Azure Monitor Opentelemetry Exporter Py

by microsoft354361d83247MITListed Oct 8, 2026Updated Oct 8, 2026

Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter".

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AI-generated overview

Guides Python developers in exporting OpenTelemetry traces, metrics, and logs to Azure Application Insights.

What it does
This skill provides reference instructions and code samples for using the azure-monitor-opentelemetry-exporter Python package to send OpenTelemetry telemetry to Application Insights. It covers trace, metric, and log exporters, authentication with DefaultAzureCredential, sampling, offline storage, sovereign clouds, and configuration options. It produces guidance and example snippets rather than executable scripts.
When to use it
Use it when building a custom OpenTelemetry pipeline in Python that needs low-level control over exporting telemetry to Application Insights. It is also relevant when configuring authentication, sampling, or offline storage for such exporters.
Requirements
Requires the azure-monitor-opentelemetry-exporter Python package, the azure-identity package, and OpenTelemetry SDK packages. Needs an Application Insights connection string and, for Microsoft Entra authentication, Azure credentials. Network access to Azure ingestion endpoints is required. No scripts are shipped.

Azure Monitor OpenTelemetry Exporter for Python

Low-level exporter for sending OpenTelemetry traces, metrics, and logs to Application Insights.

Installation

bash
pip install azure-monitor-opentelemetry-exporter

Environment Variables

bash
APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/  # Required for all auth methodsAZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication & Lifecycle

🔑 Two rules apply to every code sample below:

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

Snippets may abbreviate this setup, but production code should always follow both rules.

When to Use

ScenarioUse
Quick setup, auto-instrumentationazure-monitor-opentelemetry (distro)
Custom OpenTelemetry pipelineazure-monitor-opentelemetry-exporter (this)
Fine-grained control over telemetryazure-monitor-opentelemetry-exporter (this)

Trace Exporter

python
from azure.identity import DefaultAzureCredentialfrom opentelemetry import tracefrom opentelemetry.sdk.trace import TracerProviderfrom opentelemetry.sdk.trace.export import BatchSpanProcessorfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env to identify the resource;# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID.exporter = AzureMonitorTraceExporter(    credential=DefaultAzureCredential(),)
# Configure tracer providertrace.set_tracer_provider(TracerProvider())trace.get_tracer_provider().add_span_processor(    BatchSpanProcessor(exporter))
# Use tracertracer = trace.get_tracer(__name__)with tracer.start_as_current_span("my-span"):    print("Hello, World!")

Metric Exporter

python
from azure.identity import DefaultAzureCredentialfrom opentelemetry import metricsfrom opentelemetry.sdk.metrics import MeterProviderfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReaderfrom azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter
# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.exporter = AzureMonitorMetricExporter(    credential=DefaultAzureCredential(),)
# Configure meter providerreader = PeriodicExportingMetricReader(exporter, export_interval_millis=60000)metrics.set_meter_provider(MeterProvider(metric_readers=[reader]))
# Use metermeter = metrics.get_meter(__name__)counter = meter.create_counter("requests_total")counter.add(1, {"route": "/api/users"})

Log Exporter

python
import loggingfrom azure.identity import DefaultAzureCredentialfrom opentelemetry._logs import set_logger_providerfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandlerfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessorfrom azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter
# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.exporter = AzureMonitorLogExporter(    credential=DefaultAzureCredential(),)
# Configure logger providerlogger_provider = LoggerProvider()logger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))set_logger_provider(logger_provider)
# Add handler to Python logginghandler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)logging.getLogger().addHandler(handler)
# Use logginglogger = logging.getLogger(__name__)logger.info("This will be sent to Application Insights")

From Environment Variable

Exporters read APPLICATIONINSIGHTS_CONNECTION_STRING automatically:

python
from azure.identity import DefaultAzureCredentialfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# Connection string from environment; AAD-authenticated ingestion via DefaultAzureCredential.exporter = AzureMonitorTraceExporter(    credential=DefaultAzureCredential(),)

Azure AD Authentication

python
from azure.identity import DefaultAzureCredential, ManagedIdentityCredentialfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# 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()
exporter = AzureMonitorTraceExporter(    credential=credential)

Sampling

Use ApplicationInsightsSampler for consistent sampling:

python
from opentelemetry.sdk.trace import TracerProviderfrom opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatiofrom azure.monitor.opentelemetry.exporter import ApplicationInsightsSampler
# Sample 10% of tracessampler = ApplicationInsightsSampler(sampling_ratio=0.1)
trace.set_tracer_provider(TracerProvider(sampler=sampler))

Offline Storage

Configure offline storage for retry:

python
from azure.identity import DefaultAzureCredentialfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
exporter = AzureMonitorTraceExporter(    credential=DefaultAzureCredential(),    storage_directory="/path/to/storage",  # Custom storage path    disable_offline_storage=False  # Enable retry (default))

Disable Offline Storage

python
exporter = AzureMonitorTraceExporter(    credential=DefaultAzureCredential(),    disable_offline_storage=True  # No retry on failure)

Sovereign Clouds

python
from azure.identity import AzureAuthorityHosts, DefaultAzureCredentialfrom azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
# Azure Governmentcredential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)exporter = AzureMonitorTraceExporter(    connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.us/",    credential=credential)

Exporter Types

ExporterTelemetry TypeApplication Insights Table
AzureMonitorTraceExporterTraces/Spansrequests, dependencies, exceptions
AzureMonitorMetricExporterMetricscustomMetrics, performanceCounters
AzureMonitorLogExporterLogstraces, customEvents

Configuration Options

ParameterDescriptionDefault
connection_stringApplication Insights connection stringFrom env var
credentialAzure credential for AAD authNone
disable_offline_storageDisable retry storageFalse
storage_directoryCustom storage pathTemp directory

Best Practices

  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. Call provider.shutdown() / force_flush() at process exit to flush telemetry — providers are not context managers.
  3. Use BatchSpanProcessor for production (not SimpleSpanProcessor)
  4. Use ApplicationInsightsSampler for consistent sampling across services
  5. Enable offline storage for reliability in production
  6. Use Microsoft Entra authentication instead of instrumentation keys
  7. Set export intervals appropriate for your workload
  8. Use the distro (azure-monitor-opentelemetry) unless you need custom pipelines

Reference Files

FileContents
references/capabilities.md [blocked]Additional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.md [blocked]Dedicated non-hero examples for secondary/advanced scenarios.

Source and attribution

Source:microsoft/skillsin.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-exporter-pyat commit354361d

License: MIT

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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