Azure monitor opentelemetry exporter py

Azure Monitor OpenTelemetry Exporter for Python

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

Installation

pip install azure-monitor-opentelemetry-exporter

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

🔑 Auth & lifecycle: These exporters take a connection string by design, but for AAD-authenticated ingestion (where supported) prefer DefaultAzureCredential via the credential= parameter — see the Azure AD Authentication section. Any Azure SDK clients you create alongside the exporter should be wrapped in with/async with blocks (and async credentials from azure.identity.aio likewise).

When to Use

Scenario	Use
Quick setup, auto-instrumentation	azure-monitor-opentelemetry (distro)
Custom OpenTelemetry pipeline	azure-monitor-opentelemetry-exporter (this)
Fine-grained control over telemetry	azure-monitor-opentelemetry-exporter (this)

Trace Exporter

from azure.identity import DefaultAzureCredential
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from 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 provider
trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(exporter)
)

# Use tracer
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("my-span"):
    print("Hello, World!")

Metric Exporter

from azure.identity import DefaultAzureCredential
from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter

# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.
exporter = AzureMonitorMetricExporter(
    credential=DefaultAzureCredential(),
)

# Configure meter provider
reader = PeriodicExportingMetricReader(exporter, export_interval_millis=60000)
metrics.set_meter_provider(MeterProvider(metric_readers=[reader]))

# Use meter
meter = metrics.get_meter(__name__)
counter = meter.create_counter("requests_total")
counter.add(1, {"route": "/api/users"})

Log Exporter

import logging
from azure.identity import DefaultAzureCredential
from opentelemetry._logs import set_logger_provider
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter

# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env; AAD-authenticated ingestion via DefaultAzureCredential.
exporter = AzureMonitorLogExporter(
    credential=DefaultAzureCredential(),
)

# Configure logger provider
logger_provider = LoggerProvider()
logger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))
set_logger_provider(logger_provider)

# Add handler to Python logging
handler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)
logging.getLogger().addHandler(handler)

# Use logging
logger = logging.getLogger(__name__)
logger.info("This will be sent to Application Insights")

From Environment Variable

Exporters read APPLICATIONINSIGHTS_CONNECTION_STRING automatically:

from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Connection string from environment; AAD-authenticated ingestion via DefaultAzureCredential.
exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential(),
)

Azure AD Authentication

from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from 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:

from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatio
from azure.monitor.opentelemetry.exporter import ApplicationInsightsSampler

# Sample 10% of traces
sampler = ApplicationInsightsSampler(sampling_ratio=0.1)

trace.set_tracer_provider(TracerProvider(sampler=sampler))

Offline Storage

Configure offline storage for retry:

from azure.identity import DefaultAzureCredential
from 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

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

Sovereign Clouds

from azure.identity import AzureAuthorityHosts, DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Azure Government
credential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)
exporter = AzureMonitorTraceExporter(
    connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.us/",
    credential=credential
)

Exporter Types

Exporter	Telemetry Type	Application Insights Table
AzureMonitorTraceExporter	Traces/Spans	requests, dependencies, exceptions
AzureMonitorMetricExporter	Metrics	customMetrics, performanceCounters
AzureMonitorLogExporter	Logs	traces, customEvents

Configuration Options

Parameter	Description	Default
connection_string	Application Insights connection string	From env var
credential	Azure credential for AAD auth	None
disable_offline_storage	Disable retry storage	False
storage_directory	Custom storage path	Temp 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. 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.
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