异步文件 I/O
同步 I/O 意味着在 I/O 操作完成之前，方法被阻塞，I/O 操作完成后，方法返回其数据。使用异步 I/O，用户可以调用 BeginRead 或 BeginWrite。主线程可以继续进行其他工作，稍后，用户将能够处理数据。另外，多个 I/O 请求可以被同时挂起。
要在此数据可用时得到通知，您可以调用 EndRead 或 EndWrite，传入与您发出的 I/O 请求对应的 IAsyncResult。您还可以提供回调方法，该回调方法应调用 EndRead 或 EndWrite 以计算出读取或写入了多少字节。当许多 I/O 请求被同时挂起时，异步 I/O 可以提供较好的性能，但通常要求对您的应用程序进行一些重要的调整以使其正常工作。
Stream 类支持在同一个流上混合使用同步和异步读取及写入，而不管操作系统是否允许。Stream 根据其同步实现提供默认的异步读取和写入操作的实现，而根据其异步实现提供默认的同步读取和写入操作的实现。
当实现 Stream 的派生类时，必须为同步或异步 Read 和 Write 方法提供实现。虽然允许重写 Read 和 Write，并且异步方法（BeginRead、EndRead、BeginWrite 和 EndWrite）的默认实现将和同步方法的实现一起工作，但这不能提供最有效的性能。与之相似，如果您提供了一个异步方法的实现，同步 Read 和 Write 方法也将正常工作，但是，如果您专门实现同步方法，性能通常会更好。ReadByte 和 WriteByte 的默认实现调用带有一个元素字节数组的同步 Read 和 Write 方法。当从 Stream 派生类时，如果有内部字节缓冲区，强烈建议重写这些方法以访问内部缓冲区，这样性能将得到显著提高。
连接到后备存储器的流重写同步或异步 Read 和 Write 方法之一，以获取默认情况下另一种方法的功能。如果流不支持异步或同步操作，实施者只需让适当的方法引发异常即可。
下面的示例是一个假设的批量图像处理器的异步实现，其后是同步实现的示例。本代码用于在目录中的每个文件上执行耗费 CPU 资源的操作。有关更多信息，请参见 .NET Framework 开发人员规范中的“.NET 异步编程模型”主题。
[C#]
using System;
using System.IO;
using System.Threading;
using System.Runtime.InteropServices;
using System.Runtime.Remoting.Messaging;
public class BulkImageProcAsync
{
public const String ImageBaseName = "tmpImage-";
public const int numImages = 200;
public const int numPixels = 512*512;
// ProcessImage has a simple O(N) loop, and you can vary the number
// of times you repeat that loop to make the application more CPU-
// bound or more IO-bound.
public static int processImageRepeats = 20;
// Threads must decrement NumImagesToFinish, and protect
// their access to it through a mutex.
public static int NumImagesToFinish = numImages;
public static Object NumImagesMutex = new Object[0];
// WaitObject is signalled when all image processing is done.
public static Object WaitObject = new Object[0];
public class ImageStateObject
{
public byte[] pixels;
public int imageNum;
public FileStream fs;
}
public static void MakeImageFiles()
{
int sides = (int) Math.Sqrt(numPixels);
Console.Write("Making "+numImages+" "+sides+"x"+sides
+" images... ");
byte[] pixels = new byte[numPixels];
for(int i=0; i<numPixels; i++)
pixels[i] = (byte) i;
for(int i=0; i<numImages; i++)
{
FileStream fs = new FileStream(ImageBaseName+i+".tmp",
FileMode.Create, FileAccess.Write, FileShare.None,
8192, false);
fs.Write(pixels, 0, pixels.Length);
FlushFileBuffers(fs.Handle);
fs.Close();
}
Console.WriteLine("Done.");
}
public static void ReadInImageCallback(IAsyncResult asyncResult)
{
ImageStateObject state = (ImageStateObject) asyncResult.AsyncState;
Stream stream = state.fs;
int bytesRead = stream.EndRead(asyncResult);
if (bytesRead != numPixels)
throw new Exception("In ReadInImageCallback, got the wrong
number of bytes from the image: {0}.", bytesRead);
ProcessImage(state.pixels, state.imageNum);
stream.Close();
// Now write out the image.
