Talks
Rust for Rubyists
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Rust for Rubyists
by Steve KlabnikIn this talk titled "Rust for Rubyists" presented by Steve Klabnik at the Rocky Mountain Ruby 2015 conference, the speaker introduces the Rust programming language and its relevance to Ruby developers. Klabnik explores the importance of understanding low-level programming and how Rust can enhance Ruby development by providing performance benefits without the complexities often associated with C.
Key Points Discussed:
- Introduction to Rust and Ruby: Klabnik expresses his love for both languages and emphasizes the goal of his talk: to teach Ruby developers about the benefits of learning Rust.
- Abstraction in Programming: A central theme in the talk is the concept of abstraction. Klabnik discusses how programming languages utilize abstractions to manage complexity and the relative categorization of languages (e.g., Ruby as high-level and C as low-level).
- Performance Issues in Ruby: Ruby is described as slow when compared to other languages, and Klabnik attributes this to implementation decisions that impact performance.
- Rust's Compiled Nature: Unlike Ruby's interpreted nature, Rust is ahead-of-time compiled, which means the code is compiled into a standalone program. This results in fewer overheads during execution compared to Ruby.
- Method Resolution: The speaker illustrates how method resolution in Ruby is complex compared to Rust, where types must be explicitly defined, leading to fewer ambiguities and increased safety.
- Practical Code Comparisons: Klabnik presents comparisons between Ruby and Rust code to showcase differences in syntax, performance, and clarity, emphasizing how Rust's structure allows for better performance and fewer runtime errors.
- Learning Opportunities: He encourages Ruby developers to embrace Rust as a means to enhance their coding skills and optimize their applications. By understanding Rust, developers can improve the performance of Ruby applications without losing the expressiveness that Ruby offers.
- Conclusion: Klabnik wraps up by advocating for a culture of exploration among developers, encouraging them to appreciate the unique strengths of different programming languages rather than fostering conflict over language preferences.
Overall, the talk serves as a call to action for Ruby developers to consider integrating Rust into their skill set for better performance and reliability in their applications.
00:00:24.160
Hi everybody, I'm Steve. I am here to talk about both Rust and Ruby, two programming languages that I love.
00:00:31.009
I updated my abstract a little since it was submitted for the program, so this talk will be a bit more high-level.
00:00:37.790
I'm going to provide a brief language tutorial since I have 45 minutes, but it won't strictly be about teaching you an entire programming language in that time.
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What's important is that I want to teach you why you might want to learn about Rust and how learning Rust can help you improve your Ruby development.
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So, that's the focus of this talk: what Rust can teach us about Ruby.
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Let me briefly cover my background.
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I'm a former Rails contributor, ranking around 35th all-time before I stepped back from that.
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Now, I work for Mozilla on the Rust programming language as my full-time job.
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I love it a lot; it's great, and I have read all the documentation.
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So, when you inevitably check out Rust, if you can't figure it out, I apologize in advance, as that would be my fault.
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Now, let's talk about abstraction. The fun thing about programming languages, in general, is that they are built on abstractions.
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We discuss abstractions frequently, considering ways to create new ones and manage existing ones.
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We often categorize abstractions as good, bad, or leaky. Fundamentally, abstraction underpins programming languages.
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Interestingly, everything can be framed as an abstraction.
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We tend to think of abstractions as existing in levels, which leads to phrases like 'Turtles all the way down'.
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However, abstractions are not strictly hierarchical or tiered.
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In programming terms, we often refer to Ruby as a high-level language and C as a low-level language.
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Yet, from the perspective of an electrical engineer, C could be viewed as high-level, while assembly would be low-level.
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This relativity shapes how we categorize programming languages.
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For example, Rust can handle low-level tasks like C but offers high-level features akin to Ruby.
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So, is Rust a low-level or high-level programming language? This is where the abstraction hierarchies become muddled.
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An enlightening example is that you can develop abstractions in divergent ways.
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You start from the same premise and can arrive at seemingly incompatible conclusions.
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In computing, you can think about computation mathematically or through the assembly and machine code.
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Interestingly, modern processors don’t even run assembly or machine code directly but utilize microcode.
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This means there’s a programming level beneath assembly, linking to physical transformations in hardware.
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This chain eventually leads to areas where complexity overwhelms, leaving us uncertain about how things work.
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Thus, thinking about abstraction reveals its universality.
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Right now, I'm using an abstraction to convey my thoughts through English.
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I'm translating ideas in my brain into a structured language, which others then interpret.
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As we know, this process can encounter bugs, and the output isn't always clear.
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Everything we do is wrapped in abstraction, or what can be considered a lie.
