Use JMH (Java Microbenchmark Harness) to measure a Java method by putting the benchmark in a dedicated Maven project, defining the operation and its inputs with JMH annotations, then running the generated benchmark JAR. JMH handles important harness work, but the result is meaningful only for the workload, JVM, machine, and configuration you actually test.
Set up a JMH benchmark project
The JMH project recommends a standalone Maven project that depends on the application code being measured. This helps ensure the benchmark is initialized and run through the harness correctly. You can work from an existing project or an IDE, but the JMH README describes those routes as more complex and less reliable. For a larger codebase, keep benchmarks in a separate subproject that depends on the relevant application modules.
Generate a starter project, build it, and run its executable JAR:
mvn archetype:generate
-DinteractiveMode=false
-DarchetypeGroupId=org.openjdk.jmh
-DarchetypeArtifactId=jmh-java-benchmark-archetype
-DgroupId=org.sample
-DartifactId=test
-Dversion=1.0
cd test
mvn clean verify
java -jar target/benchmarks.jarThe JMH Maven archetype listing showed version 1.37 on Maven Central/Sonatype when accessed in 2026; check the artifact listing for current metadata. The project’s README documents this quick-start workflow. Run java -jar target/benchmarks.jar -h to see the executable JAR’s available options.
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JMH uses annotation or bytecode processing to generate synthetic benchmark code. A dependency on jmh-core alone does not provide a complete runnable setup; use a configured project such as the archetype-generated one.
Write a benchmark that measures the intended work
A benchmark method should perform the operation you want to evaluate, using state and inputs that resemble the intended use. JMH annotations let you express the benchmark, its state, and setup fixtures; the scope of that state matters when choosing whether data is shared or thread-specific.
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Make the operation’s result observable to JMH. If the result is unused, an optimizing compiler may eliminate the work. Likewise, avoid compile-time constants when you intend to measure computation: the compiler may fold the calculation before the timed operation. JMH’s sample suite includes examples for dead-code elimination, constant folding, state, setup, loops, and other benchmark-design concerns.
Be careful when benchmarking loops. A loop can change the work being measured and how the JVM optimizes it; do not assume that timing a repeated operation in one benchmark method gives the same answer as benchmarking the operation in the way your application uses it. Study relevant JMH samples and ensure the measured operation corresponds to the question you need to answer.
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Choose the benchmark mode and run configuration
Choose a mode that matches the question: throughput for operations per unit time, average time for time per operation, or sample-time measurements when you want to examine a distribution of observed latencies. The relevant units appear in JMH’s result output. Configure warmup, measurement iterations, and forks for the workload rather than treating one set of counts as universally correct.
Warmup lets the JVM initialize and compile code before timed measurements; early results can differ as those processes occur. Forks run benchmark trials in separate JVM processes and can help reveal run-to-run variation. Record the choices you make: they are part of the experiment, not incidental command-line details. OpenJDK’s microbenchmark guidance discusses warmup and the limits of microbenchmark conclusions, while the JMH samples demonstrate modes, forks, parameters, and variation.
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Compare results and report what they mean
When comparing implementations, keep the environment and configuration the same and verify that both implementations do equivalent, correct work. Choose the relevant performance measure for the question, then consider variation across forks or runs. Profiler output, such as allocation data, can add useful context when it addresses a specific concern; it does not replace a sound benchmark design.
A useful report identifies:
- The method or operation measured and the inputs or parameter values.
- The JMH mode, units, warmup and measurement settings, and number of forks.
- The JDK/JVM and machine and operating-system context.
- Any relevant profiler results and variation between runs or forks.
- Why the tested workload represents—or does not represent—the target application.
JMH measures a defined workload under a defined runtime and machine context. It cannot establish how the method will perform in every application context: a microbenchmark isolates only part of the JVM’s behavior, while a real application may introduce different data, interactions, and runtime conditions. Oracle’s JVM benchmarking guidance explains why optimizations and the surrounding application can make isolated results differ from production behavior.
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