#Perl on the rise for #DevOps

Note: I do quite a bit of development in Perl, and have my own biases, so please do take this into consideration. It is one of many languages I use, but it is by and large, my current go-to language. I’ll discuss below.

According to TIOBE (yeah, I know), Perl usage is on the rise. The linked article posits that this is for DevOps reasons. The author of the article works at a company that makes money from Perl and Python … they build (actually very good) tools. Tools that I personally use (Komodo).

The rationale is that Perl is very powerful, quite fast, extremely flexible, and ubiquitous to boot. They compare performance of Python and Perl performance, noting some of the differences, and speculating why.

Generally, I don’t normally like saying “language X is better than Y”. Languages have domains of applicability, strengths, and weaknesses. Moreover, if you have to justify your choice by making the point “Y is better than X because of Z” then you’ve largely not understood the point of the languages in the first place. I’ve made this point in the past before, but “delegitimizing” a language (such as, I dunno, the line noise meme? or use of sigils … where the latter seems to be only applied to a single language …) isn’t a good language advocacy path.

So put that aside, and lets talk DevOps. DevOps at the core is about turning processes and hardware into larger portions of an algorithmic application delivery and support. To make automation simpler, to wrapper applications that aren’t services into something that looks/acts like a service. To enable composable systems, or if you prefer the moniker used today, Software Defined systems. I’m going to focus less on the container side here, and more on the process side.

There are many tools to help enable this. Some are fairly new and undergoing rapid development. Some are more mature, others are ossified.

Generally, you need a few specific features to build elements for inclusion in a DevOps pipeline. You need the ability to build API endpoints for services that you will be running. You need the ability to link these API end points to specific functional elements. Like running a non-service based program with specific arguments. You need the ability to ingest data in common formats, and output in common formats. You need the ability to easily send signals in or out of band (depending upon how your architecture is built).

This “glue” functionality is, to a very large extent, what Perl excels at. Ok, I am talking Perl5 here. Perl6 is (literally) a new language with a similar though not identical syntax … but from what I have seen, it can do this, and far far more. But that is a topic for another time.

You can create endpoints trivially in Perl using standard modules. You can set up simple servers, or restful APIs fairly trivially without much boiler plate code (see Mojolicious on CPAN. It has significant capability in Meta-programming via various modules such as Class::MOP/Moose, and others. It has amazing multi-language capabilities with the Inline:: series (Inline::C , Inline::Python, …). It interfaces quite trivially to external libraries written in any language (FFI::Platypus). It has the ability to run external code via a tremendously powerful interface, IPC::Run, as well as with simple back ticks. It can run multi-threaded, multi-process code using threads::shared and MCE amongst many others. Its database connectivity is excellent, and it is easy to hook into (No|New)SQL DBs. It has event loops for async processes.

I could keep going, but the point is that it is fairly trivial to build responsive services for DevOps using Perl and a smattering of these tools.

This said, some of the distribution providers (I am looking at you Red Hat) are still shipping not merely ancient tool sets, but tools that have been end of lifed for years … as their current supported tools. Like the ancient Go, Python, and other tools, Perl on these distributions is so woefully out of date, that some of the modules (Mojolicious and a few others) may not work properly. This is on them, they need to decide if they want developers whom need modern tools, or not.

What I’ve been doing has been building my own tree of tools. I’ll be refreshing this soon, and putting the refreshed tree up on github shortly. These are modern versions of Perl5, Perl6, Python3, Rust, Octave, R, Julia, Node, Jupyter, and a few others, along with my build environment. These tools make DevOps and analytics generally quite easy. All batteries included as far as I can tell based upon my usage, but happy to learn of more tools we need to include.

This environment is not yet set up for containerized deployment, as it is more of an add-in to an environment, than providing a specific service. We are looking at ways of packaging/using this in a more “traditional” container scenario.

But back to Perl and DevOps. The majority of Scalable Informatics code is Perl based DevOps code, and has been for more than a decade. The code is simple, fast, well debugged. Handles very intense loads.

Tastes great, and less filling.

I’ve not felt that Perl5 was dying as a language. I’ve thought that there are many tools out there, and some of them are pretty good. Perl is just one of them. Though for the moment, it is my go-to language.

Personally I am a polyglot, and I try to use the system that gets in my way the least; allowing me to express what I need the most, with the greatest simplicity and accuracy. I think that if there is a signal in the TIOBE data, that it likely reflects this to some degree. People rediscovering solutions to problems.

Creating something new for the sake of creating something new versus using a powerful system that exists and solves problems correctly now may not be the best pattern for follow for DevOps. We’ve seen this time and again in this industry though. Some of the patterns are fads, some have longevity (even if there is no valid reason for their existence).

DevOps going forward will continue to push hard on toolchains, and those whom enable the greatest functionality with the least pain will likely be the winners. Similarly with analytics …

Make it easy to use and adopt. Make it ubiquitous. Perl has this now. Which is why I think the article might be on to something, even if the assumptions on the data are not valid.

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