Install Serverless Monitoring for Azure Functions

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Overview

This page explains how to collect traces, trace metrics, runtime metrics, and custom metrics from your Azure Functions. To collect additional metrics, install the Datadog Azure integration.

Setup

Install dependencies. Run the following commands:
```
pip install datadog-serverless-compat
pip install ddtrace
```
To use automatic instrumentation, you must use dd-trace v2.19+.
Datadog recommends using the latest versions of both datadog-serverless-compat and ddtrace to ensure you have access to enhancements and bug fixes.
Initialize the Datadog Python tracer and serverless compatibility layer. Add the following lines to your main application entry point file:
```
from datadog_serverless_compat import start
from ddtrace import tracer, patch_all

start()
patch_all()
```
(Optional) Enable runtime metrics. See Python Runtime Metrics.
(Optional) Enable custom metrics. See Metric Submission: DogStatsD.

Deploy your function.
Configure Datadog intake. Add the following environment variables to your function’s application settings:
Name Value
DD_API_KEY Your Datadog API key.
DD_SITE Your Datadog site. For example, datadoghq.com.

Name	Value
`DD_API_KEY`	Your Datadog API key.
`DD_SITE`	Your Datadog site. For example, `datadoghq.com`.

Configure Unified Service Tagging. You can collect metrics from your Azure Functions by installing the Datadog Azure integration. To correlate these metrics with your traces, first set the env, service, and version tags on your resource in Azure. Then, configure the following environment variables. You can add custom tags as DD_TAGS.

Name	Value
`DD_ENV`	How you want to tag your env for Unified Service Tagging. For example, `prod`.
`DD_SERVICE`	How you want to tag your service for Unified Service Tagging.
`DD_VERSION`	How you want to tag your version for Unified Service Tagging.
`DD_TAGS`	Your comma-separated custom tags. For example, `key1:value1,key2:value2`.

What’s next?

You can view your Azure Functions traces in Trace Explorer. Search for the service name you set in the DD_SERVICE environment variable to see your traces.
You can use the Serverless > Azure Functions page to see your traces enriched with telemetry collected by the Datadog Azure integration.

Enable/disable trace metrics

Trace metrics are enabled by default. To configure trace metrics, use the following environment variable:

DD_TRACE_STATS_COMPUTATION_ENABLED: Enables (true) or disables (false) trace metrics. Defaults to true.
Values: true, false

Troubleshooting

Enable debug logs

You can collect debug logs for troubleshooting. To configure debug logs, use the following environment variables:

DD_TRACE_DEBUG: Enables (true) or disables (false) debug logging for the Datadog Tracing Library. Defaults to false.
Values: true, false
DD_LOG_LEVEL: Sets logging level for the Datadog Serverless Compatibility Layer. Defaults to info.
Values: trace, debug, info, warn, error, critical, off

Linux Consumption plans and GitHub Actions

To use a GitHub Action to deploy to a Linux Consumption function, you must configure your workflow to use an Azure Service Principal for RBAC. See Using Azure Service Principal for RBAC as Deployment Credential.