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Julia vs Scala: What are the differences?
1. Syntax: One key difference between Julia and Scala is their syntax. Julia has a simple and flexible syntax, similar to Python, which makes it easy to read and write code. On the other hand, Scala has a more complex syntax that blends functional and object-oriented programming paradigms, making it more powerful for certain use cases but also more difficult for beginners to grasp.
2. Type System: Another major difference between Julia and Scala is their type systems. Julia is a dynamically typed language, which means that variables can hold values of any type and their type can change at runtime. This flexibility allows for more concise code but can also lead to potential runtime errors. Scala, on the other hand, is a statically typed language, which means that variables must have a specific type at compile time. This enforced type safety can prevent many common programming mistakes but can also make the code more verbose.
3. Performance: Julia and Scala also differ in terms of performance. Julia is designed to be a high-performance language, with a just-in-time (JIT) compiler that optimizes code execution. This makes Julia well-suited for scientific computing and numerical simulations, where performance is crucial. Scala, on the other hand, is not as optimized for performance as Julia, but it runs on the Java Virtual Machine (JVM), which allows it to leverage the vast ecosystem of Java libraries and frameworks.
4. Concurrency: Concurrency is another area where Julia and Scala differ. Julia has built-in support for lightweight threading, allowing multiple tasks to run concurrently. This makes it easy to write parallel code and take full advantage of modern multi-core processors. Scala, on the other hand, has a more complex concurrency model based on the Actor model, which allows developers to write scalable and fault-tolerant concurrent applications. This model requires more advanced knowledge and can be more difficult to master.
5. Community and Ecosystem: Julia and Scala also differ in terms of their community and ecosystem. Julia is a relatively new language with a growing but smaller community compared to Scala. However, Julia has gained popularity in the scientific computing community and has a growing ecosystem of packages and libraries tailored for scientific and data analysis tasks. Scala, on the other hand, has a larger and more mature community, with a wide range of libraries and frameworks available for web development, data processing, and other domains.
6. Learning Curve: The learning curve of Julia and Scala is another significant difference. Julia has a relatively low learning curve, especially for scientists and engineers familiar with Python or MATLAB, due to its simple syntax and high-level abstractions. On the other hand, Scala has a steeper learning curve, mainly because of its complex syntax and the need to understand functional programming concepts. Scala is more suitable for developers with a strong background in programming and experience with object-oriented or functional languages.
In Summary, Julia and Scala differ in syntax, type system, performance, concurrency model, community and ecosystem, and learning curve.
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.
After writing a project in Julia we decided to stick with Kotlin. Julia is a nice language and has superb REPL support, but poor tooling and the lack of reproducibility of the program runs makes it too expensive to work with. Kotlin on the other hand now has nice Jupyter support, which mostly covers REPL requirements.
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 Julia
- Fast Performance and Easy Experimentation24
- Designed for parallelism and distributed computation21
- Free and Open Source18
- Dynamic Type System17
- Multiple Dispatch16
- Calling C functions directly16
- Lisp-like Macros16
- Powerful Shell-like Capabilities10
- Jupyter notebook integration9
- REPL8
- String handling4
- Emojis as variable names4
- Interoperability3
Pros of Scala
- Static typing187
- Pattern-matching178
- Jvm177
- 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 Julia
- Immature library management system5
- Slow program start4
- JIT compiler is very slow3
- Poor backwards compatibility3
- Bad tooling2
- No static compilation2
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
