{"slug":"azure-monitor-opentelemetry-py","title":"azure-monitor-opentelemetry-py","summary":"Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation. Triggers: \"azure-monitor-opentelemetry\", \"configure_azure_monitor\", \"Application Insights\", \"OpenTelemetry distro\", \"auto-instrumentation\".","platform":"GitHub Copilot","tags":[],"authorName":"Ciza","authorSlug":"ciza","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-08-12T21:05:09.137782Z","repo":{"url":"https://github.com/microsoft/skills","stars":3075,"forks":352,"license":"MIT","updatedAt":"2026-10-02T16:39:30Z"},"bodyHtml":"
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name: azure-monitor-opentelemetry-py\ndescription: |\nAzure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation.\nTriggers: \"azure-monitor-opentelemetry\", \"configure_azure_monitor\", \"Application Insights\", \"OpenTelemetry distro\", \"auto-instrumentation\".\nlicense: MIT\nmetadata:\nauthor: Microsoft\nversion: \"1.0.0\"\npackage: azure-monitor-opentelemetry

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Azure Monitor OpenTelemetry Distro for Python

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

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Installation

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pip install azure-monitor-opentelemetry\n
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Environment Variables

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APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/  # Required for all auth methods\nAZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production\n
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Authentication & Lifecycle

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\uD83D\uDD11 Two rules apply to every code sample below:

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  1. Prefer DefaultAzureCredential for ingestion auth when supported. APPLICATIONINSIGHTS_CONNECTION_STRING identifies the target Application Insights resource, and credential=DefaultAzureCredential(...) provides Microsoft Entra authentication.\n
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    • Local dev: DefaultAzureCredential works as-is.
      • \n
    • Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.
      • \n
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    2. \n
  2. Providers are not context managers. Flush and shut down telemetry providers explicitly at process exit so buffers are exported deterministically.
    3. \n
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Snippets may abbreviate this setup, but production code should always follow both rules.

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Quick Start

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from azure.identity import DefaultAzureCredential\nfrom azure.monitor.opentelemetry import configure_azure_monitor\n\n# Connection string identifies the App Insights resource (read from APPLICATIONINSIGHTS_CONNECTION_STRING env var).\n# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID (preferred over instrumentation-key-only auth).\nconfigure_azure_monitor(\n    credential=DefaultAzureCredential(),\n)\n\n# Your application code...\n
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Explicit Connection String

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Pass the connection string explicitly by reading it from the environment variable.\nThe value includes both InstrumentationKey and IngestionEndpoint.

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import os\nfrom azure.monitor.opentelemetry import configure_azure_monitor\n\n# Read the full connection string from the environment.\n# Format: \"InstrumentationKey=<key>;IngestionEndpoint=https://<id>.in.applicationinsights.azure.com/\"\nconnection_string = os.environ[\"APPLICATIONINSIGHTS_CONNECTION_STRING\"]\n\ntry:\n    configure_azure_monitor(\n        connection_string=connection_string,\n    )\n    # Your application code...\nexcept Exception as exc:\n    raise RuntimeError(f\"Azure Monitor configuration failed: {exc}\") from exc\n
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With Flask

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from flask import Flask\nfrom azure.monitor.opentelemetry import configure_azure_monitor\n\nconfigure_azure_monitor()\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef hello():\n    return \"Hello, World!\"\n\nif __name__ == \"__main__\":\n    app.run()\n
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With Django

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# settings.py\nfrom azure.monitor.opentelemetry import configure_azure_monitor\n\nconfigure_azure_monitor()\n\n# Django settings...\n
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With FastAPI

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from fastapi import FastAPI\nfrom azure.monitor.opentelemetry import configure_azure_monitor\n\nconfigure_azure_monitor()\n\napp = FastAPI()\n\n@app.get(\"/\")\nasync def root():\n    return {\"message\": \"Hello World\"}\n
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Custom Traces

