Concurrency in Go, Clojure, Haskell and Rust

David Wagner

In the past I wrote two articles where I explored concurrency in Haskell using some examples from the talk Go Concurrency Patterns by Rob Pike.

The examples are different implementations of a simulated search engine which receives a search query and returns web, image and video results. The first version sends the search queries sequentially. Then, the program is gradually improved to become concurrent and better performing.

Earlier I presented each step in detail. Here I will only show the final form of the fake search function in four different programming languages: Go, Clojure, Haskell and Rust.


Rob Pike’s version of the search function executes the three kinds of search queries concurrently and sends the search requests to replicated back-end servers to reduce tail latency.

func search(query string) (results []Result) {
    c := make(chan Result)
    go func() { c <- First(query, Web1, Web2) } ()
    go func() { c <- First(query, Image1, Image2) } ()
    go func() { c <- First(query, Video1, Video2) } ()
    timeout := time.After(80 * time.Millisecond)
    for i := 0; i < 3; i++ {
        select {
        case result := <-c:
            results = append(results, result)
        case <-timeout:
            fmt.Println("timed out")

The implementation shows Go’s concurrency primitives: goroutines, channels, and the select statement. The Go runtime manages the goroutines which are lightweight threads of execution. Goroutines communicate via channels. The switch statement allows merging values originating from multiple channels. These constructs are all built into the language, no external library is required.


I was surprised when Rich Hickey mentioned the fake search example in his presentation on Clojure core async. I copied here the code from the slides for reference:

(defn search [query]
  (let [c (chan)
        t (timeout 80)]
    (go (>! c (<! (fastest query web1 web2))))
    (go (>! c (<! (fastest query image1 image2))))
    (go (>! c (<! (fastest query video1 video2))))
    (go (loop [i 0
               ret []]
          (if (= i 3)
            (recur (inc i)
                   (conj ret (alt! [c t] ([v] v)))))))))

Clojure’s async library uses the same primitives as Go: the syntax is LISP, but the structure of the program is identical to that of the Go version. Watch Rich Hickey’s presentation if you’re interested how this works on the JVM and in a web browser using ClojureScript.


My first implementation of the simulated search engine was in Haskell:

search30 :: SearchQuery -> IO ()
search30 query = do
    req <- timeout maxDelay $
        mapConcurrently (fastest query) [Web, Image, Video]
    printResults req

This is my favorite version of this exercise because it’s succint and expressive. We don’t see the primitives we saw in the Go and Clojure version, but an expression stating that some operations are expected to run concurrently. Writing this high-level code is possible because the threads are managed by the Haskell run-time system and I’m using the async library which exposes a powerful, composable API.


My latest addition to this collection is written in Rust, a language I’ve been learning for the last couple of weeks:

pub async fn search30(query: &SearchQuery) -> SearchResult {
    timeout(Duration::from_millis(80), async {
            fastest(query, &SearchKind::Web),
            fastest(query, &SearchKind::Image),
            fastest(query, &SearchKind::Video),
    .map(|(web, image, video)| SearchResult::new(web, image, video))
    .unwrap_or_else(|_| SearchResult::timeout())

This code looks similar to the Haskell version because we don’t see channels and explicit thread management here either. The Rust language defines the async/await syntax and the related interfaces but it delegates the concrete execution strategy to external libraries. In this example I chose the Tokio library which is a mature asynchronous run-time library, but in the future I’d like to explore other libraries too and learn more about how they work.

Asyncronous programming in Rust is a recent addition to the language. If you’re interested how this feature was designed I recommend watching Steve Klabnik’s talk.


Modern languages provide ways of expressing concurrent operations using built-in language primitives or external libraries. Writing a simulated search engine is a great exercise to learn about concurrency because it requires to think about thread creation, thread cancellation and merging results from multiple threads.