Azure Monitor OpenTelemetry Distro for Python

One-line setup for Application Insights with OpenTelemetry auto-instrumentation.

Installation

bash
pip install azure-monitor-opentelemetry

Environment Variables

bash
APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/

Quick Start

python
from azure.monitor.opentelemetry import configure_azure_monitor

# One-line setup - reads connection string from environment
configure_azure_monitor()

# Your application code...

Explicit Configuration

python
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/"
)

With Flask

python
from flask import Flask
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

app = Flask(__name__)

@app.route("/")
def hello():
    return "Hello, World!"

if __name__ == "__main__":
    app.run()

With Django

python
# settings.py
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

# Django settings...

With FastAPI

python
from fastapi import FastAPI
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

app = FastAPI()

@app.get("/")
async def root():
    return {"message": "Hello World"}

Custom Traces

python
from opentelemetry import trace
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span("my-operation") as span:
    span.set_attribute("custom.attribute", "value")
    # Do work...

Custom Metrics

python
from opentelemetry import metrics
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

meter = metrics.get_meter(__name__)
counter = meter.create_counter("my_counter")

counter.add(1, {"dimension": "value"})

Custom Logs

python
import logging
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

logger.info("This will appear in Application Insights")
logger.error("Errors are captured too", exc_info=True)

Sampling

python
from azure.monitor.opentelemetry import configure_azure_monitor

# Sample 10% of requests
configure_azure_monitor(
    sampling_ratio=0.1
)

Cloud Role Name

Set cloud role name for Application Map:

python
from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry.sdk.resources import Resource, SERVICE_NAME

configure_azure_monitor(
    resource=Resource.create({SERVICE_NAME: "my-service-name"})
)

Disable Specific Instrumentations

python
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    instrumentations=["flask", "requests"]  # Only enable these
)

Enable Live Metrics

python
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    enable_live_metrics=True
)

Azure AD Authentication

python
from azure.monitor.opentelemetry import configure_azure_monitor
from azure.identity import DefaultAzureCredential

configure_azure_monitor(
    credential=DefaultAzureCredential()
)

Auto-Instrumentations Included

| Library | Telemetry Type | |---------|---------------| | Flask | Traces | | Django | Traces | | FastAPI | Traces | | Requests | Traces | | urllib3 | Traces | | httpx | Traces | | aiohttp | Traces | | psycopg2 | Traces | | pymysql | Traces | | pymongo | Traces | | redis | Traces |

Configuration Options

| Parameter | Description | Default | |-----------|-------------|---------| | connection_string | Application Insights connection string | From env var | | credential | Azure credential for AAD auth | None | | sampling_ratio | Sampling rate (0.0 to 1.0) | 1.0 | | resource | OpenTelemetry Resource | Auto-detected | | instrumentations | List of instrumentations to enable | All | | enable_live_metrics | Enable Live Metrics stream | False |

Best Practices

1. Call configure_azure_monitor() early — Before importing instrumented libraries
2. Use environment variables for connection string in production
3. Set cloud role name for multi-service applications
4. Enable sampling in high-traffic applications
5. Use structured logging for better log analytics queries
6. Add custom attributes to spans for better debugging
7. Use AAD authentication for production workloads

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.