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Today, I want to talk about the mechanics of Ruby, thinking in terms of Ruby as a software machine. What does that machine look like, and how does it work?
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We will dig deep into the layers below our Ruby programs to understand how Ruby works, how it interacts with the operating system kernel, and how it operates on computers like Intel and ARM.
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But first, let me share a story.
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Once upon a time, there was a programmer named Matz who was very kind. Every day, he would write in Smalltalk and Perl, but he used C until one day, he decided to write an interpreter. This interpreter allowed him to write in Smalltalk, Lisp, and Perl, and he called the language he created Ruby. Over time, more people began using Ruby to write applications.
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Eventually, someone discovered that writing web applications in Ruby was a significant breakthrough. Many years passed, and developers realized that while Ruby is wonderful, it has its trade-offs. For instance, Ruby programs may not perform as fast as those written in C or Java. From that day forward, everyone learned to make engineering trade-offs to balance happiness and performance.
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Now, I have a bone to pick with people who criticize Ruby. Often, discussions about Ruby come across as condescending, focusing on its perceived shortcomings or design trade-offs.
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For instance, some people argue that Ruby is not concurrent, that the garbage collection (GC) is slow, or that Rails is overly complex. In isolation, these criticisms have some truth, but what critics often overlook is the subjective nature of design decisions. If someone were to lament not receiving a peanut butter and jelly sandwich, would you really feel obliged to make one for them? It's essential for individuals to recognize that they can create their own solutions instead of relying on others.
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That said, while many criticisms are valid, they shouldn't deter anyone from using Ruby. Instead, I propose we adopt a scientific mentality, examining these design trade-offs to understand why they exist, how they came to be, and what can be done about them moving forward.
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As scientists, we should enjoy exploring Ruby's mechanics and appreciate the whimsiness that comes with programming. Instead of complaining about GC overhead or thread management issues, let’s celebrate the fact that, today, we didn't have to use malloc at all. We'll focus on optimizing for enthusiasm!
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Let’s also acknowledge some fantastic resources, like the Real Talk podcast, which dives deep into kernels, compilers, assembly, and more. It's an excellent way to think critically about programming.
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Here's a hypothesis: Ruby is an unusual yet effective way to write C programs. If you were to pair program with me, I might mention this concept. Let me explain what I mean.
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Consider this simple Ruby program that prints the Beatles' names; it consists of five straightforward lines. In contrast, the equivalent C program is much longer and, in fact, executes slightly slower than the Ruby code. This challenges the notion that C is always faster than Ruby.
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We can write Ruby to resemble C code, and here’s how we can deconstruct that Ruby program. First, we create a Ruby module and define a singleton method. Then, we set up a fixed-size array and populate it with the Beatles' names. Lastly, we implement a for loop to print each name. The constructs used here mirror what a C extension might include.
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When writing a C extension, the first step is to include the Ruby C API by adding ruby.h, which gives you access to various functions essential for C extensions. You would then declare variables and create a reference for the Beatles module, assigning it nil. You also declare a C function for the print functionality.
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A crucial aspect of Ruby C extensions is understanding that a "VALUE" in this context is a Ruby object, which corresponds to complex pointers and unions in C. The "init_beatles" function is called when the extension loads, where you set up objects for your Ruby programs using C.
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You call module_new to create the Beatles module and attach a singleton method, which links it to the print function you declared earlier. This method accepts the module as its self object and returns a Ruby object.
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In this method, you will pre-declare several Ruby objects such as 'beatles', 'name', 'puts', 'kernel', and 'args'. To create a new fixed-size Ruby array in a C extension, use 'ruby_array_new', and for literal strings, utilize 'ruby_string_new'.
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We then get a handle on the Kernel module so we can call "puts" on it. The internal method 'rb_intern' behaves similarly to creating a symbol in Ruby. At this point, we have everything set up to print the Beatles' names.
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Now, we’ll loop through the Beatles array and print each name using the 'rb_apply' function. This function effectively sends the puts command to the Kernel module.
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It’s interesting to note that there are no calls to malloc or free in this C extension. We haven't made any direct system calls either. While we don't achieve amazing performance, we can observe how Ruby code can seamlessly translate into C constructs, providing a new gaze into Ruby's capabilities.
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We can see how Ruby handles various operations while allowing developers to avoid the intricacies normally associated with C, particularly memory management. As developers, we can become proficient without diving too deeply into the complexities of lower-level languages.
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However, the underlying reality is that Ruby executes in a way that creates garbage, which we must manage judiciously. Benchmarking simple tasks, such as arithmetic operations, offers insights into performance overhead, especially when it comes to garbage collection.
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A tool like DTrace can help us probe into running programs to observe garbage collection activities and when malloc calls occur. This sort of insight can direct our understanding of Ruby's performance.
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Using DTrace, we can map calls to object creation and free events during execution, providing a clear view of how Ruby handles memory and garbage.
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The ability to visualize the inner workings of Ruby and its relationships to memory management is key to improving performance. By analyzing these processes, we can gain valuable knowledge that will impact our programming practices.
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Furthermore, it's important to realize that Ruby is not just any programming language. MRI (Matz's Ruby Interpreter) is uniquely designed and doesn't always adhere to patterns we see in other runtimes, like JVM or V8.
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Understanding Ruby's architecture, its strengths and limitations, explains why certain design decisions were made. Emphasizing developer satisfaction over raw performance is a trade-off inherent to MRI.
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One primary takeaway is that MRI is fundamentally a scripting language that provides convenient access to text manipulation and object-oriented programming.
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Despite its utility, Ruby was not built with high concurrency in mind, contrasting it with platforms like JVM and Erlang that prioritize performance for concurrent applications.
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It's essential to accept that while Ruby may not reach the same performance heights as these other frameworks, the trade-off is its ease of use and developer satisfaction.
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Ruby’s development community largely embraces standardization, which varies from perceptions in the Western tech culture. Understanding this cultural difference provides critical insight into Ruby's evolution.
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Being aware that Ruby does not follow typical semantic versioning practices is crucial as well, impacting how developers approach upgrading and transitioning between versions.
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As we continue to engage with Ruby, it's vital to remember that MRI is just one implementation of Ruby. Supporting alternatives like JRuby and Rubinius can enable faster performance for Ruby applications.
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Encouraging these platforms allows us to explore Ruby as a language, separate from the specifics of MRI, opening up opportunities for greater efficiency.
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Through this lens, we can examine the mechanics of MRI and appreciate how these inner workings affect our development experiences.
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My suggestion is to apply the knowledge you gain from understanding Ruby internals to everyday programming tasks. Familiarize yourself with the interactions and limitations to become a better coder.
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Finally, as we wrap up this discussion, remember to take this knowledge and apply an experimental approach to your work. Measure performance, iterate, and engage with the Ruby community to enhance your skills.
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Applause! Now, let’s reflect on how MRI operates as a virtual machine and the significance of the kernel on computers like x86.
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Understanding Ruby’s environment and how it parses through code gives you tremendous insight into optimization opportunities.
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The exploration of performance through garbage collection and memory management in Ruby ultimately helps streamline the development experience.
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For developers eager to optimize, taking advantage of available tools is crucial. Tuning performance based on empirical data helps mitigate issues.
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Effective programming relies on identifying bottlenecks within your applications, enabling more focused development efforts and higher efficiency overall.
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In conclusion, remember that Ruby is a dynamic and adaptable language. It varies significantly from traditional compile-time languages.
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By engaging with Ruby at a deeper level, whether through C extensions or simply understanding its design, you can unlock numerous possibilities.
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Your ability to delve into Ruby’s mechanisms to optimize and innovate will serve as powerful assets.
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So as you go forward, keep an inquisitive mindset and transform your insights into action within your Ruby projects.
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Thank you for your time, and let's continue exploring the wondrous world of Ruby together.