Python has its pros and cons, but it is widely used nonetheless. For example, Python is often used when processing data, even if there are more suitable languages to choose from.
Why? Well, Python is relatively easy to learn. Someone with a scientific background can pick up Python much faster than, say, C. However, Python’s inherent approachability also poses a number of problems.
Whenever Python is updated, it means a large refactoring workload, which is often handled poorly or not at all. This leads to poor performance and security vulnerabilities. But maybe there’s a better way: a tool to keep your Python tasks running smoothly and securely day in and day out. Let’s see.
It’s slow, but it does the job
Python isn’t the fastest language out there, but despite its relative drawbacks, you’ll often see it used for intensive data processing operations. Think machine learning, computer vision or even pure mathematics in high-performance computing (HPC) systems.
So despite its performance reputation, very heavy workloads are often handled with Python code simply because it is so practical to use. If you have a science or math background, you can learn Python relatively easily and produce your own code that will do the job.
But as is often the case, something that is accessible can also carry a lot of risk. Inexperienced programmers writing Python code may struggle with tasks that more experienced programmers take for granted.
Refactoring is a hassle… and a risk
Like all programming languages, Python undergoes regular updates. For example, the shift from Python 2.7 to Python 3.0 brought with it a slew of features and improvements. It also means that anything written for Python 2.7 has to be “refactored” for Python 3.0 due to changes in the way Python works.
Refactoring refers to the way programmers modify a code base to respond to changes in the environment, such as a change in language version, or simply to improve existing code in some form. Without refactoring, a shift from Python 2.7 to Python 3.0 often means that the code for Python 2.7 simply doesn’t work as well, if at all.
And here’s the problem: the users who wrote the original Python code may not have the expertise to refactor. After all, they are often scientists – not experienced programmers. When inexperienced programmers try to modify code, there is a real risk of performance degradation and bugs creeping in – sometimes only visible when an edge case appears. Minor bugs become a major problem when Python code is used for critical, 24/7 purposes, such as scientific analysis.
Refactoring can also lead to unexpected performance degradation. Even if it’s only a 5% performance gain, a poorly executed code update can quickly cause much higher bills on expensive HPC platforms that pay to use.
Stick to old Python? That’s an even bigger risk
When you think about the hard work and risks involved in customizing code, it’s no surprise that users often choose to just stick with older versions of Python. Running existing code on an outdated version of Python avoids a lot of challenges because you don’t have to refactor: you keep your code exactly as it was.
Usually, software vendors will do just that – just update their software to match a new Python version when they release a new version of the software product. If you bought a specific version, say on Python 2.7, you’re stuck and have to keep using Python 2.7 no matter what.
It doesn’t sound like a big deal, but relying on outdated, unsupported building blocks for your computing is a DevSecOps nightmare. New vulnerabilities will appear and the necessary patches just won’t come. Relying on old versions of programming languages therefore entails enormous risks for your computing environment.
There is little choice – or is there?
The responsible thing to do is update the Python version when needed and edit the code running on it, but there’s just no painless way to do it. Realistically, refactoring is often not performed due to a lack of resources, with potentially costly consequences.
There is a great need for a better approach, and this is what is interesting. The situation we just described around Python versions is often seen in the computing world. For example, it is common for organizations to run versions of the Linux operating system that are no longer covered by official vendor support, risking security vulnerabilities not being patched to ensure critical applications don’t break.
That’s also a problem for language updates and other pieces of IT infrastructure. But in recent years, advanced patching solutions allow companies to extend the typical support lifecycle for multiple components, from entire operating systems to specific critical shared libraries. Hereby TuxCarewe have developed several solutions that extend the secure operation of older software beyond the vendor’s lifespan.
Run older Python apps safely and securely
What if the same could be done for language versions? Well, you can now look forward to running your older Python code, on an old version of Python – but without the risks involved. No outdated language constructs – and no vulnerabilities either.
In other words, comprehensive lifecycle support for language versions – such as Python – is becoming a reality. You’ll soon be able to keep your Python code safe without having to rewrite a single line of code, simply by getting comprehensive Python lifecycle support – giving you the same security protection as a full version upgrade.
Set to roll out at accessible prices, TuxCare’s Python Extended Lifecycle Support helps your organization deal with the tough questions surrounding older Python workloads. Keep an eye out for our announcement – coming soon.