Python OpenTelemetry auto-instrumentation with Helios
Benefit from distributed tracing and observability over microservices in Python. The Helios SDK leverages OpenTelemetry to instrument services and tests in Python.
Python is one of the most common languages used when instrumenting microservices with a distributed application using OpenTelemetry. With the Helios' Python OpenTelemetry SDK dev teams can easily instrument their application, and benefit from the deep context propagation capabilities built-into the platform to achieve the following:
- Fast troubleshooting of complex sync and async E2E flows
- Easily finding & investigating bottlenecks and other pain points in specific flows
- Applying API observability to auto-generate the API catalog and reduce MTTR when troubleshooting APIs
- Monitoring of app behavior including all the context needed to get to the root cause quickly
Instrumenting your Python microservices allows teams to unlock the power of distributed tracing and benefit from dev-first observability including 1-click access to full E2E trace visualization from logs and errors, issue reproduction, debugging tests, and more.
Check out this blog post to learn more about using OpenTelemetry in Python, including a walkthrough and an example.
Get your Python services auto-instrumented with Helios and OTel by following the installation instructions provided below.
Helios' Python OpenTelemetry SDK is available for installation from PyPI:
https://pypi.org/project/helios-opentelemetry-sdk/
Minimum Python version supported by Helios is 3.7.
An example for a trace from the Helios OpenTelemetry Sandbox that includes instrumentation of a Go service and spans:
A trace from the Helios OpenTelemetry Sandbox that propagates through many microservices, including Python
Installation docs & references
Updated 4 months ago