Question

我使用Dns.BeginGetHostEntry方法根据主机名获取主机的FQDN（主机名列表存储在SQL Server数据库中）。此方法（异步）在不到30分钟内完成对近150k记录的运行，并在存储主机名的SQL表中更新FQDN。

此解决方案运行速度过快（超过每秒300个请求的阈值）。既然允许没有。对服务器生成请求的限制，我的服务器列在顶级讲话者并要求停止运行此应用程序。我必须重建这个应用程序才能同步运行，现在需要6个多小时才能完成。

//// TotalRecords are fetched from SQL database with the Hostname (referred as host further)
for (int i = 0; i < TotalRecords.Rows.Count; i++)
{
    try
    {
        host = TotalRecords.Rows[i].ItemArray[0].ToString();
        Interlocked.Increment(ref requestCounter);
        string[] arr = new string[] { i.ToString(), host }; 
        Dns.BeginGetHostEntry(host, GetHostEntryCallback,arr);
    }
    catch (Exception ex)
    {
        log.Error("Unknown error occurred\n ", ex);
    }
}
do
{
    Thread.Sleep(0);

} while (requestCounter>0);

ListAdapter.Update（总记录）;

问题：

这种方法生成的请求数量是否有任何限制？
我理解ParallelOptions.MaxDegreeOfParallelism每秒都不能控制线程，所以有没有什么方法TPL可能是更好的选择？可以限制为否。每秒的请求数量？

Answer 1

使用SemaphoreSlim Timer来限制每个时段的请求。

[DebuggerDisplay( "Current Count = {_semaphore.CurrentCount}" )]
public class TimedSemaphoreSlim : IDisposable
{
    private readonly System.Threading.SemaphoreSlim _semaphore;
    private readonly System.Threading.Timer _timer;
    private int _releaseCount;

    public TimedSemaphoreSlim( int initialcount, TimeSpan period )
    {
        _semaphore = new System.Threading.SemaphoreSlim( initialcount );
        _timer = new System.Threading.Timer( OnTimer, this, period, period );
    }

    public TimedSemaphoreSlim( int initialCount, int maxCount, TimeSpan period )
    {
        _semaphore = new SemaphoreSlim( initialCount, maxCount );
        _timer = new Timer( OnTimer, this, period, period );
    }

    private void OnTimer( object state )
    {
        var releaseCount = Interlocked.Exchange( ref _releaseCount, 0 );
        if ( releaseCount > 0 )
            _semaphore.Release( releaseCount );
    }

    public WaitHandle AvailableWaitHandle => _semaphore.AvailableWaitHandle;
    public int CurrentCount => _semaphore.CurrentCount;

    public void Release()
    {
        Interlocked.Increment( ref _releaseCount );
    }

    public void Release( int releaseCount )
    {
        Interlocked.Add( ref _releaseCount, releaseCount );
    }

    public void Wait()
    {
        _semaphore.Wait();
    }

    public void Wait( CancellationToken cancellationToken )
    {
        _semaphore.Wait( cancellationToken );
    }

    public bool Wait( int millisecondsTimeout )
    {
        return _semaphore.Wait( millisecondsTimeout );
    }

    public bool Wait( int millisecondsTimeout, CancellationToken cancellationToken )
    {
        return _semaphore.Wait( millisecondsTimeout, cancellationToken );
    }

    public bool Wait( TimeSpan timeout, CancellationToken cancellationToken )
    {
        return _semaphore.Wait( timeout, cancellationToken );
    }

    public Task WaitAsync()
    {
        return _semaphore.WaitAsync();
    }

    public Task WaitAsync( CancellationToken cancellationToken )
    {
        return _semaphore.WaitAsync( cancellationToken );
    }

    public Task<bool> WaitAsync( int millisecondsTimeout )
    {
        return _semaphore.WaitAsync( millisecondsTimeout );
    }

    public Task<bool> WaitAsync( TimeSpan timeout )
    {
        return _semaphore.WaitAsync( timeout );
    }

    public Task<bool> WaitAsync( int millisecondsTimeout, CancellationToken cancellationToken )
    {
        return _semaphore.WaitAsync( millisecondsTimeout, cancellationToken );
    }

    public Task<bool> WaitAsync( TimeSpan timeout, CancellationToken cancellationToken )
    {
        return _semaphore.WaitAsync( timeout, cancellationToken );
    }

    #region IDisposable Support
    private bool disposedValue = false; // Dient zur Erkennung redundanter Aufrufe.

