Install Serverless Monitoring for Azure Functions

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

  1. Install dependencies. Run the following commands:

    npm install @datadog/serverless-compat
    npm install dd-trace
    

    To use automatic instrumentation, you must use dd-trace v5.25+.

    Datadog recommends pinning the package versions and regularly upgrading to the latest versions of both @datadog/serverless-compat and dd-trace to ensure you have access to enhancements and bug fixes.

  2. Start the Datadog serverless compatibility layer and initialize the Node.js tracer. Add the following lines to your main application entry point file (for example, app.js):

    require('@datadog/serverless-compat').start();
    
    // This line must come before importing any instrumented module. 
    const tracer = require('dd-trace').init()
    
  3. (Optional) Enable runtime metrics. See Node.js Runtime Metrics.

  4. (Optional) Enable custom metrics. See Metric Submission: DogStatsD.

  1. 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.

  2. 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()
    
  3. (Optional) Enable runtime metrics. See Python Runtime Metrics.

  4. (Optional) Enable custom metrics. See Metric Submission: DogStatsD.

  1. Deploy your function.

  2. Configure Datadog intake. Add the following environment variables to your function’s application settings:

    NameValue
    DD_API_KEYYour Datadog API key.
    DD_SITEYour Datadog site. For example, .
  3. 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.

    NameValue
    DD_ENVHow you want to tag your env for Unified Service Tagging. For example, prod.
    DD_SERVICEHow you want to tag your service for Unified Service Tagging.
    DD_VERSIONHow you want to tag your version for Unified Service Tagging.
    DD_TAGSYour comma-separated custom tags. For example, key1:value1,key2:value2.

What’s next?

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.