Use JMH for your Java applications with Gradle

If you want to benchmark you code, the Java Microbenchmark Harness is the tool of choice.
In our example we shall use the refill-rate-limiter project


Since refill-rate-limiter uses Gradle we will use the following plugin for gradle

plugins {
  id "me.champeau.gradle.jmh" version "0.5.3"

We shall place the Benchmark at the jmh/java/io/github/resilience4j/ratelimiter folder.

Our Benchmark should look like this.

package io.github.resilience4j.ratelimiter;

import io.github.resilience4j.ratelimiter.internal.RefillRateLimiter;
import org.openjdk.jmh.annotations.*;
import org.openjdk.jmh.infra.Blackhole;
import org.openjdk.jmh.profile.GCProfiler;
import org.openjdk.jmh.runner.Runner;
import org.openjdk.jmh.runner.RunnerException;
import org.openjdk.jmh.runner.options.Options;
import org.openjdk.jmh.runner.options.OptionsBuilder;

import java.time.Duration;
import java.util.concurrent.TimeUnit;
import java.util.function.Supplier;

public class RateLimiterBenchmark {

    private static final int FORK_COUNT = 2;
    private static final int WARMUP_COUNT = 10;
    private static final int ITERATION_COUNT = 10;
    private static final int THREAD_COUNT = 2;

    private RefillRateLimiter refillRateLimiter;

    private Supplier<String> refillGuardedSupplier;

    public static void main(String[] args) throws RunnerException {
        Options options = new OptionsBuilder()
        new Runner(options).run();

    public void setUp() {

        RefillRateLimiterConfig refillRateLimiterConfig = RefillRateLimiterConfig.custom()

        refillRateLimiter = new RefillRateLimiter("refillBased", refillRateLimiterConfig);

        Supplier<String> stringSupplier = () -> {
            return "Hello Benchmark";

        refillGuardedSupplier = RateLimiter.decorateSupplier(refillRateLimiter, stringSupplier);

    @Threads(value = THREAD_COUNT)
    @Warmup(iterations = WARMUP_COUNT)
    @Fork(value = FORK_COUNT)
    @Measurement(iterations = ITERATION_COUNT)
    public String refillPermission() {
        return refillGuardedSupplier.get();


Let’s now check the elements one by one.

By using Benchmark scope all the threads used on the benchmark scope will share the same object. We do so because we want to test how refill-rate-limiter performs in a multithreaded scenario.


We would like our results to be reported in microseconds, therefore we shall use the OutputTimeUnit.


On JMH We have various benchmark modes depending on what we want to measure.

Throughput is when we want to measure the number operations per unit of time.
AverageTime when we want to measure the average time per operation.
SampleTime when we want to sample the time for each operation including min, max time, more than just the average.
SingleShotTime: when we want to measure the time for a single operation. This can help when we want to identify how the operation will do on a cold start.

We also have the option to measure all the above.


Those options configured on the class level will apply to the benchmark methods we shall add.

Let’s also examine how the benchmark will run

We will specify the number of Threads by using the Threads annotation.

@Threads(value = THREAD_COUNT)

Also we want to warm up before we run the actual benchmarks. This way our code will be initialized, online optimizations will take place, and our runtime will adapt to the conditions before we run the benchmarks.

@Warmup(iterations = WARMUP_COUNT)

Using a Fork we shall instruct how many times the benchmark will run.

@Fork(value = FORK_COUNT)

Then we need to specify the number of iterations we want to measure/

@Measurement(iterations = ITERATION_COUNT)

We can start our test by just using

gradle jmh

The results will be save in a file.

2022-10-28T09:08:44.522+0100 [QUIET] [system.out] Benchmark result is saved to /path/refill-rate-limiter/build/reports/jmh/results.txt

Let’s examine the results.

