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/ # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
π Auth & lifecycle: This distro is configured with a connection string by design, but for AAD-authenticated ingestion (where supported) prefer
DefaultAzureCredentialvia thecredential=parameter β see the Azure AD Authentication section. Any Azure SDK clients you create alongside the exporter should be wrapped inwith/async withblocks (and async credentials fromazure.identity.aiolikewise).
Quick Start
from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry import configure_azure_monitor
# Connection string identifies the App Insights resource (read from APPLICATIONINSIGHTS_CONNECTION_STRING env var).
# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID (preferred over instrumentation-key-only auth).
configure_azure_monitor(
credential=DefaultAzureCredential(),
)
# Your application code...
Explicit Configuration
from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry import configure_azure_monitor
# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env to identify the resource;
# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID.
configure_azure_monitor(
credential=DefaultAzureCredential(),
)
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, ManagedIdentityCredential
# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
configure_azure_monitor(
credential=credential
)
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
- Pick sync OR async and stay consistent. Do not mix
azure.xxxsync clients withazure.xxx.aioasync clients in the same call path. Choose one mode per module. - Flush and shut down providers at process exit. Call the shutdown/flush APIs (e.g.
tracer_provider.shutdown(),meter_provider.shutdown(),logger_provider.shutdown()) at process exit to flush telemetry before the process terminates. - Call configure_azure_monitor() early β Before importing instrumented libraries
- Use environment variables for connection string in production
- Set cloud role name for multi-service applications
- Enable sampling in high-traffic applications
- Use structured logging for better log analytics queries
- Add custom attributes to spans for better debugging
- Use Microsoft Entra authentication for production workloads