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title: Monitor Python applications with Azure Application Insights | Microsoft Docs
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description: Provides instructions to wire up OpenCensus Python with Application Insights
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title: Monitor Python applications with Azure Monitor | Microsoft Docs
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description: Provides instructions to wire up OpenCensus Python with Azure Monitor
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services: application-insights
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keywords:
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author: reyang
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ms.author: reyang
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ms.date: 07/02/2019
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ms.date: 10/11/2019
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ms.service: application-insights
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ms.topic: conceptual
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ms.reviewer: mbullwin
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manager: carmonm
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---
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# Collect distributed traces from Python (Preview)
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# Set up Azure Monitor for your Python application
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Application Insights now supports distributed tracingof Python applications through integration with [OpenCensus](https://opencensus.io). This article will walk you step-by-step through the process of setting up OpenCensus for Python and getting your monitoring data to Application Insights.
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Azure Monitor supports distributed tracing, metric collection and logging of Python applications through integration with [OpenCensus](https://opencensus.io). This article will walk you step-by-step through the process of setting up OpenCensus for Python and getting your monitoring data to Azure Monitor.
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## Prerequisites
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- You need an Azure Subscription.
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- Python should be installed, this article uses [Python 3.7.0](https://www.python.org/downloads/), though earlier versions will likely work with minor adjustment.
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- Python should be installed, this article uses [Python 3.7.0](https://www.python.org/downloads/), though earlier versions will likely work with minor adjustments.
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If you don't have an Azure subscription, create a [free](https://azure.microsoft.com/free/) account before you begin.
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## Sign in to the Azure portal
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Sign in to the [Azure portal](https://portal.azure.com/).
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## Create Application Insights resource
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## Create Application Insights resource in Azure Monitor
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First you have to create an Application Insights resource, which will generate an instrumentation key(ikey). The ikey is then used to configure the OpenCensus SDK to send telemetry data to Application Insights.
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First you have to create an Application Insights resource in Azure Monitor, which will generate an instrumentation key(ikey). The ikey is then used to configure the OpenCensus SDK to send telemetry data to Azure Monitor.
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2. Click **Create**.
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## Install OpenCensus Azure Monitor Exporters
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## Instrumenting with OpenCensus Python SDK for Azure Monitor
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1. Install the OpenCensus Azure Monitor Exporters:
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> [!NOTE]
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> `python -m pip install opencensus-ext-azure` assumes that you have a PATH environment variable set for your Python installation. If you have not configured this, you would need to give the full directory path to where your Python executable is located which would result in a command like: `C:\Users\Administrator\AppData\Local\Programs\Python\Python37-32\python.exe -m pip install opencensus-ext-azure`.
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2. First let's generate some trace data locally. In Python IDLE, or your editor of choice, enter the following code:
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2. The SDK utilizes three Azure Monitor exporters to send different types of telemetry to Azure Monitor: trace, metrics, and logs. Take a look at [the data platform overview](https://docs.microsoft.com/azure/azure-monitor/platform/data-platform) for more details on these different types. Follow the instructions below to see how to send these different types via the three exporters.
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### Trace
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1. First let's generate some trace data locally. In Python IDLE, or your editor of choice, enter the following code.
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```python
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from opencensus.trace.samplers import ProbabilitySampler
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main()
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```
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3. Running the code will repeatedly prompt you to enter a value. With each entry, the value will be printed to the shell, and a corresponding piece of **SpanData** will be generated by the OpenCensus Python Module. The OpenCensus project defines a [_trace as a tree of spans_](https://opencensus.io/core-concepts/tracing/).
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2. Running the code will repeatedly prompt you to enter a value. With each entry, the value will be printed to the shell, and a corresponding piece of **SpanData** will be generated by the OpenCensus Python Module. The OpenCensus project defines a [_trace as a tree of spans_](https://opencensus.io/core-concepts/tracing/).
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```
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Enter a value: 4
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4. While helpful for demonstration purposes, ultimately we want to emit the SpanData to Application Insights. Modify your code from the previous step based on the following code sample:
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3. While helpful for demonstration purposes, ultimately we want to emit the `SpanData` to Azure Monitor. Modify your code from the previous step based on the following code sample:
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```python
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from opencensus.ext.azure.trace_exporter import AzureExporter
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# TODO: replace the all-zero GUID with your instrumentation key.
