Sets up Azure Monitor OpenTelemetry for Python apps with one-line Application Insights instrumentation. Auto-instruments Flask, Django, FastAPI; supports custom traces, metrics, logs.
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One-line setup for Application Insights with OpenTelemetry auto-instrumentation.
pip install azure-monitor-opentelemetry
APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/
from azure.monitor.opentelemetry import configure_azure_monitor
# One-line setup - reads connection string from environment
configure_azure_monitor()
# Your application code...
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor(
connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/"
)
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()
# settings.py
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor()
# Django settings...
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"}
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...
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"})
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)
from azure.monitor.opentelemetry import configure_azure_monitor
# Sample 10% of requests
configure_azure_monitor(
sampling_ratio=0.1
)
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"})
)
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor(
instrumentations=["flask", "requests"] # Only enable these
)
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor(
enable_live_metrics=True
)
from azure.monitor.opentelemetry import configure_azure_monitor
from azure.identity import DefaultAzureCredential
configure_azure_monitor(
credential=DefaultAzureCredential()
)
| 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 |
| 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 |
This skill is applicable to execute the workflow or actions described in the overview.