Automatic instrumentation with OpenTelemetry for Python applications

DevOps and SRE teams are transforming the process of software development. While DevOps engineers focus on efficient software applications and service delivery, SRE teams are key to ensuring reliability, scalability, and performance. These teams must rely on a full-stack observability solution that allows them to manage and monitor systems and ensure issues are resolved before they impact the business.  

Observability across the entire stack of modern distributed applications requires data collection, processing, and correlation often in the form of dashboards. Ingesting all system data requires installing agents across stacks, frameworks, and providers — a process that can be challenging and time-consuming for teams who have to deal with version changes, compatibility issues, and proprietary code that doesn't scale as systems change.      

Thanks to OpenTelemetry (OTel), DevOps and SRE teams now have a standard way to collect and send data that doesn't rely on proprietary code and has a large support community reducing vendor lock-in.  

In a previous blog, we also reviewed how to use the OpenTelemetry demo and connect it to Elastic^®, as well as some of Elastic’s capabilities with OpenTelemetry visualizations and Kubernetes. 

In this blog, we will show how to use automatic instrumentation for OpenTelemetry with the Python service of our application called Elastiflix, which helps highlight auto-instrumentation in a simple way.

The beauty of this is that there is no need for the otel-collector! This setup enables you to slowly and easily migrate an application to OTel with Elastic according to a timeline that best fits your business.

All of Elastic Observability’s APM capabilities are available with OTel data. Some of these include:

• Service maps
• Service details (latency, throughput, failed transactions)
• Dependencies between services, distributed tracing
• Transactions (traces)
• Machine learning (ML) correlations
• Log correlation

In addition to Elastic’s APM and a unified view of the telemetry data, you will also be able to use Elastic’s powerful machine learning capabilities to reduce the analysis, and alerting to help reduce MTTR.

We are going to use automatic instrumentation with Python service from the Elastiflix demo application.

We will be using the following service from Elastiflix:

Per the OpenTelemetry Automatic Instrumentation for Python documentation, you will simply install the appropriate Python packages using pip install.

If you are running the Python service on the command line, then you can use the following command:

For our application, we do this as part of the Dockerfile.

Dockerfile

As specified in the OTEL Python documentation, we will use environment variables and pass in the configuration values to enable it to connect with Elastic Observability’s APM server.  

Because Elastic accepts OTLP natively, we just need to provide the Endpoint and authentication where the OTEL Exporter needs to send the data, as well as some other environment variables.

Getting Elastic Cloud variables
You can copy the endpoints and token from Kibana^® under the path /app/home#/tutorial/apm.

You will need to copy the following environment variables:

Build the image

Run the image

Important: Note that the “OTEL_EXPORTER_OTLP_HEADERS” variable has the whitespace after Bearer escaped as “%20” — this is a requirement for Python.

You can now issue a few requests in order to generate trace data. Note that these requests are expected to return an error, as this service relies on a connection to Redis that you don’t currently have running. As mentioned before, you can find a more complete example using docker-compose here.

Exploring the Services section in Elastic APM, you’ll see the Python service displayed.

Clicking on the python-favorite-otel-auto service , you can see that it is ingesting telemetry data using OpenTelemetry.

In this blog, we discussed the following:

• How to auto-instrument Python with OpenTelemetry
• Using standard commands in a Dockerfile, auto-instrumentation was done efficiently and without adding code in multiple places

Since Elastic can support a mix of methods for ingesting data, whether it be using auto-instrumentation of open-source OpenTelemetry or manual instrumentation with its native APM agents, you can plan your migration to OTel by focusing on a few applications first and then using OpenTelemety across your applications later on in a manner that best fits your business needs.

Don’t have an Elastic Cloud account yet? Sign up for Elastic Cloud and try out the auto-instrumentation capabilities that I discussed above. I would be interested in getting your feedback about your experience in gaining visibility into your application stack with Elastic.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.