odigos VS b3-propagation

Welcome 👋 to this blog. Did you want to use odigos on your Ubuntu machine but don't know how to start? Then this blog will definitely help you and it also helps you to understand the basic aspect of the odigos project.

Disclaimer: I am one of the maintainers

Many comments complain about the complexity of using OpenTelemetry, I recommend checking out Odigos, an open-source project which makes working with OpenTelemetry much easier: https://github.com/keyval-dev/odigos

We combine OpenTelemetry and eBPF to instantly generate distributed traces without any code changes.

Check it out here: https://github.com/keyval-dev/odigos

Hi, sure here is the link to Odigos: https://github.com/keyval-dev/odigos We do not replace Grafana or any other monitoring vendor. We build them a better pipeline with higher quality signals (distributed tracing). They still do the visualization, it will just display better information.

Check out mor about Odigos, our open source project at https://github.com/keyval-dev/odigos

I've been playing with OTEL for a while, with a few backends like Jaeger and Zipkin, and am trying to figure out a way to perform end to end timing measurements across a graph of services triggered by any of several events.

Consider this scenario: There is a collection of services that talk to one another, and not all use HTTP. Say agent A0 makes a connection to agent A1, this is observed by service S0 which triggers service S1 to make calls to S2 and S3, which propagate elsewhere and return answers.

If we limit the scope of this problem to services explicitly making HTTP calls to other services, we can easily use the Propagators API [1] and use X-B3 headers [2] to propagate the trace context (trace ID, span ID, parent span ID) across this graph, from the origin through to the destination and back. This allows me to query the metrics collector (Jaeger or Zipkin) using this trace ID, look at the timestamps originating at the various services and do a T_end - T_start to determine the overall time taken by one call for a round trip across all the related services.

However, this breaks when a subset of these functions cannot propagate the B3 trace IDs for various reasons (e.g., a service is watching a specific state and acts when the state changes). I've been looking into OTEL and other related non-OTEL ways to capture metrics, but it appears there's not much research into this area though it does not seem like a unique or new problem.

Has anyone here looked at this scenario, and have you had any luck with OTEL or other mechanisms to get results?

[1] https://opentelemetry.io/docs/specs/otel/context/api-propaga...

[2] https://github.com/openzipkin/b3-propagation

[3] https://www.w3.org/TR/trace-context/

(Note that OpenTelemetry uses, by default, the W3C context propagation specification, while Istio uses the B3 context propagation specification – this can be modified).

The default format for context propagation is B3 so we use headers X-B3-TraceId and X-B3-SpanId