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Erlang vs Haskell vs Scala: What are the differences?
Introduction
This markdown code provides a comparison between Erlang, Haskell, and Scala, highlighting the key differences between these programming languages.
Concurrency Model: Erlang is known for its built-in support for concurrency and parallelism with lightweight processes and message passing. It follows the actor model, where processes communicate by exchanging messages. On the other hand, Haskell primarily follows the purely functional programming paradigm and uses lazy evaluation, with concurrency achieved through higher-level abstractions like software transactional memory. Scala combines both object-oriented and functional programming and provides actors for concurrent and distributed programming, similar to Erlang.
Type Systems: Erlang has a dynamic type system, which means that variable types are checked at runtime. It provides flexibility but can lead to potential runtime errors. Haskell, in contrast, has a strong static type system with type inference, ensuring type safety at compile-time. It leverages the Hindley-Milner type system and allows for more robust code and better error checking. Scala, similar to Haskell, adopts a statically typed system with type inference but supports both object-oriented and functional programming styles.
Pattern Matching: Erlang is known for its powerful pattern matching capabilities, which allow matching complex data structures easily. It uses pattern matching extensively in functions and case statements. Haskell also provides strong pattern matching support, which is deeply integrated into the language and enables concise and expressive code. Scala, although supporting pattern matching, it is not as deeply integrated as in Erlang or Haskell, limiting its scope and expressiveness.
Tooling and Libraries: Erlang provides a mature and stable runtime system, along with a wide range of libraries and frameworks tailored for distributed, fault-tolerant systems, such as OTP (Open Telecom Platform). Haskell has a vibrant ecosystem with a strong focus on academia and research, offering numerous libraries for various domains. However, the tooling and package management in Haskell can be less mature and may require additional configuration. Scala, being built on top of the Java Virtual Machine (JVM), benefits from Java's extensive libraries and tooling ecosystems. It has a wide range of libraries available for different use cases, making it popular for building scalable and enterprise-grade applications.
Syntax and Programming Paradigm: Erlang's syntax is more declarative and focuses on concurrency and fault-tolerance, making it suitable for building distributed systems. Haskell emphasizes functional programming and has a mathematical syntax, allowing for concise and expressive code. Scala combines object-oriented and functional paradigms, offering a more flexible syntax that can cater to different programming styles. It provides the ability to intermix imperative, functional, and object-oriented code within a single language.
Community and Industry Adoption: Erlang has a smaller but dedicated community, primarily used in telecommunications and distributed systems. It has proven its reliability and scalability in telecom infrastructure. Haskell, widely used in academia and research, has a smaller community compared to mainstream languages but has gained popularity in finance and fintech sectors. Scala, being interoperable with Java, has gained significant adoption in the industry, especially for building large-scale, scalable applications. It has a larger community and is supported by many organizations.
In summary, Erlang excels in concurrency and fault-tolerant systems, Haskell emphasizes purity and type safety, and Scala combines object-oriented and functional paradigms with strong industry adoption. Each language has its strengths and can be chosen depending on the specific requirements of the project.
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 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 Erlang
- Real time, distributed applications62
- Concurrency Support62
- Fault tolerance58
- Soft real-time36
- Open source32
- Message passing22
- Functional programming22
- Immutable data16
- Works as expected14
- Facebook chat uses it at backend6
- Practical5
- Knowledgeable community5
- Bullets included4
- WhatsApp uses it at backend1
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
- Mill build tool1
- DSL1
- Rich Static Types System and great Concurrency support1
- Naturally enforce high code quality1
- Akka Streams1
- Akka1
- Reactive Streams1
- First class citizen Big Data Support1
- Easy embedded DSLs1
- Freedom to choose the right tools for a job0
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Cons of Erlang
- Languange is not popular demand1
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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