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Perl vs Scala: What are the differences?
Developers describe Perl as "Highly capable, feature-rich programming language with over 26 years of development". Perl is a general-purpose programming language originally developed for text manipulation and now used for a wide range of tasks including system administration, web development, network programming, GUI development, and more. 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.
Perl and Scala can be primarily classified as "Languages" tools.
"Lots of libraries", "Open source" and "Text processing" are the key factors why developers consider Perl; whereas "Static typing", "Jvm" and "Pattern-matching" are the primary reasons why Scala is favored.
Perl 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 Perl with 435 GitHub stars and 152 GitHub forks.
According to the StackShare community, Scala has a broader approval, being mentioned in 437 company stacks & 324 developers stacks; compared to Perl, which is listed in 133 company stacks and 64 developer stacks.
I intend to use a programming language which I'll use as AWS runtime and write a script that will comb through tons of files in a directory and its subdirectories and search for simple text regular expressions and process and write the matches in a file as output. I have heard that Perl is good for regex based search but I also want the performance to be good as it will have to go through tons of files for IO. In this post: https://filia-aleks.medium.com/aws-lambda-battle-2021-performance-comparison-for-all-languages-c1b441005fd1, I see that Rust works well as AWS Lambda runtime with very good performance. Which one should I choose as my AWS lambda runtime for this problem? Golang is also an option as it is fast as per the above link.
I used to work in a Perl shop and must admit that the language is very simple for tasks like these, but as you mentioned it's not fast at execution time. I'm now a Go programmer professionally but I taught myself the language while in college purely out of interest and eventually found my way to the job, not the other way around. I've recently been learning a little rust because of how much that language comes up in conversations around Go. I find the concept of the borrow checker nice but I have to admit I feel lost like I am in most flavors of new fancy framework js. That's not to say Rust is really anything like js, but the learning appears the same to me as someone who's convinced they could learn just about any programming language if it was necessary (over time I've seen procedural, OOP, declarative and functional stuff but never programming logic outside of the prolog code I wrote in school).
Go isn't made for your specific task at hand but it's a very easy language to pick up and it has good directory traversal standard library code and good regex (even though with time perl's has been optimized to be faster and I think it's written in C++) but more than anything Go is "cloud native" programming in that an awful lot of new microservice tech stacks are centered around it, docker and kubernetes are written in it, and there's a thriving community whose focus is generally web-first and performance-oriented. This means for your use case there might already be a large cohort of gophers that have asked the stackoverflow questions for you
I personally would push you towards the NYT Profiler for Perl before I would towards Rest, but that's because I know you wouldn't waste any time being able to get to the task at hand and then make it go faster, and I expect all but a few rustaceans would be able to do so with the same speed.
Whatever you pick I wish you the very best of luck!
Basically, I am looking for a good language that compiles to Java and JavaScript(and can use their libraries/frameworks). These JVM languages seem good to me, but I have no interest in Android. Which programming language is the best of these? I am looking for one with high money and something functional.
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 Perl
- Lots of libraries72
- Open source66
- Text processing61
- Powerful54
- Unix-style49
- Regex47
- Stable37
- Concise syntax32
- Hackerish29
- Easy to use22
- Swiss army chainsaw16
- Code Less Do More13
- CPAN12
- Freedom9
- All purpose8
- Readability5
- Familiar5
- Many ways to do it5
- Community5
- Object-Oriented4
- Modular4
- Smart (does alot for you)4
- Postmodern3
- It's the best one-off task language3
- For a man2
- Good man pages2
- Auto case variables1
- Single Source Library (CPAN)1
- Multi-threaded support1
- Multiparadigm1
- C-style1
- Hashes1
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 Perl
- Messy $/@/% syntax4
- No exception handling3
- Bad OO support2
- "1;"2
- No OS threads2
- Variables are global by default1
- Copy-on-create for interpreter-based threads1
- Barewords1
- Errors/warnings are ignored by default1
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