What is Clojure and what are its top alternatives?
Top Alternatives to Clojure
- Scala
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. ...
- Haskell
It is a general purpose language that can be used in any domain and use case, it is ideally suited for proprietary business logic and data analysis, fast prototyping and enhancing existing software environments with correct code, performance and scalability. ...
- Common Lisp
Lisp was originally created as a practical mathematical notation for computer programs, influenced by the notation of Alonzo Church's lambda calculus. It quickly became the favored programming language for artificial intelligence (AI) research. As one of the earliest programming languages, Lisp pioneered many ideas in computer science, including tree data structures, automatic storage management, dynamic typing, conditionals, higher-order functions, recursion, and the self-hosting compiler. [source: wikipedia] ...
- Elixir
Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain. ...
- Julia
Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. ...
- Erlang
Some of Erlang's uses are in telecoms, banking, e-commerce, computer telephony and instant messaging. Erlang's runtime system has built-in support for concurrency, distribution and fault tolerance. OTP is set of Erlang libraries and design principles providing middle-ware to develop these systems. ...
- Golang
Go 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. ...
- Kotlin
Kotlin is a statically typed programming language for the JVM, Android and the browser, 100% interoperable with Java ...
Clojure alternatives & related posts
- Static typing188
- Pattern-matching179
- Jvm177
- Scala is fun172
- Types138
- Concurrency95
- Actor library88
- Solve functional problems86
- Open source83
- Solve concurrency in a safer way80
- Functional44
- Generics23
- Fast23
- It makes me a better engineer18
- Syntactic sugar17
- Scalable13
- First-class functions10
- Type safety10
- Interactive REPL9
- Expressive8
- SBT7
- Implicit parameters6
- Case classes6
- Used by Twitter4
- JVM, OOP and Functional programming, and static typing4
- Rapid and Safe Development using Functional Programming4
- Object-oriented4
- 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
- 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
related Scala posts
I am new to Apache Spark and Scala both. I am basically a Java developer and have around 10 years of experience in Java.
I wish to work on some Machine learning or AI tech stacks. Please assist me in the tech stack and help make a clear Road Map. Any feedback is welcome.
Technologies apart from Scala and Spark are also welcome. Please note that the tools should be relevant to Machine Learning or Artificial Intelligence.
Lumosity is home to the world's largest cognitive training database, a responsibility we take seriously. For most of the company's history, our analysis of user behavior and training data has been powered by an event stream--first a simple Node.js pub/sub app, then a heavyweight Ruby app with stronger durability. Both supported decent throughput and latency, but they lacked some major features supported by existing open-source alternatives: replaying existing messages (also lacking in most message queue-based solutions), scaling out many different readers for the same stream, the ability to leverage existing solutions for reading and writing, and possibly most importantly: the ability to hire someone externally who already had expertise.
We ultimately migrated to Kafka in early- to mid-2016, citing both industry trends in companies we'd talked to with similar durability and throughput needs, the extremely strong documentation and community. We pored over Kyle Kingsbury's Jepsen post (https://aphyr.com/posts/293-jepsen-Kafka), as well as Jay Kreps' follow-up (http://blog.empathybox.com/post/62279088548/a-few-notes-on-kafka-and-jepsen), talked at length with Confluent folks and community members, and still wound up running parallel systems for quite a long time, but ultimately, we've been very, very happy. Understanding the internals and proper levers takes some commitment, but it's taken very little maintenance once configured. Since then, the Confluent Platform community has grown and grown; we've gone from doing most development using custom Scala consumers and producers to being 60/40 Kafka Streams/Connects.
We originally looked into Storm / Heron , and we'd moved on from Redis pub/sub. Heron looks great, but we already had a programming model across services that was more akin to consuming a message consumers than required a topology of bolts, etc. Heron also had just come out while we were starting to migrate things, and the community momentum and direction of Kafka felt more substantial than the older Storm. If we were to start the process over again today, we might check out Pulsar , although the ecosystem is much younger.
To find out more, read our 2017 engineering blog post about the migration!
- Purely-functional programming87
- Statically typed65
- Type-safe58
- Great community38
- Open source38
- Composable29
- Built-in concurrency29
- Built-in parallelism28
- Referentially transparent22
- Generics19
- Intellectual satisfaction14
- Type inference13
- If it compiles, it's correct11
- Flexible7
- Monads7
- Great type system4
- Proposition testing with QuickCheck4
- One of the most powerful languages *(see blub paradox)*3
- Great maintainability of the code2
- Fun2
- Purely-functional Programming2
- Kind system2
- Reliable2
- Highly expressive, type-safe, fast development time2
- Type classes2
- Better type-safe than sorry2
- Pattern matching and completeness checking2
- Best in class thinking tool2
- Orthogonality0
- Predictable0
- Too much distraction in language extensions7
- Error messages can be very confusing7
- Libraries have poor documentation4
- No best practices3
- No good ABI3
- Sometimes performance is unpredictable2
- Poor packaging for apps written in it for Linux distros2
- Slow compilation1
related Haskell posts
Why I am using Haskell in my free time?
