opentelemetry-specification VS jaeger

One update to this: we proposed replacing the count suffix in OpenTelemetry with total to match Prometheus/OpenMetrics. That discussion resulted in the count suffix being removed from the OpenTelemetry semantic conventions. We'll soon update our metric from being called function.calls.count to just function.calls and the generated Prometheus queries will refer to function_calls_total. That resolves one of the main conflicts between the two specs.

OpenTelemetry is a standard for implementing telemetry in your applications. It provides a specification, containing the requirements that all implementations should follow as well as some implementations for major languages, including an API and a SDK to interact with it.

At this point, we are about to do the real work where we have to configure OpenTelemetry and export telemetry data to New Relic. Exporting this kind of data relies on a specific protocol; the OpenTelemetry Protocol or OTLP.

OpenTelemetry provides instrumentation libraries for your application. The development of these libraries is guided by the OpenTelemetry specification. The OpenTelemetry specification describes the cross-language requirements and design expectations for all OpenTelemetry implementations in various programming languages.

It follows a specification-driven development. The OpenTelemetry specification has design and implementation guidelines for how the instrumentation libraries should be implemented. In addition, it provides client libraries in all the major programming languages which follow the specification.

Today we learned how to manually configure OpenTelemetry for Python to connect to Lightstep (this also works for any Observability back-end that ingests the OTLP format). We also learned how to link related services together through manual context propagation.

According to the opentelemetry specification for messaging systems, When a process receives messages in a batch it is impossible for this process to determine the parent span for the span that it is currently creating.

That's why you have otel logging: https://github.com/open-telemetry/opentelemetry-specificatio...

OTLP protocol for shipping telemetry data

Lightstep ingests data in native OpenTelemetry Protocol (OTLP) format, so we will use the OTLP Exporter. The exporter can be called either otlp or follow the naming format otlp/. We could call it otlp/bob if we wanted to. We're calling our exporter otlp/ls to signal to us that we are using the OTLP exporter to send the data to Lightstep.

Otro servicio open source para tracing es Jaeger que sí soporta OpenTelemetry

Now that Quickwit is an official Jaeger backend, we have published a step-by-step guide to tracing your Node.js application using Quickwit and Jaeger. https://quickwit.io/blog/quickwit-as-official-jaeger-backend

I know about tools such as tracing, jaeger or tracy. While having a complete tracing could be a potential solution, these tools don't work with no_std.

In our specific implementation, we used Jaeger, which is based on the OpenTracing API.

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.

Jaeger is an open-source end-to-end tracing tool designed to help developers monitor and troubleshoot transactions in distributed environments. The goal is to simplify how developers debug a set of distributed services, which is far more complex than dealing with a single monolith.

Jaeger is an open source distributed tracing tool. It can store trace data in both Cassandra and Elasticsearch.

Jaeger is a distributed tracing tool initially built by Uber and released as open source in 2015. Jaeger is also a Cloud Native Computing Foundation graduate project and was influenced by Dapper and OpenZipkin. It is used for monitoring and troubleshooting microservices-based distributed systems.