// Using asynchronous I/O here appears not to be best practice.
// It ends up swamping the threadpool, because the threadpool
// threads are blocked on I/O requests that were just queued to
// the threadpool.
FileStream fs = new
FileStream(ImageBaseName+state.imageNum+".done",
FileMode.Create, FileAccess.Write, FileShare.None, 4096,
false);
fs.Write(state.pixels, 0, numPixels);
fs.Close();
// This application model uses too much memory.
// Releasing memory as soon as possible is a good idea,
// especially global state.
state.pixels = null;
// Record that an image is finished now.
lock(NumImagesMutex)
{
NumImagesToFinish--;
if (NumImagesToFinish==0)
{
Monitor.Enter(WaitObject);
Monitor.Pulse(WaitObject);
Monitor.Exit(WaitObject);
}
}
}
public static void ProcessImage(byte[] pixels, int imageNum)
{
Console.WriteLine("ProcessImage "+imageNum);
// Perform some CPU-intensive operation on the image.
for(int i=0; i<processImageRepeats; i++)
for(int j=0; j<numPixels; j++)
pixels[j] += 1;
Console.WriteLine("ProcessImage "+imageNum+" done.");
}
public static void ProcessImagesInBulk()
{
Console.WriteLine("Processing images... ");
long t0 = Environment.TickCount;
NumImagesToFinish = numImages;
AsyncCallback readImageCallback = new
AsyncCallback(ReadInImageCallback);
for(int i=0; i<numImages; i++)
{
ImageStateObject state = new ImageStateObject();
state.pixels = new byte[numPixels];
state.imageNum = i;
// Very large items are read only once, so you can make the
// buffer on the FileStream very small to save memory.
FileStream fs = new FileStream(ImageBaseName+i+".tmp",
FileMode.Open, FileAccess.Read, FileShare.Read, 1, true);
state.fs = fs;
fs.BeginRead(state.pixels, 0, numPixels, readImageCallback,
state);
}
// Determine whether all images are done being processed.
// If not, block until all are finished.
bool mustBlock = false;
lock (NumImagesMutex)
{
if (NumImagesToFinish > 0)
mustBlock = true;
}
if (mustBlock)
{
Console.WriteLine("All worker threads are queued. Blocking
until they complete. numLeft: {0}.", NumImagesToFinish);
Monitor.Enter(WaitObject);
Monitor.Wait(WaitObject);
Monitor.Exit(WaitObject);
}
long t1 = Environment.TickCount;
Console.WriteLine("Total time processing images: {0} ms",
(t1-t0));
}
public static void Cleanup()
{
for(int i=0; i<numImages; i++)
{
File.Delete(ImageBaseName+i+".tmp");
File.Delete(ImageBaseName+i+".done");
}
}
public static void TryToClearDiskCache()
{
// Try to force all pending writes to disk, and clear the
// disk cache of any data.
