Examples for using Opentelmetry in a Kubernetes environment.
Opentelemetry and Kubernetes are two of the most popular CNCF projects.
Kubernetes is an already accepted solution for a variety of orchestration usecases, it has been tried and tested for deploying a cluster of microservices at different scales. In a distributed environment with multiple microservices, a kubernetes cluster can get quite complex as the microservices interact with each other. In such a setup, if there is no uniform layer for observability, the MTTD (Mean Time To Detect) and the MTTR (Mean Time To Resolve) can significantly go up. Having a standard observability layer would help in this situation by allowing for fetching the telemetry data (metrics, traces, logs) and displaying it in a dashboarding tool such as Grafana to trace out the flow and pinpoint the problem.
Opentelemetry is aimed at providing a standard protocol and tools for observability. It defines the opentelemetry specification for instrumenting telemetry data. The benefit of having a standard specification for telemetry data is in using the data with other tools down the line. The instrumented data can then be integrated with tools like Jaeger, Zipkin, Prometheus, etc. or with some vendor-specific tools.
This experiment demonstrates how to set up a single application with a single opentelemetry collector. The application is a simple FastAPI application that has been auto-instrumented using the opentelemetry-instrument command line tool. The collector is configured to receive telemetry data from the application and log the telemetry data.
Following applications are defined in this repository.
| Application | Instrumentation | Description | Reference |
|---|---|---|---|
| Simple Fastapi Application | Auto Instrumentation | A simple FastAPI application that is instrumented with OpenTelemetry using the OpenTelemetry Auto Instrumentation for Python. It listens on the port 8000, and generates telemetry data for each request. | README.md |