Proper Python Instrumentation: 5 Things to Keep In Mind
Mukul Khanna
on
March 17, 2022
Proper Python Instrumentation: 5 Things to Keep In Mind
Python’s USP as a programming language is that it’s flexible, easy to use, and quick to get started and iterate with. These virtues have led it to become the most popular programming language in 2022 and be used by millions of developers.
As Python applications continue to multiply and scale to cater to millions of users worldwide, instrumentation and monitoring tools play a role more crucial than ever – in building robust, performant software. This is because maintaining and optimizing an application is as challenging as (if not more) building it. And if you have any experience with scaling apps, you’d know how quickly things can get out of hand.
Developed to remedy this dynamic and fragile nature of web apps, instrumentation tools enable you to measure, track, analyze, and optimize software performance. In this post, to give you some more context, we’ll start by defining instrumentation (in a software sense) and understanding its significance. Finally, we will look at five things to keep in mind for effectively instrumenting your Python applications.
- Instrumentation: What and Why?
- How to Effectively Instrument Using Python
- #1 Deciding Between Manual and Automatic Instrumenting
- #2 Decide if You’re Instrumenting via API or Config
- #3 Excess of Everything is Bad
- #4 Standardize What to Trace
- #5 Choosing the right tool
- Scout Python Instrumentation
Instrumentation: What and Why?
Instrumentation, in the realm of software and web applications, refers to the process of quantifying aspects of an application’s performance — collecting metrics that speak to its status and health. These can be measures of response rates, database or external API calls, function processing times, memory allocation, etc.
It, therefore, helps to think of instrumentation as a foundational component of an application management system that can have monitoring services to enable tracking, alerting, analysis, troubleshooting, debugging, and optimization.
Continuing on the premise we set up in the introduction, it is critical to employ systems that can instrument (measure) your applications and simultaneously use that data to monitor, aggregate, identify, summarise, and alert you about noteworthy trends and issues in your application. This is how instrumentation serves as an effective window into the internal workings of your software.
Your work doesn’t end when you ship the product; that’s where the actual game starts. Now let’s talk about how you can get started with instrumenting your Python applications.
How to Effectively Instrument Using Python
As you can imagine, it is very much possible to manually add some custom code in your project that captures and logs system information (memory, CPU usage, etc.), response times, error rates, and other valuable metrics. And this might work well enough for a small-scale, pet project application. However, maintaining this functionality for hundreds of endpoints across dozens of modules becomes intractable as your application scales.
This is where established (auto) instrumentation tools come into the picture. Along with their full-fledged application performance monitoring (APM) services, toolkits like ScoutAPM, out of the box, provide automatic instrumentation with several of the most popular Python libraries like —
Scout automatically detects these libraries during initialization and do not require explicit configuration. To set up instrumentation, all you need is to install your preferred library/framework of choice, and Scout takes care of the rest.