What is Nim and what are its top alternatives?
Nim is a statically typed, compiled systems programming language known for its efficiency, readability, and flexibility. It offers features like metaprogramming, support for multiple paradigms, and a simple syntax that resembles Python. However, Nim's ecosystem is still evolving, with fewer libraries and tools compared to more established languages. Developers may also find transitioning to Nim challenging due to its unique syntax and concepts.
Rust: Rust is a systems programming language focused on safety, speed, and concurrency. Key features include memory safety without garbage collection, fearless concurrency, and a powerful package management system. Pros: strong community support, great performance. Cons: steeper learning curve than Nim, stricter borrowing rules.
Go: Go is a simple and efficient programming language designed for building reliable and efficient software. Key features include built-in support for concurrency, static typing, and a straightforward syntax. Pros: easy to learn, excellent performance. Cons: lacks some advanced language features present in Nim.
D: D is a systems programming language with a focus on combining the performance of compiled languages with the productivity of high-level languages. Key features include powerful metaprogramming capabilities, support for functional and imperative programming styles, and a rich standard library. Pros: strong performance, extensive library support. Cons: smaller community compared to Nim.
Crystal: Crystal is a compiled language that offers the power of low-level programming with the elegance of high-level languages. Key features include static typing, native performance, and a Ruby-like syntax. Pros: high performance, easy to learn. Cons: smaller ecosystem compared to Nim.
Julia: Julia is a high-level, high-performance language for technical computing known for its speed and ease of use. Key features include a sophisticated compiler, multiple dispatch, and built-in support for parallel computing. Pros: excellent performance, seamless integration with other languages. Cons: may not be as well-suited for systems programming as Nim.
Swift: Swift is a powerful and intuitive programming language developed by Apple for iOS, macOS, watchOS, and tvOS development. Key features include safety features to avoid mistakes and modern syntax. Pros: easy to learn, great performance. Cons: primarily tailored for Apple platforms.
Haskell: Haskell is a functional programming language known for its strong type system, lazy evaluation, and purity. Key features include type inference, high-level abstractions, and a rich ecosystem of libraries. Pros: expressive syntax, robust type system. Cons: steep learning curve for newcomers.
Scala: Scala is a modern multi-paradigm programming language designed to express common programming patterns in a concise, elegant, and type-safe way. Key features include functional programming, object-oriented programming, and compatibility with Java. Pros: seamless Java interoperability, strong static typing. Cons: complex language features may be overwhelming for beginners.
Kotlin: Kotlin is a statically typed programming language that runs on the Java Virtual Machine (JVM) and can also be compiled to JavaScript or native code. Key features include null safety, concise syntax, and seamless interoperability with Java. Pros: reduces boilerplate code, excellent tooling. Cons: adoption outside of Android development is still growing.
Clojure: Clojure is a dynamic, functional programming language that targets the Java Virtual Machine (JVM), designed for building robust and scalable systems. Key features include immutable data structures, concurrency abstractions, and a Lisp-like syntax. Pros: simplicity and expressiveness, great support for concurrency. Cons: may not be as performant as Nim for low-level systems programming tasks.
Top Alternatives to Nim
GolangGo is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language. ...
RustRust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory. ...
PythonPython is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...
CrystalCrystal is a programming language that resembles Ruby but compiles to native code and tries to be much more efficient, at the cost of disallowing certain dynamic aspects of Ruby. ...
DD is a language with C-like syntax and static typing. It pragmatically combines efficiency, control, and modeling power, with safety and programmer productivity. ...
OCamlIt is an industrial strength programming language supporting functional, imperative and object-oriented styles. It is the technology of choice in companies where a single mistake can cost millions and speed matters, ...
JavaScriptJavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...
