Ruby might be faster than you think
johnhawthorn.com
johnhawthorn.com
To be fair, I do not think that is a "mistake" as such. I have written Ruby professionally for 6 years or so and have committed to several Ruby open source projects and haven't seen an innocus `nil` sitting at the end of a loop, to prevent array allocation.
The argument would be fair, if it wasn't idiomatic Ruby.
More like - knowing internals of a language will allow one to gain more performance out of it. That has been true for almost every programming language, but general speaking the goal of a VM based language is to not require that _specialized_ knowledge.
Go is the opposite. It's great, as you say, because it's dirt simple. It's a brutalist get-the-job-done kind of language, and I think if I were to start a company working with other engineers, I'd absolutely choose Go for that reason. It's easy to read. It's easy to reason about. And there's very little implicitness in it.
Are you aware that you're referencing the Python mantra with that? Feel free to Google it, it's from 2004.
There should be one-- and preferably only one --obvious way to do it.
the Python mantra with that?
While I love Ruby, I was looking forward to some of that "one way to do things" simplicity I'd been promised by Python.
Boy... were my hopes crushed. There are a lot of possible ways to do any given thing, from iteration to package/import structures, etc.
For the most part it seems like the proliferation of options in Python is pretty sane; I can generally see why each choice was made to address existing pain points. So kudos to Python for that. But man, did they leave "one way to do it" behind a loooong time ago.
And they honestly never really adhered to it either. It was just a knee-jerk reaction to a (I believe Haskell) presentation that said something like "we got n ways to do x", where n was in the double digits and x was something extremely uninteresting like looping over an array.
Can't really recall details though, it was old knowledge by the time I Heard about it around 2010 and a quick Google didn't help me dig it up
with some being slower than others.
It's certainly easy to think of situations where they do matter, but unless your project is FaaS (Fibonacci As A Service) probably not.
All the inefficiencies add up, at scale. a 3% inefficiency means you're spending ~3% more on compute. CI takes longer. Dev velocity decreases.
It's idiomatic Ruby in a very particular case that likely was explicitly chosen to demonstrate such a dramatic effect.
You're _usually_ not returning implicit arrays from loops in production code. Parallel assignments, when they're used, are almost always in the first line of an initialize method, not the returned line of an enumerable block.
Ruby code, perhaps more than most code, is written for readability and “beauty”. It’s a part of Ruby culture that I greatly appreciate. But if you care about performance, you will act differently, regardless of language. And the whole point of this code is to show that if you care about performance above all else, there’s of plenty of room to maneuver in interpreted Ruby.
~/scripts > ruby fib.rb
2.3346780000720173
~/scripts > ruby --yjit fib.rb
1.5913339999970049
Running the original fib benchmark (i.e., without the author's technique to eliminate the array allocation) on an M1 Pro, I see:
CRuby 3.3.1:
2.058589000022039
CRuby 3.3.1 w/ YJIT:
1.4314430000958964
TruffleRuby 24.0.1 (Native):
0.20155820800573565
TruffleRuby 24.0.1 (JVM):
0.1336908749944996
Running the new prime_counter benchmark that the crystalruby author mentions in another thread¹, I see:
Crystal 1.12.1 (LLVM 18.1.4) w/ crystalruby 0.2.0 in CRuby 3.3.1:
0.34096299996599555
CRuby 3.3.1:
2.9615250000497326
CRuby 3.3.1 w/ YJIT:
1.640430000028573
TruffleRuby 24.0.1 (Native):
0.2504862080095336
TruffleRuby 24.0.1 (JVM):
0.25282600001082756
¹ -- https://news.ycombinator.com/item?id=40153218
² -- The method_source gem used by crystalruby catches exceptions and matches against the message² for some conditional handling. TruffleRuby 24.0 now uses to Prism as its parser and Prism has an exception message with slightly different wording from CRuby. Consequently, method_source's handling doesn't work with Prism. It's hard to say where the compatibility issue lies, since exception messages aren't stable APIs. We'll get it sorted out.
³ -- https://github.com/banister/method_source/blob/06f21c66380c6...
That's impressive numbers for running the unoptimized code. I might give TruffleRuby a shot!
It is, indeed, a fundamental goal of ruby that there are multiple ways to write the same thing, and that the programmer should not need to understand nuances of the compiler.
"I need to guess how the compiler works. If I'm right, and I'm smart enough, it's no problem. But if I'm not smart enough, and I'm really not, it causes confusion. The result will be unexpected for an ordinary person. This is an example of how orthogonality is bad." -matz 2003.
