Common Lisp vs Haskell vs Scala

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Common Lisp

151
194
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116
Haskell

971
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+ 1
491
Scala

7.4K
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Advice on Common Lisp, Haskell, and Scala
Needs advice
on
Java
and
Common Lisp

Hello everyone! I’m interested in learning AI development, and after doing a little bit of research, I’ve learned that Common Lisp and Java are the top languages for AI. Which one should I learn? What are the differences? Are they hard to learn? If anyone can help with this, it’d be very appreciated. Thank you!

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Replies (4)
Václav Hodek
CEO, lead developer at Localazy · | 5 upvotes · 40.5K views
Recommends
Java

Java is far more popular and you can use other JVM-based languages such as Kotlin (I would recommend Kotlin over Java). Also, for Java, there are many more libraries, tools, etc. Also, if you learn Java, you can do almost anything - mobile (Android), web, and desktop apps - without "hacks". There is native support for all of these.

As with any programming language, it's not hard to learn the syntax but it's hard to understand the ecosystem, know libraries, best practices, etc. From that point of view, I would also prefer java - more tools, more libraries, more resources, guides, how-tos, etc.

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Recommends
Python

I'd recommend Python due to the fact that many AI libraries and frameworks are specifically developed for the Python ecosystem.

Java is good for general purpose programming: Web, Mobile and Desktop, however doesn't really have many native libraries supporting AI Development.

As for LISP, again it has some support,, however Python seems to be the leading edge in AI development

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پیمان سبزی زاده
Recommends
Python

Hi Excuse me if I wrote the text badly because I do not know much English My suggestion is to choose Python for artificial intelligence because it has both comfortable and powerful syntax. Python is currently the best language for artificial intelligence It is better to go and learn Python and then learn one of the artificial intelligence frameworks and enter it.

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Recommends
Java

I have not much idea about Lisp, but have been a Java professional since last 20 odd years. And I would say Java along with Python is one of the best languages for AI.

AI works on the concept of algorithms, and Java is algorithm based. Also Java has it's own AI libraries that can be reused. You have Java AI libraries for Expert Systems, Neural Networks, Natural Language Processing.

Also Java being a widely used language, brings with it certain advantages, ease of usage, debugging, has a large user base and support groups. And above all JVM helps you to create on single app, that can run on any platform. And it's features of garbage collection, simplifying work with large scale projects makes it better.

Hope this helps.

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Needs advice
on
Scala
Node.js
and
Go

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)

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Replies (1)
David Annez
Head of Engineering at loveholidays · | 4 upvotes · 111.8K views

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.

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Decisions about Common Lisp, Haskell, and Scala
Chose
Python
over
Scala

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.

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Timm Stelzer
Software Engineer at Flexperto GmbH · | 18 upvotes · 227.2K 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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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.

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Pros of Common Lisp
Pros of Haskell
Pros of Scala
  • 23
    Flexibility
  • 18
    High-performance
  • 16
    Comfortable: garbage collection, closures, macros, REPL
  • 12
    Stable
  • 12
    Lisp
  • 6
    Can integrate with C (via CFFI)
  • 6
    Code is data
  • 5
    Multi paradigm
  • 4
    Easy Setup
  • 4
    Lisp is fun
  • 3
    Macros
  • 2
    Elegant
  • 2
    Open source
  • 2
    Purelly functional
  • 1
    Parentheses
  • 85
    Purely-functional programming
  • 64
    Statically typed
  • 57
    Type-safe
  • 38
    Great community
  • 38
    Open source
  • 29
    Built-in concurrency
  • 29
    Composable
  • 28
    Built-in parallelism
  • 22
    Referentially transparent
  • 19
    Generics
  • 13
    Type inference
  • 13
    Intellectual satisfaction
  • 11
    If it compiles, it's correct
  • 7
    Monads
  • 7
    Flexible
  • 4
    Great type system
  • 4
    Proposition testing with QuickCheck
  • 3
    One of the most powerful languages *(see blub paradox)*
  • 2
    Kind system
  • 2
    Reliable
  • 2
    Highly expressive, type-safe, fast development time
  • 2
    Type classes
  • 2
    Better type-safe than sorry
  • 2
    Pattern matching and completeness checking
  • 2
    Purely-functional Programming
  • 2
    Best in class thinking tool
  • 2
    Great maintainability of the code
  • 2
    Fun
  • 0
    Orthogonality
  • 0
    Predictable
  • 188
    Static typing
  • 179
    Pattern-matching
  • 177
    Jvm
  • 171
    Scala is fun
  • 138
    Types
  • 95
    Concurrency
  • 88
    Actor library
  • 86
    Solve functional problems
  • 83
    Open source
  • 80
    Solve concurrency in a safer way
  • 44
    Functional
  • 23
    Generics
  • 23
    Fast
  • 17
    Syntactic sugar
  • 17
    It makes me a better engineer
  • 13
    Scalable
  • 10
    Type safety
  • 10
    First-class functions
  • 9
    Interactive REPL
  • 8
    Expressive
  • 7
    SBT
  • 6
    Case classes
  • 6
    Implicit parameters
  • 4
    JVM, OOP and Functional programming, and static typing
  • 4
    Object-oriented
  • 4
    Rapid and Safe Development using Functional Programming
  • 4
    Used by Twitter
  • 3
    Functional Proframming
  • 2
    Spark
  • 2
    Beautiful Code
  • 2
    Safety
  • 2
    Growing Community
  • 1
    Akka
  • 1
    Reactive Streams
  • 1
    Easy embedded DSLs
  • 1
    Mill build tool
  • 1
    DSL
  • 1
    Rich Static Types System and great Concurrency support
  • 1
    Naturally enforce high code quality
  • 1
    Akka Streams
  • 0
    Freedom to choose the right tools for a job

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Cons of Common Lisp
Cons of Haskell
Cons of Scala
  • 4
    Too many Parentheses
  • 1
    No hygienic macros
  • 1
    Standard did not evolve since 1994
  • 1
    Small library ecosystem
  • 7
    Too much distraction in language extensions
  • 6
    Error messages can be very confusing
  • 4
    Libraries have poor documentation
  • 3
    No best practices
  • 3
    No good ABI
  • 2
    Sometimes performance is unpredictable
  • 2
    Poor packaging for apps written in it for Linux distros
  • 1
    Slow compilation
  • 10
    Slow compilation time
  • 6
    Multiple ropes and styles to hang your self
  • 3
    Complicated subtyping
  • 3
    Too few developers available
  • 1
    My coworkers using scala are racist against other stuff

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What is Common Lisp?

Lisp was originally created as a practical mathematical notation for computer programs, influenced by the notation of Alonzo Church's lambda calculus. It quickly became the favored programming language for artificial intelligence (AI) research. As one of the earliest programming languages, Lisp pioneered many ideas in computer science, including tree data structures, automatic storage management, dynamic typing, conditionals, higher-order functions, recursion, and the self-hosting compiler. [source: wikipedia]

What is Haskell?

It is a general purpose language that can be used in any domain and use case, it is ideally suited for proprietary business logic and data analysis, fast prototyping and enhancing existing software environments with correct code, performance and scalability.

What is Scala?

Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.

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Blog Posts

Aug 28 2019 at 3:10AM

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What are some alternatives to Common Lisp, Haskell, and Scala?
Clojure
Clojure is designed to be a general-purpose language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming. Clojure is a compiled language - it compiles directly to JVM bytecode, yet remains completely dynamic. Clojure is a dialect of Lisp, and shares with Lisp the code-as-data philosophy and a powerful macro system.
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.
Racket
It is a general-purpose, multi-paradigm programming language based on the Scheme dialect of Lisp. It is designed to be a platform for programming language design and implementation. It is also used for scripting, computer science education, and research.
Java
Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!
C
See all alternatives