Regtransfers/azure-monitor-opentelemetry-py

@ 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 enablelivemetrics; Enable Live Metrics stream; False

@ Best Practices

1. Call configureazuremonitor() 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.
• never 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.