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Elm vs Scala: What are the differences?
Elm and Scala are both functional programming languages, but there are key differences that set them apart in terms of their features and capabilities. 1. Static vs Dynamic Typing: Elm uses static typing, meaning that type errors are caught at compile time, offering more safety and robustness in code. On the other hand, Scala utilizes dynamic typing, which allows for more flexibility but can lead to runtime errors due to type mismatches. 2. Concurrency: Scala provides built-in support for concurrency with features like Akka actors and Futures, making it more suitable for building highly concurrent systems. In contrast, Elm focuses on simplicity and purity, avoiding direct support for mutable state or concurrency primitives. 3. Tooling: Scala has a rich ecosystem of tools and libraries, making it easier for developers to find solutions and integrate them into their projects. Elm, being a smaller language, has a more limited set of tools and libraries available, which can sometimes hinder development speed and scalability. 4. Scalability: Scala is known for its scalability and performance, being able to handle large, complex systems with ease. Elm, while efficient for building front-end web applications, may face challenges when scaling up to more intricate or demanding projects. 5. Syntax: The syntax of Elm is more opinionated and constrained, following a specific design philosophy that emphasizes clarity and simplicity. Scala, on the other hand, allows for more flexibility in coding styles and paradigms, catering to a wider range of preferences and practices. 6. Type Inference: Elm features strong type inference capabilities, reducing the need for explicit type annotations and making code more concise and readable. Scala also supports type inference but with some limitations, requiring more annotations in certain scenarios to ensure type safety.
In Summary, Elm and Scala differ in their approach to typing, concurrency, tooling, scalability, syntax, and type inference, catering to different needs and preferences in the realm of functional programming languages.
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 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 Elm
- Code stays clean45
- Great type system44
- No Runtime Exceptions40
- Fun33
- Easy to understand28
- Type safety23
- Correctness22
- JS fatigue17
- Ecosystem agrees on one Application Architecture12
- Declarative12
- Friendly compiler messages10
- Fast rendering8
- If it compiles, it runs7
- Welcoming community7
- Stable ecosystem5
- 'Batteries included'4
- Package.elm-lang.org2
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 Elm
- No typeclasses -> repitition (i.e. map has 130versions)3
- JS interop can not be async2
- JS interoperability a bit more involved2
- More code is required1
- No JSX/Template1
- Main developer enforces "the correct" style hard1
- No communication with users1
- Backwards compability breaks between releases1
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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What is Elm?
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