opentelemetry-js VS prom-client

To enable only @opentelemetry/instrumentation-http and @opentelemetry/instrumentation-express you can run this command below.

JavaScript

> OpenTelemetry is a marketing-driven project, designed by committee, implemented naively and inefficiently, and guided by the primary goal of allowing Fortune X00 CTOs to tick off some boxes on their strategy roadmap documents.

I'm the founder of highlight.io. On the consumer side as a company, we've seen a lot of value of from OTEL; we've used it to build out language support for quite a few customers at this point, and the community is very receptive.

Here's an example of us putting up a change: https://github.com/open-telemetry/opentelemetry-js/pull/4049

Do you mind sharing why you think no-one should be using it? Some reasoning would be nice.

That's traces? I was wondering if I could use https://github.com/open-telemetry/opentelemetry-js/tree/main...

Nothing like Faro for now. However, https://github.com/open-telemetry/opentelemetry-js can be used to achieve the same result and OpenObserve has great support for Opentelemetry.

Can it be integrated with Grafana Faro or OpenTelemetry?

4. Register automatic instrumentations. For example, you can automatically trace all XHR requests, fetch requests, page loads, and user interactions. With distributed tracing, you should always prefer automatic instrumentation whenever possible to reduce maintenance and leverage existing conventions. The document load instrumentation allows you to treat the server as the parent span to a given page load, from which the client will then be the root span to everything in the server. This is an extremely powerful concept that allows traces to viewed from the perspective of the user, encapsulating all requests and user interactions in a single trace with no manual instrumentation!

We also trialed Sentry's APM tool (also marketed as a distributed tracing tool). While it had pretty charts, dashboards, niceties like core web vitals, and integrated well with Sentry's error product, its utility as a distributed tracing tool is significantly less powerful than tools like Honeycomb. You will end up using a large amount of your user's bandwidth sending telemetry data that can't fully be leveraged in the Sentry UI. When I last used (in April 2021), the spans of a given trace could only be viewed in a specific part the UI and they couldn't be searched for in queries or used in charts. I'm unsure if this has been updated. But this is not the worse part. Because Sentry uses its own data model for traces, it is not compatible with open source standards such as OpenTelemetry or OpenTracing! The sales team will not tell you this during the trial. This means our entire backend, which was already instrumented with OpenTracing, would now also need to instrument Sentry's tracing (...if they supported the language) in order to connect frontend traces to backend traces. Each team I met with their sales team, I said the same thing: support OpenTelemetry, otherwise you are asking for us to further isolate our backend and frontend teams.

It looks like they have heard this opinion, as they have recently published a blog post about the evolution of the distributed tracing API, citing incompatibility with OpenTelemetry due to their data model. It will require a very large change for them to support this. Meanwhile, OpenTelemetry can be used with any tracing vendor, a large number of languages, the other major instrumentation standards (OpenTracing, OpenCensus), and any trace propagation format.

- [1]: https://github.com/open-telemetry/opentelemetry-js

Consumer lag will quickly show any functional or technical issue with your event stream. By using the code examples from this blogpost, you can avoid having to dive into the SDKs yourself. Of course, you can adopt the metric collection to send the metric to the logs or to another metrics system like prometheus, datadog, or open telemetry.

I would work on getting Open Telemetry pointed at an “all-in-one” Jaegar instance and move on from there: https://www.jaegertracing.io/docs/1.25/getting-started/ https://github.com/open-telemetry/opentelemetry-js Various cloud providers may have a way to collect/view traces as well, but, Jaegar and the Open Telemetry Collector are the open source way to do that. The projects are in the process of converging in some ways — everything is in flux.

However, for this tutorial, the Prom-client package will be used as it has user-friendly documentation, a large user base and strong community support. Here is a link to its documentation and npm page.

prom-client

This approach will work as long as you don’t need to run multiple instances of the application in cluster mode. For collecting metrics in cluster mode, prom-client provides a good example with metric aggregation. But what if you’re running the application through the process manager PM2, where you don’t have access to the running cluster?

You would need to build a custom endpoint if it's a custom application that Prometheus can scrape but there are many libraries for that like prom-client for NodeJS or prometheus-net for C#. The list of client libraries can be found here.

For node, try this: https://github.com/siimon/prom-client

The metric itself is process_start_time_seconds (from the prom-client NodeJS package), which returns the timestamp in seconds corresponding to the start time of the process.

Consumer lag will quickly show any functional or technical issue with your event stream. By using the code examples from this blogpost, you can avoid having to dive into the SDKs yourself. Of course, you can adopt the metric collection to send the metric to the logs or to another metrics system like prometheus, datadog, or open telemetry.

There is a node client library for backend dev.