Optimizing the Wrong Code

First written on October 23, 2014

4 min. read

Today I was going through some of our worst queries on Skylight and trying to find some low-hanging fruit to improve performance with the least effort possible. I like Skylight because it’s a tool designed to reduce noise. Whenever you get into the optimization mindset, noise is your worst enemy. It turns the most innocent pursuit of necessary speed improvements into a rabbit hole chase of diminishing gains.

After a few successful optimizations for which I successfully added table indices and compound indices to dramatically speed up some queries on very large tables, I found that our Report Card had a ridiculously slow SQL query in it taking basically 50% of the request time.

Request Trace from Skylight

Much to my surprise, that terribly slow (about 700 ms) query appeared very simple:

SELECT  "users".* FROM "users"  WHERE ("users"."username" ILIKE ?) LIMIT 1

ILIKE is a Postgres-specific case insensitive LIKE comparison.

Since I had spent so much time researching, analyzing, and implementing table indices my first instinct was to research the possibility of adding an index to this ILIKE clause and I did find some interesting information about using Postgres’ Trigram extension to make a relatively fast GIN index on this relatively tricky clause.

I got started on adding a migration to our database to enable the pg_trgm extension in Postgres but remembered that database extension support was only added to Rails’ schema.rb files in Rails 4.0 and for now at least we’re still running a patched Rails 3.2 so I wouldn’t be able to make this migration portable without switching to a structure.sql file which is a nope.

Then to clear my mind a little bit I searched for the line of code that was triggering this rogue query.


Now, I understand that using hardcoded SQL strings in Rails feels weird. That said — and I’ll admit I forgot what I base this opinion on — I consider Arel a private Rails API. It may not change often and unpredictably but I will treat it as if can. Harcoded SQL strings on the other hand are unlikely to surprisingly break with no deprecation notice. I’m aware someone could extend this line of argument to say mean things about ActiveRecord but I think that’s silly. ActiveRecord has an API and when it changes we generally tend to hear about it ahead of time.

What really bothers me here is that this User.arel_table[:username].matches(params[:id]) is completely obfuscating the ILIKE statement it generates. If you don’t call to_sql on this thing, or see the full SQL output in your console or on something like Skylight, you won’t notice it’s doing something potentially very slow.

And then it hit me. Why the hell were we doing an ILIKE query? If a user matches the username in a URL, it should be displayed. It’s not like the goal of this action was to display usernames that start with params[:id] and maybe end with something else like a where("username ILIKE ?", "#{params[:id]}%") would. No this was purely focused on dealing with the problem of case. If someone typed in OlivierLacan we want to look for the username olivierlacan or OLIVIERLACAN or OlIvIeRlAcAn.

We could use User.where("lower(username) = ?", id.downcase).first instead and considering we already have an index on the username column this would be much faster. In the best cases that would take the query down to 270 ms.

That’s when I remembered that we had an internal column named system_username where we simply lowercase the username column every time it’s updated. That meant I could let Ruby deal with the (easy) job of downcasing a string:

User.where(system_username: params[:id].downcase)
# or

Query speed? 1 milisecond. Instead of optimizing the database for inefficient code, I ended up rewriting the code to be less silly and take advantage of prior optimizations we had (thankfully) done. I didn’t have to install a Postgres extension usually reserved for full-text search, and — more importantly — I didn’t have to create a potentially costly index on an operation (the case insensitive comparison) that ended up being unnecessary.

PS: I’ll be following this post up with a new one detailing my methodology with the more indexing-focused performance optimizations I mentioned at the beginning. If you have any tips and tricks in your toolbag, please send me a quick email to let me know so I don’t look silly. Here are some of mine.