consider that to be a beginner's guide.

Zipkin, which was developed by Twitter, is an open source tool for distributed tracing that can also be used to troubleshoot latency issues in your application. While Zipkin is Java-based, py_zipkin is an implementation for Python.