Nim alternatives & related posts
Golang
- High-performance553
- Simple, minimal syntax397
- Fun to write364
- Easy concurrency support via goroutines303
- Fast compilation times273
- Goroutines195
- Statically linked binaries that are simple to deploy181
- Simple compile build/run procedures151
- Backed by google137
- Great community137
- Garbage collection built-in53
- Built-in Testing47
- Excellent tools - gofmt, godoc etc44
- Elegant and concise like Python, fast like C40
- Awesome to Develop37
- Used for Docker26
- Flexible interface system26
- Great concurrency pattern25
- Deploy as executable24
- Open-source Integration21
- Easy to read19
- Fun to write and so many feature out of the box17
- Go is God17
- Powerful and simple14
- Easy to deploy14
- Its Simple and Heavy duty14
- Concurrency14
- Best language for concurrency13
- Safe GOTOs11
- Rich standard library11
- Clean code, high performance10
- Easy setup10
- High performance10
- Simplicity, Concurrency, Performance9
- Cross compiling8
- Single binary avoids library dependency issues8
- Hassle free deployment8
- Used by Giants of the industry7
- Simple, powerful, and great performance7
- Gofmt7
- Garbage Collection6
- WYSIWYG5
- Very sophisticated syntax5
- Excellent tooling5
- Keep it simple and stupid4
- Widely used4
- Kubernetes written on Go4
- No generics2
- Looks not fancy, but promoting pragmatic idioms1
- Operator goto1
- You waste time in plumbing code catching errors42
- Verbose25
- Packages and their path dependencies are braindead23
- Google's documentations aren't beginer friendly16
- Dependency management when working on multiple projects15
- Automatic garbage collection overheads10
- Uncommon syntax8
- Type system is lacking (no generics, etc)7
- Collection framework is lacking (list, set, map)5
- Best programming language3
- A failed experiment to combine c and python1
related Golang posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)
We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.
Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.
#FrameworksFullStack #Languages
- Guaranteed memory safety145
- Fast132
- Open source88
- Minimal runtime75
- Pattern matching72
- Type inference63
- Algebraic data types57
- Concurrent57
- Efficient C bindings47
- Practical43
- Best advances in languages in 20 years37
- Safe, fast, easy + friendly community32
- Fix for C/C++30
- Stablity25
- Zero-cost abstractions24
- Closures23
- Extensive compiler checks20
- Great community20
- Async/await18
- No NULL type18
- Completely cross platform: Windows, Linux, Android15
- No Garbage Collection15
- Great documentations14
- High-performance14
- Generics12
- Super fast12
- High performance12
- Safety no runtime crashes11
- Fearless concurrency11
- Compiler can generate Webassembly11
- Macros11
- Guaranteed thread data race safety11
- Helpful compiler10
- RLS provides great IDE support9
- Prevents data races9
- Easy Deployment9
- Real multithreading8
- Painless dependency management8
- Good package management7
- Support on Other Languages5
- Type System1
- Hard to learn28
- Ownership learning curve24
- Unfriendly, verbose syntax12
- High size of builded executable4
- Many type operations make it difficult to follow4
- No jobs4
- Variable shadowing4
- Use it only for timeoass not in production1
related Rust posts
Hello!
I'm a developer for over 9 years, and most of this time I've been working with C# and it is paying my bills until nowadays. But I'm seeking to learn other languages and expand the possibilities for the next years.
Now the question... I know Ruby is far from dead but is it still worth investing time in learning it? Or would be better to take Python, Golang, or even Rust? Or maybe another language.
Thanks in advance.
Sentry's event processing pipeline, which is responsible for handling all of the ingested event data that makes it through to our offline task processing, is written primarily in Python.
For particularly intense code paths, like our source map processing pipeline, we have begun re-writing those bits in Rust. Rust’s lack of garbage collection makes it a particularly convenient language for embedding in Python. It allows us to easily build a Python extension where all memory is managed from the Python side (if the Python wrapper gets collected by the Python GC we clean up the Rust object as well).
