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Logging and Monitoring

This step covers how to monitor your Azure Communication Services (ACS) resource and configure SDK logging in Python.

1. SDK Logging Configuration

The ACS Python SDK uses the standard logging library. You can configure it to output logs to the console or a file.

import logging
import sys

# Configure logging to output to console
handler = logging.StreamHandler(stream=sys.stdout)
logger = logging.getLogger('azure.communication')
logger.setLevel(logging.DEBUG)
logger.addHandler(handler)

# Use ACS client as usual
from azure.communication.identity import CommunicationIdentityClient
# ...

2. Azure Monitor Integration

ACS is integrated with Azure Monitor. You can view metrics and logs in the Azure Portal.

  1. Navigate to your ACS resource.
  2. Under Monitoring, select Metrics.
  3. Choose a metric, such as SMS Sent or Email Sent.
  4. You can also create Alerts based on these metrics.

3. Application Insights Setup

To get deeper insights into your Python application's performance and dependencies, integrate with Azure Application Insights.

pip install opencensus-ext-azure opencensus-ext-flask

import logging
from opencensus.ext.azure.log_exporter import AzureLogHandler

logger = logging.getLogger(__name__)
logger.addHandler(AzureLogHandler(connection_string='InstrumentationKey=<your-instrumentation-key>'))

logger.warning("Monitoring ACS operation performance.")

4. Custom Telemetry for ACS Operations

You can add custom telemetry to track specific ACS operations, such as the time it takes to send an email or handle a call.

import time
from opencensus.stats import stats as stats_module
from opencensus.stats import measure as measure_module
from opencensus.stats import view as view_module

# Define measure and view
m_email_latency = measure_module.MeasureFloat("email_latency", "Latency of email sends", "ms")
v_email_latency = view_module.View("email_latency_view", "Email latency view", [], m_email_latency, view_module.DistributionAggregation([10, 50, 100, 200, 500, 1000]))

# Register view
stats_module.stats_recorder.new_view_manager().register_view(v_email_latency)

# Record telemetry
start_time = time.time()
# send_email_op()
latency = (time.time() - start_time) * 1000
stats_module.stats_recorder.new_measurement_map().measure_float_put(m_email_latency, latency).record()

5. Diagnostic Settings

To store logs for long-term analysis, configure diagnostic settings.

  1. Navigate to your ACS resource.
  2. Under Monitoring, select Diagnostic settings.
  3. Click Add diagnostic setting.
  4. Select the logs you want to collect (e.g., SMS Operational Logs, Email Operational Logs).
  5. Choose a destination, such as a Log Analytics workspace.

6. KQL Queries for Monitoring

Once your logs are in a Log Analytics workspace, you can query them using Kusto Query Language (KQL).

SMS Sent Summary

ACSSmsOperational
| where OperationName == "SmsSend"
| summarize Count=count() by ResultType, ResultSignature, bin(TimeGenerated, 1h)
| render timechart

Email Delivery Success Rate

ACSEmailOperational
| where OperationName == "EmailSend"
| summarize SuccessCount=countif(ResultType == "Succeeded"), TotalCount=count()
| extend SuccessRate = (SuccessCount * 100.0) / TotalCount

See Also

Sources