OpenTelemetry Tracing in < 200 lines of code
OpenTelemetry tracing simplifies to structured logging and context propagation, using spans for metadata. It supports distributed tracing via HTTP headers and requires instrumentation to capture trace data for monitoring.
Read original articleOpenTelemetry tracing can often seem complex to developers, who may view it as a "black box." However, it can be simplified into a model that resembles structured logging combined with context propagation. Tracing involves creating spans, which are essentially enhanced log entries that include essential fields like name, timestamp, duration, and IDs (trace ID, span ID, and parent span ID). Each trace is a collection of these spans, allowing developers to track the flow of requests through their applications.
To implement tracing, developers can create a simple library that generates spans and manages context. This library can be expanded to support distributed tracing, which allows trace context to be passed between different systems. This is achieved by including trace information in HTTP headers, following a standardized format. Instrumentation, or wrapping existing code to track operations, is also crucial for effective tracing.
The article concludes with a demonstration of how to send trace data to a service like Honeycomb, emphasizing the importance of structured data for effective monitoring and debugging in distributed systems.
- OpenTelemetry tracing simplifies to structured logging and context propagation.
- Spans are key components of tracing, containing essential metadata.
- Distributed tracing allows tracking across multiple systems using standardized HTTP headers.
- Instrumentation is necessary for wrapping code to capture trace data.
- Sending trace data to services like Honeycomb enhances monitoring capabilities.
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However, if you want to figure out how it works you might be scared, it is not lightweight. I just spent a few days digging through the Azure Application Insights NodeJS code base which integrates with OpenTelemetry packages. It's an utter mess, a huge amount of abstractions. Adding it to the project brought 100 MB and around 40 extra packages.
for example, we translate our loosely OTEL-based telemetry into a format which was consumable by any otel collector.
shim a few fields, et voila! can be read by Jaeger-UI. free trace tree visualization.
Setting this up in the mess that is Python/Gunicorn/asgi/wsgi/celery/Django has not been as easy
I have used application insights on Azure on my day job but I was wondering is there a simple self hosted collector/UI to use?
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