Python
- Great libraries1.2K
- Readable code964
- Beautiful code847
- Rapid development788
- Large community691
- Open source438
- Elegant393
- Great community282
- Object oriented273
- Dynamic typing221
- Great standard library77
- Very fast60
- Functional programming55
- Easy to learn51
- Scientific computing46
- Great documentation35
- Productivity29
- Easy to read28
- Matlab alternative28
- Simple is better than complex24
- It's the way I think20
- Imperative19
- Very programmer and non-programmer friendly18
- Free18
- Powerfull language17
- Machine learning support17
- Fast and simple16
- Scripting14
- Explicit is better than implicit12
- Ease of development11
- Clear and easy and powerfull10
- Unlimited power9
- Import antigravity8
- It's lean and fun to code8
- Print "life is short, use python"7
- Python has great libraries for data processing7
- Rapid Prototyping6
- Readability counts6
- Now is better than never6
- Great for tooling6
- Flat is better than nested6
- Although practicality beats purity6
- I love snakes6
- High Documented language6
- There should be one-- and preferably only one --obvious6
- Fast coding and good for competitions6
- Web scraping5
- Lists, tuples, dictionaries5
- Great for analytics5
- Easy to setup and run smooth4
- Easy to learn and use4
- Plotting4
- Beautiful is better than ugly4
- Multiple Inheritence4
- Socially engaged community4
- Complex is better than complicated4
- CG industry needs4
- Simple and easy to learn4
- It is Very easy , simple and will you be love programmi3
- Flexible and easy3
- Many types of collections3
- If the implementation is easy to explain, it may be a g3
- If the implementation is hard to explain, it's a bad id3
- Special cases aren't special enough to break the rules3
- Pip install everything3
- List comprehensions3
- No cruft3
- Generators3
- Import this3
- Powerful language for AI3
- Can understand easily who are new to programming2
- Should START with this but not STICK with This2
- A-to-Z2
- Because of Netflix2
- Only one way to do it2
- Better outcome2
- Batteries included2
- Good for hacking2
- Securit2
- Procedural programming1
- Best friend for NLP1
- Slow1
- Automation friendly1
- Sexy af1
- Ni0
- Keep it simple0
- Powerful0
- Still divided between python 2 and python 353
- Performance impact28
- Poor syntax for anonymous functions26
- GIL22
- Package management is a mess19
- Too imperative-oriented14
- Hard to understand12
- Dynamic typing12
- Very slow12
- Indentations matter a lot8
- Not everything is expression8
- Incredibly slow7
- Explicit self parameter in methods7
- Requires C functions for dynamic modules6
- Poor DSL capabilities6
- No anonymous functions6
- Fake object-oriented programming5
- Threading5
- The "lisp style" whitespaces5
- Official documentation is unclear.5
- Hard to obfuscate5
- Circular import5
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- The benevolent-dictator-for-life quit4
- Not suitable for autocomplete4
- Meta classes2
- Training wheels (forced indentation)1
related Python posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)
We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.
Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.
#FrameworksFullStack #Languages
- Compiles to efficient native code38
- Ruby inspired syntax36
- Performance oriented - C-like speeds32
- Gem-like packages, called Shards23
- Can call C code using Crystal bindings20
- Super Fast18
- Typed Ruby <318
- Open Source17
- Minimal Runtime14
- Cute11
- Clean code9
- Concurrent9
- Productive9
- Great community4
- Powerful2
- Program compiled into a single binary2
- Simplicity2
- Meta-Programming (via Macros)2
- Feels like duck types, safe like static types2
- Null Safety2
- Type inference2
- Has builtin LLVM support library1
- Statically linked binaries that are simple to deploy1
- Fun to write1
- High-performance1
- Simple, minimal syntax1
- Compile time statically safe macros1
- Concise1
- Statically Safe Monkey Patching1
- Fibers1
- Spawn1
- Meta-programming1
- Productivity1
- Elegant1
- Cross-platform1
- Small community13
- No windows support3
- No Oracle lib1
related Crystal posts
I’m trying to find the best programming language for programming a video game. Should I use Crystal or JavaScript to create the game?
- Performance69
- Low-level49
- Portability36
- Hardware level29
- Embedded apps19
- Pure13
- Performance of assembler9
- Ubiquity8
- Great for embedded6
- Old4
- Compiles quickly4
- No garbage collection to slow it down3
- OpenMP2
- Gnu/linux interoperable2
- Low-level5
- No built in support for parallelism (e.g. map-reduce)3
- Lack of type safety3
- No built in support for concurrency3
related C lang posts
Why Uber developed H3, our open source grid system to make geospatial data visualization and exploration easier and more efficient:
We decided to create H3 to combine the benefits of a hexagonal global grid system with a hierarchical indexing system. A global grid system usually requires at least two things: a map projection and a grid laid on top of the map. For map projection, we chose to use gnomonic projections centered on icosahedron faces. This projects from Earth as a sphere to an icosahedron, a twenty-sided platonic solid. The H3 grid is constructed by laying out 122 base cells over the Earth, with ten cells per face. H3 supports sixteen resolutions: https://eng.uber.com/h3/
(GitHub Pages : https://uber.github.io/h3/#/ Written in C w/ bindings in Java & JavaScript )
One important decision for delivering a platform independent solution with low memory footprint and minimal dependencies was the choice of the programming language. We considered a few from Python (there was already a reasonably large Python code base at Thumbtack), to Go (we were taking our first steps with it), and even Rust (too immature at the time).
