opentelemetry-demo VS jaeger

Here is a typical trace from the OpenTelemetry demo project.

# test suite based on https://github.com/open-telemetry/opentelemetry-demo/tree/main/test/tracetesting/frontend-service type: Test spec: id: frontend-view-cart name: 'Frontend: View cart' description: Simulate a user viewing the shopping cart trigger: type: http httpRequest: url: http://${var:FRONTEND_ADDR}/api/cart?userId=2491f868-88f1-4345-8836-d5d8511a9f83 method: GET headers: - key: Content-Type value: application/json specs: - name: It called the frontend with success selector: span[tracetest.span.type="general" name="Tracetest trigger"] assertions: - attr:tracetest.response.status = 200 - name: It retrieved the cart items correctly selector: span[name="oteldemo.CartService/GetCart"] assertions: - attr:rpc.grpc.status_code = 0

Contributing to the OpenTelemetry Demo is a great way to get involved and showcase your skills in the OpenTelemetry community. It's a real-world example of OpenTelemetry in action, and by actively contributing, you enhance your understanding and improve the project's quality.

Trace-based testing added to OpenTelemetry Demo

Examples and the tutorial in this blog post use the OpenTelemetry Astronomy Shop Demo to show what you can do with OpenTelemetry and New Relic. This application is built and maintained by the OpenTelemetry open-source community, and it provides a real-world example of a distributed application that’s been instrumented with OpenTelemetry. In the Deploying the OpenTelemetry Astronomy Shop demo app section, you’ll have an opportunity to get hands-on experience spinning up your own version of this application. You’ll learn how to:

Take this https://github.com/open-telemetry/opentelemetry-demo

I mean, you really just need experience instrumenting apps and tinkering with them to play with OTEL. https://github.com/open-telemetry/opentelemetry-demo is a good start. You can modify the collector to point to any backend of your choice.

I got to play around with these newer features last December, after a months-long hiatus, and it was really cool to see the evolution of the product. If you follow my work, you’ll know that I play in both the Kubernetes and Nomad worlds. Today, I’ll be taking you on a quick little guided tour of Tracetest, using Traces from the OpenTelemetry Demo App to give you a feel for how it works. The whole setup will be running on HashiCorp Nomad. \

​ By having an observability infrastructure gather information about a set of API/microservices, we can have a concise view of the operation of these services and start thinking in an observability-driven way to test your software. ​ Tracetest can help. When given an API endpoint, Tracetest checks observability traces to see if this API is behaving as intended. ​ For example, let’s try to test an OpenTelemetry Astronomy Store which has the exact same use cases that we want to check. ​ To test the "Add product to the shopping cart" task, we can create a test, define a URL and payload in the trigger section that we send to the Cart API and use the specs to define our assertions, checking if the API was called with the correct Product ID and if this product was persisted correctly. ​

In my latest Nomadification Project (TM), I got the OpenTelemetry Demo App to run on Nomad (with HashiQube, of course). To do this, I used the OpenTelemetry Demo App Helm Chart as my guide. In doing this, and other Nomadifications, I realized that I’ve never gone through the process of explaining the conversion process from Kubernetes manifests to Nomad jobspecs.

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.