teknql / tapestry

Weave loom fibers into your Clojure

Tapestry

Next generation concurrency primitives for Clojure built on top of Project Loom

About

Project Loom is bringing first-class fibers to the JVM! Tapestry seeks to bring ergonomic clojure APIs for working with Loom.

What are Fibers and Why do I care?

Fibers behave similarly to OS level threads, but are much lighter weight to spawn, allowing potentially millions of them to exist.

Clojure already has the wonderful core.async and manifold libraries but writing maximally performant code in either requires the abstraction (channels, or promises) to leak all over your code (as you return channels or promise chains) to avoid blocking. Furthermore you frequently have to think about which executor will handle the blocking code.

Loom moves handling parking to the JVM runtime level making it possible for "blocking" code to be executed in a highly parallel fashion without the developer having to explicitly opt into the behavior. This is similar to how Golang and Haskell achieve their highly performant parallelism.

Some great further reading on the topic:

• What color is your function - A mental exploration of the leaking of the abstraction.
• Async Rust Book Async Intro - A good explaination of why we want async. The rest of this book is fantastic in terms of understanding how async execution works under the hood. In manifold and core.async the JVM executor is mostly analogous to the rust's concept of the Executor. In a language like Rust, without a runtime, being explicit and "colorizing functions" makes sense, but with a run-time we can do better.
• Project Loom Wiki - Internal design notes of Loom.

Project State

Tapestry is in beta. As the APIs in loom have stabilized so too has tapestry. Still, breaking changes may happen as we evolve the library. Pre-1.0.0 we will use the minor version to denote breaking changes.

Tapestry is being used in production for several of Teknql's projects and has more or less replaced both clojure.core/future and manifold.deferred/future.

Installation

Add to your deps.edn:

teknql/tapestry {:mvn/version "0.3.0-SNAPSHOT"}

Installing JDK 19

You will need to be running a recent JDK 19 release

You can download the latest version from the JDK 19 Site.

On linux, installing it looks something like this:

tar -xvzf openjdk-19_linux-x64_bin.tar.gz
sudo mv jdk-19/ /usr/lib/jvm/jdk-19
cd /usr/lib/jvm
sudo rm default default-runtime
sudo ln -s jdk-19 $PWD/default
sudo ln -s jdk-19 $PWD/default-runtime

Then you will need to add the --enable-preview flag to your JVM opts. In deps.edn it looks like:

{:aliases
 {:preview
  {:jvm-opts ["--enable-preview"]}}}

clojure -A:preview

Alternatively, you can install a loom preview build via sdkman:

$ sdk install java 17.ea.2.lm-open
    ...
Installing: java 17.ea.2.lm-open
Done installing!

Do you want java 17.ea.2.lm-open to be set as default? (Y/n): n
$ sdk use java 17.ea.2.lm-open

Using java version 17.ea.2.lm-open in this shell.

The sdkman maintainers update the loom preview build often. To find the current version identifier for the loom preview build run sdk list java and look for the identifier containing "lm-open".

Showcase

Full API documentation can be seen in the tapestry.core ns itself. Right now we can't build the documentation using clj-doc. You can track the issue here

Here is a demo of some of the basics.

Spawning a Fiber

(require '[tapestry.core :refer [fiber fiber-loop]])

;; Spawning a Fiber behaves very similarly to `future` in standard clojure, but
;; runs in a Loom Fiber and returns a tapestry.core.Fiber which implements IDeref.
@(fiber (+ 1 2 3 4))
;; => 10


;; Or, Like `core.async`'s `go-loop'

@(fiber-loop [i 0]
   (if (= i 5)
     (* 2 i)
     (do (Thread/sleep 100)
         (recur (inc i)))))
;; => 10, after aprox 500ms of sleeping

Interrupting and introspecting a Fiber

(require '[tapestry.core :refer [fiber interrupt! alive?]]')

(let [f (fiber (Thread/sleep 10000))]
  (alive? f) ;; true
  (interrupt! f)
  (alive? f) ;; false
  @f ;; Raises java.lang.InterruptedException))

Timeouts

Tapestry supports setting timeouts on fibers which will cause them to be interrupted (with a java.lang.InterruptedException) when the timeout is hit.

(require '[tapestry.core :refer [fiber timeout! alive?]]')