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A lesson that resonated with me from college is that the fundamental role of operating systems is to maintain this illusion.
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For instance, every program on your computer believes it is the only program running.
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It assumes access to the entire memory space, even when that isn't true.
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The operating system utilizes virtual memory to manage this illusion, providing the impression of infinite RAM.
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A funny memory from my childhood: I saw an ad for a program called RAM Doubler that claimed to double your memory.
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Being a nerdy kid, I was skeptical, knowing it was a virtual memory implementation.
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This program allowed the Mac OS to mimic virtual memory, which was vital in a time of limited memory.
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Essentially, every action we undertake is enshrined in this tapestry of lies.
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One of my favorite sayings is that another term for 'eventually consistent' is 'inconsistent'.
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Why are these lies acceptable? Ultimately, we can only process so much information at any given time.
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If I had to consider quantum mechanics while moving my hand, it would be a daunting task.
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So, we create abstractions that allow us to overlook certain details.
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It's impossible to consider every detail all the time.
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What I aim to discuss today is a bit about Rust and Ruby!
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I'd like to help you develop a more intuitive understanding of Ruby internals.
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I will present abstractions, leading me to build some delightful lies.
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This slide is necessary because I gave this talk for the first time last week.
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I realized that Koichi Sasada, the author of MRI, was in the audience.
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I felt it necessary to preemptively apologize for potentially misrepresenting his work.
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Fortunately, Koichi provided valuable feedback, which I've now incorporated.
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If you're interested in this topic, I highly recommend Pat Shaughnessy's book, 'Ruby Under a Microscope'.
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It beautifully explains how Ruby works and serves as a great introduction to programming languages.
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Ruby was designed for programmer happiness; that's what Matz had in mind.
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To achieve this happiness, some performance details were overlooked.
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Thus, we find Ruby slow compared to many programming languages.
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However, we must remember that comparisons of language speeds relate to their implementations, not the language itself.
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For example, JRuby is considerably faster, showcasing differing Ruby implementations.
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So why is Ruby perceived as slow?
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Let's discuss some internal mechanics and decisions that contribute to Ruby's performance.
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Initially, a Ruby program processes a code input through the interpreter.
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The first step is tokenization, where the code becomes a sequence of tokens.
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The parser then checks for valid order and coherence among these tokens.
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For instance, an invocation like '+ 2 2' is valid in that tokens exist, yet lacks correct ordering.
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The next stage is compilation; Ruby compiles parsed representations.
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This compilation can lead to confusion, as Ruby does compile its operations.
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Ultimately, compiled code runs on MRI, Matsumoto's Ruby interpreter.
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A significant difference introduced from Ruby 1.8 to 1.9 is this compilation step.
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Ruby interpreters are limited in their understanding of Ruby; they function as separate virtual machines.
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The interpreter transforms Ruby code into instructions that the VM utilizes.
00:08:06.200
This process often involves working within Docker containers or cloud VM images.
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This metaphor of multiple layers of machines is aptly illustrated when considering programming.
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To better visualize these steps, here's a practical Ruby example.
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The operation '2 + 2' is valid, and when executed, the code gets tokenized.
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Tokenization translates '2 + 2' into a representation consisting of two integers and a plus operation.
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Parsing converts it into an abstract syntax tree (AST), structuring the operation for the interpreter.
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This AST eventually compiles into MRI bytecode for execution.
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The interpreter processes each step, running the plus operation in an adorable sequence.
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Now, let's compare Ruby's internal workings with those of other languages.
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Most programming languages operate under a similar model of tokenization, parsing, and compilation.
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However, the distinct ways of representation give rise to unique programming styles.
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For example, in human languages, certain expressions may be simpler in one language compared to another.
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German offers compound words describing detailed concepts, which English likes to unpack through long explanations.
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In Japanese, verbs always come at the end of the sentence, creating entirely different constructs.
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Thus, across different languages, programming goals often change in response to particular features.
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The focus of my discussion will revolve around performance comparisons.
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Let's delve into the intrigue of Rust as a programming language.
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As a small visual joke, I’ve playfully added 'st' to the top of Ruby.
00:10:04.410
I jokingly mentioned that I'm only programming in languages starting with 'ru'.
00:10:10.530
In the Rust community, we affectionately refer to our crab mascot, Ferris.
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Rust is an ahead-of-time compiled programming language.
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This means that rather than interpreting the code each time, you compile it once.
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With Rust, when you call the Rust compiler on a file, it outputs a standalone program.
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In contrast to Ruby, where every execution involves tokenizing, parsing, and compilation.
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There’s an overhead in dynamically interpreted languages that ahead-of-time compilation can circumvent.
