I’ve now worked with “The Cloud” for long enough to see that there’s still a long way to go before it becomes materially better than, say, the oldschool method of renting a couple of servers with a co-location company and running your software there. The latest fad is Serverless, which makes me feel a lot like we’ve arrived in 1970.
A long, long time ago, I was pretending to study business administration while teaching myself to code in C. The university was in two worlds: we had a lab with “personal computers” but also terminals to the mini computer and assignments had to be made on either one depending on the professors’ preferences. But both systems were at least “on-line” systems–interactive, with immediate feedback. A friend of mine was not so lucky: he had an assignment for one of his courses in civil engineering that had to be completed in Pascal and had to be handed in as a print-out from the university’s mainframe.
He called me in for help, because he never wrote a line of code before. So I hopped over to his place, a Turbo Pascal floppy disk in hand, and assumed that we would make short work of the assignment. And we did, even though I never coded in Pascal before: the IDE, even back then, was marvellous and a quick succession of trial-and-error got us the results we wanted (they were some simple engineering calculations, much quicker done on even the most primitive of calculators, but such are course assignments. At least it was easy for us to verify the program was correct).
We hopped into the car and drove over the the university’s computer center, a ’70s brutalist concrete bunker. We sat behind a terminal, typed in the code, surrounded it with the required IBM Job Control Language that the teaching assistant helpfully added to the assignment, and hit “Enter” (not “Return”, actually “Enter” - Enter The Job). A couple of minutes later, the printer started and spit out a couple of pages. Between gibberish in all caps, there it was: a compiler error. Back to square one - Turbo Pascal and IBM Mainframe Pascal clearly had some differences. A couple of hours and reams of wasted paper later, we had what my friend wanted: the results of a good run. The numbers matched our notes, and we could retreat to the nearest bar for a well-earned beer.
Ever since that experience, I have stressed the value of feedback and especially of quick feedback. Even in this minimal example, we spent more time tweaking the code for the dialect than we spent on writing it in the first place. Slow feedback loops kill performance, and if you don’t believe me, find an online version of The Beer Distribution Game and play it. You’ll be surprised.
The pinnacle of interactivity was, and still is, Smalltalk. I worked in that language for a couple of years and having your system compile and run most of your tests in less than a second is addictive. It is incredible how much your performance increases on a fully interactive programming system but it takes first-hand experience to fully appreciate it. It’s a strange system, a strange language, a tough sell; therefore Smalltalk is still in a very undeserved state of limbo.
After my Smalltalk years, I met a lot of large Java systems. They started out as horrible hairballs, but when the idea of “dependency injection” got a foothold, things started to get better. Except for the return of the Job Control Language, this time disguised in XML and carrying friendlier names like “Spring”. Internally, the code was reasonably clean and modular, but telling the computer how to run it took pretty much the same amount of typing, this time not in a programming language but in a structured markup language. That language lacked all the facilities that apply to writing good code, so principles like “Don’t Repeat Yourself” went overboard and copy/paste programming ensued. New business logic? New controller, twenty lines of Java boilerplate, 10 lines of Java business logic, 50 lines of XML to wire it to the rest of the system.
At least, in hindsight, XML was a blessing: your editor would tell you whether your “this is how the code is wired up” description had the right structure, and later on even whether you used names that existed in your Java codebase.
These systems took ages to compile, hours to run all the unit tests, and clearly interactivity went down the drain. So “split it all up” was not a bad idea. It was an unnecessary idea, mostly driven by the shortfalls of mainstream programming languages, but necessity is the mother of invention - even though your average Java business app was much simpler than the Smalltalk IDE before you added a single line of code to write your app, it was already too complex to maintain and “divide and conquer” was the answer. Service Oriented Architecture, and later microservices, was born. Split your codebase, split your teams, create a lot of opportunities for mediocre coders to grow into mediocre engineering managers, everybody was happy. Including your hardware vendor, because suddenly you needed much more hardware to run the same workloads. Because networks are slow, and whereas it can be argued whether a three tier system is actually a distributed computing system, a microservices system certainly qualifies for that label.
The return of the Job Control Language this time was in the form of, again, configuration data on how to run your microservice. Microservices were somewhat fatter than the very fine-grained objects of the old days, so there was less of it, but still - it was there. The feedback cycle became worse as well: in the monolith-with-XML days, your XML editor would get you mostly there and a quick local compile and run would leave you all but certain that your configuration was working. XML, however, was universally rejected in favour of things like JSON, Yaml, HCL, Toml - all free of structure, with zero indication whether a computer would find your prose gibberish or the next Shakespeare play until you actually pushed your code to some test cluster. It suddenly felt a lot like being back at that university interacting with a mainframe, but at least you still owned the hardware and could introspect all the way down, especially if you were doing “DevOps” meaning mostly that you had administrative access to your hardware.
Microservices have trouble scaling, and they are very complex. Most companies that employ them have no need for them, but the systems and programming languages they employ are sufficiently lacking that this stacking of complexity on top of complexity becomes a necessity. It seems that, contrary to what everybody is saying, software developers are in plentiful and cheap supply so wasting a lot of their talent makes economic sense over the perceived cost of adapting more powerful programming systems. C’est la vie. The feedback cycle is truly broken - testing a microservice is merely testing a cog in a machine and no guarantee that the cog will fit the machine - but we just throw more bodies at the problem because Gartner tells us this is the future.
So, we’re now at the next phase of this game: maintaining these very complex systems is hard and not your core business, so outsource it (simplifying it would cost too many managers’ jobs, so that is not an option). The Cloud was born, first as a marketing label for an old business model (offering “virtual private servers” to the public), but more and more as a marketing label for an even older business model (the mainframe - we run it, we own it, you lease capacity).
Apparently, Worse Is Better and you can do worse than Virtual Private Servers, so through a short-lived detour through containerizing microservices and deploying them on a distributed scheduler like Mesos, Nomad, or Kubernetes, we have arrived at “Serverless”. You deploy individual stateless functions. But not inside a Java monolith, that is old, but on top of a distributed system. You would have been laughed out of the door if you had proposed that in 2000, and you should be laughed out of the door right now, but such is the power of marketing. So what have we now? A “mono repo” codebase, because clearly a Git repository per function in your system would be too much, a large deployment descriptor per fine-grained component, which Spring maybe called “Controller” but is now called “Function”, and instead of combining them all on your desktop, you send them off to someone else’s mega-mainframe. You deploy, get an error message, and login to CloudWatch to see what actually happened - it’s all batch-driven, just like the bad old days, so progress is slow. At least we’re not having to walk to the printer on every try, but that pretty much sums up the progress of the last half century. Oh, and a “Function” will be able to handle a single request concurrently, so here’s your AWS hosting bill, we needed a lot of instances, I hope you won’t have a heart attack. Yes, we run workloads that your nephew can run on his Raspberry Pi 4, but this is the future of enterprise.
History will repeat itself, of course. Conceptually, hiding a lot of the scaling and coordination machinery is not a bad idea; programming systems like Erlang and OTP have shown for decades how well that can work and Elixir is giving the platform a well-deserved surge in popularity. But there’s a big difference here: a platform like OTP handles pretty much everything that a platform like AWS Lambda handles, but it does it in a single programming language. Tools of the trade are available: you can refactor Erlang code, you can write Elixir macros, all to keep the system clean, malleable, and free of accidental complexity.
It is called “Configuration as Code”, and it truly is a great idea (but hardly a new one). But XML is not code, and neither is JSON, nor YAML, nor HCL. There is an essential quality lacking in all of these markup languages, and that causes all the copy/paste programming I am currently seeing everywhere, whether a team deploys on Nomad or uses Terraform or CloudFormation to whip up mega complex clusters to run a marketing site. This level of complexity is not sustainable, and my fear is that it will be solved the way our industry likes to solve the problems they have created themselves: by adding more complexity on top if it.
Not by going back through our history and figuring out what caused the Personal Computer Revolution. Which is a shame.