Azure Monitor Opentelemetry Distro client library for Python - version 1.2.0

The Azure Monitor Distro of Opentelemetry Python provides multiple installable components available for an Opentelemetry Azure Monitor monitoring solution. It allows you to instrument your Python applications to capture and report telemetry to Azure Monitor via the Azure monitor exporters.

This distro automatically installs the following libraries:

Officially supported instrumentations

OpenTelemetry instrumentations allow automatic collection of requests sent from underlying instrumented libraries. The following is a list of OpenTelemetry instrumentations that come bundled in with the Azure monitor distro. These instrumentations are enabled by default. See the Usage section below for how to opt-out of these instrumentations.

Instrumentation Supported library Name Supported versions
Azure Core Tracing OpenTelemetry azure_sdk
OpenTelemetry Django Instrumentation django link
OpenTelemetry FastApi Instrumentation fastapi link
OpenTelemetry Flask Instrumentation flask link
OpenTelemetry Psycopg2 Instrumentation psycopg2 link
OpenTelemetry Requests Instrumentation requests link
OpenTelemetry UrlLib Instrumentation urllib All
OpenTelemetry UrlLib3 Instrumentation urllib3 link

If you would like to add support for another OpenTelemetry instrumentation, please submit a feature request. In the meantime, you can use the OpenTelemetry instrumentation manually via it's own APIs (i.e. instrument()) in your code. See this for an example.

Key concepts

This package bundles a series of OpenTelemetry and Azure Monitor components to enable the collection and sending of telemetry to Azure Monitor. For MANUAL instrumentation, use the configure_azure_monitor function. AUTOMATIC instrumentation is not yet supported.

The Azure Monitor OpenTelemetry exporters are the main components in accomplishing this. You will be able to use the exporters and their APIs directly through this package. Please go the exporter documentation to understand how OpenTelemetry and Azure Monitor components work in enabling telemetry collection and exporting.

Currently, all instrumentations available in OpenTelemetry are in a beta state, meaning they are not stable and may have breaking changes in the future. Efforts are being made in pushing these to a more stable state.

Getting started


To use this package, you must have:

Install the package

Install the Azure Monitor Opentelemetry Distro with pip:

pip install azure-monitor-opentelemetry


You can use configure_azure_monitor to set up instrumentation for your app to Azure Monitor. configure_azure_monitor supports the following optional arguments. All pass-in parameters take priority over any related environment variables.

Parameter Description Environment Variable
connection_string The connection string for your Application Insights resource. The connection string will be automatically populated from the APPLICATIONINSIGHTS_CONNECTION_STRING environment variable if not explicitly passed in. APPLICATIONINSIGHTS_CONNECTION_STRING
logger_name The name of the Python logger under which telemetry is collected. N/A
instrumentation_options A nested dictionary that determines which instrumentations to enable or disable. Instrumentations are referred to by their Library Names. For example, {"azure_sdk": {"enabled": False}, "flask": {"enabled": False}, "django": {"enabled": True}} will disable Azure Core Tracing and the Flask instrumentation but leave Django and the other default instrumentations enabled. The OTEL_PYTHON_DISABLED_INSTRUMENTATIONS environment variable explained below can also be used to disable instrumentations. N/A

You can configure further with OpenTelemetry environment variables such as: | Environment Variable | Description | |-------------|----------------------| | OTEL_SERVICE_NAME, OTEL_RESOURCE_ATTRIBUTES | Specifies the OpenTelemetry Resource associated with your application. | | OTEL_LOGS_EXPORTER | If set to None, disables collection and export of logging telemetry. | | OTEL_METRICS_EXPORTER | If set to None, disables collection and export of metric telemetry. | | OTEL_TRACES_EXPORTER | If set to None, disables collection and export of distributed tracing telemetry. | | OTEL_BLRP_SCHEDULE_DELAY | Specifies the logging export interval in milliseconds. Defaults to 5000. | | OTEL_BSP_SCHEDULE_DELAY | Specifies the distributed tracing export interval in milliseconds. Defaults to 5000. | | OTEL_TRACES_SAMPLER_ARG | Specifies the ratio of distributed tracing telemetry to be sampled. Accepted values are in the range [0,1]. Defaults to 1.0, meaning no telemetry is sampled out. | | OTEL_PYTHON_DISABLED_INSTRUMENTATIONS | Specifies which of the supported instrumentations to disable. Disabled instrumentations will not be instrumented as part of configure_azure_monitor. However, they can still be manually instrumented with instrument() directly. Accepts a comma-separated list of lowercase Library Names. For example, set to "psycopg2,fastapi" to disable the Psycopg2 and FastAPI instrumentations. Defaults to an empty list, enabling all supported instrumentations. | | OTEL_EXPERIMENTAL_RESOURCE_DETECTORS | An experimental OpenTelemetry environment variable used to specify Resource Detectors to be used to generate Resource Attributes. This is an experimental feature and the name of this variable and its behavior can change in a non-backwards compatible way. Defaults to "azure_app_service,azure_vm" to enable the Azure Resource Detectors for Azure App Service and Azure VM. To add or remove specific resource detectors, set the environment variable accordingly. See the OpenTelemetry Python Resource Detector Documentation for more. |

Azure monitor OpenTelemetry Exporter configurations

You can pass Azure monitor OpenTelemetry exporter configuration parameters directly into configure_azure_monitor. See additional configuration related to exporting here.



Samples are available here to demonstrate how to utilize the above configuration options.

Monitoring in Azure Functions

Trace correlation

Tracked incoming requests coming into your Python application hosted in Azure Functions will not be automatically correlated with telemetry being tracked within it. You can manually achieve trace correlation by extract the TraceContext directly as shown below:

import azure.functions as func

from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry import trace
from opentelemetry.propagate import extract

# Configure Azure monitor collection telemetry pipeline

def main(req: func.HttpRequest, context) -> func.HttpResponse:
   # Store current TraceContext in dictionary format
   carrier = {
      "traceparent": context.trace_context.Traceparent,
      "tracestate": context.trace_context.Tracestate,
   tracer = trace.get_tracer(__name__)
   # Start a span using the current context
   with tracer.start_as_current_span(

Logging issues

The Azure Functions worker itself sends logging telemetry itself without the use of the azure monitor sdk (the call to configure_azure_monitor()). This will cause you to possibly experience duplicate telemetry entries when sending logging telemetry. Our recommendation to customers is to use solely the SDK as it will allow much more rich telemetry and features than using the built in one provided by the Azure Functions worker. You can turn off the Azure Functions telemetry logger by clearing the list of handlers of your logger.

root_logger = logging.getLogger()
for handler in root_logger.handlers[:]:

Be sure to call the above BEFORE any loggers or the call to configure_azure_monitor() is setup.

You may also disable logging through Azure Functions configuration.


  "logging": {
    "logLevel": {
      "default": "None",


  "logger": {
    "categoryFilter": {
      "defaultLevel": "None",


The exporter raises exceptions defined in Azure Core.

Next steps

Check out the documentation for more.


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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact with any additional questions or comments.

Additional documentation