osm VS zipkin

If you'd like to go a bit deeper into service mesh technology, be sure to check out the resources listed below then head over to the Open Service Mesh with Azure Kubernetes Service lab to get hands-on and see a service mesh in action! Open Service Mesh is an open-source, lightweight service mesh that is easy to install and operate, so I encourage you to take it for a spin 🚀

osm-edge forked from Open Service Mesh is a lightweight, extensible, cloud-native, SMI-compatible service mesh built purposely for Edge computing. osm-edge uses lightweight programmable proxy Pipy as a sidecar proxy.

osm-edge is built on top of Open Service Mesh (OSM) v1.1.0 codebase and is a lightweight service mesh for resource-sensitive cloud environments and edge computing scenarios. It uses osm as the control plane and Pipy as the data plane and features high performance, low resources, simplicity, ease of use, scalability, and compatibility (x86/arm64 support).

osm-edge is a fork of open service mesh and we will strive to keep this fork in sync with its upstream and propose back major changes and/or feature proposals to upstream for broader benefits of the community. Both OSM and osm-edge are hosted on Github. If you have any feature request, question, or comment, we’d love to have you join the rapidly-growing community via Github Issues, Pull Requests, or osm slack channel!

Link: https://github.com/openservicemesh/osm/issues/4840

Azure Arc-enabled Kubernetes allows us to attach and configure Kubernetes clusters running anywhere. Open Service Mesh (OSM) is a lightweight, extensible, Cloud Native service mesh that allows users to uniformly manage, secure, and get out-of-the-box observability features for highly dynamic microservice environments. The Open Service Mesh (OSM) extension is a managed service mesh for Arc-enabled Kubernetes clusters that is lightweight and extensible.

Pull Request template from the Open Service Mesh project

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.