Azure Monitor OpenTelemetry Distro para Python

Configuração em uma linha para Application Insights com auto-instrumentação OpenTelemetry.

Instalação

pip install azure-monitor-opentelemetry

Variáveis de Ambiente

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

Início Rápido

from azure.monitor.opentelemetry import configure_azure_monitor

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

# Your application code...

Configuração Explícita

from azure.monitor.opentelemetry import configure_azure_monitor

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

Com 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()

Com Django

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

configure_azure_monitor()

# Django settings...

Com 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"}

Traces Personalizados

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...

Métricas Personalizadas

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"})

Logs Personalizados

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)

Amostragem

from azure.monitor.opentelemetry import configure_azure_monitor

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

Nome da Função na Nuvem

Defina o nome da função na nuvem para o Mapa de Aplicações:

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"})
)

Desabilitar Instrumentações Específicas

from azure.monitor.opentelemetry import configure_azure_monitor

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

Habilitar Métricas ao Vivo

from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    enable_live_metrics=True
)

Autenticação Azure AD

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

configure_azure_monitor(
    credential=DefaultAzureCredential()
)

Auto-Instrumentações Incluídas

Opções de Configuração

Melhores Práticas

1. Chame configure_azure_monitor() cedo — Antes de importar bibliotecas instrumentadas
2. Use variáveis de ambiente para a string de conexão em produção
3. Defina o nome da função na nuvem para aplicações multisserviço

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