We ended up writing it in C. It was easy to meet all requirements with only one external dependency for implementing the web server, clearly no challenges running it on any of the Linux distributions we were maintaining, and arguably the implementation with the smallest memory footprint given the choices above.
- Compile-time function execution16
- Makes functional programming style easier12
- Productive12
- Much easier to do Concurrent/Parallel vs C/C++12
- Simple but Powerful template-based generics11
- Powerful static function to avoid macro11
- Meta program is much easier to read/write vs. C++10
- It support unittest etc9
- Assembler is support directly in the language9
- System program language like C++ and C9
- Supports code covarge directly in the compiler9
- Metaprogramming7
- Supports both manuel memory and garbage collection7
- Plugs directly into C6
- Easy to translate from Java and C# to D6
- Feels and looks like C, so it's easy to learn5
- Amazing developer productivity4
- Fast2
- Performance2
- Syntax uniformity across pre-compile/compile/runtime1
related D posts
- Satisfying to write7
- Pattern matching6
- Also has OOP4
- Very practical4
- Easy syntax3
- Extremely powerful type inference3
- Efficient compiler1
- Small community3
- Royal pain in the neck to compile large programs1
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JavaScript
- Can be used on frontend/backend1.7K
- It's everywhere1.5K
- Lots of great frameworks1.2K
- Fast898
- Light weight746
- Flexible425
- You can't get a device today that doesn't run js392
- Non-blocking i/o286
- Ubiquitousness237
- Expressive191
- Extended functionality to web pages55
- Relatively easy language49
- Executed on the client side46
- Relatively fast to the end user30
- Pure Javascript25
- Functional programming21
- Async15
- Full-stack13
- Future Language of The Web12
- Setup is easy12
- Its everywhere12
- Because I love functions11
- JavaScript is the New PHP11
- Like it or not, JS is part of the web standard10
- Easy9
- Can be used in backend, frontend and DB9
- Expansive community9
- Everyone use it9
- Easy to hire developers8
- Most Popular Language in the World8
- For the good parts8
- Can be used both as frontend and backend as well8
- No need to use PHP8
- Powerful8
- Evolution of C7
- Its fun and fast7
- It's fun7
- Nice7
- Versitile7
- Hard not to use7
- Popularized Class-Less Architecture & Lambdas7
- Agile, packages simple to use7
- Supports lambdas and closures7
- Love-hate relationship7
- Photoshop has 3 JS runtimes built in7
- 1.6K Can be used on frontend/backend6
- Client side JS uses the visitors CPU to save Server Res6
- It let's me use Babel & Typescript6
- Easy to make something6
- Can be used on frontend/backend/Mobile/create PRO Ui6
- Client processing5
- What to add5
- Everywhere5
- Scope manipulation5
- Function expressions are useful for callbacks5
- Stockholm Syndrome5
- Promise relationship5
- Clojurescript5
- Only Programming language on browser4
- Because it is so simple and lightweight4
- Easy to learn and test1
- Easy to understand1
- Not the best1
- Subskill #41
- Hard to learn1
- Test21
- Test1
- Easy to learn1
- Hard 彤0
- A constant moving target, too much churn22
- Horribly inconsistent20
- Javascript is the New PHP15
- No ability to monitor memory utilitization9
- Shows Zero output in case of ANY error8
- Thinks strange results are better than errors7
- Can be ugly6
- No GitHub3
- Slow2
- HORRIBLE DOCUMENTS, faulty code, repo has bugs0
related JavaScript posts
Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.
But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.
But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.
Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark