Rails Performance and the root of all evil

BY Doug Breaker

May 09, 2016

Donald Knuth wrote an often quoted paper in the 70s which is still referenced when talking about performance in web apps today.

Premature optimization is the root of all evil.

In my line of work, it is sometimes invoked as a sort of apology; an excuse for why more time wasn't spent on performance: "This sucks, but at least we didn't.....prematurely optimize!"

My job is to fix performance issues and help other developers write well-performing code, so I'm not a huge fan of this quote. We shouldn't let an out of context quote guide our development strategy, no matter the source!

When things bother me, I like to stop to take a closer look. Here's the full quote:

We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.

I translate this to: "When optimizing, don't get distracted by things that don't matter."

I would never translate this to: "Hold off on optimizing things until it really hurts."

Identify What Matters

Some developers see performance work like they see taxes. It's the annoying obligation that comes after code has been written. The fun is over, your bad decisions have come back to haunt you, and you are stuck fiddling with technical trivialities that you wish would just go away.

Other devs stay away from performance work, remaining blissfully ignorant. They assume it is difficult, mysterious and elusive.

But what do we mean by performance work? I break it into 2 types:

  1. "Baking in" performance with best practices and experience. In Rails, this would be proactively using pagination, having your db indexes in order, having a good idea what SQL Active Record is firing, using .includes where necessary, keeping track of where caching is going to be useful, etc. Not baking in these best practices along the way is akin to taking a mortgage out on your app. You are instantly creating debt and your options will be to "pay it off later" or bankruptcy.

  2. Measuring, identifying and handling performance issues. This is what I call "the problem of figuring out what the problem is." This is the work that can feel difficult, cumbersome and mysterious.

It's much easier to build performance into an app than it is to hunt down random performance problems as they bubble up. Baking in optimization is not what Knuth means by "premature." Let's see a bit more context around the Knuth quote:

We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.

Yet we should not pass up our opportunities in that critical 3%. A good programmer will not be lulled into complacency by such reasoning, he will be wise to look carefully at the critical code; but only after that code has been identified.

The context here is how to approach optimization, not whether to optimize or not. I would summarize his advice as:

  1. Don't be complacent (by avoiding optimization altogether)
  2. Once optimizing, it's easy to spend hours optimizing things that don't matter (this is evil)
  3. Instead, immediately identify the critical 3% that requires optimization

Measure Twice, Cut Once

Knuth is on a roll, so let's give him a bit more page time:

It is often a mistake to make a priori judgments about what parts of a program are really critical, since the universal experience of programmers who have been using measurement tools has been that their intuitive guesses fail

Got that? Knuth wants us to measure and discover where the problems are, not guess at the issues and dive right into trying to fix them.

I'm a huge fan of using my gut to guide investigation into issues - but it's never worth refactoring or committing code until there is tangible proof of where the problem is. It's normal to be wrong, or for the story to be more complicated than you expect.

This is another reason why performance issue work might sound "hard." Endless hours spent down dead ends chasing "leads", fiddling around with things, coding and coding and coding...

Here are other things I see hours wasted on that I recommend skipping:

Here is what I find to be the quickest path towards issue resolution:

If you don't have enough measurement around the problem, it's easy to make more. For example, once you know which action you care about, add rack-mini-profiler to your Gemfile, hit a few pages, and immediately get more detail.

If more than an hour or two has gone by and you have no idea where the problem is, don't despair. You can always try the "The Rude Macgyver." Delete half of the code path, check to see if the problem disappeared. If it did, you know the problem is in the removed code, so put half of that back in and then evaluate. Repeat until you have narrowed down a line or two to blame. In a Rails app you can rip out partials, or the view entirely, comment out parts of the controller, old helper methods, before filters, etc.

Evil Optimizations

I’ve seen many underperforming Rails apps. In most cases, I walk away certain that the app, the business and the customers would be better off if performance issues were treated more proactively vs. reactively.

The favorite part of my job is when developers I work with agree and code more defensively, catching performance issues before they are introduced into the codebase. However, along with this increased attention, a new risk emerges - this is what Knuth is actually warning us about. The risk of eagerly optimizing "all the things" without measurement.

Here's a list of common "optimizations" I regularly see Rails devs tempted to chase down:

Disclaimer: You may like these challenges! By all means, geek out, measure and explore. But most modern web apps do not require these types of optimizations. Default to spending time evaluating database relationships and Active Record usage, figuring out if the UI is sane, or measuring a custom abstraction that someone in the company built on top of Rails 3 years ago.

Non-Evil Optimizations

If your app has customers and is growing, and you don't proactively spend time optimizing popular features, heads up: it's likely you will eventually get complaints, downtime, and potentially a loss of business as your dataset and usage grows. It's never premature to "drive defensively." Optimizations like these are probably never going to be waste of your time:

Nothing But the Knuth

My advice is to take a reasonable amount of time to bake in performance as you build. When trouble arises, figure out what the critical 3% is via measurement. Do this before allowing yourself to worry about esoteric topics or starting to shuffle code around.


Sudara Williams is a full-stack kinda guy who enjoys chewing on the tough bits at Rubytune, a Rails performance consultancy. He wrote his first (and last) programming book in the 6th grade. He built and managed an IT support business in Santa Fe, then returned to app development and fell in love with ruby in 2005. He later worked at Engine Yard, helping clients such as Github, Groupon and New Relic do the hard stuff. He runs Ramen Music and alonetone, the open source artist platform.