我有一个50米的ArrayList，我想测量存储这么多对象所需的时间.似乎所有的JMH模式都是基于时间的，我们无法真正控制@Benchmark下代码的执行数量.例如，我如何确保以下代码在每个fork上运行50万次？
@Benchmark
@BenchmarkMode(Mode.SampleTime)
@OutputTimeUnit(TimeUnit.NANOSECONDS)
@Fork(5)
public void run(BenchmarkState state) {
try {
state.queue.add(System.nanoTime());
} catch (Exception e) {
e.printStackTrace();
}
}
您可以创建基准类(ArrayListBenchmark)和runner类(BenchmarkRunner).
ArrayListBenchmark类中，您可以添加BenchmarkRunner类中，设置要添加到的项目数Note:根据你的环境，增加5000万件物品可能会抛出OutOfMemoryError.
基准类:
import java.util.List;
import java.util.ArrayList;
import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.Level;
import org.openjdk.jmh.annotations.Param;
import org.openjdk.jmh.annotations.Scope;
import org.openjdk.jmh.annotations.Setup;
import org.openjdk.jmh.annotations.State;
import org.openjdk.jmh.infra.Blackhole;
public class ArrayListBenchmark {
@State(Scope.Thread)
public static class ThreadState {
@Param({})
private int items;
private List<Long> list;
@Setup(Level.Iteration)
public void setup() {
list = new ArrayList<>();
}
}
@Benchmark
public void addItems(ThreadState state, Blackhole blackhole) {
blackhole.consume(addItems(state.list, state.items));
}
private static boolean addItems(List<Long> list, int items) {
for (int i = 0; i < items; i++) {
list.add(System.nanoTime());
}
return true;
}
}
基准 run 者等级:
import java.util.Locale;
import java.util.concurrent.TimeUnit;
import org.openjdk.jmh.annotations.Mode;
import org.openjdk.jmh.results.format.ResultFormatType;
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 org.openjdk.jmh.runner.options.TimeValue;
public class BenchmarkRunner {
private static final String ITEMS = "items";
private static final String N_50_000_000 = "50000000";
public static void main(String[] args) throws RunnerException {
runArrayListBenchmark();
}
public static void runArrayListBenchmark() throws RunnerException {
Options options = new OptionsBuilder()
.include(ArrayListBenchmark.class.getSimpleName())
.mode(Mode.AverageTime)
.timeUnit(TimeUnit.NANOSECONDS)
.warmupTime(TimeValue.seconds(1))
.warmupBatchSize(1)
.warmupIterations(5)
.measurementTime(TimeValue.milliseconds(100))
.measurementBatchSize(1)
.measurementIterations(10)
.param(ITEMS, N_50_000_000)
.operationsPerInvocation(Integer.parseInt(N_50_000_000))
.threads(1)
.forks(5)
.shouldFailOnError(true)
.shouldDoGC(true)
.resultFormat(ResultFormatType.CSV)
.result("target/" + ArrayListBenchmark.class.getSimpleName().toLowerCase(Locale.ENGLISH) + ".csv")
.build();
new Runner(options).run();
}
Output:
Result "ArrayListBenchmark.addItems":
50.023 ±(99.9%) 0.768 ns/op [Average]
(min, avg, max) = (48.094, 50.023, 53.020), stdev = 1.551
CI (99.9%): [49.256, 50.791] (assumes normal distribution)
Benchmark (items) Mode Cnt Score Error Units
ArrayListBenchmark.addItems 50000000 avgt 50 50.023 ± 0.768 ns/op