    private void CheckDisposed()
    {
        if ( disposedValue )
        {
            throw new ObjectDisposedException( nameof( TimedSemaphoreSlim ) );
        }
    }

    protected virtual void Dispose( bool disposing )
    {
        if ( !disposedValue )
        {
            if ( disposing )
            {
                _timer.Dispose();
                _semaphore.Dispose();
            }

            disposedValue = true;
        }
    }

    public void Dispose()
    {
        Dispose( true );
    }
    #endregion
}

样本用法

IEnumerable<string> bunchOfHosts = GetBunchOfHosts();
IList<IPHostEntry> result;

using ( var limiter = new TimedSemaphoreSlim( 300, 300, TimeSpan.FromSeconds( 1 ) ) )
{
    result = bunchOfHosts.AsParallel()
        .Select( e =>
        {
            limiter.Wait();
            try
            {
                return Dns.GetHostEntry( e );
            }
            finally
            {
                limiter.Release();
            }
        } )
        .ToList();
}

Answer 2

纯粹的异步解决方案。

它使用一个nuget包Nite.AsyncEx和System.Reactive它执行错误处理并提供DNS的结果，因为它们以IObservable<IPHostEntry>

的形式出现

这里有很多事情要发生。您需要将reactive extensions理解为标准async programming。可能有很多方法可以实现以下结果，但这是一个有趣的解决方案。

using System;
using System.Collections.Concurrent;
using System.Threading.Tasks;
using System.Linq;
using System.Collections.Generic;
using System.Diagnostics;
using System.Net;
using System.Reactive.Disposables;
using System.Reactive.Linq;
using Nito.AsyncEx;
using System.Threading;

#pragma warning disable CS4014 // Because this call is not awaited, execution of the current method continues before the call is completed

public static class EnumerableExtensions
{
    public static IEnumerable<Func<U>> Defer<T, U>
        ( this IEnumerable<T> source, Func<T, U> selector) 
        => source.Select(s => (Func<U>)(() => selector(s)));
}


public class Program
{
    /// <summary>
    /// Returns the time to wait before processing another item
    /// if the rate limit is to be maintained
    /// </summary>
    /// <param name="desiredRateLimit"></param>
    /// <param name="currentItemCount"></param>
    /// <param name="elapsedTotalSeconds"></param>
    /// <returns></returns>
    private static double Delay(double desiredRateLimit, int currentItemCount, double elapsedTotalSeconds)
    {
        var time = elapsedTotalSeconds;
        var timeout = currentItemCount / desiredRateLimit;
        return timeout - time;
    }

    /// <summary>
    /// Consume the tasks in parallel but with a rate limit. The results
    /// are returned as an observable.
    /// </summary>
    /// <typeparam name="T"></typeparam>
    /// <param name="tasks"></param>
    /// <param name="rateLimit"></param>
    /// <returns></returns>
    public static IObservable<T> RateLimit<T>(IEnumerable<Func<Task<T>>> tasks, double rateLimit){
        var s = System.Diagnostics.Stopwatch.StartNew();
        var n = 0;
        var sem = new  AsyncCountdownEvent(1);

        var errors = new ConcurrentBag<Exception>();

        return Observable.Create<T>
            ( observer =>
            {

                var ctx = new CancellationTokenSource();
                Task.Run
                    ( async () =>
                    {
                        foreach (var taskFn in tasks)
                        {
                            n++;
                            ctx.Token.ThrowIfCancellationRequested();

                            var elapsedTotalSeconds = s.Elapsed.TotalSeconds;
                            var delay = Delay( rateLimit, n, elapsedTotalSeconds );
                            if (delay > 0)
                                await Task.Delay( TimeSpan.FromSeconds( delay ), ctx.Token );

                            sem.AddCount( 1 );
                            Task.Run
                                ( async () =>
                                {
                                    try
                                    {
                                        observer.OnNext( await taskFn() );
                                    }
                                    catch (Exception e)
                                    {
                                        errors.Add( e );
                                    }
                                    finally
                                    {
                                        sem.Signal();
                                    }
                                }
                                , ctx.Token );
                        }
                        sem.Signal();
                        await sem.WaitAsync( ctx.Token );
                        if(errors.Count>0)
                            observer.OnError(new AggregateException(errors));
                        else
                            observer.OnCompleted();
                    }
                      , ctx.Token );

                return Disposable.Create( () => ctx.Cancel() );
            } );
    }

    #region hosts



    public static string [] Hosts = new [] { "google.com" }

    #endregion


    public static void Main()
    {
        var s = System.Diagnostics.Stopwatch.StartNew();

        var rate = 25;

        var n = Hosts.Length;

        var expectedTime = n/rate;

        IEnumerable<Func<Task<IPHostEntry>>> dnsTaskFactories = Hosts.Defer( async host =>
        {
            try
            {
                return await Dns.GetHostEntryAsync( host );
            }
            catch (Exception e)
            {
                throw new Exception($"Can't resolve {host}", e);
            }
        } );