I have 3 reasons for it. I am looking for:
Fun.
Improve functional programming skill.
Improve problem-solving skill.
Laziness and mathematical abstractions behind Haskell makes it a wonderful language.
It is Pure functional, it helps me to write better Scala code.
Highly expressive language gives elegant ways to solve coding puzzle.
Common Lisp
- Flexibility24
- High-performance21
- Comfortable: garbage collection, closures, macros, REPL17
- Stable13
- Lisp12
- Code is data7
- Can integrate with C (via CFFI)6
- Multi paradigm6
- Lisp is fun5
- Easy Setup4
- Macros4
- Elegant3
- Open source3
- Parentheses2
- Purelly functional2
- DSLs1
- Generic functions1
- Still decades ahead of almost all programming languages1
- Will still be relevant 100 years from now1
- Best programming language1
- Simple syntax1
- Powerful1
- Clean semantics1
- Can implement almost any feature as a library1
- Formal specification, multiple implementations1
- Multiple values1
- CLOS/MOP1
- Too many Parentheses4
- Standard did not evolve since 19942
- No hygienic macros1
- Small library ecosystem1
related Common Lisp posts
Hello everyone! I’m interested in learning AI development, and after doing a little bit of research, I’ve learned that Common Lisp and Java are the top languages for AI. Which one should I learn? What are the differences? Are they hard to learn? If anyone can help with this, it’d be very appreciated. Thank you!
Elixir
- Concurrency169
- Functional155
- Erlang vm130
- Great documentation110
- Great tooling103
- Immutable data structures84
- Open source79
- Pattern-matching76
- Easy to get started61
- Actor library58
- Functional with a neat syntax29
- Ruby inspired28
- Homoiconic24
- Erlang evolved23
- Beauty of Ruby, Speed of Erlang/C21
- Fault Tolerant17
- High Performance13
- Simple13
- Good lang10
- Stinkin' fast, no memory leaks, easy on the eyes9
- Doc as first class citizen9
- Pipe Operator9
- Resilient to failure7
- Fun to write6
- OTP5
- GenServer takes the guesswork out of background work5
- Fast, Concurrent with clean error messages4
- Idempotence4
- Not Swift4
- Pattern matching4
- Error isolation2
- Easy to use1
- Dynamic Typing1
- Fewer jobs for Elixir experts11
- Smaller userbase than other mainstream languages7
- Elixir's dot notation less readable ("object": 1st arg)5
- Dynamic typing4
- Difficult to understand1
- Not a lot of learning books available1
related Elixir posts
When you think about test automation, it’s crucial to make it everyone’s responsibility (not just QA Engineers'). We started with Selenium and Java, but with our platform revolving around Ruby, Elixir and JavaScript, QA Engineers were left alone to automate tests. Cypress was the answer, as we could switch to JS and simply involve more people from day one. There's a downside too, as it meant testing on Chrome only, but that was "good enough" for us + if really needed we can always cover some specific cases in a different way.
Another major decision was to adopt Elixir and Phoenix Framework - the DX (Developer eXperience) is pretty similar to what we know from RoR, but this tech is running on the top of rock-solid Erlang platform which is powering planet-scale telecom solutions for 20+ years. So we're getting pretty much the best from both worlds: minimum friction & smart conventions that eliminate the excessive boilerplate AND highly concurrent EVM (Erlang's Virtual Machine) that makes all the scalability problems vanish. The transition was very smooth - none of Ruby developers we had decided to leave because of Elixir. What is more, we kept recruiting Ruby developers w/o any requirement regarding Elixir proficiency & we still were able to educate them internally in almost no time. Obviously Elixir comes with some more tools in the stack: Credo , Hex , AppSignal (required to properly monitor BEAM apps).
Julia
- Fast Performance and Easy Experimentation18
- Designed for parallelism and distributed computation18
- Free and Open Source14
- Multiple Dispatch13
- Calling C functions directly12
- Dynamic Type System12
- Lisp-like Macros12
- Powerful Shell-like Capabilities8
- REPL5
- Jupyter notebook integration4
- String handling2
- Emojis as variable names2
- Interoperability1
- Immature library management system5
- Slow program start4
- JIT compiler is very slow3
- Poor backwards compatibility3
- Bad tooling2
- No static compilation2
related Julia posts
Erlang
- Concurrency Support59
- Real time, distributed applications59
- Fault tolerance55
- Soft real-time34
- Open source30
- Functional programming20
- Message passing19
- Immutable data14
- Works as expected12
- Facebook chat uses it at backend4
- Practical3
- Knowledgeable community3
- Bullets included2
related Erlang posts
Another major decision was to adopt Elixir and Phoenix Framework - the DX (Developer eXperience) is pretty similar to what we know from RoR, but this tech is running on the top of rock-solid Erlang platform which is powering planet-scale telecom solutions for 20+ years. So we're getting pretty much the best from both worlds: minimum friction & smart conventions that eliminate the excessive boilerplate AND highly concurrent EVM (Erlang's Virtual Machine) that makes all the scalability problems vanish. The transition was very smooth - none of Ruby developers we had decided to leave because of Elixir. What is more, we kept recruiting Ruby developers w/o any requirement regarding Elixir proficiency & we still were able to educate them internally in almost no time. Obviously Elixir comes with some more tools in the stack: Credo , Hex , AppSignal (required to properly monitor BEAM apps).
