Key Metrics And Best Practices To Follow With Gunicorn

Gunicorn, also known as Green Unicorn, is a type of web server gateway interface. It is designed for use with Python applications on the web. If you have ever used Unicorn for Ruby, you likely know the importance of using, optimizing, and understanding Gunicorn for performance issues. Knowing more about it can help you monitor key metrics to optimize its performance.

Key Metrics to Check

Taking advantage of understanding Gunicorn performance is one of the first steps toward ensuring everything is running smoothly. There are several variables that you can tweak, when you are monitoring Gunicorn, and each variable has a different effect on the performance of the server. You can manipulate each one to get different effects.

Remember, each Python application can have a different reaction, so you might need to do a bit of experimenting to determine the way yours will react. Part of the reason for that could be because your application source is written slightly differently. No matter the way you designed the app, however, each component can change the performance of the app.

By taking a look at each of those variables, you can get a better look at what is going on behind the scenes. For example, looking at “log.error” will show you how often you receive error messages during load and request handling. Similarly, checking for critical errors will show how often critical errors happen as your server runs.

What Should Your Metrics Be?

You can check the number of requests each server processes every second. And checking “request.duration” will show you how long each request has taken to handle, and that is measured in milliseconds. If you check “logexception,” you will find the number of exceptional logs that were recorded. It is equally important to look at how often you receive warning messages since that can be indicative of a severe issue you may have. You can also look at the processing agent for each of your requests to ensure they are getting recorded correctly.

Do your research to determine how to measure each of the metrics. For example, some of them might need to be kept higher than others. On the other hand, some of them will need to remain a bit lower. The right tools will ensure everything is working as it should.

Best Practices for Choosing Tools

There are a few features to look for when choosing tools to help you monitor things. For instance, some offer ways of pinpointing wait times’ root causes since you can get more insight into the metrics’ composition. They might give you the HTTP status code for each application procedure.

A good tool will also offer a dashboard to help you easily view information on the status of your metrics. That way, you can keep the data in one place and analyze it easily. You should be able to configure real-time alerts to keep you updated on critical changes in the structure. And it should be a tool that is not difficult to set up, so you can spend more time monitoring.