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Ruby vs Scala: What are the differences?
Key Differences between Ruby and Scala
Ruby and Scala are both popular programming languages that are widely used for various purposes. While they share certain similarities, such as being object-oriented and supporting functional programming paradigms, there are several key differences that set them apart. Here are six important differences between Ruby and Scala:
Type System: Ruby is a dynamically typed language, meaning that variable types are determined at runtime. On the other hand, Scala is a statically typed language, where variable types are checked at compile time. This difference allows Scala to catch type-related errors earlier in the development process, making it more suitable for large-scale projects.
Scalability: Scala is known for its scalability, making it an ideal choice for building high-performance, concurrent applications. It leverages the Java Virtual Machine (JVM) to achieve native thread support and efficient memory management. Ruby, on the other hand, is not optimized for parallelism and may struggle to handle heavy workloads or large datasets.
Language Syntax: Ruby focuses on simplicity and readability, using a more expressive syntax that emphasizes writing code in a natural language-like style. Scala, on the other hand, has a more complex syntax that combines object-oriented and functional programming constructs. This difference makes Ruby easier to learn and write, while Scala offers more flexibility and power for experienced developers.
Concurrency: Scala provides built-in support for concurrency through its actor-based model, allowing developers to easily write concurrent and distributed applications. Ruby, on the other hand, relies on thread-based concurrency, which can be more challenging to manage and prone to issues like race conditions and deadlocks.
Community and Ecosystem: Ruby has a vibrant and passionate community, with a wide range of open-source libraries and frameworks to choose from. It is especially popular in web development, with Ruby on Rails being one of the most widely used frameworks. Scala, being a more niche language, has a smaller community but is backed by strong industry support, particularly in the big data and distributed systems domains.
Performance: Due to its dynamic nature, Ruby may suffer from slower performance compared to statically-typed languages like Scala. While this may not be a significant concern for small to medium-sized applications, it can become a bottleneck for computationally intensive tasks or high-traffic systems. Scala, with its static typing and JVM optimization, generally offers better performance.
In summary, Ruby and Scala differ in their type systems, scalability, syntax, concurrency models, community size, and performance characteristics. Understanding these differences can help developers make informed decisions about which language to use based on their specific requirements and project constraints.
Hello!
I'm a developer for over 9 years, and most of this time I've been working with C# and it is paying my bills until nowadays. But I'm seeking to learn other languages and expand the possibilities for the next years.
Now the question... I know Ruby is far from dead but is it still worth investing time in learning it? Or would be better to take Python, Golang, or even Rust? Or maybe another language.
Thanks in advance.
Hi Caue, I don't think any language is dead in 2022, and we still see a lot of Cobol and Fortran out there, so Ruby is not going to die for sure. However, based on the market, you'll be better off learning Goland and Python. For example, for data science, machine learning, and similar areas, Python is the default language while backend API, services, and other general purpose Goland is becoming the preferred.
I hope this helps.
I feel most productive using go. It has all the features I need and doesn't throw road blocks in your way as you learn. Rust is the most difficult to learn as borrow checking and other features can puzzle a newcomer for days. Python is a logical next step as it has a huge following, many great libraries, and one can find a gig using python in a heartbeat. Ruby isn't awful, it's just not that popular as the others.
Another reason to use python is that it is not compiled. You can muck around in the interpreter until you figure things out. OTOH, that makes it less performant. You really need to think about your use cases, your interest in lower-lever versus high-level coding, and so on.
I enjoy coding in Python. I think it's minimalistic and readable syntax and lang features are just unparalleled. They are perfect for prototyping and for the software engineering in general. If I'm not wrong Gitlab marked Python as #2 popular language after JavaScript. Beyond that, Python ecosystem and areas of usage are enormous. In areas like ML/DL, it's important to know Python to leverage variety of existing tools and frameworks.
Then, I have learned and worked with Golang. I use it where I think I would need a slightly better performance than in Python. Plus, relatively small and self-contained executable is a great thing to have. If you plan to write distributed systems, extend Kubernetes or do similar things I think Golang is a great choice. It's also simple and straightforward, especially when you want to do effective multithreading. Although I don't like that Golang is more low-level than Python. Sometimes I feel like I need to implement myself too much things.
