https://DevOpsCloud.io -- Cloud Monk Losang Jinpa, Ph.D., MCSE/MCT, GitOps DevOps Engineer

Return to Concurrency Programming, Languages That Support Functional Programming

Concurrent programming has become increasingly important as systems aim to utilize the full potential of multi-core processors and distributed computing environments. Various programming languages offer different models and primitives to handle concurrency, each with its unique approach and syntax. This summary explores several key languages that support concurrent programming, highlighting their features and providing examples.

Go, or Golang, introduced a simple and efficient model for concurrency using goroutines and channels. Goroutines are lightweight threads managed by the Go runtime, while channels are typed conduits used to communicate between goroutines. ```go go func() {

   fmt.Println("Hello from a goroutine")

}() ``` [Go's official documentation](https://golang.org/doc/) provides extensive information on using goroutines and channels for concurrent programming.

Java offers built-in support for concurrent programming through the `Thread` class and the `java.util.concurrent` package, which includes a rich set of synchronization primitives and concurrent collections. ```java new Thread<sup>1)</sup>.start(); ``` For more details, refer to the [Java Concurrency Utilities documentation](https://docs.oracle.com/javase/tutorial/essential/concurrency/).

Erlang uses the actor model for concurrency, where each actor is a process that communicates with other actors using message passing. This model excels in building fault-tolerant and distributed systems. ```erlang spawn(fun() → io:format(“Hello from an actor~n”) end). ``` [Erlang's documentation](http://erlang.org/doc/) covers its concurrency model and how to use it effectively.

Rust provides concurrency features built around the principles of ownership and type safety, preventing data races at compile time. It offers threads, message passing, and shared state concurrency. ```rust std::thread::spawn(]] | programming_languages_that_support_concurrency | [[ {

   println!("Hello from a thread");

}); ``` The [Rust Book](https://doc.rust-lang.org/book/ch16-00-concurrency.html) details Rust's approach to safe concurrency.

Python supports concurrency through the `threading` and `multiprocessing` modules, allowing for both multi-threading and multi-processing paradigms. ```python import threading threading.Thread(target=lambda: print(“Hello from a thread”)).start() ``` Python's concurrency can be explored further in the [Python documentation](https://docs.python.org/3/library/concurrency.html).

C++11 introduced a standard thread library, including support for threads, mutexes, condition variables, and futures, allowing for modern concurrent programming in C++. ```cpp std::thread([](){ std::cout « “Hello from a thread\n”; }).join(); ``` For more on C++ concurrency, consult the [C++ Standard Library documentation](https://en.cppreference.com/w/cpp/thread).

JavaScript handles concurrency through an event-driven model, using callbacks, promises, and async/await syntax for asynchronous programming. ```javascript async function hello() {

   console.log(await Promise.resolve("Hello from async/await"));

} hello(); ``` The [MDN Web Docs](https://developer.mozilla.org/en-US/docs/Learn/JavaScript/Asynchronous) provide comprehensive guides on JavaScript's asynchronous features.

Haskell offers concurrency through lightweight threads and supports various synchronization mechanisms, including software transactional memory (STM). ```haskell forkIO $ putStrLn “Hello from a thread” ``` The [Haskell Wiki](https://wiki.haskell.org/Concurrency) has resources on concurrency in Haskell.

Scala, running on the JVM, integrates concurrent programming through futures and an actor system (via libraries like Akka), combining functional programming with an effective concurrency model. ```scala scala.concurrent.Future { println(“Hello from a future”) } ``` For an introduction to concurrency in Scala, see the [Scala documentation](https://docs.scala-lang.org/overviews/core/futures.html).

Clojure, a functional Lisp on the JVM, provides concurrency mechanisms such as software transactional memory (STM), agents, and atoms, focusing on immutable data structures and side-effect-free functions. ```clojure (future (println “Hello from a future”)) ``` Explore [Clojure's concurrency primitives](https://clojure.org/reference/concurrency) for more information.

Swift uses Grand Central Dispatch (GCD) for concurrency, offering a powerful and easy way to execute code concurrently on multicore hardware. ```swift DispatchQueue.global().async {

   print("Hello from a dispatch queue")

} ``` The [Swift concurrency documentation](https://docs.swift.org/swift-book/LanguageGuide/Concurrency.html) details its approach to concurrent execution.

== Kotlin: Coroutines

==

Kotlin introduces coroutines for managing concurrency in a more efficient and simpler way compared to threads, fully supported on the JVM and Android. ```kotlin GlobalScope.launch {

   println("Hello from a coroutine")

} ``` [Kotlin's coroutines guide](https://kotlinlang.org/docs/coroutines-guide.html) provides extensive information on using coroutines.

Concurrent programming paradigms vary widely among different programming languages, each offering unique constructs, models, and primitives to harness the power of modern processors and distributed systems effectively. Developers should choose the language and model that best fits their application's requirements, considering factors like performance, safety, and ease of use. For in-depth exploration of concurrency in any specific language, consulting the official language documentation and relevant community resources is highly recommended.