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Julia vs Ruby: What are the differences?

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Key Differences between Julia and Ruby

Julia and Ruby are both programming languages with distinct characteristics and purposes. Here are six key differences between Julia and Ruby:

  1. Execution Speed: Julia is designed to be fast and efficient, with a just-in-time (JIT) compiler that allows it to perform at speeds comparable to traditional statically-typed languages. On the other hand, Ruby is an interpreted language, which generally results in slower execution speeds.

  2. Type System: Julia has a strong type system that supports multiple dispatch and allows for efficient handling of complex mathematical operations. It offers the ability to explicitly define types and perform type inference. In contrast, Ruby has a dynamic type system, where variables are dynamically typed and type checking is performed at runtime.

  3. Concurrent Programming: Julia has built-in support for concurrent and parallel programming, making it suitable for high-performance computing and scientific computing tasks. Ruby, on the other hand, has limited support for concurrency and relies on external libraries and frameworks for parallel processing.

  4. Syntax: Julia has a syntax that resembles traditional scientific programming languages like MATLAB and Python, making it easy for users familiar with these languages to adopt Julia. Ruby, on the other hand, has a more flexible and expressive syntax, allowing for more concise code and a more readable style.

  5. Community and Ecosystem: Ruby has a large and active community with a rich ecosystem of libraries and frameworks for various purposes, including web development, automation, and scripting. Julia, although growing, has a smaller community and a more focused ecosystem primarily centered around scientific and numerical computing.

  6. Design Philosophy: Julia was designed with the goal of combining the high-level expressiveness of dynamic languages like Python and MATLAB with the performance of statically-typed languages like C and Fortran. On the other hand, Ruby was designed with an emphasis on simplicity, readability, and developer happiness, prioritizing ease of use and programmer productivity.

In summary, Julia and Ruby differ in terms of execution speed, type system, support for concurrent programming, syntax, community and ecosystem, and design philosophy.

Decisions about Julia and Ruby
Ing. Alvaro Rodríguez Scelza
Software Systems Engineer at Ripio · | 12 upvotes · 390K views

I was considering focusing on learning RoR and looking for a work that uses those techs.

After some investigation, I decided to stay with C# .NET:

  • It is more requested on job positions (7 to 1 in my personal searches average).

  • It's been around for longer.

  • it has better documentation and community.

  • One of Ruby advantages (its amazing community gems, that allows to quickly build parts of your systems by merely putting together third party components) gets quite complicated to use and maintain in huge applications, where building and reusing your own components may become a better approach.

  • Rail's front end support is starting to waver.

  • C# .NET code is far easier to understand, debug and maintain. Although certainly not easier to learn from scratch.

  • Though Rails has an excellent programming speed, C# tends to get the upper hand in long term projects.

I would avise to stick to rails when building small projects, and switching to C# for more long term ones.

Opinions are welcome!

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Timm Stelzer
VP Of Engineering at Flexperto GmbH · | 18 upvotes · 664.8K views

We have a lot of experience in JavaScript, writing our services in NodeJS allows developers to transition to the back end without any friction, without having to learn a new language. There is also the option to write services in TypeScript, which adds an expressive type layer. The semi-shared ecosystem between front and back end is nice as well, though specifically NodeJS libraries sometimes suffer in quality, compared to other major languages.

As for why we didn't pick the other languages, most of it comes down to "personal preference" and historically grown code bases, but let's do some post-hoc deduction:

Go is a practical choice, reasonably easy to learn, but until we find performance issues with our NodeJS stack, there is simply no reason to switch. The benefits of using NodeJS so far outweigh those of picking Go. This might change in the future.

PHP is a language we're still using in big parts of our system, and are still sometimes writing new code in. Modern PHP has fixed some of its issues, and probably has the fastest development cycle time, but it suffers around modelling complex asynchronous tasks, and (on a personal note) lack of support for writing in a functional style.

We don't use Python, Elixir or Ruby, mostly because of personal preference and for historic reasons.

Rust, though I personally love and use it in my projects, would require us to specifically hire for that, as the learning curve is quite steep. Its web ecosystem is OK by now (see https://www.arewewebyet.org/), but in my opinion, it is still no where near that of the other web languages. In other words, we are not willing to pay the price for playing this innovation card.

Haskell, as with Rust, I personally adore, but is simply too esoteric for us. There are problem domains where it shines, ours is not one of them.

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Alexander Nozik
Senior researcher at MIPT · | 3 upvotes · 183.9K views
Migrated
from
JuliaJulia
to
KotlinKotlin

After writing a project in Julia we decided to stick with Kotlin. Julia is a nice language and has superb REPL support, but poor tooling and the lack of reproducibility of the program runs makes it too expensive to work with. Kotlin on the other hand now has nice Jupyter support, which mostly covers REPL requirements.