Now, about Rust. It's my second try to learn Rust. First time I decided to learn Golang as I understood it in 30mins or so while I was struggling to compile/do anything meaningful there for quite a bit. So I personally don't think Rust is super easy. I have got back to learning Rust as it's going to fill one of gaps in my problem solving toolkit - let me write low-level system programs (e.g. linux kernel modules). I don't want to learn "obsolete" C/C++ (my reasons are similar to why Google has recently introduced Carbon - a replacement for C/C++ codebases). If you are not going to tight your life with system-like programming, Rust may be an overkill for you.
Finally, I have never coded in Ruby, so are not going to comment it.
Since you are very experienced, picking up a language will not take you more than a week. Rust is a very new language. Many startups are still experimenting with it. Golang is very popular nowadays. You can see a lot of golang jobs in the market. The best part is, compiled code is single binary and has a minimal footprint. Rails is a compelling framework; believe me, many websites like Shopify, GitHub, GitLab, etc., are powered by the rails framework. You can also leverage the power of metaprogramming in Ruby. Python is memory and CPU intensive. It is not as performant as the other three. If you want to go into Data Science, Python is the language. Good luck, buddy. Feel free to connect with me: https://twitter.com/avirajkhare00
Because it opens endless possibilities you can do anything and everything you want to. from ai to app development to web development.
I'm almost same position as you. 8 years same company with c#. I tried both Python and Golang. I like working with Golang. Check this litte go doc. After reading this document and following its examples, I decided to work with "go" https://www.openmymind.net/assets/go/go.pdf
Either Python or Golang, for all the enlightened reasons already mentionned in all advices/comments :) Enjoy!
Basically, I am looking for a good language that compiles to Java and JavaScript(and can use their libraries/frameworks). These JVM languages seem good to me, but I have no interest in Android. Which programming language is the best of these? I am looking for one with high money and something functional.
Edit: Kotlin was originally on this list but I removed it since I had no interest in Android
Clojure is a Lisp dialect, so if you like Lisp that's probably the way to go. Scala is more popular and broadly used, and has a larger job market especially for data engineering. Both are functional but Scala is more interoperable with Java libraries, probably a big factor in its popularity. I prefer Scala for a number of reasons, but in terms of jobs Scala is the clear leader.
Scala has more momentum. It is good for back-end programming. The popular big data framework Spark is written in Scala. Spark is a marketable skill.
If you need to program something very dynamic like old school A.I., Clojure is attractive. You would chose Scala if prefer a statically typed language, and Clojure if you prefer a dynamically typed language.
It's not clear exactly what you mean by "high money", you mean financial support to the language, money paid for a job, economic health of the market the language is positioned on?
In any case, it's very hard to give any advice here, since you'd need to provide details on the intended usage, what sector, kind of product/service, team size, potential customer type... Both languages are very general purpose and decently supported, each have its own pros and cons, both are functional as approach, and neither is really mainstream.
Finding the best server-side tool for building a personal information organizer that focuses on performance, simplicity, and scalability.
performance and scalability get a prototype going fast by keeping codebase simple find hosting that is affordable and scales well (Java/Scala-based ones might not be affordable)
I've picked Node.js here but honestly it's a toss up between that and Go around this. It really depends on your background and skillset around "get something going fast" for one of these languages. Based on not knowing that I've suggested Node because it can be easier to prototype quickly and built right is performant enough. The scaffolding provided around Node.js services (Koa, Restify, NestJS) means you can get up and running pretty easily. It's important to note that the tooling surrounding this is good also, such as tracing, metrics et al (important when you're building production ready services).
You'll get more scalability and perf from go, but balancing them out I would say that you'll get pretty far with a well built Node.JS service (our entire site with over 1.5k requests/m scales easily and holds it's own with 4 pods in production.
Without knowing the scale you are building for and the systems you are using around it it's hard to say for certain this is the right route.
We're moving from Java to Kotlin with our Microservice Stack (Spring Boot) because it is excellently supported by framework and tools and the learning curve is not very steep Kotlin is way more straightforward and convenient to use while providing less boilerplate and more strictness, which finally leads to better code, which is more readable, maintainable and less error-prone. We especially like Kotlin's (functional) data structures, which are, e.g. compared to Scala, easier to understand and don't require deep knowledge in functional programming.
A developer and project manager from our team X says the following about our use of Rails at i22:
"We use Rails to build stable and flexible backend systems. Rails is extremely good for managing data structures and quickly setting up new systems. It is the perfect base for most use cases."
I asked the same Team X member why the team prefers to work with Ruby on Rails, rather than Python and Django:
"Because Python is a scripting language and from my point of view not suitable for building stable web services. Python is for me rather good for scripts and fast small tools. Not for stable business applications. And if I want it fast I prefer Go."
I am working in the domain of big data and machine learning. I am helping companies with bringing their machine learning models to the production. In many projects there is a tendency to port Python, PySpark code to Scala and Scala Spark.
This yields to longer time to market and a lot of mistakes due to necessity to understand and re-write the code. Also many libraries/apis that data scientists/machine learning practitioners use are not available in jvm ecosystem.
Simply, refactoring (if necessary) and organising the code of the data scientists by following best practices of software development is less error prone and faster comparing to re-write in Scala.
Pipeline orchestration tools such as Luigi/Airflow is python native and fits well to this picture.
I have heard some arguments against Python such as, it is slow, or it is hard to maintain due to its dynamically typed language. However cost/benefit of time consumed porting python code to java/scala alone would be enough as a counter-argument. ML pipelines rarerly contains a lot of code (if that is not the case, such as complex domain and significant amount of code, then scala would be a better fit).
In terms of performance, I did not see any issues with Python. It is not the fastest runtime around but ML applications are rarely time-critical (majority of them is batch based).
I still prefer Scala for developing APIs and for applications where the domain contains complex logic.
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!
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.
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.
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.
We needed to incorporate Big Data Framework for data stream analysis, specifically Apache Spark / Apache Storm. The three options of languages were most suitable for the job - Python, Java, Scala.
The winner was Python for the top of the class, high-performance data analysis libraries (NumPy, Pandas) written in C, quick learning curve, quick prototyping allowance, and a great connection with other future tools for machine learning as Tensorflow.
The whole code was shorter & more readable which made it easier to develop and maintain.
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.
Pros of Ruby
- Programme friendly607
- Quick to develop538
- Great community492
- Productivity469
- Simplicity432
- Open source274
- Meta-programming235
- Powerful208
- Blocks157
- Powerful one-liners140
- Flexible70
- Easy to learn59
- Easy to start52
- Maintainability42
- Lambdas38
- Procs31
- Fun to write21
- Diverse web frameworks19
- Reads like English14
- Makes me smarter and happier10
- Rails9
- Elegant syntax9
- Very Dynamic8
- Matz7
- Programmer happiness6
- Object Oriented5
- Elegant code4
- Friendly4
- Generally fun but makes you wanna cry sometimes4
- Fun and useful4
- There are so many ways to make it do what you want3
- Easy packaging and modules3
- Primitive types can be tampered with2
Pros of Scala
- Static typing188
- Pattern-matching178
- Jvm175
- Scala is fun172
- Types138
- Concurrency95
- Actor library88
- Solve functional problems86
- Open source81
- Solve concurrency in a safer way80
- Functional44
- Fast24
- Generics23
- It makes me a better engineer18
- Syntactic sugar17
- Scalable13
- First-class functions10
- Type safety10
- Interactive REPL9
- Expressive8
- SBT7
- Case classes6
- Implicit parameters6
- Rapid and Safe Development using Functional Programming4
- JVM, OOP and Functional programming, and static typing4
- Object-oriented4
- Used by Twitter4
- Functional Proframming3
- Spark2
- Beautiful Code2
- Safety2
- Growing Community2
- DSL1
- Rich Static Types System and great Concurrency support1
- Naturally enforce high code quality1
- Akka Streams1
- Akka1
- Reactive Streams1
- Easy embedded DSLs1
- Mill build tool1
- Freedom to choose the right tools for a job0
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Cons of Ruby
- Memory hog7
- Really slow if you're not really careful7
- Nested Blocks can make code unreadable3
- Encouraging imperative programming2
- No type safety, so it requires copious testing1
- Ambiguous Syntax, such as function parentheses1
Cons of Scala
- Slow compilation time11
- Multiple ropes and styles to hang your self7
- Too few developers available6
- Complicated subtyping4
- My coworkers using scala are racist against other stuff2