Working with JMH (Microbenchmarking)
1. Setting Up JMH Project
Step Detail Generate mvn archetype:generate -Dgroup=org.openjdk.jmh -DartifactId=jmh-archetypeDependencies jmh-core, jmh-generator-annprocess Build Shade JMH runner JAR Run java -jar target/benchmarks.jar
2. Creating Benchmark Methods (@Benchmark)
Example: Simple benchmark
@ Benchmark public int hashCode (MyState s) { return s.value. hashCode (); }
Rule Detail Return result Or use Blackhole No I/O Unless intentionally measuring it
3. Using Benchmark Modes
Mode Reports Throughput ops/time AverageTime time/op SampleTime Sampled time distribution SingleShotTime One iteration (cold) All All of the above
4. Configuring Warmup (@Warmup)
Param Default iterations 5 time 10 s batchSize 1 Purpose Reach steady state (JIT, caches)
5. Configuring Measurement (@Measurement)
Param Use iterations 5–10 typical time 10 s typical forks JVM forks (default 5) threads Concurrent worker count
6. Using State Objects (@State)
Example: State
@ State (Scope.Benchmark) public static class MyState { String value; @ Setup public void init () { value = "hello" ; } }
Aspect Detail Lifecycle Linked to Scope Injection Pass as @Benchmark method param
7. Understanding Scope
Scope Sharing Benchmark One instance for all threads Group Shared in @Group methods Thread Per-thread instance
8. Using Setup and TearDown (@Setup, @TearDown)
Level Runs Trial Once per benchmark run Iteration Each iteration Invocation Every @Benchmark call (use sparingly — bias risk)
9. Avoiding Dead Code Elimination (Blackhole)
Example: Blackhole
@ Benchmark public void multi (Blackhole bh) { bh. consume ( work1 ()); bh. consume ( work2 ()); }
Issue Fix Unused result Return it OR Blackhole.consume() Constant folding Read input from @State
10. Running Benchmarks
Command Use java -jar benchmarks.jarRun all java -jar benchmarks.jar PatternFilter by name regex -prof gcAllocation profiler -prof async:libPath=...;output=flamegraphasync-profiler -rf json -rff out.jsonJSON output
11. Analyzing Results
Field Meaning Score Mean throughput / time Error 99.9% confidence half-width Ratio Compare to baseline Visualize jmh-visualizer (web UI)
12. Understanding JMH Pitfalls
Warning: Microbenchmarks are easy to write but hard to interpret. Always: (1) cross-check with macro-benchmark, (2) verify dead-code isn't elided, (3) use multiple forks, (4) account for warmup, (5) compare on the same JVM/hardware.
Pitfall Effect Constant folding JIT precomputes result Inlining hides cost Use Blackhole No warmup Measures interpreter Single fork Class init bias