JMH是用於編寫精確Java微基準(zhǔn)測(cè)試的框架，能避免JVM優(yōu)化導(dǎo)致的測(cè)量偏差。 1. 使用Maven或Gradle添加jmh-core和jmh-generator-annprocess依賴並啟用註解處理。 2. 編寫基準(zhǔn)測(cè)試方法並用@Benchmark、@BenchmarkMode、@Warmup、@Measurement、@Fork等註解配置參數(shù)。 3. 將耗時(shí)操作的返回值通過return或Blackhole.consume()防止被JIT優(yōu)化消除。 4. 用@State(Scope.Thread)定義狀態(tài)類並在@Setup方法中初始化測(cè)試數(shù)據(jù)以避免污染基準(zhǔn)方法。 5. 通過mvn clean install和java -jar運(yùn)行測(cè)試並分析平均時(shí)間、誤差和單位等結(jié)果。 6. 僅在比較算法、數(shù)據(jù)結(jié)構(gòu)或JVM配置時(shí)使用微基準(zhǔn)測(cè)試，不能替代全系統(tǒng)負(fù)載測(cè)試。正確使用JMH可準(zhǔn)確回答“哪種實(shí)現(xiàn)更快”的問題，並得出可靠結(jié)論。

When measuring the performance of Java code, simply using System.currentTimeMillis() or System.nanoTime() can lead to misleading results due to JVM optimizations like JIT compilation, dead code elimination, and warm-up effects. That's where the Java Microbenchmark Harness (JMH) comes in — a robust framework designed specifically for writing and running accurate microbenchmarks.

What Is JMH?

JMH is an open-source project developed as part of the OpenJDK. It helps developers write reliable benchmarks by handling common pitfalls automatically. It's widely used in performance-critical applications, libraries, and frameworks (like JDK itself) to compare small units of code under controlled conditions.

You don't just use JMH — you design benchmarks with it. It ensures proper warm-up, prevents code elimination, manages threading, and provides statistical analysis of results.

Setting Up JMH in Your Project

The easiest way to get started is by using Maven or Gradle.

With Maven:

Add this to your pom.xml :

 <dependency>
    <groupId>org.openjdk.jmh</groupId>
    <artifactId>jmh-core</artifactId>
    <version>1.37</version>
</dependency>
<dependency>
    <groupId>org.openjdk.jmh</groupId>
    <artifactId>jmh-generator-annprocess</artifactId>
    <version>1.37</version>
    <scope>provided</scope>
</dependency>

Then enable annotation processing in your IDE or build tool so that @Benchmark methods are processed correctly.

Alternatively, use the JMH Maven plugin to generate a self-contained benchmark JAR:

 mvn archetype:generate \
    -DinteractiveMode=false \
    -DarchetypeGroupId=org.openjdk.jmh \
    -DarchetypeArtifactId=jmh-java-benchmark-archetype \
    -DgroupId=com.example \
    -DartifactId=jmh-demo

This creates a project with everything preconfigured.

Writing Your First Benchmark

Here's a simple example comparing two ways of concatenating strings: StringBuilder vs String .

 @Benchmark
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.NANOSECONDS)
@Fork(1)
@Warmup(iterations = 3, time = 1)
@Measurement(iterations = 5, time = 1)
public String testStringBuilder() {
    StringBuilder sb = new StringBuilder();
    sb.append("hello");
    sb.append("world");
    return sb.toString();
}

@Benchmark
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.NANOSECONDS)
@Fork(1)
@Warmup(iterations = 3, time = 1)
@Measurement(iterations = 5, time = 1)
public String testStringConcat() {
    String s = "hello";
    s = "world";
    return s;
}

Key annotations explained:

• @Benchmark : Marks the method to be measured.
• @BenchmarkMode : Defines what metric to collect. Mode.AverageTime means nanoseconds per operation.
• @OutputTimeUnit : Sets the time unit for results.
• @Fork : Number of separate JVM processes to run. Helps isolate results.
• @Warmup : How many iterations to run before actual measurement (lets JIT optimize).
• @Measurement : How many iterations to use for collecting data.

Run the benchmark via:

 mvn clean install
java -jar target/benchmarks.jar

Common Pitfalls & Best Practices

Even with JMH, incorrect usage can skew results. Here are key things to watch:

• Avoid dead code elimination : If the result isn't used or returned, JVM may optimize the whole thing away. Always return a value from your benchmark method when appropriate.

   @Benchmark
    public String myBenchmark() {
        // do work
        return result; // important!
    }

• Use Blackhole to consume values when you don't want to return them:

   @Benchmark
    public void useBlackhole(Blackhole bh) {
        String result = expensiveOperation();
        bh.consume(result);
    }

• Don't put setup logic inside the benchmark method . Use @Setup :

   @State(Scope.Thread)
    public static class MyState {
        public List<String> data;
  
        @Setup
        public void setup() {
            data = Arrays.asList("a", "b", "c");
        }
    }
  
    @Benchmark
    public void processList(MyState state) {
        // use state.data
    }

• Understand Scope in @State :

  • Scope.Thread : Each thread gets its own instance.
  • Scope.Benchmark : Shared across all threads (use carefully).
  • Scope.Group : For threads within a thread group.

• Control parallelism with @Threads and @Fork to simulate real-world concurrency.

Interpreting Results

Sample output:

 Benchmark Mode Cnt Score Error Units
MyBenchmark.testStringBuilder avgt 5 8.234 ± 0.198 ns/op
MyBenchmark.testStringConcat avgt 5 12.451 ± 0.302 ns/op

This tells you that on average, StringBuilder was faster than string concatenation in this test.

JMH also reports throughput ( Mode.Throughput ), sample times, and even generates flame graphs if configured.

When Should You Use JMH?

• Comparing two algorithms (eg, sorting, hashing).
• Evaluating data structure performance (eg, ArrayList vs LinkedList ).
• Testing impact of JVM flags or GC settings.
• Validating performance assumptions before merging optimizations.

But remember: microbenchmarks aren't a substitute for real-world load testing . They show isolated behavior, not system-wide performance.

Basically, if you're trying to answer “Is this loop faster than that one?” or “Does this cache strategy reduce allocation?”, JMH is the right tool — as long as you use it right.

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