Benchmark                                                         Mode       Cnt      Score   Error   Units
RateLimiterBenchmark.refillPermission                            thrpt        20     13.594 ± 0.217  ops/us
RateLimiterBenchmark.refillPermission                             avgt        20      0.147 ± 0.002   us/op
RateLimiterBenchmark.refillPermission                           sample  10754462      0.711 ± 0.025   us/op
RateLimiterBenchmark.refillPermission:refillPermission·p0.00    sample                  ≈ 0           us/op
RateLimiterBenchmark.refillPermission:refillPermission·p0.50    sample                0.084           us/op
RateLimiterBenchmark.refillPermission:refillPermission·p0.90    sample                0.125           us/op
RateLimiterBenchmark.refillPermission:refillPermission·p0.95    sample                0.125           us/op
RateLimiterBenchmark.refillPermission:refillPermission·p0.99    sample                0.209           us/op
RateLimiterBenchmark.refillPermission:refillPermission·p0.999   sample              139.008           us/op
RateLimiterBenchmark.refillPermission:refillPermission·p0.9999  sample              935.936           us/op
RateLimiterBenchmark.refillPermission:refillPermission·p1.00    sample            20709.376           us/op
RateLimiterBenchmark.refillPermission                               ss        20     14.700 ± 4.003   us/op

As we can see we have the modes listed.
Count is the number of iterations. Apart from throughput where we measure the operations by time, the rest is time per operation.
Throughput,Average and Single shot are straightforward, Sample lists the percentiles. Error is the margin of error.

That’s it! Happy benchmarking

Gradle: Push to Maven Repository

If you are a developer sharing your artifacts is a common task, that needs to be in place from the start.

In most teams and companies a Maven repository is already setup, this repository would be used mostly through CI/CD tasks enabling developers to distribute the generated artifacts.

In order to make the example possible we shall spin up a Nexus repository using Docker Compose.

First let’s create a default password and the directory containing the plugins Nexus will download. As of now the password is clear text, this will serve us for doing our first action. By resetting the admin password on nexus the file shall be removed, thus there won’t be a file with a clear text password.

mkdir nexus-data
cat a-test-password > admin.password

Then onwards to the Compose file:

    image: sonatype/nexus3
      - 8081:8081
      INSTALL4J_ADD_VM_PARAMS: "-Xms2703m -Xmx2703m -XX:MaxDirectMemorySize=2703m"
      - nexus-data:/nexus-data
      - ./admin.password:/nexus-data/admin.password

Let’s examine the file.
By using INSTALL4J_ADD_VM_PARAMS we override the Java command that will run the Nexus server, this way we can instruct to use more memory.
Because nexus takes too long on initialization we shall create a Docker volume. Everytime we run the compose file, the initialization will not happen from start, instead will use the results of the previous initialization. By mounting the admin password created previously we have a predefined password for the service.

By issuing the following command, the server will be up and running:

docker compose up

You can find more on Compose on the Developers Essential Guide to Docker Compose.

After some time nexus will have been initialized and running, therefore we shall proceed to our Gradle configuration.

We will keep track of the version on the file

version 1.0-SNAPSHOT

The build.gradle file:

plugins {
    id 'java'
    id 'maven-publish'

group 'org.example'
version '1.0-SNAPSHOT'

repositories {

dependencies {
    testImplementation 'org.junit.jupiter:junit-jupiter-api:5.8.1'
    testRuntimeOnly 'org.junit.jupiter:junit-jupiter-engine:5.8.1'

test {

publishing {
    publications {
        mavenJava(MavenPublication) {
            groupId 'org.example'
            artifactId 'gradle-push'
            version version
            versionMapping {
                usage('java-api') {
                usage('java-runtime') {
            pom {
                name = 'gradle-push'
                description = 'Gradle Push to Nexus'
                url = ''
                licenses {
                    license {
                        name = 'The Apache License, Version 2.0'
                        url = ''
                developers {
                    developer {
                        id = 'John'
                        name = 'John Doe'
                        email = ''
                scm {
                    connection = 'scm:git:'
                    developerConnection = 'scm:git:'
                    url = ''

    repositories {
        maven {
            def releasesRepoUrl = "http://localhost:8081/repository/maven-releases/"
            def snapshotsRepoUrl = "http://localhost:8081/repository/maven-snapshots/"
            url = version.endsWith('SNAPSHOT') ? snapshotsRepoUrl : releasesRepoUrl

            credentials {
                username "admin"
                password "a-test-password"

The plugin to be used is the maven-publish plugin. If we examine the file, we identify that the plugin generates a pom maven file which shall be used in order to execute the deploy command to Nexus. The repositories are configured on the repositories section including the nexus users we defined previously. Whether the version is a SNAPSHOT or a release the corresponding repository endpoint will be picked. As we see clear text password is used. On the next blog we will examine how we can avoid that.

