Instrument Python Apps with Azure Monitor

Azure Monitor OpenTelemetry Distro for Python

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

Installation

pip install azure-monitor-opentelemetry

Environment Variables

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

Quick Start

from azure.monitor.opentelemetry import configure_azure_monitor

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

# Your application code...

Explicit Configuration

from azure.monitor.opentelemetry import configure_azure_monitor

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

With Flask

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

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

configure_azure_monitor()

# Django settings...

With FastAPI

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

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

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

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

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:

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

from azure.monitor.opentelemetry import configure_azure_monitor

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

Enable Live Metrics

from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    enable_live_metrics=True
)

Azure AD Authentication

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.