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Asked: 2026-05-31T07:56:22+00:00

I was just listening to a lecture at coursera ( https://www.coursera.org/saas/ ) and the

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I was just listening to a lecture at coursera (https://www.coursera.org/saas/) and the professor was saying that everything in Ruby is an object and that every method call is calling a send method on an object, passing some params to it. This includes numbers, arrays and other basic classes.

I went on Google and looked for efficiency benchmarks and I found the following: http://benchmarksgame.alioth.debian.org/u32/which-programs-are-fastest.html

While it’s not shocking that a compiled language is faster than an interpreted one, the performance difference between (Ruby, Python) and Java for instance is shocking.

Even if there’s a way to compile ruby code (I have not researched this topic), I think the efficiency problem would still be there due to the core “problem” in the language:
Basic operations are being too heavy: 1+1 takes many more CPU cycles to complete.

I love Ruby. I love the high level aspect of meta programming and I think this is where the future should be heading, and I agree, sometimes we need to compromise certain thing in order to be more effective: I don’t see myself optimizing my code in assembly in order to save a few extra milliseconds. However, when we do 1+1 in C, it’s not exponentially increasing the amount of time a basic operation is taking!

My question is how are you guys dealing with operation intensive programs? We have a Ruby on Rails project we have been developing for about a year now and we’re at a point we’ll start doing some machine learning with geolocation traversal and prioritization.

I hope you understand my concerns and offer reasonable suggestions 🙂

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1 Answer

  1. Editorial Team

    This is not such a bad question as it seems looking at the comments. The only problem is the wrongly used word exponential, but the described problem is real.

    I have similar Ruby usage patterns as you describe – I’m doing a lot of natural language processing in Ruby, which involves machine learning as well.
    I use the following techniques to overcome Ruby performance issues:

    1. Use C libraries with Ruby interface whenever applicable. E.g. I would not use Ruby implementation of SVM or decision trees, since there are much faster implementations in C available.

    2. Write my own Ruby wrappers for C implementation if they are not available. Usually this is not much problematic – I use RubyInline gem extensively to glue Ruby with C code.

    3. Patch Ruby memory management or manually control its garbage collector.

    4. Consider JRuby as your Ruby platform – you would get easy access to fast Java libraries for machine learning (Weka) and the like and general performance of your application would be preserved or even better.

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