go-kit VS jaeger

https://github.com/go-kit/kit

. Web backend (with various frameworks available) . Web Assembly (one of them is vugu framework) . Microservices (some frameworks: Go Micro, Go Kit, Gizmo, Kite) . Fragments services (Term mentioned by @jeffotoni in a microservices discussion group) . Lambdas (FaaS example) . Client Server . Terminal applications (using the tview lib) . IoT (some frameworks) . Bots (some here) . Client Applications using Web technology . Desktop using Qt+QML, Native Win Lib (example Qt, Qt widgets, Qml) . Network Applications . Protocol applications . REST Applications . SOAP Applications . GraphQL Applications . RPC Applications . TCP Applications . gRPC Applications . WebSocket Applications . GopherJS (compiles Go to JavaScript)

To implement service discovery in our GoLang microservice we will use GoKit, a toolkit for microservices that provides support to auth, log, service discovery, tracing and more. For this starter code the mod already installed, you can skip this step

My company uses go-kit

For reference my company Go projects are built with (go-kit)[https://gokit.io/] design patterns.

5. kit. The kit framework is a programming toolkit for building robust, reliable, and maintainable microservices in Golang. It is a collection of packages and best practices that offer businesses of all sizes a thorough, reliable, and trustworthy way to create microservices. Go is a fantastic general-purpose language, but microservices need some specialized assistance. As a result, the kit framework offers infrastructure integration, system observability, and Remote Procedure Call (RPC) safety. Golang is a first-class language for creating microservices in any organization thanks to its composition of numerous closely related packages that together form an opinionated framework for building substantial Service-Oriented Architectures (SOAs).It was created with interoperability in mind, and developers are free to select the platforms, databases, components, and architectural styles that best suit their needs. The disadvantage of using go-kit is that it has a high overhead for adding API to the service because of how heavily it relies on interfaces. Documentation Link: https://github.com/go-kit/kit

At first I was confused but this GitHub user/org is completely different from the massively popular go-kit/kit https://github.com/go-kit/kit

https://github.com/go-kit/kit#related-projects

go-micro seems like it does a bit too much, like service discovery and balancing within the framework when that's likely better handled by an Envoy/Istio.

I think the more interesting aspect of this is the framework being used: https://github.com/micro/micro

I haven't dug into it at all yet, but at a glance it looks like it's aiming to do something similar to what Go kit (https://gokit.io/) or Finagle (https://twitter.github.io/finagle/) does, where it gives you a nice abstraction for defining your "service" and then handles all the supplementary aspects (service discovery, serialization, retry/circuit breaker logic, rate limiting, hooks for logging, tracing, and metrics, etc) so you don't have to build those from scratch every time.

I don't know if any of those other frameworks could really be considered very "successful" outside the original organizations they were built for (it seems like the industry has bet more on service meshes and API gateway products), but I'd probably be more inclined to start with one of them than making a new framework.

Jaeger maps the flow of requests and data as they traverse a distributed system. These requests may make calls to multiple services, which may introduce their own delays or errors. https://www.jaegertracing.io/

As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had an idea of what API route was slow, there wasn’t much visibility into the actual bottleneck.

This is where our performance product comes in: we’re rethinking a tracing/performance tool that focuses on bridging the gap between the client and server.

What’s unique about our approach is that we lean heavily into creating traces from the frontend. For example, if you’re using our Next.js SDK, we automatically connect browser HTTP requests with server-side code execution, all from the perspective of a user. We find this much more powerful because you can understand what part of your frontend codebase causes a given trace to occur. There’s an example here [3].

From an instrumentation perspective, we’ve built our SDKs on-top of OTel, so you can create custom spans to expand highlight-created traces in server routes that will transparently roll up into the flame graph you see in our UI. You can also send us raw OTel traces and manually set up the client-server connection if you want. [4] Here’s an example of what a trace looks like with a database integration using our Golang GORM SDK, triggered by a frontend GraphQL query [5] [6].

In terms of how it's built, we continue to rely heavily on ClickHouse as our time-series storage engine. Given that traces require that we also query based on an ID for specific groups of spans (more akin to an OLTP db), we’ve leveraged the power of CH materialized views to make these operations efficient (described here [7]).

To try it out, you can spin up the project with our self hosted docs [8] or use our cloud offering at app.highlight.io. The entire stack runs in docker via a compose file, including an OpenTelemetry collector for data ingestion. You’ll need to point your SDK to export data to it by setting the relevant OTLP endpoint configuration (ie. environment variable OTEL_EXPORTER_OTLP_LOGS_ENDPOINT [9]).

Overall, we’d really appreciate feedback on what we’re building here. We’re also all ears if anyone has opinions on what they’d like to see in a product like this!

[1] https://github.com/highlight/highlight/blob/main/LICENSE

[2] https://www.jaegertracing.io

[3] https://app.highlight.io/1383/sessions/COu90Th4Qc3PVYTXbx9Xe...

[4] https://www.highlight.io/docs/getting-started/native-opentel...

[5] https://static.highlight.io/assets/docs/gorm.png

[6] https://github.com/highlight/highlight/blob/1fc9487a676409f1...

[7] https://highlight.io/blog/clickhouse-materialized-views

[8] https://www.highlight.io/docs/getting-started/self-host/self...

[9] https://opentelemetry.io/docs/concepts/sdk-configuration/otl...

For the request following, something like jeager https://www.jaegertracing.io/, because you are talking more about tracing than necessarily logging. For just monitoring, https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack would be the starting point, then it depends. Nginx gives metrics out of the box, then you can pull in the dashboard like https://grafana.com/grafana/dashboards/14314-kubernetes-nginx-ingress-controller-nextgen-devops-nirvana/ , or full metal with something like service mesh monitoring which would provably fulfil most of the requirements

Have you checked out Jaeger [1]? It is lightweight enough for a personal project, but featureful enough to really help "turn on the lightbulb" with other engineers to show them the difference between logging/monitoring and tracing.

[1] https://www.jaegertracing.io/

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.

Jaeger is an open-source, distributed tracing system that monitors and troubleshoots the flow of requests through complex, microservices-based applications, providing a comprehensive view of system interactions.

However, ensuring fault tolerance in distributed systems is not at all easy. These systems are complex, with multiple nodes or components working together. A failure in one node can cascade across the system if not addressed timely. Moreover, the inherently distributed nature of these systems can make it challenging to pinpoint the exact location and cause of fault - that is why modern systems rely heavily on distributed tracing solutions pioneered by Google Dapper and widely available now in Jaeger and OpenTracing. But still, understanding and implementing fault tolerance becomes not just about addressing the failure but predicting and mitigating potential risks before they escalate.

In this article, we'll use HoneyComb.io as our tracing backend. While there are other tools in the market, some of which can be run on your local machine (e.g., Jaeger), I chose HoneyComb because of their complementary tools that offer improved monitoring of the service and insights into its behavior.

The best way to do this, is with the help of tracing tools such as paid tools such as Honeycomb, or your own instance of the open source Jaeger offering, or perhaps Encore's built in tracing system.

In this example, I will create 3 Node.js services (shipping, notification, and courier) using Amplication, add traces to all services, and show how to analyze trace data using Jaeger.