Newrelic Python for Monitoring and Performance Optimization

Newrelic Python provides a robust set of tools for monitoring and performance optimization, allowing developers to identify bottlenecks and optimize their code for better performance.

With Newrelic Python, you can monitor your application's performance in real-time, identifying slow SQL queries, high memory usage, and other issues that can impact user experience.

Newrelic Python's transaction tracing feature allows you to drill down into specific transactions and identify the root cause of performance issues.

By using Newrelic Python, developers can optimize their code for better performance, reducing latency and improving user experience.

Monitoring is a crucial aspect of ensuring your Python application is running smoothly. You can view the big picture of your app with the Summary page.

The New Relic UI provides a high-level summary of your app, including Apdex (user satisfaction) and key transactions specific to your business. You can also enable distributed tracing to see activity across an architecture with many services.

To drill down into performance details, you can examine code-level transaction traces, database query traces, and error traces. This will give you a clear understanding of where issues are occurring in your app.

The New Relic UI also allows you to view logs for your APM and infrastructure data in context. This makes troubleshooting easier and faster, as you can see log messages related to your errors and traces directly in your app's UI.

Here are some tools you can use to extend agent instrumentation:

You can also track key transactions specific to your business and create custom dashboards for important metrics. This will help you stay on top of your app's performance and make data-driven decisions.

To install the New Relic Python agent, you need to create a New Relic account and ensure your system meets the system requirements. Make sure you have the necessary permissions to install the agent.

You can install the Python agent by following these simple steps: download and install the Python package, create a config file, and integrate the agent with your application. This will work for most setups, especially if you use a supported web framework like Django, WSGI, or Gunicorn.

If you use an unsupported framework, you'll need to add some code to your app and/or web server files. Don't worry, it's still a straightforward process.

Here's a quick rundown of the installation process:

Alternatively, you can install the newrelic-telemetry-sdk package using pip. If that fails, you can download the library from its GitHub page and install it manually.

To install New Relic APM for your Python application, you'll need to add the newrelic package to your requirements file and create a newrelic.ini file in your project directory. You'll also need to edit the newrelic.ini file to enable monitoring and log file path.

Here are the steps to install New Relic APM:

1. Add newrelic to your requirements file.

2. Create a newrelic.ini file in your project directory.

3. Edit the newrelic.ini file to enable monitoring.

4. Enable the log file path.

5. Set the NEW_RELIC_CONFIG_FILE environment variable to point to the newrelic.ini file.

To check if New Relic is installed, execute into your container and run "pip3 list" to verify that the newrelic package is installed. If not, double-check the installation process.

Additional reading: Python save Html to File

To get started with New Relic Python, you'll need to install the package using pip. This can be done by running the command `newrelic-admin generate-config $YOUR_LICENSE_KEY newrelic.ini`.

The next step is to validate the agent configuration and test the connection to the data collector service. This can be achieved by running the command `newrelic-admin validate-config newrelic.ini`.

To integrate the agent with your web application, you can use the `newrelic-admin wrapper` script. This script should be modified to prefix the existing startup command and options with `newrelic-admin run-program`. Additionally, set the `NEW_RELIC_CONFIG_FILE` environment variable to the name of the configuration file you created earlier.

Here is a step-by-step guide to integrating the agent:

Alternatively, you can manually integrate the agent by adding the following lines at the top of your Python WSGI script file:

import newrelic.agent

newrelic.agent.initialize('/path/to/newrelic.ini')

By following these steps, you'll be able to integrate the New Relic agent with your Python web application.

Broaden your view: Newrelic Python Agent

To get started with using the New Relic package, you'll need to install it via pip. This is done by running a command in your terminal, which will generate the agent configuration file with your license key.

The configuration file is essential for setting up the agent, and you can generate it by running the command `newrelic-admin generate-config $YOUR_LICENSE_KEY newrelic.ini`. This will create a file named `newrelic.ini` with your license key.

Once you have the configuration file, you'll need to validate the agent configuration and test the connection to the New Relic data collector service. This is done by running the command `newrelic-admin validate-config newrelic.ini`.

To integrate the agent with your web application, you can use the `newrelic-admin wrapper` script. This script should be modified to prefix the existing startup command and options with `newrelic-admin run-program`. You'll also need to set the `NEW_RELIC_CONFIG_FILE` environment variable to the name of the configuration file you created earlier.

Alternatively, you can manually integrate the agent by adding the following lines at the top of your Python WSGI script file: `import newrelic.agent; newrelic.agent.initialize('/path/to/newrelic.ini')`. This is useful if you're using mod_wsgi.

To start or restart your Python web application or WSGI server, simply run the command that starts it, and the agent should be integrated and collecting data.

Here is a step-by-step guide to integrating the agent:

To get the most out of our API usage, we first need to set up the necessary libraries, including requests for making HTTP requests and pandas for organizing data into a tabular format.

The required headers for accessing the New Relic API are crucial for extracting metrics. We specified the API endpoint and defined the start time for the time range we want to query.

By looping through the list of applications, we can extract the required metrics for each application using the API. This process is essential for creating a comprehensive report.

The data is then organized into a dictionary and appended to a list of dictionaries that will later be used to create a Pandas DataFrame. This allows us to easily manipulate and analyze the data.

Finally, we created a CSV file that contains the extracted metrics by using the pandas.DataFrame.to_csv() method. The CSV file is saved to a directory called reports in the current working directory.

Worth a look: Python Read Html File

The New Relic Telemetry SDK is a game-changer for Python developers. It allows you to send data into New Relic using the Python urllib3 library.

This SDK provides a Python library for sending data into New Relic, making it easy to see dimensional metrics, events, logs, and spans/traces in New Relic.

With the Telemetry SDK, you don't have to use an agent to get started.

Curious to learn more? Check out: Python Sdk Azure

The Python agent is open source software, which means you can browse its source code and send improvements.

This level of transparency and community involvement is a huge advantage, allowing you to tailor the agent to your specific needs.

You can also create your own fork and build it, giving you complete control over the code.

For more information, see the README.