我编写了一个计时器，用于衡量任何多线程应用程序中特定代码的性能。在下面的计时器中，它还将使用x毫秒的数量填充地图。我将使用这张地图作为直方图的一部分来进行进一步的分析，例如调用百分比的百分比等等。
public static class StopWatch {
public static ConcurrentHashMap<Long, Long> histogram = new ConcurrentHashMap<Long, Long>();
/**
* Creates an instance of the timer and starts it running.
*/
public static StopWatch getInstance() {
return new StopWatch();
}
private long m_end = -1;
private long m_interval = -1;
private final long m_start;
private StopWatch() {
m_start = m_interval = currentTime();
}
/**
* Returns in milliseconds the amount of time that has elapsed since the timer was created. If the
* <code>stop</code> method has been invoked, then this returns instead the elapsed time between the creation of
* the timer and the moment when <code>stop</code> was invoked.
*
* @return duration it took
*/
public long getDuration() {
long result = 0;
final long startTime = m_start;
final long endTime = isStopWatchRunning() ? currentTime() : m_end;
result = convertNanoToMilliseconds(endTime - startTime);
boolean done = false;
while (!done) {
Long oldValue = histogram.putIfAbsent(result, 1L);
if (oldValue != null) {
done = histogram.replace(result, oldValue, oldValue + 1);
} else {
done = true;
}
}
return result;
}
/**
* Returns in milliseconds the amount of time that has elapsed since the last invocation of this same method. If
* this method has not previously been invoked, then it is the amount of time that has elapsed since the timer
* was created. <strong>Note</strong> that once the <code>stop</code> method has been invoked this will just
* return zero.
*
* @return interval period
*/
public long getInterval() {
long result = 0;
final long startTime = m_interval;
final long endTime;
if (isStopWatchRunning()) {
endTime = m_interval = currentTime();
} else {
endTime = m_end;
}
result = convertNanoToMilliseconds(endTime - startTime);
return result;
}
/**
* Stops the timer from advancing. This has an impact on the values returned by both the
* <code>getDuration</code> and the <code>getInterval</code> methods.
*/
public void stop() {
if (isStopWatchRunning()) {
m_end = currentTime();
}
}
/**
* What is the current time in nanoseconds?
*
* @return returns back the current time in nanoseconds
*/
private long currentTime() {
return System.nanoTime();
}
/**
* This is used to check whether the timer is alive or not
*
* @return checks whether the timer is running or not
*/
private boolean isStopWatchRunning() {
return (m_end <= 0);
}
/**
* This is used to convert NanoSeconds to Milliseconds
*
* @param nanoseconds
* @return milliseconds value of nanoseconds
*/
private long convertNanoToMilliseconds(final long nanoseconds) {
return nanoseconds / 1000000L;
}
}
例如，这是我使用上面的计时器类来测量多线程应用程序中特定代码的性能的方法：
StopWatch timer = StopWatch.getInstance();
//... some code here to measure
timer.getDuration();
现在我的问题是 - 从我的直方图中计算请求的平均值，中位数，第95和第99百分位数的最佳方法是什么？我的意思是说，我想在我的StopWatch类中添加某些方法，这将完成所有计算，例如查找平均值，中位数，第95和第99百分位数。
然后我可以直接使用StopWatch实例来获取它。
我的直方图将如下所示：
key - 表示毫秒数
value - 表示花费了很多毫秒的调用次数。
答案 0 :(得分：3)
平均值很容易实现。中位数是第50百分位数，因此您只需要一个有效的单百分位方法，并为中位数创建实用方法。有several variations of Percentile calculation，但是这个应该生成与Microsoft Excel PERCENTILE.INC函数相同的结果。
import java.util.Map;
import java.util.SortedSet;
import java.util.concurrent.ConcurrentSkipListSet;
public class HistogramStatistics
{
public static Double average(final Map<Long, Long> histogram)
{
return HistogramStatistics.mean(histogram);
}
public static Double mean(final Map<Long, Long> histogram)
{
double sum = 0L;
for (Long value : histogram.keySet())
{
sum += (value * histogram.get(value));
}
return sum / (double) HistogramStatistics.count(histogram);
}
public static Double median(final Map<Long, Long> histogram)
{
return HistogramStatistics.percentile(histogram, 0.50d);
}
public static Double percentile(final Map<Long, Long> histogram, final double percent)
{
if ((percent < 0d) || (percent > 1d))
{
throw new IllegalArgumentException("Percentile must be between 0.00 and 1.00.");
}
if ((histogram == null) || histogram.isEmpty())
{
return null;
}
double n = (percent * (HistogramStatistics.count(histogram).doubleValue() - 1d)) + 1d;
double d = n - Math.floor(n);
SortedSet<Long> bins = new ConcurrentSkipListSet<Long>(histogram.keySet());
long observationsBelowBinInclusive = 0L;
Long lowBin = bins.first();
Double valuePercentile = null;
for (Long highBin : bins)
{
observationsBelowBinInclusive += histogram.get(highBin);
if (n <= observationsBelowBinInclusive)
{
if ((d == 0f) || (histogram.get(highBin) > 1L))
{
lowBin = highBin;
}
valuePercentile = lowBin.doubleValue() + ((highBin - lowBin) * d);
break;
}
lowBin = highBin;
}
return valuePercentile;
}
public static Long count(final Map<Long, Long> histogram)
{
long observations = 0L;
for (Long value : histogram.keySet())
{
observations += histogram.get(value);
}
return observations;
}
}
答案 1 :(得分：1)
给出直方图（频率列表），如下所示
Value | Frequency
------+----------
1 | 5
2 | 3
3 | 1
4 | 7
5 | 2
..
每个Value在您的数据集中出现Frequency次。
public static double getMean (ConcurrentHashMap<Long,Long> histogram)
{
double mean = 0;
double a = 0;
double b = 0;
TreeSet<Long> values = histogram.keySet();
for (Long value : values)
{
// a = a + (value x frequency)
a = a + (value * histogram.get(value));
// b = b + frequency
b = b + histogram.get(value);
}
// mean = SUM(value x frequency) / SUM(frequency)
mean = (a / b);
return mean;
}
答案 2 :(得分：0)
您可能希望将测得的持续时间四舍五入到某个所需的分辨率，例如10或100毫秒为单位，以便您的地图不会因所有可能的延迟值而肿。
在最坏的情况和内存位置优势下，也可以使用数组而不是映射进行O（1）查找。
此外，您可以使用LongAdder或AtomicLong，而不是while (!done)中的getDuration()循环。
要可靠地计算合并的直方图上的百分位数，可以查看HBPE作为参考实现。免责声明：我是作者。