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Andrew Carpenter
Chief Software Architect at Xelex Digital, LLC · | 16 upvotes · 436.2K views

In 2015 as Xelex Digital was paving a new technology path, moving from ASP.NET web services and web applications, we knew that we wanted to move to a more modular decoupled base of applications centered around REST APIs.

To that end we spent several months studying API design patterns and decided to use our own adaptation of CRUD, specifically a SCRUD pattern that elevates query params to a more central role via the Search action.

Once we nailed down the API design pattern it was time to decide what language(s) our new APIs would be built upon. Our team has always been driven by the right tool for the job rather than what we know best. That said, in balancing practicality we chose to focus on 3 options that our team had deep experience with and knew the pros and cons of.

For us it came down to C#, JavaScript, and Ruby. At the time we owned our infrastructure, racks in cages, that were all loaded with Windows. We were also at a point that we were using that infrastructure to it's fullest and could not afford additional servers running Linux. That's a long way of saying we decided against Ruby as it doesn't play nice on Windows.

That left us with two options. We went a very unconventional route for deciding between the two. We built MVP APIs on both. The interfaces were identical and interchangeable. What we found was easily quantifiable differences.

We were able to iterate on our Node based APIs much more rapidly than we were our C# APIs. For us this was owed to the community coupled with the extremely dynamic nature of JS. There were tradeoffs we considered, latency was (acceptably) higher on requests to our Node APIs. No strong types to protect us from ourselves, but we've rarely found that to be an issue.

As such we decided to commit resources to our Node APIs and push it out as the core brain of our new system. We haven't looked back since. It has consistently met our needs, scaling with us, getting better with time as continually pour into and expand our capabilities.

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Thomas Miller
Talent Co-Ordinator at Tessian · | 16 upvotes · 254.8K views

In December we successfully flipped around half a billion monthly API requests from our Ruby on Rails application to some new Python 3 applications. Our Head of Engineering has written a great article as to why we decided to transition from Ruby on Rails to Python 3! Read more about it in the link below.

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Mike Fiedler
Enterprise Architect at Warby Parker · | 3 upvotes · 249.5K views

When I was evaluating languages to write this app in, I considered either Python or JavaScript at the time. I find Ruby very pleasant to read and write, and the Ruby community has built out a wide variety of test tools and approaches, helping e deliver better software faster. Along with Rails, and the Ruby-first Heroku support, this was an easy decision.

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Pros of Julia
Pros of Ruby
  • 25
    Fast Performance and Easy Experimentation
  • 22
    Designed for parallelism and distributed computation
  • 19
    Free and Open Source
  • 17
    Dynamic Type System
  • 17
    Calling C functions directly
  • 16
    Multiple Dispatch
  • 16
    Lisp-like Macros
  • 10
    Powerful Shell-like Capabilities
  • 10
    Jupyter notebook integration
  • 8
    REPL
  • 4
    String handling
  • 4
    Emojis as variable names
  • 3
    Interoperability
  • 608
    Programme friendly
  • 538
    Quick to develop
  • 492
    Great community
  • 469
    Productivity
  • 432
    Simplicity
  • 274
    Open source
  • 235
    Meta-programming
  • 208
    Powerful
  • 157
    Blocks
  • 140
    Powerful one-liners
  • 70
    Flexible
  • 59
    Easy to learn
  • 52
    Easy to start
  • 42
    Maintainability
  • 38
    Lambdas
  • 31
    Procs
  • 21
    Fun to write
  • 19
    Diverse web frameworks
  • 14
    Reads like English
  • 10
    Makes me smarter and happier
  • 9
    Rails
  • 9
    Elegant syntax
  • 8
    Very Dynamic
  • 7
    Matz
  • 6
    Programmer happiness
  • 5
    Object Oriented
  • 4
    Elegant code
  • 4
    Friendly
  • 4
    Generally fun but makes you wanna cry sometimes
  • 4
    Fun and useful
  • 3
    There are so many ways to make it do what you want
  • 3
    Easy packaging and modules
  • 2
    Primitive types can be tampered with

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Cons of Julia
Cons of Ruby
  • 5
    Immature library management system
  • 4
    Slow program start
  • 3
    JIT compiler is very slow
  • 3
    Poor backwards compatibility
  • 2
    Bad tooling
  • 2
    No static compilation
  • 7
    Memory hog
  • 7
    Really slow if you're not really careful
  • 3
    Nested Blocks can make code unreadable
  • 2
    Encouraging imperative programming
  • 1
    No type safety, so it requires copious testing
  • 1
    Ambiguous Syntax, such as function parentheses

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What is Julia?

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.

What is Ruby?

Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.

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What are some alternatives to Julia and Ruby?
Python
Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
R Language
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
MATLAB
Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
Rust
Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory.
Golang
Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.
See all alternatives