A majority of enterprises are using a version of Python that’s a year or more older, according to new research, and it’s costing them dearly.
Statistics from JetBrains’ annual Python Developers Survey show over three-quarters (83%) of enterprises are using older versions, highlighting a broad “distribution across the latest and older versions of the Python runtime”.
They’re not particularly fussed, however, with the majority of respondents stating that they were satisfied with the performance of the version they’re currently using.
More than half (53%) said the version they’re using “meets all my needs” while 25% admitted they simply haven’t had time to update.
Breaking down usage rates, JetBrains’ study showed just under a quarter (21%) reported using version 3.11, released in October 2022, while 35% said they’re working with version 3.12.
Just 15%, the study found, are using 3.13, the latest version of the programming language.
Notably, the study showed that a significant number of developers are using Docker and containers to execute code, meaning the volume of enterprises working with older versions was “surprising”.
“With containers, just pick the latest version of Python in the container,” the company noted in a blog post detailing its findings. “Since everything is isolated, you don’t need to worry about its interactions with the rest of the system, for example, Linux’s system Python.”
JetBrains added that it expects containerization to “provide more flexibility” for enterprises and “ease our transition” to the latest version of Python.
Upgrading Python has big performance benefits
A key talking point in the JetBrains survey centers around the potential ease of use improvements enterprises are missing out on by failing to upgrade.
More recent versions include features that ease quality of life for developers, but also big performance gains, the company noted.
“The 83% of developers running on older versions of Python may be missing out on much more than they realize,” the firm said. “It’s not just that they are missing some language features, such as the except keyword, or a minor improvement to the standard library, such as tomllib.”
“Python 3.11, 3.12, and 3.13 all include major performance benefits, and the upcoming 3.14 will include even more.”
Drilling down into the potential gains, JetBrains noted that 48% of those using Python 3.11 can unlock code run gains of ~11% by upgrading to 3.13 - and that’s achieved while using ~10-15% less memory.
“If they are on the 27% still on 3.10 or older, their code gets a whopping ~42% speed increase (with no code changes), and memory use can drop by ~20-30%,” JetBrains added.
Speaking to ITPro, Daniele Volpe, software engineer at SecureFlag, said while updating to newer versions isn’t critical, the advantages are clear and should be capitalized on.
“Running old Python versions does add up over time,” Volpe said. “You’re basically paying for slower runtimes and higher cloud costs when newer versions give you efficiency for free.”
“It’s not always urgent, but ignoring it means you’re leaving money and performance on the table”.
Volpe noted that the impact on developers has a knock-on effect which ultimately exacerbates existing efficiency and performance-related issues.
Slower runtimes mean slower feedback loops, which slows down the overall efficiency of the development lifecycle.
“Developers spend more time waiting on tests, builds, or jobs to finish,” he said. “Plus, sticking with older versions means missing out on language improvements that could make their day-to-day work smoother.”
Upgrading might be a hassle – but just take the plunge
Volpe said that it’s no surprise a significant portion of respondents are satisfied with the performance of older Python versions. It’s a long-running practice and most teams “stick with what works until something breaks”.
“Upgrading feels like hassle and risk, so they put it off,” he said. “Comfort and inertia often win out, even if the gains from moving forward would be worth it.”
Maurice Kalinowski, product director at Qt, echoed Volpe’s comments on this front, adding that “updating any technical dependency is always a concern to a development team”.
“There can be regressions, both on functional or performance level, incompatible changes or even removed functionality,” Kalinowski said.
“This applies even more to the underlying programming language, as it touches not just one component but your whole solution.”
Simply put, if it isn’t broken, don’t try and fix it.
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