Now it is sufficient to run

gradle publish

This will deploy the binary to the snapshot repository.

You can find the source code on GitHub.

refill-rate-limiter: A Resilience4j-based rate limiter

It’s been a while since I’ve been working on this project. It is a rate limiter based on the atomic rate limiter from Resilience4j. The key difference is that the atomic rate limiter gets the permissions assigned per cycle while the refill-rate-limiter will split the permissions in a cycle and gradually refill them. In an essence it simulates a token bucket algorithm however it uses the atomic rate limiter internals

Using it is very simple.
Import it using maven.


And use it in your code base.

        RefillRateLimiterConfig refillRateLimiterConfig = new RefillRateLimiterConfig.Builder()
                .limitRefreshPeriod(Duration.of(2, ChronoUnit.SECONDS))

        refillRateLimiter = new RefillRateLimiter("default", refillRateLimiterConfig, io.vavr.collection.HashMap.empty());

        boolean allowed = refillRateLimiter.acquirePermission(1);

Just like Resilience4j it also has Apache Licence V2.
It spawned from an pr on the Resilience4j, since the merge time took longer I made it available as a standalone implementation. It would not hurt if you thumps up on pr so it ends up to the place it belongs 😉

A description of the algorithm can be found on GitHub. Benchmarks and integrations will follow.

New Book Day: A Developer’s Essential Guide to Docker Compose

As Developers nowadays we have a wide variety of Software components and Cloud Services to use. This was a scenario that we could not even imagine in the past.
I still remember when we had to setup our Application Servers and Databases on top of Bare metal servers.
This burst of computing functionality in the form of the Cloud and managed services, allowed us to be able to utilise more tools for our applications and build better products.
Issues like orchestrating workloads, isolating and shipping them went to a whole different level.
As a result containerization came to the rescue.
Docker took over the microservice world and became the dominant solution to deploy microservices and in certain cases even to deploy Databases and Brokers.
This brings us to the development process. Production deployments is a huge chapter on its own. Platform engineers needs to take care of the security, the scaling and robustness of container based deployments.
But the development process is also affected:

  • SQL/NoSQL Databases as well as purpose-built databases like InfluxDB need to be available locally
  • Scenarios of Microservices applications need to be tested
  • Other Components such as brokers need to be available for testing

A step forward to the challenges mentioned above is to utilise Docker and its rich functionality. As convenient as it is to spin up containers locally you still end up managing containers, volumes and networks. Most of the times those have been spinned up add-hoc or with some scripts that a team has to maintain.

Docker Compose is one of the solutions to the problems described. With Compose you can spin up multiple containers locally organised using yaml files.
Here are some of the benefits when used during development:

  • The containers are organised and can spin up or shut down in an organized way
  • Applications can be placed on different networks
  • Volumes can be managed and attached to containers in a managed way
  • Containers resolve each other’s location through a DNS automatically, manual linking is not needed.

I have been using Compose for years. It helped me to make my development process much more efficient. Also in certain cases it helped me on actual production deployments. Writing this book was an opportunity for me to advocate for Docker Compose.



Thanks to the amazing people at Packt publishing it was possible to write this book and give back to the community.

The book is focused on various aspects of Compose.

In the beginning it will be an extensive look on Docker Compose, how it is implemented, how it interacts with the Docker engine, the available commands as well as the functionality it provides.

Onwards we will dive deep in day to day development using Compose. We will spin up complex infrastructure locally, as well as simulate Microservice environments using Compose. We will take this concept further and incrementally simulate things that we have to deal with a production deployment, as well as issue workarounds. Lastly we will use Compose for CI/CD jobs on popular solutions like Github Actions, Travis, and BitBucket Pipelines.

The last part of the book is all about deploying to production. All the knowledge acquired previously can be used for actual production deployments. A production deployment comes with some standards:

  • Infrastructure as Code
  • Container registry
  • Networks
  • Load Balancing
  • Autoscaling

The above, in combination with the knowledge we accumulated so far using Compose, will be used for a deployment on AWS and Azure, the most popular Cloud providers. This will be done also with the extra help of Infrastructure as Code by using Terraform.

Lastly since many production deployments nowadays reside on Kubernetes we shall build a bridge between Compose and Kubernetes by migrating the existing Compose deployment using Kompose.

You can find the book on Amazon  as well as on the Packt portal.
Happy Learning!