In Praise Of Reinventing The Wheel

The original Ferris Wheel at the 1893 World Columbian Exposition in Chicago (Source: Public Domain via Wikipedia)


There is a strong principle in software engineering of reusing code wherever possible. It’s considered so important, that it’s got its own TLA: DRY (“Don’t Repeat Yourself”). In other words, don’t reinvent the wheel.

There are obvious benefits to this. Nobody wants to type the same piece of code over and over. So write it once as a function and reuse it wherever you need to, freeing yourself up to build other stuff. Better still, reuse someone else’s functions.

Indeed, the ready availability of open-source libraries and frameworks for almost every popular requirement under the sun, means that you almost never have to develop your own low level software. Thank goodness. Software development would get really tedious if you had to start out writing basic I/O routines with each new project.

Even new developers can get sophisticated projects up and running fairly quickly. Github and StackOverflow have seen to that.

The principle is taken to an extreme by some web developers, who include the entire jQuery library on their webpage in order to implement some simple form validation. I’m all in favour of simplifying JavaScript development, second only to abolishing it altogether. But including the whole library is a tad overkill.

And this highlights a problem with treating DRY as dogma.

When we rely on existing libraries and frameworks, we don’t get a good idea of what’s really happening within our code base. Yes, we can read the library code to get the gist of what’s going on. But we never really do.

There’s a really good reason to try to implement your own routines, if only in your test projects. You’ll get an appreciation of the lower level algorithms that your applications depend on. You may even discover how difficult it is to get some of these algorithms to run quickly or to use a small amount of memory.

This will lead you to the core principles of algorithmic analysis that are fundamental to computer science. Along the way, you’ll pick up a really good sense of what works well and what doesn’t, what’s fast and what’s slow, what’s memory efficient and what’s a complete hog. Hopefully, you can reflect those lessons in your own production application code.

So the next time you have a chance, try to implement your own I/O routines or other low level algorithms and see how you compare to other published libraries.

Remember, DRY is a principle, not a dogma.

Sometimes there is merit in repeating yourself (or someone else).