JMH alternatives and similar libraries
Based on the "Performance analysis" category.
Alternatively, view JMH alternatives based on common mentions on social networks and blogs.
7.6 5.9 L1 JMH VS JITWatchLog analyser / visualiser for Java HotSpot JIT compiler. Inspect inlining decisions, hot methods, bytecode, and assembly. View results in the JavaFX user interface.
6.0 1.4 L4 JMH VS honest-profilerA sampling JVM profiler without the safepoint sample bias
5.0 0.0 L2 JMH VS jHiccupjHiccup is a non-intrusive instrumentation tool that logs and records platform "hiccups" - including the JVM stalls that often happen when Java applications are executed and/or any OS or hardware platform noise that may cause the running application to not be continuously runnable.
3.1 8.9 JMH VS SniffySniffy - interactive profiler, testing and chaos engineering tool for Java
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of JMH or a related project?
SBT plugin for running OpenJDK JMH benchmarks.
JMH about itself:
JMH is a Java harness for building, running, and analysing nano/micro/milli/macro benchmarks written in Java and other languages targeting the JVM.
Please read nanotrusting nanotime and other blog posts on micro-benchmarking (or why most benchmarks are wrong) and make sure your benchmark is valid, before you set out to implement your benchmarks.
|Plugin version||Default JMH version||Notes|
||profilers now in JMH core|
||support JDK 11|
||support JDK 11|
||support of GraalVM|
||JMH bugfix release|
||minor bugfix release|
||minor bugfix release|
||async profiler, flame-graphs|
Not interesting versions are skipped in the above listing. Always use the newest which has the JMH version you need. You should stick to the latest version at all times anyway of course.
Adding to your project
Since sbt-jmh is an AutoPlugin all you need to do in order to activate it in
your project is to add the below line to your
// project/plugins.sbt addSbtPlugin("pl.project13.scala" % "sbt-jmh" % "0.4.3")
and enable it in the projects where you want to (useful in multi-project builds, as you can enable it only where you need it):
// build.sbt enablePlugins(JmhPlugin)
If you define your project in a
Build.scala, you also need the following import:
You can read more about auto plugins in sbt on it's documentation page.
Write your benchmarks in
src/main/scala. They will be picked up and instrumented by the plugin.
JMH has a very specific way of working (it generates loads of code), so you should prepare a separate project for your benchmarks. In it, just type
run in order to run your benchmarks.
All JMH options work as expected. For help type
run -h. Another example of running it is:
jmh:run -i 3 -wi 3 -f1 -t1 .*FalseSharing.*
Which means "3 iterations" "3 warmup iterations" "1 fork" "1 thread". Please note that benchmarks should be usually executed at least in 10 iterations (as a rule of thumb), but more is better.
For "real" results we recommend to at least warm up 10 to 20 iterations, and then measure 10 to 20 iterations again. Forking the JVM is required to avoid falling into specific optimisations (no JVM optimisation is really "completely" predictable)
If your benchmark should be a module in a multimodule project and needs access to another modules test classes then you
might want to define your benchmarks in
src/test as well (because Intellij does not support "compile->test" dependencies).
While this is not directly supported it can be achieved with some tweaks. Assuming the benchmarks live in a module
bench and need access
to test classes from
anotherModule, you have to define this dependency in your main
lazy val bench = project.dependsOn(anotherModule % "test->test").enablePlugins(JmhPlugin)
bench/build.sbt you need to tweak some settings:
sourceDirectory in Jmh := (sourceDirectory in Test).value classDirectory in Jmh := (classDirectory in Test).value dependencyClasspath in Jmh := (dependencyClasspath in Test).value // rewire tasks, so that 'jmh:run' automatically invokes 'jmh:compile' (otherwise a clean 'jmh:run' would fail) compile in Jmh := (compile in Jmh).dependsOn(compile in Test).value run in Jmh := (run in Jmh).dependsOn(Keys.compile in Jmh).evaluated
run -h to get a full list of run as well as output format options.
Useful hint: If you plan to aggregate the collected data you should have a look at the available output formats (
For example it's possible to keep the benchmark's results as csv or json files for later regression analysis.
Using Java Flight Recorder / async-profiler.
sbt-jmh-s integration with async-profiler and Java Flight Recorder has been contributed to the
JMH project as of JMH 1.25 and removed from this project. Please migrate to using
-prof jfr /
-prof jfr:help /
-prof async:help to list available options.
The examples are scala-fied examples from the original JMH repo, check them out, and run them!
