Transcript:

Hello and welcome to the first OmbuCast by Ombu Labs. In this screencast we'll be taking a look at the derailed_benchmarks gem, and how you can use it to benchmark your Rails application and find, and hopefully fix bottlenecks in your code.

There are two ways you can benchmark your application, one is statically, which uses your Gemfile to check for any gems which might be too big - which take too much memory - and the other one is dynamically. To be able to run these, you'll need to be able to boot your Rails application in production mode locally. To be able to run the benchmarks I'll be showing, you'll need both the derailed_benchmarks gem and also the stackprof gem.

To show how the gem works, I created a test application, which just returns an array - a JSON - which has many users, and it's a Rails 5.2 app. So it probably won't be similar to results you may get from your own applications, but it's good enough for, just showing how to apply these techniques.

So the first thing that you should do is finding out which one of your endpoints is slow, be it in production or in development (usually you'll want to optimize your production endpoints). And when you do that, you will need to measure how many iterations per second the endpoint can do. This is to get a baseline, an idea of how many, or, how fast, your endpoint initially is, and then when you optimize, you can measure if the optimization was good or not. So our first step then would be to run the IPS task that derailed_benchmarks provides and specify that our path to hit will be /users.json .

PATH_TO_HIT=/users.json bundle exec derailed exec perf:ips

So we run this task and it will give us a number, which is the IPS, the iterations per second, in this case it's 263. And this is the starting point, so any modifications that we do to the code should not make it fall under 263 iterations per second for this endpoint in particular. So to start optimizing, one thing we can do is to run the stackprof task that derailed_benchmarks provides, to find where there are any potential bottlenecks.

PATH_TO_HIT=/users.json bundle exec derailed exec perf:stackprof

Like before, we specify the /users.json endpoint, and run the stackprof task. And we get this output from stackprof . The methods on top are the ones that took the most CPU time: the logger, garbage collection. And then there's one that probably jumps out, which is Time.parse , which we're using in our controller here, and it's not exactly a very time consuming task to parse the time, but since it's hard-coded, we can use Time.at , which is slightly more performant than Time.parse , and it will accomplish the same result. So what we can do here is to check in a Rails console.

2 . 4 . 2 : 001 > Time . parse ( "2018-02-12 12:00:00 UTC" ) . to_i => 1518436800

We can cast to integer, and we get the time that we can use for passing to Time.at , and then we should do the same for the second one.

If you apply this kind of refactor in a real application, note that these should probably be constants and not just magic numbers. So now we can run the stackprof task again, and, yeah, Time.parse should now be gone. This doesn't necessarily mean that your iterations per second will be better, it's a good indicator, but you still need to double check. So we can check our IPS again, and, they're roughly the same as before, which makes sense since it's a very small modification, and also a very small app. What we can try and do is make other changes to our app, and one such change can be the optimization of this code here.

users = User . where ( "send_reminder_at > ?" , hour_12 ) . map do | user | { id : user . id , email : user . email , full_name : user . full_name } end

This is actually instantiating one user per iteration, so for each user, we have to instantiate one object - one user object. So to save memory, we can introduce a gem, it's called pluck_to_hash . It acts like ActiveRecord's pluck , but it produces a hash like the JSON we're trying to produce. So let's try to install it, and see how our iterations per second change, if they change.

users_scope = User . where ( "send_reminder_at > ?" , hour_12 ) . where ( "created_at < ?" , cut_off_date ) users = users_scope . pluck_to_hash ( :id , :email , :full_name )

And now we can run our IPS task again, and measure the change. And yeah, that's a noticeable difference, there's about 60 more iterations per second. Like I mentioned before though, this is a small scale application, so you may have similar results, or you may not, it depends on your application. Since our Rails application is serving a JSON, another gem we can use is called OJ . It stands for "optimized JSON", and it's a faster JSON parser than the default that Rails uses. We'll install it in our app by adding it to the Gemfile . And then we need to create an initializer, and we can check our updated IPS.

# config/initializers/oj.rb Oj . optimize_rails

And that's another performance improvement there, it almost - 400 iterations per second from 320 and 270 before that, that's almost 100%, you should keep in mind though that all of these improvements should be done with a good test suite, since you should make sure that these changes don't break compatibility with your current application. And again, these modifications probably don't make much sense in an application this small, but hopefully you can apply them to your larger Rails applications.

And on a final note, you should benchmark, improve, rinse and repeat. I hope you found these techniques useful, let me know in the comments if you have doubts about any of them, and I will see you on the next episode.