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However, this isn't a strict limitation; other practices can manipulate these models.
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For example, one could pre-generate the MRI bytecode and load it on demand.
00:11:04.150
Thus, the methodology and culture of a programming language greatly influence how efficiently we can code.
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Let’s discuss how methods are called in Ruby.
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The operation 'foo' can be complicated, and for those unfamiliar with Ruby's method resolution, here's a diagram.
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In this process, we check the current object's method table, searching for our method.
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If the method isn't found, Ruby checks parent classes, continuing this resolution process.
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If Ruby fails to identify the method, it’ll invoke 'method missing,' essentially allowing for flexibility in method calls.
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So, this can lead to unforeseen complexities in understanding method behavior.
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Time to present an example: a simple Ruby function accepting one argument that returns five.
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Ruby must navigate its complex method resolution when invoking the function.
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Next, here's a Rust equivalent to illustrate a stark contrast.
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The Rust function is clearer, detailing its types and functionality without ambiguity.
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In Rust, you must define the entry point of your program with the 'main' function.
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The approach is simpler, with explicit types leading to more stringent control.
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Rust compiles down to assembly code involving a straightforward sequence of instructions.
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The assembly structure is often succinct and readily understandable.
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Much effort in Rust emphasizes low-level programming comprehensibility.
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Perception of difficulty differs among programmers; what might be simple for some may appear complex to others.
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Many find assembly code intuitive compared to high-level frameworks like Rails.
00:13:05.780
There are different strengths and weaknesses in programming, affecting preference in language choice.
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I’m interested in showcasing various Rust and Ruby functionalities.
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In Ruby, one can instantiate a class, form its attributes, and define a return statement.
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Rust takes a different approach—favoring structures and implementations.
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This diverges significantly from Ruby’s object-oriented expectations.
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In Rust, you define a structure and then implement the behavior separately.
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Using Rust, this delineation fosters clarity of data and behavior.
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The compilation yield is more concise, leading to optimized performance.
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So, expect a multitude of assembly lines from Rust.
00:14:03.300
Optimizations are possible, enhancing generated code performance.
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Rust’s compiler leverages optimization strategies to enhance execution efficiency.
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Ruby, conversely, performs limited optimizations as a general rule.
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Let’s examine how Ruby operates in comparison to Rust.
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In Ruby, we specify class behavior through modules and mix-ins.
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So you can build classes to achieve desired functionality through behaviors.
00:14:41.770
In contrast, Rust implements traits and structures distinctly.
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The separation of attributes and behaviors creates limitations.
00:14:53.210
There is explicit type binding in Rust, which results in rigorous methods.
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This leads to reduced ambiguity when executing function calls.
00:15:05.620
While flexibility exists in Ruby, it’s crucial to define traits in Rust clearly.
00:15:12.340
By requiring the method to be defined, Rust grants more safety in function calls.
00:15:19.540
So, Rust brings type safety and structure that Ruby may lack.
00:15:26.590
With this rigidity comes better performance, reducing overhead.
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Let's explore some practical code comparisons next.
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When we write functions in Rust, they need clear parameter types.
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Rust captures implementations efficiently while retaining program clarity.
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Moreover, a brief glance at these structures clarifies programming intentions.
00:15:56.050
Let’s wrap this segment by underscoring the necessity of performance considerations.
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The message here is that Rust excels at performance where Ruby slows.
00:16:11.790
Ruby, despite its slow nature, remains beloved by many developers.
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Both languages possess unique advantages; exploring both can enhance your skills.
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We often misjudge programming languages, causing unnecessary conflict.
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Embracing the distinctions among languages is vital for new programmers.
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It's frustrating to see individuals demonstrating disdain towards others’ chosen languages.
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In crucial ways, every language adds value to the programming landscape.
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This promotes exploration and trying out new languages whenever possible.
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I encourage everyone to appreciate the multifaceted nature of coding.
00:17:03.650
Let’s foster a culture of exploration and curiosity, rather than resentment.
00:17:10.130
Ultimately, programming languages ought to unite rather than divide us.
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Now, I’ll take a moment to present additional Rust functionalities and discuss their merits.
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Rust shines in the realm of safety and performance, especially in systems programming.
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Rust helps developers create reliable, efficient applications, reducing runtime errors.
00:17:36.389
Ruby developers can also benefit from understanding Rust's functionalities.
00:17:42.820
This perspective opens the door to reduce bottlenecks while harnessing Ruby's expressiveness.
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By leveraging Rust, it's possible to add performance to Ruby applications without sacrificing ease of development.
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Thank you, everyone, for being an engaging audience!
00:18:00.600
I'm happy to take any questions!
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