从直方图中计算平均值和百分位数？ [英] Calculating average and percentiles from a histogram map?
问题描述
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，它还将填充地图，其中有多少次调用需要x毫秒。我将使用这张地图作为直方图的一部分进行进一步的分析，例如调用百分比的话，等等。
I have written a timer which will measure the performance of a particular code in any multithreaded application. In the below timer, it will also populate the map with how many calls took x milliseconds. I will use this map as part of my histogram to do further analysis, like what percentage of calls took this much milliseconds and etc.
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my的方式在timer类上面测量我的多线程应用程序中特定代码的性能：
For example, this is the way I will use my above timer class to measure the performance of a particular code in my multithreading application:
StopWatch timer = StopWatch.getInstance();
//... some code here to measure
timer.getDuration();
现在我的问题是 - 计算平均值，中位数，第95百分位数和第99百分位数的最佳方法是什么？直方图中的请求？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，例如找到平均值，中位数，第95和第99百分位数。
Now my question is - What is the best way to calculate average, median, 95th and 99th percentile of the request from my histogram map? I mean to say, I want to add certain methods in my StopWatch class only which will does all the calculation like finding the average, median, 95th and 99th percentile.
然后我可以直接使用 StopWatch 实例来获取它。
And then I can just directly get it by using StopWatch instance.
我的直方图将如下所示：
My histogram map will look like this:
key - 表示毫秒数
key - means number of milliseconds
value - 表示调用的次数很多毫秒。
value - means number of calls that took that much milliseconds.
推荐答案
平均值很容易实现。中位数是第50个百分位数，因此您只需要一个有效的单百分位方法，并为中位数创建一个实用方法。 Percentile计算的几种变体，但这个应该生成与Microsoft相同的结果Excel PERCENTILE.INC函数。
The mean is straightforward to implement. Median is the 50th percentile, so you just need a single percentile method that works, and create a utility method for the median. There are several variations of Percentile calculation, but this one should generate the same results as the Microsoft Excel PERCENTILE.INC function.
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;
}
}
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