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Native Clojure with GraalVM


Clojure is one of the most powerful programming languages out there. It’s unique approach to functional programming, being a dynamically typed language is very powerful. It’s a dialect of Lisp, one of the oldest and most powerful languages itself. Lisp was one of the first to introduce concepts like first class functions or anonymous functions, garbage collection and more that are still used to this day.



GraalVM is a Java virtual machine that was develpoed by the Oracle Corporation in 2019. One of the very cool features of GraalVM is Ahead-of-Time (AOT) compilation, which is what we’re gonna discuss here. Ahead of time compilation allows for a blazingly faster startup time and much less memory footprint. Startup time is crucial in cases where you need your services to restart with zero to no down time, or as fast as possible, and in many other cases.

Building a native image of your Clojure app

Using GraalVM with Clojure is pretty easy and straightforward. Start by first downloading the GraalVM binaries for your selected Java version from https://github.com/graalvm/graalvm-ce-builds/releases, unpack it and make sure its in your system’s PATH. Once the GraalVM command line tools are recognized in the system, you can install native-image using:

gu install native-image

After that, make sure that your project.clj in your clojure project contains the :main and that ahead of time (AOT) compilation is enabled:

(defproject graalvm_test "0.1.0-SNAPSHOT"
  :description "FIXME: write description"
  :url "http://example.com/FIXME"
  :license {:name "EPL-2.0 OR GPL-2.0-or-later WITH Classpath-exception-2.0"
            :url "https://www.eclipse.org/legal/epl-2.0/"}
  :dependencies [[org.clojure/clojure "1.10.1"]]

  :repl-options {:init-ns graalvm-test.core})

  ;; add the main namespace
  :main graalvm-test.core

  ;; add aot to the build profile
  :profiles {:uberjar {:aot :all}}

Finally, you’ll need to add a main function to your clojure project in the main namespace you specified.

(defn -main 
  [& args]
  (println "Hello World"))

Now, in order to build a native image, we have to build an uberjar first:

lein do clean, uberjar

And now you can run the native-image command:

native-image --initialize-at-build-time \
             --no-server \
             -jar ./target/hello-world-0.1.0-SNAPSHOT-standalone.jar \

You can now run your native image:


And to put it in action, let’s compare the time difference between running the jar vs. the native image:

$ time java -jar target/hello-world-0.1.0-SNAPSHOT-standalone.jar
Hello World
java -jar target/hello-world-0.1.0-SNAPSHOT-standalone.jar  2.17s user 0.20s system 181% cpu 1.309 total

$ time ./hello-world-0.1.0-SNAPSHOT-standalone
Hello World
./hello-world-0.1.0-SNAPSHOT-standalone  0.00s user 0.01s system 29% cpu 0.034 total

from 2.17 seconds to less than a millisecond of startup speed, amazing!

Building a native image of your clojure projects isn’t always this straightforward, though. Some clojure libraries uses dynamic class loading to load some of its components, and for that you’ll need to supply a reflection configuration file to GraalVM in order for it to load the class on runtime.

Native Carmine: Building a native image of clojure’s redis client

Carmine is a very powerful Redis client for Clojure. It’s also one of the clojure libraries that uses dynamic class loading for some of its components (i.e: a class named org.apache.commons.pool2.impl.EvictionPolicy). Let’s say how to build a native image of a clojure project that uses Carmine.

Let’s start by generating a new project:

lein new carmine_graalvm

We’ll add the carmine dependency to project.clj, along with what we did initially; adding the main and enabling aot compilation:

  :dependencies [[org.clojure/clojure "1.10.1"]
                  [com.taoensso/carmine "3.1.0"]]
  :main carmine-graalvm.core
  :profiles {:uberjar {:aot :all}}

Next, we’ll need to add some code that communicates with the redis server we want to test. Add this to your core.clj:

(ns carmine-graalvm.core       
  (:require [taoensso.carmine :as car])
(defmacro wcar* [& body] `(car/wcar {} ~@body))

(defn -main                    
  [& args]                     
  (println (wcar* (car/ping)))
  (println (wcar* (car/info "server"))))

This code sends the command PING to the redis server which replies with PONG and then asks redis for the server info and prints that out.

Before building the native image; let’s make sure our code runs by running lein run. Make sure that the redis server is running in your local machine.

$ lein run
# Server
os:Linux 5.11.0-34-generic x86_64

Now we can just build the source by running:

lein do clean, uberjar

and then build the native image by:

native-image --initialize-at-build-time \
             --no-server \
             -jar ./target/carmine_graalvm-0.1.0-SNAPSHOT-standalone.jar

now running ./carmine_graalvm-0.1.0-SNAPSHOT-standalone will give an exception:

Exception in thread "main" java.lang.IllegalArgumentException: Unable to create org.apache.commons.pool2.impl.EvictionPolicy instance of type org.apache.commons.pool2.impl.DefaultEvictionPolicy

This is because as we mentioned, carmine uses dynamic class loading. To successfully build a native image of our project, we’ll need to supply a reflection configuration file that’ll let GraalVM know that this is a dynamic class and is going to be loaded in the future. Add this to a file named reflect-config.json:

    "allPublicConstructors" : true

and when building the native image we’ll use:


and that’ll make our final build command:

native-image --initialize-at-build-time \
             --no-server \
             -H:ConfigurationFileDirectories=./path/to/config/dir \
             -jar ./target/carmine_graalvm-0.1.0-SNAPSHOT-standalone.jar

Comparing the startup time between the two variants

java -jar target/carmine_graalvm-0.1.0-SNAPSHOT-standalone.jar  5.81s user 0.34s system 237% cpu 2.591 total

./carmine_graalvm-0.1.0-SNAPSHOT-standalone  0.01s user 0.01s system 106% cpu 0.014 total

Amazing! From 5.81 seconds to less than a millisecond! You can also check the memory usage and compare it in the two processes.