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Groovy vs Scala: What are the differences?
Developers describe Groovy as "A dynamic language for the Java platform". Groovy builds upon the strengths of Java but has additional power features inspired by languages like Python, Ruby and Smalltalk. It makes modern programming features available to Java developers with almost-zero learning curve. On the other hand, Scala is detailed as "A pure-bred object-oriented language that runs on the JVM". 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.
Groovy and Scala belong to "Languages" category of the tech stack.
"Java platform" is the top reason why over 38 developers like Groovy, while over 177 developers mention "Static typing" as the leading cause for choosing Scala.
Groovy and Scala are both open source tools. It seems that Scala with 11.8K GitHub stars and 2.75K forks on GitHub has more adoption than Groovy with 1.49K GitHub stars and 414 GitHub forks.
According to the StackShare community, Scala has a broader approval, being mentioned in 437 company stacks & 324 developers stacks; compared to Groovy, which is listed in 79 company stacks and 73 developer stacks.
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.
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 Groovy
- Java platform43
- Much more productive than java32
- Concise and readable28
- Very little code needed for complex tasks27
- Dynamic language21
- Nice dynamic syntax for the jvm12
- Very fast9
- Can work with JSON as an object6
- Easy to setup6
- Supports closures (lambdas)5
- Literal Collections5
- Developer Friendly2
- Optional static typing2
- Syntactic sugar2
Pros of Scala
- Static typing187
- Scala is fun172
- Actor library88
- Solve functional problems86
- Open source83
- Solve concurrency in a safer way80
- It makes me a better engineer18
- Syntactic sugar17
- First-class functions10
- Type safety10
- Interactive REPL9
- Implicit parameters6
- Case classes6
- Used by Twitter4
- JVM, OOP and Functional programming, and static typing4
- Rapid and Safe Development using Functional Programming4
- Functional Proframming3
- Beautiful Code2
- Growing Community2
- Rich Static Types System and great Concurrency support1
- Naturally enforce high code quality1
- Akka Streams1
- Reactive Streams1
- Easy embedded DSLs1
- Mill build tool1
- Freedom to choose the right tools for a job0
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Cons of Groovy
- Groovy Code can be slower than Java Code3
- Objects cause stateful/heap mess1
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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