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Copy file name to clipboardExpand all lines: articles/azure-monitor/app/opencensus-python-dependency.md
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Track your outgoing Django requests with the OpenCensus `django` integration.
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> [!NOTE]
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> The only outgoing Django requests that are tracked are calls made to a database. For requests made to the Django application, see [incoming requests](./opencensus-python-request.md#tracking-django-applications).
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> The only outgoing Django requests that are tracked are calls made to a database. For requests made to the Django application, see [incoming requests](./opencensus-python-request.md#track-django-applications).
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Download and install `opencensus-ext-django` from [PyPI](https://pypi.org/project/opencensus-ext-django/) and add the following line to the `MIDDLEWARE` section in the Django `settings.py` file.
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title: Incoming Request Tracking in Azure Application Insights with OpenCensus Python | Microsoft Docs
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title: Incoming request tracking in Application Insights with OpenCensus Python | Microsoft Docs
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description: Monitor request calls for your Python apps via OpenCensus Python.
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ms.topic: conceptual
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ms.date: 03/22/2023
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# Track incoming requests with OpenCensus Python
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OpenCensus Python and its integrations collect incoming request data. Track incoming request data sent to your web applications built on top of the popular web frameworks `django`, `flask` and `pyramid`. Application Insights receives the data as `requests` telemetry
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OpenCensus Python and its integrations collect incoming request data. You can track incoming request data sent to your web applications built on top of the popular web frameworks Django, Flask, and Pyramid. Application Insights receives the data as `requests` telemetry.
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First, instrument your Python application with latest [OpenCensus Python SDK](./opencensus-python.md).
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First, instrument your Python application with the latest [OpenCensus Python SDK](./opencensus-python.md).
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## Tracking Django applications
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## Track Django applications
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1. Download and install `opencensus-ext-django` from [PyPI](https://pypi.org/project/opencensus-ext-django/) and instrument your application with the `django` middleware. Incoming requests sent to your `django` application will be tracked.
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1. Download and install `opencensus-ext-django` from [PyPI](https://pypi.org/project/opencensus-ext-django/). Instrument your application with the `django` middleware. Incoming requests sent to your Django application are tracked.
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2. Include `opencensus.ext.django.middleware.OpencensusMiddleware` in your `settings.py` file under `MIDDLEWARE`.
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1. Include `opencensus.ext.django.middleware.OpencensusMiddleware` in your `settings.py` file under `MIDDLEWARE`.
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```python
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MIDDLEWARE= (
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)
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```
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3. Make sure AzureExporter is configured properly in your `settings.py` under `OPENCENSUS`. For requests from urls that you don't wish to track, add them to `EXCLUDELIST_PATHS`.
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1. Make sure AzureExporter is configured properly in your `settings.py` under `OPENCENSUS`. For requests from URLs that you don't want to track, add them to `EXCLUDELIST_PATHS`.
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```python
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OPENCENSUS= {
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}
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```
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You can find a Django sample application in the sample Azure Monitor OpenCensus Python samples repository located [here](https://github.com/Azure-Samples/azure-monitor-opencensus-python/tree/master/azure_monitor/django_sample).
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## Tracking Flask applications
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You can find a Django sample application in the [Azure Monitor OpenCensus Python samples repository](https://github.com/Azure-Samples/azure-monitor-opencensus-python/tree/master/azure_monitor/django_sample).
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## Track Flask applications
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1. Download and install `opencensus-ext-flask`from [PyPI](https://pypi.org/project/opencensus-ext-flask/)and instrument your application with the `flask` middleware. Incoming requests sent to your `flask` application will be tracked.
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1. Download and install `opencensus-ext-flask`from [PyPI](https://pypi.org/project/opencensus-ext-flask/). Instrument your application with the `flask` middleware. Incoming requests sent to your Flask application are tracked.
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```python
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```
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2. You can also configure your `flask` application through `app.config`. For requests fromurls that you don't wish to track, add them to `EXCLUDELIST_PATHS`.
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1. You can also configure your `flask` application through `app.config`. For requests fromURLs that you don't want to track, add them to `EXCLUDELIST_PATHS`.
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```python
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app.config['OPENCENSUS'] = {
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```
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> [!NOTE]
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> To run Flask under uWSGI in a Docker environment, you must first add `lazy-apps = true` to the uWSGI configuration file (uwsgi.ini). For more information, see the [issue description](https://github.com/census-instrumentation/opencensus-python/issues/660).
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You can find a Flask sample application that tracks requests in the Azure Monitor OpenCensus Python samples repository located [here](https://github.com/Azure-Samples/azure-monitor-opencensus-python/tree/master/azure_monitor/flask_sample).
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> To run Flask under uWSGI in a Docker environment, you must first add `lazy-apps = true` to the uWSGI configuration file (uwsgi.ini). For more information, see the [issue description](https://github.com/census-instrumentation/opencensus-python/issues/660).
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## Tracking Pyramid applications
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You can find a Flask sample application that tracks requests in the [Azure Monitor OpenCensus Python samples repository](https://github.com/Azure-Samples/azure-monitor-opencensus-python/tree/master/azure_monitor/flask_sample).
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1. Download and install `opencensus-ext-django`from [PyPI](https://pypi.org/project/opencensus-ext-pyramid/) and instrument your application with the `pyramid` tween. Incoming requests sent to your `pyramid` application will be tracked.
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## Track Pyramid applications
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1. Download and install `opencensus-ext-django`from [PyPI](https://pypi.org/project/opencensus-ext-pyramid/). Instrument your application with the `pyramid` tween. Incoming requests sent to your Pyramid application are tracked.