Postmates built a tool called Bazaar that helps onboard new partners and handles several routine tasks, like nightly emails to merchants alerting them about items that are out of stock.
Since they ran Bazaar across multiple instances, the team needed to avoid sending multiple emails to their partners by obtaining lock across multiple hosts. To solve their challenge, they created and open sourced ConsulMutEx, and an Elixir module for acquiring and releasing locks with Consul and other backends.
It works with Consul’s KV store, as well as other backends, including ets, Erlang’s in-memory database.
Golang
- High-performance530
- Simple, minimal syntax387
- Fun to write354
- Easy concurrency support via goroutines295
- Fast compilation times267
- Goroutines189
- Statically linked binaries that are simple to deploy177
- Simple compile build/run procedures148
- Backed by google134
- Great community131
- Garbage collection built-in50
- Built-in Testing42
- Excellent tools - gofmt, godoc etc41
- Elegant and concise like Python, fast like C38
- Awesome to Develop34
- Used for Docker25
- Flexible interface system24
- Great concurrency pattern22
- Deploy as executable22
- Open-source Integration19
- Fun to write and so many feature out of the box16
- Easy to read15
- Its Simple and Heavy duty14
- Go is God14
- Powerful and simple13
- Easy to deploy13
- Concurrency11
- Best language for concurrency11
- Safe GOTOs10
- Rich standard library10
- Clean code, high performance9
- Easy setup9
- Simplicity, Concurrency, Performance8
- High performance8
- Hassle free deployment8
- Used by Giants of the industry7
- Single binary avoids library dependency issues7
- Cross compiling6
- Simple, powerful, and great performance6
- Excellent tooling5
- Very sophisticated syntax5
- Gofmt5
- WYSIWYG5
- Garbage Collection5
- Widely used4
- Kubernetes written on Go4
- Keep it simple and stupid3
- No generics1
- Operator goto1
- You waste time in plumbing code catching errors41
- Verbose25
- Packages and their path dependencies are braindead22
- Google's documentations aren't beginer friendly15
- Dependency management when working on multiple projects15
- Automatic garbage collection overheads10
- Uncommon syntax8
- Type system is lacking (no generics, etc)6
- Collection framework is lacking (list, set, map)2
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
- Interoperable with Java66
- Functional Programming support51
- Null Safety46
- Backed by JetBrains41
- Official Android support41
- Concise33
- Modern Multiplatform Applications32
- Expressive Syntax25
- Coroutines24
- Target to JVM23
- Open Source22
- Statically Typed16
- Practical elegance16
- Type Inference15
- Android support15
- Better Java11
- Pragmatic10
- Readable code10
- Powerful as Scala, simple as Python, plus coroutines <39
- Lambda7
- Better language for android7
- Expressive DSLs7
- Target to JavaScript7
- Less boilerplate code6
- Used for Android5
- Less code4
- Fast Programming language4
- Functional Programming Language3
- Less boiler plate code3
- Friendly community3
- Native2
- Latest version of Java1
- Java interop makes users write Java in Kotlin7
- Frequent use of {} keys4
- Hard to make teams adopt the Kotlin style2
- Nonullpointer Exception2
- Friendly community1
- Slow compiler1
- No boiler plate code1
related Kotlin posts
Hi Community! Trust everyone is keeping safe. I am exploring the idea of building a #Neobank (App) with end-to-end banking capabilities. In the process of exploring this space, I have come across multiple Apps (N26, Revolut, Monese, etc) and explored their stacks in detail. The confusion remains to be the Backend Tech to be used?
What would you go with considering all of the languages such as Node.js Java Rails Python are suggested by some person or the other. As a general trend, I have noticed the usage of Node with React on the front or Node with a combination of Kotlin and Swift. Please suggest what would be the right approach!
In our company we have think a lot about languages that we're willing to use, there we have considering Java, Python and C++ . All of there languages are old and well developed at fact but that's not ideology of araclx. We've choose a edge technologies such as Node.js , Rust , Kotlin and Go as our programming languages which is some kind of fun. Node.js is one of biggest trends of 2019, same for Go. We want to grow in our company with growth of languages we have choose, and probably when we would choose Java that would be almost impossible because larger languages move on today's market slower, and cannot have big changes.