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Practicality Beats Purity - Pure SQL vs ORMs

I’ve been using some form of a database throughout the entirety of my career. Sometimes single-file databases, sometimes full servers. Sometimes testing them, sometimes designing them, but a lot of times I was optimizing them. Even when learning programming with my dad, most of the apps I built were about storing and managing some type of data.

With all those years of experience, I definitely understand enough to know that I’m by no means an expert at any of it. There are several (an understatement) mechanisms by which folks make the best use of their database servers, almost all of them are tradeoffs in memory usage, space, look-up times, results retrieval, backup mechanisms, etc.

As expected, each database mechanism has their own quirks and optimizations, but the common theme is the language which you use to retrieve information: Structured Query Language (SQL). Different database engines implement different extensions to this language, some of which add powerful functionality, some of which just add confusion. But in general, SQL has been very successful in standardization across the industry.

This next chapter in the Practicality Beats Purity series covers the tradeoffs when using direct SQL queries to a database vs programming language abstractions that do it for you, like ORMs.

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Practicality Beats Purity - Microservices vs Monoliths

In recent years there’s a growing trend to move away from large all-in-one applications. These “monoliths”, developed with one codebase and delivered as one large system, are hard to maintain. In their place, the industry now favors splitting-off the component systems into individual services. As separate “microservices”, they perform the smallest functions possible grouped into logical units. They are independent deliverables, deployable, replaceable and upgradeable on their own.

Going further into the Practicality Beats Purity series, this article will cover the implications of transitioning to a microservices architecture.

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Practicality Beats Purity - Modularity

Continuing on the Practicality Beats Purity series, today we’re talking about modularity. While written with python in mind, the discussion here applies to any language that’s highly modular and with a large ecosystem.

As is touted frequently, python is quite famous for being a “batteries included” language with a vast ecosystem of modules and packages that provide almost every possible utility or function you’ll ever need. When building large applications, it’s a great idea to make use of this environment and not reinvent the wheel. This makes rapid development and prototyping real easy.

However, you must keep in mind that every new dependency added is one more variable that you have little to no control over. While you may not write the code yourself, there’s still cost incurred in keeping up with the most recent versions of your dependency and watching for security flaws and their respective fixes. It’s also important to pay attention to the size of the community around those dependencies, their interaction with other modules, responsiveness to reported bugs, and the size of supporting documentation both official (like read-the-docs) and unofficial (like stack overflow).

Following we discuss some of the costs.

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Practicality Beats Purity - Intro and Test Pass Rates Topic

A few hours later, I find myself sitting in the “comforts” of my cubicle. The discussion replaying over and over in my head: “An interface with this behavior will integrate with most common language libraries, with no special client code”, I said. The response was: “But then it’s not a design, and the company already decided that’s the route we’re taking.”

I’ve spent many years of my career involved in buzzword dogma discussions. It’s present at all levels of software development, from basic principles, to scheduling, to implementation, its interfaces, its tests, the execution, the infrastructure that runs it and its release mechanisms. Most of the time, people lose track of why or what they are building in favor of claiming they are using some common buzzword, regardless of the effects on architecture, ease of use, customer experience or maintenance costs. My experience shows they don’t even know why the buzzword technology does things a certain way or why someone chose it in the first place. Factual or data-based counterargument results in an almost “religious” discussion and even shaming.

Given today’s ease of communication and the ability to share our experiences, it’s great that we try to educate other folks on the problems we typically face throughout our lives and careers. Especially the principles used in managing their solutions.

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