如何获得方法的执行时间? 是否有Timer实用程序类来计时任务所需的时间等?

在谷歌上的大多数搜索都返回调度线程和任务的计时器的结果,这不是我想要的。


当前回答

在Java 8中引入了一个名为Instant的新类。根据文件:

Instant represents the start of a nanosecond on the time line. This class is useful for generating a time stamp to represent machine time. The range of an instant requires the storage of a number larger than a long. To achieve this, the class stores a long representing epoch-seconds and an int representing nanosecond-of-second, which will always be between 0 and 999,999,999. The epoch-seconds are measured from the standard Java epoch of 1970-01-01T00:00:00Z where instants after the epoch have positive values, and earlier instants have negative values. For both the epoch-second and nanosecond parts, a larger value is always later on the time-line than a smaller value.

这可以用于:

Instant start = Instant.now();
try {
    Thread.sleep(7000);
} catch (InterruptedException e) {
    e.printStackTrace();
}
Instant end = Instant.now();
System.out.println(Duration.between(start, end));

打印pt7.001。

其他回答

如果您不使用工具,并且希望对执行时间较短的方法进行计时,那么只需执行多次,每次将执行次数增加一倍,直到达到1秒左右。因此,系统调用的时间。纳米时间等,也没有系统的准确性。nanoTime确实对结果有很大影响。

    int runs = 0, runsPerRound = 10;
    long begin = System.nanoTime(), end;
    do {
        for (int i=0; i<runsPerRound; ++i) timedMethod();
        end = System.nanoTime();
        runs += runsPerRound;
        runsPerRound *= 2;
    } while (runs < Integer.MAX_VALUE / 2 && 1000000000L > end - begin);
    System.out.println("Time for timedMethod() is " + 
        0.000000001 * (end-begin) / runs + " seconds");

当然,使用挂钟也有一些注意事项:jit编译、多线程/进程等的影响。因此,您需要首先执行该方法很多次,以便JIT编译器完成它的工作,然后重复此测试多次,并使用最短的执行时间。

您可以使用Perf4j。非常酷的实用工具。用法简单

String watchTag = "target.SomeMethod";
StopWatch stopWatch = new LoggingStopWatch(watchTag);
Result result = null; // Result is a type of a return value of a method
try {
    result = target.SomeMethod();
    stopWatch.stop(watchTag + ".success");
} catch (Exception e) {
    stopWatch.stop(watchTag + ".fail", "Exception was " + e);
    throw e; 
}

更多信息可以在开发者指南中找到

编辑:项目似乎已死

如果java有更好的功能支持就好了,这样需要测量的动作就可以被包装到一个块中:

measure {
   // your operation here
}

在java中,这可以通过匿名函数来完成,看起来太冗长了

public interface Timer {
    void wrap();
}


public class Logger {

    public static void logTime(Timer timer) {
        long start = System.currentTimeMillis();
        timer.wrap();
        System.out.println("" + (System.currentTimeMillis() - start) + "ms");
    }

    public static void main(String a[]) {
        Logger.logTime(new Timer() {
            public void wrap() {
                // Your method here
                timeConsumingOperation();
            }
        });

    }

    public static void timeConsumingOperation() {
        for (int i = 0; i<=10000; i++) {
           System.out.println("i=" +i);
        }
    }
}
new Timer(""){{
    // code to time 
}}.timeMe();



public class Timer {

    private final String timerName;
    private long started;

    public Timer(String timerName) {
        this.timerName = timerName;
        this.started = System.currentTimeMillis();
    }

    public void timeMe() {
        System.out.println(
        String.format("Execution of '%s' takes %dms.", 
                timerName, 
                started-System.currentTimeMillis()));
    }

}

您可以使用Metrics库,它提供了各种测量工具。添加依赖关系:

<dependencies>
    <dependency>
        <groupId>io.dropwizard.metrics</groupId>
        <artifactId>metrics-core</artifactId>
        <version>${metrics.version}</version>
    </dependency>
</dependencies>

并为您的环境配置它。

方法可以用@Timed进行注释:

@Timed
public void exampleMethod(){
    // some code
}

或者用Timer包装的一段代码:

final Timer timer = metricsRegistry.timer("some_name");
final Timer.Context context = timer.time();
// timed code
context.stop();

聚合的指标可以导出到控制台、JMX、CSV或其他。

@定时指标输出示例:

com.example.ExampleService.exampleMethod
             count = 2
         mean rate = 3.11 calls/minute
     1-minute rate = 0.96 calls/minute
     5-minute rate = 0.20 calls/minute
    15-minute rate = 0.07 calls/minute
               min = 17.01 milliseconds
               max = 1006.68 milliseconds
              mean = 511.84 milliseconds
            stddev = 699.80 milliseconds
            median = 511.84 milliseconds
              75% <= 1006.68 milliseconds
              95% <= 1006.68 milliseconds
              98% <= 1006.68 milliseconds
              99% <= 1006.68 milliseconds
            99.9% <= 1006.68 milliseconds