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/

When to Use

Trace Exporter

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Create exporter
exporter = AzureMonitorTraceExporter(
    connection_string="InstrumentationKey=xxx;..."
)

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

# Create exporter
exporter = AzureMonitorMetricExporter(
    connection_string="InstrumentationKey=xxx;..."
)

# 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 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

# Create exporter
exporter = AzureMonitorLogExporter(
    connection_string="InstrumentationKey=xxx;..."
)

# 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.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Connection string from environment
exporter = AzureMonitorTraceExporter()

Azure AD Authentication

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

exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential()
)

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.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

exporter = AzureMonitorTraceExporter(
    connection_string="...",
    storage_directory="/path/to/storage",  # Custom storage path
    disable_offline_storage=False  # Enable retry (default)
)

Disable Offline Storage

exporter = AzureMonitorTraceExporter(
    connection_string="...",
    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

Configuration Options

Best Practices

1. Use BatchSpanProcessor for production (not SimpleSpanProcessor)
2. Use ApplicationInsightsSampler for consistent sampling across services
3. Enable offline storage for reliability in production
4. Use AAD authentication instead of instrumentation keys
5. Set export intervals appropriate for your workload
6. Use the distro (azure-monitor-opentelemetry) unless you need custom pipelines

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior deployment configurations, rollback procedures, and incident post-mortems. Avoid re-discovering infrastructure patterns.

# Check for prior infrastructure context before starting
python3 execution/memory_manager.py auto --query "deployment configuration and patterns for Azure Monitor Opentelemetry Exporter Py"

Storing Results

After completing work, store infrastructure decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Deployment pipeline: configured blue-green deployment with health checks on port 8080" \
  --type technical --project <project> \
  --tags azure-monitor-opentelemetry-exporter-py devops

Multi-Agent Collaboration

Broadcast deployment changes so frontend and backend agents update their configurations accordingly.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Deployed infrastructure changes — updated CI/CD pipeline with new health check endpoints" \
  --project <project>

Playbook Integration

Use the ship-saas-mvp or full-stack-deploy playbook to sequence this skill with testing, documentation, and deployment verification.

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