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```python
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defmain(global_config, **settings):
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'.pyramid_middleware.OpenCensusTweenFactory')
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```
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2. You can configure your `pyramid` tween directly in the code. For requests fromurls that you don't wish to track, add them to `EXCLUDELIST_PATHS`.
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1. You can configure your `pyramid` tween directly in the code. For requests fromURLs that you don't want to track, add them to `EXCLUDELIST_PATHS`.
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```python
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settings = {
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config = Configurator(settings=settings)
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```
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## Tracking FastAPI applications
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## Track FastAPI applications
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OpenCensus doesn't have an extension for FastAPI. To write your own FastAPI middleware, complete the following steps:
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OpenCensus doesn't have an extension for FastAPI. To write your own FastAPI middleware:
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1. The following dependencies are required:
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1. The following dependencies are required:
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- [fastapi](https://pypi.org/project/fastapi/)
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- [uvicorn](https://pypi.org/project/uvicorn/)
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In a production setting, we recommend that you deploy [uvicorn with gunicorn](https://www.uvicorn.org/deployment/#gunicorn).
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2. Add [FastAPI middleware](https://fastapi.tiangolo.com/tutorial/middleware/). Make sure that you set the span kind server: `span.span_kind = SpanKind.SERVER`.
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1. Add [FastAPI middleware](https://fastapi.tiangolo.com/tutorial/middleware/). Make sure that you set the span kind server: `span.span_kind = SpanKind.SERVER`.
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3. Run your application. Calls made to your FastAPI application should be automatically trackedand telemetry should be logged directly to Azure Monitor.
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1. Run your application. Calls made to your FastAPI application should be automatically tracked. Telemetry should be logged directly to Azure Monitor.
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```python
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# Opencensus imports
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title: Statsbeat in Azure Application Insights | Microsoft Docs
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title: Statsbeat in Application Insights | Microsoft Docs
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description: Statistics about Application Insights SDKs and Auto-Instrumentation
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ms.topic: conceptual
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ms.date: 08/24/2022
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ms.custom: references_regions
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# Statsbeat in Azure Application Insights
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# Statsbeat in Application Insights
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Statsbeat collects essential and non-essential[custom metric](../essentials/metrics-custom-overview.md) about Application Insights SDKs and auto-instrumentation. Statsbeat serves three benefits for Azure Monitor Application Insights customers:
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- Service Health and Reliability (outside-in monitoring of connectivity to ingestion endpoint)
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- Support Diagnostics (self-help insights and CSS insights)
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- Product Improvement (insights for design optimizations)
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Statsbeat collects essential and nonessential[custom metrics](../essentials/metrics-custom-overview.md) about Application Insights SDKs and autoinstrumentation. Statsbeat serves three benefits for Azure Monitor Application Insights customers:
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- Service health and reliability (outside-in monitoring of connectivity to ingestion endpoint)
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- Support diagnostics (self-help insights and CSS insights)
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- Product improvement (insights for design optimizations)
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Statsbeat data is stored in a Microsoft data store. It doesn't impact customers' overall monitoring volume and cost.
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Statsbeat data is stored in a Microsoft data store. It doesn't affect customers' overall monitoring volume and cost.
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Statsbeat doesn't support [Azure Private Link](../../automation/how-to/private-link-security.md).
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Statsbeat doesn't support [Azure Private Link](../../automation/how-to/private-link-security.md).
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## What data does Statsbeat collect?
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Statsbeat collects essential and non-essential metrics.
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Statsbeat collects essential and nonessential metrics.
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#### [Java](#tab/java)
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To disable non-essential Statsbeat, add the below configuration to your config file.
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To disable nonessential Statsbeat, add the following configuration to your config file:
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```json
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}
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```
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You can also disable this feature by setting the environment variable `APPLICATIONINSIGHTS_STATSBEAT_DISABLED` to true (which will then take precedence over disabledspecified in the json configuration).
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You can also disable this feature by setting the environment variable `APPLICATIONINSIGHTS_STATSBEAT_DISABLED` to `true`. This setting then takes precedence over `disabled`, which is specified in the JSON configuration.
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*Use the [Click Analytics Auto collection plugin](javascript-feature-extensions.md) via npm to emit these attributes.
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>[!TIP]
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> To understand how to effectively use the Click Analytics plugin, please refer to [this section](javascript-feature-extensions.md#how-to-effectively-use-the-plugin).
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> To understand how to effectively use the Click Analytics plugin, please refer to [this section](javascript-feature-extensions.md#use-the-plug-in).
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### Open the workbook
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The workbook can be found in the gallery under 'public templates'. The workbook will be shown in the section titled **"Product Analytics using the Click Analytics Plugin"** as shown in the following image:
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Collection of custom measurements: Use this collection to report named measurements associated with the telemetry item. Typical use cases are:
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- The number of queue items processed by Request telemetry.
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- The time that a customer took to finish the wizard step completing Event telemetry.
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**Collection of custom measurements:** Use this collection to report named measurements associated with the telemetry item. Typical use cases are:
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- The size of the dependency telemetry payload.
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- The number of queue items processed by request telemetry.
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- The time that a customer took to finish the wizard step completing event telemetry.
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> Custom measurements are associated with the telemetry item they belong to. They're subject to sampling with the telemetry item that contains those measurements. To track a measurement that has a value independent from other telemetry types, use [Metric telemetry](../articles/azure-monitor/app/api-custom-events-metrics.md).
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> Custom measurements are associated with the telemetry item they belong to. They're subject to sampling with the telemetry item that contains those measurements. To track a measurement that has a value independent from other telemetry types, use [metric telemetry](../articles/azure-monitor/app/api-custom-events-metrics.md).
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