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DOCS-9940: Serverless Monitoring for Azure Functions (#27294)
* initiali commit * adding content * Apply suggestions from code review Co-authored-by: Duncan Harvey <35278470+duncanpharvey@users.noreply.github.com> * more edits * last updates * adding to side nav * Update content/en/serverless/azure_functions/_index.md Co-authored-by: Duncan Harvey <35278470+duncanpharvey@users.noreply.github.com> * Update _index.md * resolving edits --------- Co-authored-by: Duncan Harvey <35278470+duncanpharvey@users.noreply.github.com>
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‎config/_default/menus/main.en.yaml

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

‎content/en/serverless/azure_functions/_index.md

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---
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title: Install Serverless Monitoring for Azure Functions
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---
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## Overview
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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][6].
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## Setup
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{{< programming-lang-wrapper langs="nodejs,python" >}}
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{{< programming-lang lang="nodejs" >}}
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1. **Install dependencies**. Run the following commands:
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```shell
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npm install @datadog/serverless-compat
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npm install dd-trace
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```
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To use [automatic instrumentation][1], you must use `dd-trace` v5.25+.
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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.
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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`):
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```js
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require('@datadog/serverless-compat').start();
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// This line must come before importing any instrumented module.
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const tracer = require('dd-trace').init()
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```
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3. (Optional) **Enable runtime metrics**. See [Node.js Runtime Metrics][2].
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4. (Optional) **Enable custom metrics**. See [Metric Submission: DogStatsD][3].
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[1]: /tracing/trace_collection/automatic_instrumentation/?tab=singlestepinstrumentation
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[2]: /tracing/metrics/runtime_metrics/nodejs/?tab=environmentvariables
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[3]: /metrics/custom_metrics/dogstatsd_metrics_submission/?code-lang=nodejs
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{{< /programming-lang >}}
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{{< programming-lang lang="python" >}}
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1. **Install dependencies**. Run the following commands:
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```shell
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pip install datadog-serverless-compat
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pip install ddtrace
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```
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To use [automatic instrumentation][1], you must use `dd-trace` v2.19+.
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Datadog recommends using the latest versions of both `datadog-serverless-compat` and `ddtrace` to ensure you have access to enhancements and bug fixes.
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2. **Initialize the Datadog Python tracer and serverless compatibility layer**. Add the following lines to your main application entry point file:
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```python
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from datadog_serverless_compat import start
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from ddtrace import tracer, patch_all
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start()
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patch_all()
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```
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3. (Optional) **Enable runtime metrics**. See [Python Runtime Metrics][2].
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4. (Optional) **Enable custom metrics**. See [Metric Submission: DogStatsD][3].
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[1]: /tracing/trace_collection/automatic_instrumentation/?tab=singlestepinstrumentation
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[2]: /tracing/metrics/runtime_metrics/python/
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[3]: /metrics/custom_metrics/dogstatsd_metrics_submission/?code-lang=python
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{{< /programming-lang >}}
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{{< /programming-lang-wrapper >}}
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5. **Deploy your function**.
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6. **Configure Datadog intake**. Add the following environment variables to your function's application settings:
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| Name | Value |
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| ---- | ----- |
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| `DD_API_KEY` | Your [Datadog API key][1]. |
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| `DD_SITE` | Your [Datadog site][2]. For example, {{< region-param key=dd_site code="true" >}}. |
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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`.
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| Name | Value |
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| ---- | ----- |
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| `DD_ENV` | How you want to tag your env for [Unified Service Tagging][9]. For example, `prod`. |
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| `DD_SERVICE` | How you want to tag your service for [Unified Service Tagging][9]. |
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| `DD_VERSION` | How you want to tag your version for [Unified Service Tagging][9]. |
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| `DD_TAGS` | Your comma-separated custom tags. For example, `key1:value1,key2:value2`. |
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## What's next?
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- 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.
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- You can use the [Serverless > Azure Functions][5] page to see your traces enriched with telemetry collected by the [Datadog Azure integration][6].
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### Enable/disable trace metrics
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[Trace metrics][3] are enabled by default. To configure trace metrics, use the following environment variable:
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`DD_TRACE_STATS_COMPUTATION_ENABLED`
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: Enables (`true`) or disables (`false`) trace metrics. Defaults to `true`.
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**Values**: `true`, `false`
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## Troubleshooting
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### Enable debug logs
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You can collect [debug logs][7] for troubleshooting. To configure debug logs, use the following environment variables:
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`DD_TRACE_DEBUG`
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: Enables (`true`) or disables (`false`) debug logging for the Datadog Tracing Library. Defaults to `false`.
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**Values**: `true`, `false`
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`DD_LOG_LEVEL`
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: Sets logging level for the Datadog Serverless Compatibility Layer. Defaults to `info`.
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**Values**: `trace`, `debug`, `info`, `warn`, `error`, `critical`, `off`
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### Linux Consumption plans and GitHub Actions
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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].
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[1]: /account_management/api-app-keys/#add-an-api-key-or-client-token
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[2]: /getting_started/site
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[3]: /tracing/metrics/metrics_namespace/
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[4]: https://app.datadoghq.com/apm/traces
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[5]: https://app.datadoghq.com/functions?cloud=azure&entity_view=function
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[6]: /integrations/azure/
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[7]: /tracing/troubleshooting/tracer_debug_logs/#enable-debug-mode
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[8]: https://github.com/Azure/functions-action?tab=readme-ov-file#using-azure-service-principal-for-rbac-as-deployment-credential
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[9]: /getting_started/tagging/unified_service_tagging/

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