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if __name__ == "__main__":
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main()
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```
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5. Now when you run the Python script, you should still be prompted to enter values, but now only the value is being printed in the shell.
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4. Now when you run the Python script, you should still be prompted to enter values, but now only the value is being printed in the shell. The `SpanData` created will be sent to Azure Monitor. You can find the emitted span data under `dependencies`.
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### Metrics
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1. First, let's generate some local metric data. We will create a simple metric to track the number of times the user presses enter.
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```python
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from datetime import datetime
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from opencensus.stats import aggregation as aggregation_module
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from opencensus.stats import measure as measure_module
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from opencensus.stats import stats as stats_module
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from opencensus.stats import view as view_module
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from opencensus.tags import tag_map as tag_map_module
2. Running the code will repeatedly prompt you to press enter. A metric is created to track the number of enters pressed. With each entry, the value will be incremented and the metric information will be displayed in the console, with the current value and the current timestamp when the metric was updated.
3. While helpful for demonstration purposes, ultimately we want to emit the metric data to Azure Monitor. Modify your code from the previous step based on the following code sample:
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```python
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from datetime import datetime
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from opencensus.ext.azure import metrics_exporter
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from opencensus.stats import aggregation as aggregation_module
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from opencensus.stats import measure as measure_module
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from opencensus.stats import stats as stats_module
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from opencensus.stats import view as view_module
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from opencensus.tags import tag_map as tag_map_module
4. The exporter will send metric data to Azure Monitor at a fixed interval, the default being every 15 seconds. We are tracking a single metric so this metric data, with whatever value and timestamp it contains, will be sent every interval. You can find the data under `customMetrics`.
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### Logs
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1. First, let's generate some local log data.
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```python
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import logging
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logger = logging.getLogger(__name__)
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def valuePrompt():
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line = input("Enter a value: ")
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logger.warning(line)
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def main():
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while True:
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valuePrompt()
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if __name__ == "__main__":
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main()
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```
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2. The code will ask continuously prompt for a value to be entered. A log entry is emitted for every value that is entered containing the said value.
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```
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Enter a value: 24
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Enter a value: 55
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Enter a value: 123
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Enter a value: 90
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```
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3. While helpful for demonstration purposes, ultimately we want to emit the metric data to Azure Monitor. Modify your code from the previous step based on the following code sample:
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```python
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import logging
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from opencensus.ext.azure.log_exporter import AzureLogHandler
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logger = logging.getLogger(__name__)
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# TODO: replace the all-zero GUID with your instrumentation key.
4. The exporter will send log data to Azure Monitor. You can find the data under `traces`.
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## Start monitoring in the Azure portal
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## View your data with queries
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1. You can view the telemetry data that was sent from your application through the Logs(Analytics) tab.
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2. For telemetry sent with the Azure Monitor trace exporter, incoming requests will show up under `requests` and out-going/in process requests will show up under `dependencies`.
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3. For telemetry sent with the Azure Monitor metrics exporter, metrics sent will show up under `customMetrics`.
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4. For telemetry sent with the Azure Monitor logs exporter, logs will show up under `traces` and exceptions will show up under `exceptions`.
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5. Take a look at [Logs in Azure Monitor](https://docs.microsoft.com/azure/azure-monitor/platform/data-platform-logs) for more detailed information about how to use queries and logs.
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## OpenCensus for Python
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* [OpenCensus Python on GitHub](https://github.com/census-instrumentation/opencensus-python)
* [Availability tests](../../azure-monitor/app/monitor-web-app-availability.md): Create tests to make sure your site is visible on the web.
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* [Smart diagnostics](../../azure-monitor/app/proactive-diagnostics.md): These tests run automatically, so you don't have to do anything to set them up. They tell you if your app has an unusual rate of failed requests.
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* [Metric alerts](../../azure-monitor/app/alerts.md): Set alerts to warn you if a metric crosses a threshold. You can set them on custom metrics that you code into your app.
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