Benchmarking Scala Code with JMH

Creating the first Java Microbenchmark Harness (JMH) project using SBT

April 18, 2021


We identified in the previous post that JMH is a jvm tool that can help benchmark the source code. Till now, we have used it to benchmark the Java code. But, since it is a jvm, it must be capable of benchmarking other jvm based languages. In this post, I pick up Scala, jvm language, to benchmark the code.

Integrate JMH with SBT

Let us start by creating a new Scala project with SBT. I will use the below giter8 template to produce the project structure.

sbt new scala/scala-seed.g8 --name=benchmarks

The next step is to configure JMH with the new project. We can achieve it by adding JMH plugin. Create a new file plugins.sbt under the project directory and add the below content to plugins.sbt.

addSbtPlugin("pl.project13.scala" % "sbt-jmh" % "0.4.0")

We must enable the above plugin through build.sbt.


Now, we can start experimenting with benchmarks. We are going to use the same JMH annotations as we have seen in the previous post. The giter8 template has created example package inside directory src/main/scala. I am going to rename example package to gaurav. I will create a new class MyBenchmark.scala in the new package. Similar to the java example, I am going to benchmark the code to sum all elements inside a list.

import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.infra.Blackhole

class MyBenchmark {

  def testMethod(blackHole: Blackhole): Double = {
    val list: List[Int] = List.range(1, Integer.MAX_VALUE/100)
    val sum: Double = list.sum

As we have observed in earlier posts, I am returning a value from testMethod. Additionally, I am using BlackHole to avoid jvm optimization.

Executing JMH project in SBT

Unlike, maven which creates a JMH jar to execute the project, SBT can perform the operations from its console. Use the below command to both compile and execute the project. You can also use sbt jmh:compile, to just compile the project.

sbt jmh:run

I find it quite handy and quick, as compared to maven. Once executed, you can observe jmh log lines

[info] running (fork) org.openjdk.jmh.Main

Similar to what we have seen earlier, jmh, by default, will:

  • execute 5 warm up iterations,
  • execute 5 fork iterations,
  • the mode will be Throughput, and
  • The default TimeUnit will be SECONDS

It produced the below result for me. It means that it is executing approx 69342 operations per sec.

[info] # Run progress: 80.00% complete, ETA 00:01:40
[info] # Fork: 5 of 5
[info] # Warmup Iteration   1: 58060.100 ops/s
[info] # Warmup Iteration   2: 64730.638 ops/s
[info] # Warmup Iteration   3: 69149.250 ops/s
[info] # Warmup Iteration   4: 63715.739 ops/s
[info] # Warmup Iteration   5: 66027.235 ops/s
[info] Iteration   1: 70228.232 ops/s
[info] Iteration   2: 60943.758 ops/s
[info] Iteration   3: 63144.950 ops/s
[info] Iteration   4: 63729.494 ops/s
[info] Iteration   5: 63557.685 ops/s
[info] Result "com.gaurav.MyBenchmark.testMethod":
[info]   69342.082 ±(99.9%) 4236.598 ops/s [Average]
[info]   (min, avg, max) = (54795.094, 69342.082, 75332.645), stdev = 5655.737
[info]   CI (99.9%): [65105.484, 73578.681] (assumes normal distribution)

Benchmark Configuration

Now, the project is up and running, the next step is to configure the jmh. We can configure the project for:

  • Modes - Throughput, AverageTime, SampleTime, SingleShotTime, and All
  • Iterations - Fork iterations, Warmup iterations and Measurements iterations

Below is my sample code. I have configured the code to use AverageTime, to run a couple of fork, warmup and measurement iterations.

import org.openjdk.jmh.annotations.{Benchmark, BenchmarkMode, Fork, Measurement, Mode, Warmup}
import org.openjdk.jmh.infra.Blackhole

class MyBenchmark {

    @Fork(value = 2)
    @Warmup(iterations = 2)
    @Measurement(iterations = 2)
    def testMethod(blackHole: Blackhole): Double = {
        val list: List[Int] = List.range(1, 1000)
        val sum: Double = list.sum

Below is the output. As you can observe in the highlighted lines below, jmh first prints the summary of the configuration. The feature is quite handy. You can go through to make sure jmh is doing the right thing.

[info] # Warmup: 2 iterations, 10 s each
[info] # Measurement: 2 iterations, 10 s each
[info] # Timeout: 10 min per iteration
[info] # Threads: 1 thread, will synchronize iterations
[info] # Benchmark mode: Average time, time/op
[info] # Benchmark: com.gaurav.MyBenchmark.testMethod
[info] # Run progress: 0.00% complete, ETA 00:01:20
[info] # Fork: 1 of 2
[info] # Warmup Iteration   1: ≈ 10⁻⁵ s/op
[info] # Warmup Iteration   2: ≈ 10⁻⁵ s/op
[info] Iteration   1: ≈ 10⁻⁵ s/op
[info] Iteration   2: ≈ 10⁻⁵ s/op
[info] # Run progress: 50.00% complete, ETA 00:00:40
[info] # Fork: 2 of 2
[info] # Warmup Iteration   1: ≈ 10⁻⁵ s/op
[info] # Warmup Iteration   2: ≈ 10⁻⁵ s/op
[info] Iteration   1: ≈ 10⁻⁵ s/op
[info] Iteration   2: ≈ 10⁻⁵ s/op
[info] Result "com.gaurav.MyBenchmark.testMethod":
[info]   ≈ 10⁻⁵ s/op
[info] # Run complete. Total time: 00:01:20


In this article we have seen that jmh not only works with Java, but also with Scala, a jvm language. We have gone through a hand-on example of configuring a new Scala project with jmh plugin. You can refer to JMH Github Scala Samples for more in depth examples.

JMH Github
JMH Github Scala Samples
JMH Javadox

© 2022, Gaurav Gaur