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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.
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.
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()
(Optional) Enable runtime metrics. See Node.js Runtime Metrics.
(Optional) Enable custom metrics. See Metric Submission: DogStatsD.
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, . |
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 . |
DD_SERVICE
environment variable to see your traces.Trace metrics are enabled by default. To configure trace metrics, use the following environment variable:
DD_TRACE_STATS_COMPUTATION_ENABLED
true
) or disables (false
) trace metrics. Defaults to true
.Values: true
, false
You can collect debug logs for troubleshooting. To configure debug logs, use the following environment variables:
DD_TRACE_DEBUG
true
) or disables (false
) debug logging for the Datadog Tracing Library. Defaults to false
.Values: true
, false
DD_LOG_LEVEL
info
.Values: trace
, debug
, info
, warn
, error
, critical
, off
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.