DOCS-9940: Serverless Monitoring for Azure Functions

config/_default/menus/main.en.yaml

@@ -3024,21 +3024,26 @@ menu:
       parent: serverless
       identifier: serverless_container_apps
       weight: 5
+    - name: Azure Functions
+      url: serverless/azure_functions
+      parent: serverless
+      identifier: serverless_azure_functions
+      weight: 6
     - name: Google Cloud Run
       url: serverless/google_cloud_run
       parent: serverless
       identifier: serverless_gcr
-      weight: 6
+      weight: 7
     - name: Glossary
       url: serverless/glossary
       parent: serverless
       identifier: serverless_glossary
-      weight: 7
+      weight: 8
     - name: Guides
       url: serverless/guide/
       identifier: serverless_guides
       parent: serverless
-      weight: 8
+      weight: 9
     - name: Network Monitoring
       url: network_monitoring/
       pre: network

content/en/serverless/azure_functions/_index.md

@@ -0,0 +1,131 @@
+---
+title: 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][6].
+
+## Setup
+
+{{< programming-lang-wrapper langs="nodejs,python" >}}
+{{< programming-lang lang="nodejs" >}}
+1. **Install dependencies**. Run the following commands:
+   ```shell
+   npm install @datadog/serverless-compat
+   npm install dd-trace
+   ```
+
+   To use [automatic instrumentation][1], 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`):
+
+   ```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][2].
+
+4. (Optional) **Enable custom metrics**. See [Metric Submission: DogStatsD][3].
+
+[1]: /tracing/trace_collection/automatic_instrumentation/?tab=singlestepinstrumentation
+[2]: /tracing/metrics/runtime_metrics/nodejs/?tab=environmentvariables
+[3]: /metrics/custom_metrics/dogstatsd_metrics_submission/?code-lang=nodejs
+{{< /programming-lang >}}
+{{< programming-lang lang="python" >}}
+1. **Install dependencies**. Run the following commands:
+   ```shell
+   pip install datadog-serverless-compat
+   pip install ddtrace
+   ```
+
+   To use [automatic instrumentation][1], 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:
+
+   ```python
+   from datadog_serverless_compat import start
+   from ddtrace import tracer, patch_all
+
+   start()
+   patch_all()
+   ```
+
+3. (Optional) **Enable runtime metrics**. See [Python Runtime Metrics][2].
+
+4. (Optional) **Enable custom metrics**. See [Metric Submission: DogStatsD][3].
+
+[1]: /tracing/trace_collection/automatic_instrumentation/?tab=singlestepinstrumentation
+[2]: /tracing/metrics/runtime_metrics/python/
+[3]: /metrics/custom_metrics/dogstatsd_metrics_submission/?code-lang=python
+{{< /programming-lang >}}
+{{< /programming-lang-wrapper >}}
+
+5. **Deploy your function**.
+
+6. **Configure Datadog intake**. Add the following environment variables to your function's application settings:
+
+   | Name | Value |
+   | ---- | ----- |
+   | `DD_API_KEY` | Your [Datadog API key][1]. |
+   | `DD_SITE` | Your [Datadog site][2]. For example, {{< region-param key=dd_site code="true" >}}. |
+
+7. **Configure Unified Service Tagging**. You can collect metrics from your Azure Functions by installing the [Datadog Azure integration][6]. 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][9]. For example, `prod`. |
+   | `DD_SERVICE` | How you want to tag your service for [Unified Service Tagging][9].  |
+   | `DD_VERSION` | How you want to tag your version for [Unified Service Tagging][9]. |
+   | `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][4]. Search for the service name you set in the `DD_SERVICE` environment variable to see your traces.
+- You can use the [Serverless > Azure Functions][5] page to see your traces enriched with telemetry collected by the [Datadog Azure integration][6].
+
+### Enable/disable trace metrics
+
+[Trace metrics][3] 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][7] 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][8].
+
+
+[1]: /account_management/api-app-keys/#add-an-api-key-or-client-token
+[2]: /getting_started/site
+[3]: /tracing/metrics/metrics_namespace/
+[4]: https://app.datadoghq.com/apm/traces
+[5]: https://app.datadoghq.com/functions?cloud=azure&entity_view=function
+[6]: /integrations/azure/
+[7]: /tracing/troubleshooting/tracer_debug_logs/#enable-debug-mode
+[8]: https://github.com/Azure/functions-action?tab=readme-ov-file#using-azure-service-principal-for-rbac-as-deployment-credential
+[9]: /getting_started/tagging/unified_service_tagging/