在进行性能测试时，经常需要测量代码执行的时间，以评估其效率。本文将通过C#代码示例，展示如何进行性能测试，并分析不同方法获取时间戳的性能差异。
首先，来看一段用于性能测试的C#代码。这段代码通过循环执行空操作（NOP）来测试循环本身的时间消耗，然后分别测试了使用Environment.TickCount、DateTime.Now.Ticks和Stopwatch.ElapsedTicks获取时间戳的时间消耗。
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Diagnostics;
namespace TimerPerformance
{
class Program
{
static void Main(string[] args)
{
Console.WriteLine("Performance Tests");
Console.WriteLine("Stopwatch Resolution (nS): " + (1000000000.0 / Stopwatch.Frequency).ToString());
RunTests();
Console.WriteLine("Tests Finished, press any key to stop...");
Console.ReadKey();
}
public static long DummyValue;
public static void RunTests()
{
const int loopEnd = 1000000;
Stopwatch watch = new Stopwatch();
Console.WriteLine();
Console.WriteLine("Reference Loop (NOP) Iterations: " + loopEnd);
watch.Reset();
watch.Start();
for (int i = 0; i < loopEnd; ++i)
{
DummyValue += i;
}
watch.Stop();
Console.WriteLine("Reference Loop (NOP) Elapsed Time (ms): " + (((double)watch.ElapsedTicks / Stopwatch.Frequency * 1000).ToString()));
Console.WriteLine();
Console.WriteLine("Query Environment.TickCount");
watch.Reset();
watch.Start();
for (int i = 0; i < loopEnd; ++i)
{
DummyValue += Environment.TickCount;
}
watch.Stop();
Console.WriteLine("Query Environment.TickCount Elapsed Time (ms): " + (((double)watch.ElapsedTicks / Stopwatch.Frequency * 1000).ToString()));
Console.WriteLine();
Console.WriteLine("Query DateTime.Now.Ticks");
watch.Reset();
watch.Start();
for (int i = 0; i < loopEnd; ++i)
{
DummyValue += DateTime.Now.Ticks;
}
watch.Stop();
Console.WriteLine("Query DateTime.Now.Ticks Elapsed Time (ms): " + (((double)watch.ElapsedTicks / Stopwatch.Frequency * 1000).ToString()));
Console.WriteLine();
Console.WriteLine("Query Stopwatch.ElapsedTicks");
watch.Reset();
watch.Start();
for (int i = 0; i < loopEnd; ++i)
{
DummyValue += watch.ElapsedTicks;
}
watch.Stop();
Console.WriteLine("Query Stopwatch.ElapsedTicks Elapsed Time (ms): " + (((double)watch.ElapsedTicks / Stopwatch.Frequency * 1000).ToString()));
}
}
}
通过在不同硬件上运行这段代码，得到了以下结果：
| 硬件 | Empty Loop | Environment.TickCount | DateTime.Now.Ticks |
|---|---|---|---|
| AMD Opteron 4174 HE 2.3 GHz | 8.7 ms | 16.6 ms | 2227 ms |
| AMD Athlon 64 X2 5600+ 2.9 GHz | 6.8 ms | 15.1 ms | 1265 ms |
| Intel Core 2 Quad Q9550 2.83 GHz | 2.1 ms | 4.9 ms | 557.8 ms |
| Azure A1 (Intel Xeon E5-2660 2.2 GHz) | 5.2 ms | 19.9 ms | 168.1 ms |
从结果可以看出，DateTime.Now的调用时间远大于空循环和Environment.TickCount。这是因为DateTime.Now的调用时间大约为1-2微秒，而Environment.TickCount的调用时间大约为600纳秒。
例如，一个HTTP请求需要测量响应时间和吞吐量（数据传输速率），它需要为从Web服务器接收到的每个数据块获取一个时间戳。在操作完成之前，至少需要3个时间戳（开始、响应、结束）来测量响应时间和下载时间。如果测量吞吐量（数据传输速率），则取决于接收到的数据块数量。这对于多线程访问来说更加糟糕。因为Environment.TickCount和DateTime.Now都是共享资源，所有调用都必须经过它们的同步机制，这意味着它们不能并行化。
实际上，像Crawler-Lib Engine这样的真实系统可以在相对较好的硬件上每秒执行20,000-30,000个HTTP请求。因此，很明显，时间测量对最大吞吐量有影响。
有些人可能会争辩说DateTime.Now比Environment.TickCount更精确。这是部分正确的。这里有一个代码片段，用于测量时间戳的粒度：
if (Environment.TickCount > int.MaxValue - 60000)
throw new InvalidOperationException("Tick Count will overflow in the next minute, test can't be run");
var startTickCount = Environment.TickCount;
var currentTickCount = startTickCount;
int minGranularity = int.MaxValue;
int maxGranularity = 0;
while (currentTickCount < startTickCount + 1000)
{
var tempMeasure = Environment.TickCount;
if (tempMeasure - currentTickCount > 0)
{
minGranularity = Math.Min(minGranularity, tempMeasure - currentTickCount);
maxGranularity = Math.Max(maxGranularity, tempMeasure - currentGranularity);
}
currentTickCount = tempMeasure;
Thread.Sleep(0);
}
Console.WriteLine("Environment.TickCount Min Granularity: " + minGranularity + ", Max Granularity: " + maxGranularity + "ms");
Console.WriteLine();
var startTime = DateTime.Now;
var currentTime = startTime;
double minGranularityTime = double.MaxValue;
double maxGranularityTime = 0.0;
while (currentTime < startTime + new TimeSpan(0, 0, 1))
{
var tempMeasure = DateTime.Now;
if ((tempMeasure - currentTime).TotalMilliseconds > 0)
{
minGranularityTime = Math.Min(minGranularityTime, (tempMeasure - currentTime).TotalMilliseconds);
maxGranularityTime = Math.Max(maxGranularityTime, (tempMeasure - currentTime).TotalMilliseconds);
}
currentTime = tempMeasure;
Thread.Sleep(0);
}
Console.WriteLine("DateTime Min Granularity: " + minGranularityTime + ", Max Granularity: " + maxGranularityTime + "ms");
在几台机器上运行这个代码片段，发现Environment.TickCount的粒度约为16毫秒（15.6毫秒），这是默认的系统范围计时器分辨率。系统范围计时器分辨率可以使用函数更改为1毫秒，但通常不建议这样做，因为它会影响所有应用程序。