The results will look somewhat like this:
... [info] # Run progress: 92.86% complete, ETA 00:00:15 [info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.7.0_60.jdk/Contents/Home/jre/bin/java [info] # VM options: <none> [info] # Fork: 1 of 1 [info] # Warmup: 2 iterations, single-shot each [info] # Measurement: 3 iterations, single-shot each [info] # Threads: 1 thread, will synchronize iterations [info] # Benchmark mode: Single shot invocation time [info] # Benchmark: org.openjdk.jmh.samples.JMHSample_02_BenchmarkModes.measureSingleShot [info] # Warmup Iteration 1: 100322.000 us [info] # Warmup Iteration 2: 100556.000 us [info] Iteration 1: 100162.000 us [info] Iteration 2: 100468.000 us [info] Iteration 3: 100706.000 us [info] [info] Result : 100445.333 ±(99.9%) 4975.198 us [info] Statistics: (min, avg, max) = (100162.000, 100445.333, 100706.000), stdev = 272.707 [info] Confidence interval (99.9%): [95470.135, 105420.532] [info] [info] [info] # Run progress: 96.43% complete, ETA 00:00:07 [info] # VM invoker: /Library/Java/JavaVirtualMachines/jdk1.7.0_60.jdk/Contents/Home/jre/bin/java [info] # VM options: <none> [info] # Fork: 1 of 1 [info] # Warmup: 2 iterations, single-shot each, 5000 calls per batch [info] # Measurement: 3 iterations, single-shot each, 5000 calls per batch [info] # Threads: 1 thread, will synchronize iterations [info] # Benchmark mode: Single shot invocation time [info] # Benchmark: org.openjdk.jmh.samples.JMHSample_26_BatchSize.measureRight [info] # Warmup Iteration 1: 15.344 ms [info] # Warmup Iteration 2: 13.499 ms [info] Iteration 1: 2.305 ms [info] Iteration 2: 0.716 ms [info] Iteration 3: 0.473 ms [info] [info] Result : 1.165 ±(99.9%) 18.153 ms [info] Statistics: (min, avg, max) = (0.473, 1.165, 2.305), stdev = 0.995 [info] Confidence interval (99.9%): [-16.988, 19.317] [info] [info] [info] Benchmark Mode Samples Mean Mean error Units [info] o.o.j.s.JMHSample_22_FalseSharing.baseline thrpt 3 692.034 179.561 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.baseline:reader thrpt 3 199.185 185.188 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.baseline:writer thrpt 3 492.850 7.307 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.contended thrpt 3 706.532 293.880 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.contended:reader thrpt 3 210.202 277.801 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.contended:writer thrpt 3 496.330 78.508 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.hierarchy thrpt 3 1751.941 222.535 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.hierarchy:reader thrpt 3 1289.003 277.126 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.hierarchy:writer thrpt 3 462.938 55.329 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.padded thrpt 3 1745.650 83.783 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.padded:reader thrpt 3 1281.877 47.922 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.padded:writer thrpt 3 463.773 104.223 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.sparse thrpt 3 1362.515 461.782 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.sparse:reader thrpt 3 898.282 415.388 ops/us [info] o.o.j.s.JMHSample_22_FalseSharing.sparse:writer thrpt 3 464.233 49.958 ops/us
Advanced: Using custom Runners
It is possible to hand over the running of JMH to an
App implemented by you, which allows you to programmatically
access all test results and modify JMH arguments before you actually invoke it.
To use a custom runner class with
runMain, simply use it:
jmh:runMain com.example.MyRunner -i 10 .* –
an example for this is available in [plugin/src/sbt-test/sbt-jmh/runMain](plugin/src/sbt-test/sbt-jmh/runMain) (open the
To replace the runner class which is used when you type
jmh:run, you can set the class in your build file –
an example for this is available in [plugin/src/sbt-test/sbt-jmh/custom-runner](plugin/src/sbt-test/sbt-jmh/custom-runner) (open the
Yes, pull requests and opening issues is very welcome!
The plugin is maintained at an best-effort basis -- submitting a PR is the best way of getting something done :-)
You can locally publish the plugin with:
sbt '; project plugin; ^publishLocal'
Please test your changes by adding to the [scripted test suite][sbt-jmh/plugin/src/sbt-test/sbt-jmh/] which can be run with:
sbt '; project plugin; ^scripted'
Special thanks for contributing async-profiler and flame-graphs support and other improvements go to @retronym of Lightbend's Scala team.