byte[] bytes = new byte[100*(1<<20)];
for(int i=0; i<bytes.Length; i++)
bytes[i] = 0;
bytes = null;
GC.Collect();
Thread.Sleep(2000);
}
public static void Main(String[] args)
{
Console.WriteLine("Bulk image processing sample application,
using async IO.");
Console.WriteLine("Simulates applying a simple transformation to
"+numImages+" /"images/"");
Console.WriteLine("(Async FileStream & Threadpool benchmark)");
Console.WriteLine("Warning - this test requires "+(numPixels *
numImages * 2)+" bytes of temporary space");
if (args.Length==1)
{
processImageRepeats = Int32.Parse(args[0]);
Console.WriteLine("ProcessImage inner loop - {0}.",
processImageRepeats);
}
MakeImageFiles();
TryToClearDiskCache();
ProcessImagesInBulk();
Cleanup();
}
[DllImport("KERNEL32", SetLastError=true)]
private static extern void FlushFileBuffers(IntPtr handle);
}
以下是同一假设的同步示例。
[C#]
using System;
using System.IO;
using System.Threading;
using System.Runtime.InteropServices;
using System.Runtime.Remoting.Messaging;
public class BulkImageProcSync
{
public const String ImageBaseName = "tmpImage-";
public const int numImages = 200;
public const int numPixels = 512*512;
// ProcessImage has a simple O(N) loop, and you can vary the number
// of times you repeat that loop to make the application more CPU-
// bound or more IO-bound.
public static int processImageRepeats = 20;
public static void MakeImageFiles()
{
int sides = (int) Math.Sqrt(numPixels);
Console.Write("Making "+numImages+" "+sides+"x"+sides+"
images... ");
byte[] pixels = new byte[numPixels];
for(int i=0; i<numPixels; i++)
pixels[i] = (byte) i;
for(int i=0; i<numImages; i++)
{
FileStream fs = new FileStream(ImageBaseName+i+".tmp",
FileMode.Create, FileAccess.Write, FileShare.None,
8192, false);
fs.Write(pixels, 0, pixels.Length);
FlushFileBuffers(fs.Handle);
fs.Close();
}
Console.WriteLine("Done.");
}
public static void ProcessImage(byte[] pixels, int imageNum)
{
Console.WriteLine("ProcessImage "+imageNum);
// Perform some CPU-intensive operation on the image.
for(int i=0; i<processImageRepeats; i++)
for(int j=0; j<numPixels; j++)
pixels[j] += 1;
Console.WriteLine("ProcessImage "+imageNum+" done.");
}
public static void ProcessImagesInBulk()
{
Console.WriteLine("Processing images... ");
long t0 = Environment.TickCount;
byte[] pixels = new byte[numPixels];
for(int i=0; i<numImages; i++)
{
FileStream input = new FileStream(ImageBaseName+i+".tmp",
FileMode.Open, FileAccess.Read, FileShare.Read,
4196, false);
input.Read(pixels, 0, numPixels);
input.Close();
ProcessImage(pixels, i);
FileStream output = new FileStream(ImageBaseName+i+".done",
FileMode.Create, FileAccess.Write, FileShare.None,
4196, false);
output.Write(pixels, 0, numPixels);
output.Close();
}
long t1 = Environment.TickCount;
Console.WriteLine("Total time processing images: {0} ms",
(t1-t0));
}
public static void Cleanup()
{
for(int i=0; i<numImages; i++)
{
File.Delete(ImageBaseName+i+".tmp");
File.Delete(ImageBaseName+i+".done");
}
}
public static void TryToClearDiskCache()
{
byte[] bytes = new byte[100*(1<<20)];
for(int i=0; i<bytes.Length; i++)
bytes[i] = 0;
bytes = null;
GC.Collect();
Thread.Sleep(2000);
}
public static void Main(String[] args)
{
Console.WriteLine("Bulk image processing sample application,
using synchronous I/O");
Console.WriteLine("Simulates applying a simple transformation to
"+numImages+" /"images/"");
Console.WriteLine("(ie, Sync FileStream benchmark)");
Console.WriteLine("Warning - this test requires "+(numPixels *
numImages * 2)+" bytes of tmp space");
if (args.Length==1)
{
processImageRepeats = Int32.Parse(args[0]);
Console.WriteLine("ProcessImage inner loop –
"+processImageRepeats);
}
MakeImageFiles();
TryToClearDiskCache();
ProcessImagesInBulk();
Cleanup();
}
[DllImport("KERNEL32", SetLastError=true)]
private static extern void FlushFileBuffers(IntPtr handle);
}