        IObservable<IPHostEntry> results = RateLimit( dnsTaskFactories, rate );

        results
            .Subscribe( result =>
            {
                Console.WriteLine( "result " + DateTime.Now + " " + result.AddressList[0].ToString() );
            },
            onCompleted: () =>
            {
                Console.WriteLine( "Completed" );

                PrintTimes( s, expectedTime );
            },
            onError: e =>
            {
                Console.WriteLine( "Errored" );

                PrintTimes( s, expectedTime );

                if (e is AggregateException ae)
                {
                    Console.WriteLine( e.Message );
                    foreach (var innerE in ae.InnerExceptions)
                    {
                        Console.WriteLine( $"     " + innerE.GetType().Name + " " + innerE.Message );
                    }
                }
                else
                {
                        Console.WriteLine( $"got error " + e.Message );
                }
            }

            );

        Console.WriteLine("Press enter to exit");
        Console.ReadLine();
    }

    private static void PrintTimes(Stopwatch s, int expectedTime)
    {
        Console.WriteLine( "Done" );
        Console.WriteLine( "Elapsed Seconds " + s.Elapsed.TotalSeconds );
        Console.WriteLine( "Expected Elapsed Seconds " + expectedTime );
    }
}

最后几行输出

result 5/23/2017 3:23:36 PM 84.16.241.74
result 5/23/2017 3:23:36 PM 84.16.241.74
result 5/23/2017 3:23:36 PM 157.7.105.52
result 5/23/2017 3:23:36 PM 223.223.182.225
result 5/23/2017 3:23:36 PM 64.34.93.5
result 5/23/2017 3:23:36 PM 212.83.211.103
result 5/23/2017 3:23:36 PM 205.185.216.10
result 5/23/2017 3:23:36 PM 198.232.125.32
result 5/23/2017 3:23:36 PM 66.231.176.100
result 5/23/2017 3:23:36 PM 54.239.34.12
result 5/23/2017 3:23:36 PM 54.239.34.12
result 5/23/2017 3:23:37 PM 219.84.203.116
Errored
Done
Elapsed Seconds 19.9990118
Expected Elapsed Seconds 19
One or more errors occurred.
     Exception Can't resolve adv758968.ru
     Exception Can't resolve fr.a3dfp.net
     Exception Can't resolve ads.adwitserver.com
     Exception Can't resolve www.adtrader.com
     Exception Can't resolve trak-analytics.blic.rs
     Exception Can't resolve ads.buzzcity.net

我无法粘贴完整代码，因此这里是指向主机列表的代码的链接。

https://gist.github.com/bradphelan/084e4b1ce2604bbdf858d948699cc190

Answer 3

您是否考虑过使用webpack.local.settings.js图书馆？它有一种非常方便的方法来限制同一类型的并发操作。此外，它还有机会按limiting the buffer size限制整个管道。

基本上你需要创建的是一个管道：

条目BufferBlock，用于存储您的所有TPL Dataflow项
一个TransformBlock，它将接受TotalRecords并返回结果
BatchBlock将收集结果并create a batch of results
一个ActionBlock，最终用结果更新您的数据库。

所以你的代码可能是这样的：

host

我建议您阅读整篇How-to section on MSDN，以便更好地了解您可以使用此库做些什么，或者继续阅读official documentation。

顺便说一句，您可以use the SqlBulkCopy class to update the database，如果它符合您的要求，通常比使用// buffer limited to 30 items in queue // all other items would be postponed and added to queue automatically // order in queue is preserved var hosts = new BufferBlock<string>(new DataflowBlockOptions { BoundedCapacity = 30 }); // get a host and perform a dns search operation var handler = new TransformBlock<string, IPHostEntry>(host => Dns.GetHostEntry(host), // no more than 5 simultaneous requests at a time new ExecutionDataflowBlockOptions { MaxDegreeOfParallelism = 10 }); // gather results in an array of size 500 var batchBlock = new BatchBlock<IPHostEntry>(500); // get the resulting array and save it to database var batchSave = new ActionBlock<IPHostEntry[]>(r => GetHostEntryCallback(r)); // link all the blocks to automatically propagate items along the pipeline var linkOptions = new DataflowLinkOptions { PropagateCompletion = true }; hosts.LinkTo(handler, linkOptions); handler.LinkTo(batchBlock, linkOptions); batchBlock.LinkTo(batchSave, linkOptions); // provide the data to pipeline for (var i = 0; i < TotalRecords.Rows.Count; ++i) { var host = TotalRecords.Rows[i].ItemArray[0].ToString(); // async wait for item to be sent to pipeline // will throttle starting with 31th item in a buffer queue await hosts.SendAsync(host); } // pipeline is complete now, just wait it finishes hosts.Complete(); // wait for the last block to finish it's execution await batchSave.Completion; // notify user that update is over的常规更新更快。

限制没有。 Dns.BeginGetHostEntry方法生成的每秒请求数或使用任务并行库（TPL）

3 个答案: