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Clojure vs Haskell vs Scala: What are the differences?
Introduction
Clojure, Haskell, and Scala are three popular programming languages that are widely used in the field of functional programming. While all three languages share some common features and concepts, they also have key differences that set them apart from each other. In this Markdown code, we will explore and provide the key differences between Clojure, Haskell, and Scala.
Syntax and Paradigm: Clojure is a dialect of Lisp and follows a Lisp-like syntax. It is primarily a functional programming language but also supports imperative programming. Haskell, on the other hand, is a purely functional programming language that is based on lambda calculus. It has a distinct and strong type system, and its syntax is different from mainstream programming languages. Scala, unlike Clojure and Haskell, is a multi-paradigm language that combines both object-oriented and functional programming. Its syntax is similar to Java and has support for imperative, object-oriented, and functional programming.
Type System: Clojure is a dynamically-typed language which means its type checking is done at runtime. It has a flexible type system that allows programmers to work with a mix of data types. Haskell, in contrast, has a static type system with strong type inference. It ensures type safety at compile-time, which helps in catching errors early. Scala, being a statically-typed language, provides a rich type system with type inference. It supports both static typing and type inference, allowing developers to choose their preferred level of type safety.
Concurrency and Parallelism: Clojure provides built-in support for concurrent programming with its Software Transactional Memory (STM) system. It allows easy and safe coordination of multiple threads and provides constructs like atoms, agents, and refs for managing shared state. Haskell, being a purely functional language, encourages the use of immutable data and supports concurrent programming through lightweight threads and software transactional memory. Scala, being a multi-paradigm language, offers a range of concurrency abstractions including threads, futures, actors, and the Akka toolkit.
Integration with JVM: Clojure is designed to run on the Java Virtual Machine (JVM) and seamlessly integrates with existing Java libraries and frameworks. It allows interoperability with Java code and makes use of the extensive Java ecosystem. Haskell, on the other hand, has its own compiler and runtime, and can also be compiled to native code. While it can interface with C libraries, it does not have the same level of interoperability with Java as Clojure. Scala, similar to Clojure, is designed to run on the JVM and provides seamless interoperability with Java. It can also make use of existing Java libraries and frameworks.
Tooling and Community Support: Clojure has a vibrant and growing community with a wide range of libraries and tools. It has a well-established build tool called Leiningen and a package manager called Clojars. Haskell, being a mature language, also has a strong community support with a rich ecosystem of libraries and tools. It has a build tool called Cabal and a package manager called Hackage. Scala, being widely adopted in the industry, has a large and active community. It has a build tool called sbt and a package manager called Scala's package index (or sbt's organization).
Learning Curve and Adoption: Clojure has a simpler syntax compared to Haskell and Scala, making it relatively easier to learn for those familiar with Lisp. It is widely used in the industry for its simplicity and ease of integration with existing Java codebases. Haskell, with its unique syntax and strong type system, has a steeper learning curve. It is mainly used in academia and for projects that require strong type safety. Scala, being a multi-paradigm language, has a wider adoption compared to Clojure and Haskell. Its syntax is more familiar to programmers coming from object-oriented languages like Java, which contributes to its popularity in industry.
In Summary, Clojure is a dynamically-typed Lisp dialect with support for both functional and imperative programming. Haskell is a purely functional language with a strong type system and a steep learning curve. Scala is a multi-paradigm language that combines object-oriented and functional programming and has wide industry adoption.
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.
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.
We’re a new startup so we need to be able to deliver quick changes as we find our product market fit. We’ve also got to ensure that we’re moving money safely, and keeping perfect records. The technologies we’ve chosen mix mature but well maintained frameworks like Django, with modern web-first and api-first front ends like GraphQL, NextJS, and Chakra. We use a little Golang sparingly in our backend to ensure that when we interact with financial services, we do so with statically compiled, strongly typed, and strictly limited and reviewed code.
You can read all about it in our linked blog post.
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.
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.
Pros of Clojure
- It is a lisp117
- Persistent data structures100
- Concise syntax100
- jvm-based language90
- Concurrency89
- Interactive repl81
- Code is data76
- Open source61
- Lazy data structures61
- Macros57
- Functional49
- Simplistic23
- Immutable by default22
- Excellent collections20
- Fast-growing community19
- Multiple host languages15
- Simple (not easy!)15
- Practical Lisp15
- Because it's really fun to use10
- Addictive10
- Community9
- Web friendly9
- Rapid development9
- It creates Reusable code9
- Minimalist8
- Programmable programming language6
- Java interop6
- Regained interest in programming5
- Compiles to JavaScript4
- Share a lot of code with clojurescript/use on frontend3
- EDN3
- Clojurescript1
Pros of Haskell
- Purely-functional programming90
- Statically typed66
- Type-safe59
- Open source39
- Great community38
- Built-in concurrency31
- Built-in parallelism30
- Composable30
- Referentially transparent24
- Generics20
- Type inference15
- Intellectual satisfaction15
- If it compiles, it's correct12
- Flexible8
- Monads8
- Great type system5
- Proposition testing with QuickCheck4
- One of the most powerful languages *(see blub paradox)*4
- Purely-functional Programming4
- Highly expressive, type-safe, fast development time3
- Pattern matching and completeness checking3
- Great maintainability of the code3
- Fun3
- Reliable3
- Best in class thinking tool2
- Kind system2
- Better type-safe than sorry2
- Type classes2
- Predictable1
- Orthogonality1
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 Clojure
- Cryptic stacktraces11
- Need to wrap basically every java lib5
- Toxic community4
- Good code heavily relies on local conventions3
- Tonns of abandonware3
- Slow application startup3
- Usable only with REPL1
- Hiring issues1
- It's a lisp1
- Bad documented libs1
- Macros are overused by devs1
- Tricky profiling1
- IDE with high learning curve1
- Configuration bolierplate1
- Conservative community1
- Have no good and fast fmt0
Cons of Haskell
- Too much distraction in language extensions9
- Error messages can be very confusing8
- Libraries have poor documentation5
- No good ABI3
- No best practices3
- Poor packaging for apps written in it for Linux distros2
- Sometimes performance is unpredictable2
- Slow compilation1
- Monads are hard to understand1
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
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