In this particular example, the fact that assignments return the value of the right-hand side is well-known and used frequently in Ruby code. The fact that arrays have to be allocated is obvious. The fact that allocations have a runtime cost is obvious. The only thing that isn't obvious is that the return value allocation of assignments whose value isn't used are optimized away. If you know that, you'll think of appending the nil to activate that optimization. Characterizing the lack of that step as a "mistake" only makes sense if the goal for your code is to maximize performance -- which in this case, most unusually for Ruby, it was.
I had a very hot loop (runtime of 15 minutes) that I wanted to speed up. I profiled it over and over again in IntelliJ, identifying every possible allocation I could eliminate. When I was done there were zero allocations in the hot loop and the thing ran something like 4x faster than it had previously.
At that point, looking at the code, I realized that what I had written was very similar to how I'd have implemented it in Rust—I allocated a bunch of structs upfront and then "borrowed" them into the various parts of the algorithm. Since it was so close in style to Rust anyway, I decided to port it over and see if I could get any more performance out of it by being closer to the metal.
It turned out that the difference in performance between the Rust version and the Java version was statistically insignificant. I tried a few different optimization settings but didn't manage to get Rust to be any faster than Java's JIT.
This was eye opening to me. Now that most runtimes have JIT compilers, I suspect that far more important than choosing the right language is deeply understanding how the language you're working with works under the hood so you can eliminate hot spots and unnecessary allocations.
To pick on your example, back when Android used Dalvik as a plain interpreter, followed by a basic JIT method tracing, they implemented floating point math on native code.
Nowadays the JIT takes care of it,
https://developer.android.com/reference/android/util/FloatMa...
Unfortunely it has taken us several decades to catch up to Lisp and BASIC (the original Dartmouth BASIC), were already offering in the 1970's.
Related to Ruby perf, I still hear folks worried about rails “not being able to scale”. Let me say something controversial (and clearly wrong): Rails is the only framework that has proven it _can_ scale. GitHub, Shopify, AirBnb, Stripe all use rails and have scaled successfully. Very few other frameworks have that track record.
There’s plenty of reasons to not use rails, but scaling issues doesn’t feel like a strong one to me.
Citation? Seems like a pretty extraordinary claim.
And a well known fruity brand used to have all their online services on Web Objects for several years.
But, for the sake of truth: - AirBnB migrated from Rails to a micro-services architecture (which I think, they regretted doing too early - I read that somewhere I believe) - Stripe never used Rails: they use Ruby (and Sinatra for the Web part - i.e. dashboard).
But it's true that Github and Shopify both use and scaled Rails monoliths. There are showing the way :)
This particular example crossed this painful divide 1 million times. I found it interesting that despite this disadvantage, the Crystal implementation was still able to take the lead over my identical, naively written Ruby implementation (warts and all). As the author of this post points out, for trivial operations crossing the interface at high frequency, finely tuned Ruby will easily take the lead!
That said, I still believe there are times where having the ability to write and interface with a performant, precompiled language (that is somewhat familiar to the average Rubyist) in an ergonomic way that avoids the need to context switch can be beneficial. Sure, performance is unlikely to match a finely tuned (but arguably more difficult to maintain) C or Rust extension and ergonomics are unlikely to match an approach that sticks to pure Ruby, but it exposes a new middle ground, which at times, may just hit the right spot!
I'd imagine realistic examples of where this type of library could be useful might include:
- Providing an easy way to expose and use high-quality Crystal shards from within your Ruby program.
- Allowing you to easily write performant CPU or memory-intensive procedures for which reusable native libraries do not exist, and where the majority of the overall execution time can be spent within Crystal.
- As a way to glue several different smaller Crystal shared objects together into a single application using Ruby glue code, allowing you to avoid some of the high compile times you might typically see with a large monolithic binary.
I would definitely not suggest this library has any business:
- Blindly replacing swaths of Ruby methods, without any tangible performance metrics to back this decision.
- Replacing code that is already highly performant in pure Ruby (whether that's code that lends itself well to being JIT'd, is backed by an existing native library etc.)
Funnily enough, if you take a look at the commit history of the project, you'll notice that last week I actually replaced the referenced example with one that better demonstrates a performance difference (even compared against YJIT) and crosses the FFI divide only once. This came as a result of having to introduce a Reactor to get the library to play nice in multi-threaded Ruby applications, which regrettably added even more overhead to the FFI interface and further hammers home the point that this library is not going to perform well in cases where you need to jump between Crystal and Ruby at high frequency.
On the content of the post: "Now it’s Ruby that’s 5 times faster than Crystal!!! And 20x faster than our original version. Though most likely that’s some cost from the FFI, or something similar, though that does seem like a surprising amount of overhead."
There are tools to provide a definitive answer to this, and no, FFI is not a silver bullet solution to slowness of interpreted (or JIT compiled but dynamically typed) languages.