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from opentelemetry import trace\nfrom azure.monitor.opentelemetry import configure_azure_monitor\n\nconfigure_azure_monitor()\n\ntracer = trace.get_tracer(__name__)\n\nwith tracer.start_as_current_span(\"my-operation\") as span:\n    span.set_attribute(\"custom.attribute\", \"value\")\n    # Do work...\n
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Custom Metrics

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from opentelemetry import metrics\nfrom azure.monitor.opentelemetry import configure_azure_monitor\n\nconfigure_azure_monitor()\n\nmeter = metrics.get_meter(__name__)\ncounter = meter.create_counter(\"my_counter\")\n\ncounter.add(1, {\"dimension\": \"value\"})\n
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Custom Logs

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import logging\nfrom azure.monitor.opentelemetry import configure_azure_monitor\n\nconfigure_azure_monitor()\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\nlogger.info(\"This will appear in Application Insights\")\nlogger.error(\"Errors are captured too\", exc_info=True)\n
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Sampling

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from azure.monitor.opentelemetry import configure_azure_monitor\n\n# Sample 10% of requests\nconfigure_azure_monitor(\n    sampling_ratio=0.1\n)\n
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Cloud Role Name

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Set cloud role name for Application Map:

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from azure.monitor.opentelemetry import configure_azure_monitor\nfrom opentelemetry.sdk.resources import Resource, SERVICE_NAME\n\nconfigure_azure_monitor(\n    resource=Resource.create({SERVICE_NAME: \"my-service-name\"})\n)\n
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Disable Specific Instrumentations

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from azure.monitor.opentelemetry import configure_azure_monitor\n\nconfigure_azure_monitor(\n    instrumentations=[\"flask\", \"requests\"]  # Only enable these\n)\n
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Enable Live Metrics

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from azure.monitor.opentelemetry import configure_azure_monitor\n\nconfigure_azure_monitor(\n    enable_live_metrics=True\n)\n
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Azure AD Authentication

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from azure.monitor.opentelemetry import configure_azure_monitor\nfrom azure.identity import DefaultAzureCredential, ManagedIdentityCredential\n\n# Local dev: DefaultAzureCredential. In production, set AZURE_TOKEN_CREDENTIALS=prod or use a specific credential.\ncredential = DefaultAzureCredential()\n# Or use a specific credential directly in production:\n# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes\n# credential = ManagedIdentityCredential()\n\nconfigure_azure_monitor(\n    credential=credential\n)\n
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Auto-Instrumentations Included

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LibraryTelemetry Type
FlaskTraces
DjangoTraces
FastAPITraces
RequestsTraces
urllib3Traces
httpxTraces
aiohttpTraces
psycopg2Traces
pymysqlTraces
pymongoTraces
redisTraces
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Configuration Options

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ParameterDescriptionDefault
connection_stringApplication Insights connection stringFrom env var
credentialAzure credential for AAD authNone
sampling_ratioSampling rate (0.0 to 1.0)1.0
resourceOpenTelemetry ResourceAuto-detected
instrumentationsList of instrumentations to enableAll
enable_live_metricsEnable Live Metrics streamFalse
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Best Practices

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  1. Pick sync OR async and stay consistent. Do not mix azure.xxx sync clients with azure.xxx.aio async clients in the same call path. Choose one mode per module.
    2. \n
  2. Call provider.shutdown() / force_flush() at process exit to flush telemetry — providers are not context managers.
    3. \n
  3. Call configure_azure_monitor() early — Before importing instrumented libraries
    4. \n
  4. Use environment variables for connection string in production
    5. \n
  5. Set cloud role name for multi-service applications
    6. \n
  6. Enable sampling in high-traffic applications
    7. \n
  7. Use structured logging for better log analytics queries
    8. \n
  8. Add custom attributes to spans for better debugging
    9. \n
  9. Use Microsoft Entra authentication for production workloads
    10. \n
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Reference Files

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FileContents
references/capabilities.mdAdditional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.mdDedicated non-hero examples for secondary/advanced scenarios.
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