(let [f (fiber (Thread/sleep 10000))]
  (timeout! f 100)
  (alive? f) ;; true
  (Thread/sleep 200)
  (alive? f) ;; false
  @f ;; Raises java.util.concurrent.TimeoutException))

You can also specify a default value

(require '[tapestry.core :refer [fiber timeout! alive?]]')

(let [f (fiber (Thread/sleep 10000))]
  (timeout! f 100 :default)
  @f ;; => :default))

You can use dynamic bindings to set a timeout on a bunch of fibers. Note that each fiber will have a timeout that starts from when the fiber was spawned.

(require '[tapestry.core :refer [fiber alive? with-timeout]]')

(with-timeout 100 ;; Accepts a duration or number of millis
  (let [f (fiber (Thread/sleep 10000))]
    @f ;; raises java.util.concurrent.TimeoutException
    ))

Processing Sequences

(require '[tapestry.core :refer [parallelly asyncly pfor]]
         '[clj-http.client :as clj-http])

(def urls
  ["https://google.com"
   "https://bing.com"
   "https://yahoo.com"])

;; We can also run a function over a sequence, spawning a fiber for each item.
(->> urls
     (parallelly clj-http/get))

;; We can using the built in `pfor` macro to evaluate a `for` expression in parallel. Note that unlike
;; clojure.core/for, this is not lazy.
(pfor [url urls]
  (clj-http/get url))

;; Similalry, if we don't care about the order of items being maintained, and instead just want
;; to return results as quickly as possible

(doseq [resp (asyncly clj-http/get urls)]
  (println "Got Response!" (:status resp)))

Bounded Parallelism

;; We can control max parallelism for fibers
(require '[tapestry.core :refer [parallelly fiber]])

;; Note that you can also use `with-max-parallelism` within a fiber body
;; which will limit parallelism of all newly spawned fibers. Consider the following
;; in which we process up to 3 orders simultaneously, and each order can process up to 2
;; tasks in parallel.
(defn process-order!
  [order]
  (with-max-parallelism 2
    (let [internal-notification-success? (fiber (send-internal-notification! order))
          shipping-success?     (fiber (ship-order! order))
          receipt-success?      (fiber (send-receipt! order))]
      {:is-notified @internal-notification-success?
       :is-shipped  @shipping-success?
       :has-receipt @receipt-success?})))
(with-max-parallelism 3
  (let [order-a-summary (process-order! order-a)
        order-b-summary (process-order! order-b)
        order-c-summary (process-order! order-c)
        order-d-summary (process-order! order-d)]
    {:a @order-a-summary
     :b @order-b-summary
     :c @order-c-summary
     :d @order-d-summary})


;; You can also bound the parallelism of sequence processing functions by specifying
;; an optional bound:

(asyncly 3 clj-http/get urls)

(parallelly 3 clj-http/get urls)

Manifold Support

(require '[manifold.stream :as s]
         '[tick.alpha.api :as t]
         '[tapestry.core :refer [periodically parallelly asyncly]])

;; tapestry.core/periodically behaves very similar to manfold's built in periodically,
;; but runs each task in a fiber. You can terminate it by closing the stream.
(let [count     (atom 0)
        generator (periodically (t/new-duration 1 :seconds) #(swap! count inc))]
    (->> generator
         (s/consume #(println "Count is now:" %)))
    (Thread/sleep 5000)
    (s/close! generator))

;; Also, `parallelly` and `asyncly` both suppport manifold streams, allowing you to describe parallel
;; execution pipelines
(->> (s/stream)
     (paralelly 5 some-operation)
     (asyncly 5 some-other-operation)
     (s/consume #(println "Got Result" %)))

Advisories

None at the moment

CLJ Kondo Config

Add the following to your .clj-kondo/config.edn

{:lint-as {tapestry.core/fiber-loop clojure.core/loop
           tapestry.core/pfor       clojure.core/for}}

Long Term Wish List

• Consider whether we can drop manifold. What do streams look like?
• Implement structured concurrency using either java built-in APIs (currently gated) or see if clojure affords us a nicer API.
• Consider implement linking ala erlang
• Consider implementing an OTP-like interface
• Consider cljs support
• (parallelize ...) macro to automatically re-write call graphs