while (true)
{
var msg = queue1.GetMessage();
if (msg != null)
{
didSomething = true;
// do something with it
queue1.DeleteMessage(msg);
}
msg = queue2.GetMessage();
if (msg != null)
{
didSomething = true;
// do something with it
queue2.DeleteMessage(msg);
}
// ...
if (!didSomething) Thread.Sleep(TimeSpan.FromSeconds(1)); // so I don't enter a tight loop with nothing to do
}
0
以下是我们当前的实现，以更好的方式完成您的请求（或者我们认为） . 也就是说，这段代码还需要一些 heavy 清理工作 . 不过，这是0.1的功能版本 .
public class WorkerRole : RoleEntryPoint
{
public override void Run()
{
var logic = new WorkerAgent();
logic.Go(false);
}
public override bool OnStart()
{
// Initialize our Cloud Storage Configuration.
AzureStorageObject.Initialize(AzureConfigurationLocation.AzureProjectConfiguration);
return base.OnStart();
}
}
public class WorkerAgent
{
private const int _resistance_to_scaling_larger_queues = 9;
private Dictionary<Type, int> _queueWeights = new Dictionary<Type, int>
{
{typeof (Queue1.Processor), 1},
{typeof (Queue2.Processor), 1},
{typeof (Queue3.Processor), 1},
{typeof (Queue4.Processor), 1},
};
private readonly TimeSpan _minDelay = TimeSpan.FromMinutes(Convert.ToDouble(RoleEnvironment.GetConfigurationSettingValue("MinDelay")));
private readonly TimeSpan _maxDelay = TimeSpan.FromMinutes(Convert.ToDouble(RoleEnvironment.GetConfigurationSettingValue("MaxDelay")));
protected TimeSpan CurrentDelay { get; set; }
public Func<string> GetSpecificQueueTypeToProcess { get; set; }
/// <summary>
/// This is a superset collection of all Queues that this WorkerAgent knows how to process, and the weight of focus it should receive.
/// </summary>
public Dictionary<Type, int> QueueWeights
{
get
{
return _queueWeights;
}
set
{
_queueWeights = value;
}
}
public static TimeSpan QueueWeightCalibrationDelay
{
get { return TimeSpan.FromMinutes(15); }
}
protected Dictionary<Type, DateTime> QueueDelays = new Dictionary<Type, DateTime>();
protected Dictionary<Type, AzureQueueMetaData> QueueMetaData { get; set; }
public WorkerAgent(Func<string> getSpecificQueueTypeToProcess = null)
{
CurrentDelay = _minDelay;
GetSpecificQueueTypeToProcess = getSpecificQueueTypeToProcess;
}
protected IProcessQueues CurrentProcessor { get; set; }
/// <summary>
/// Processes queue request(s).
/// </summary>
/// <param name="onlyProcessOnce">True to only process one time. False to process infinitely.</param>
public void Go(bool onlyProcessOnce)
{
if (onlyProcessOnce)
{
ProcessOnce(false);
}
else
{
ProcessContinuously();
}
}
public void ProcessContinuously()
{
while (true)
{
// temporary hack to get this started.
ProcessOnce(true);
}
}
/// <summary>
/// Attempts to fetch and process a single queued request.
/// </summary>
public void ProcessOnce(bool shouldDelay)
{
PopulateQueueMetaData(QueueWeightCalibrationDelay);
if (shouldDelay)
{
Thread.Sleep(CurrentDelay);
}
var typesToPickFrom = new List<Type>();
foreach(var item in QueueWeights)
{
for (var i = 0; i < item.Value; i++)
{
typesToPickFrom.Add(item.Key);
}
}
var randomIndex = (new Random()).Next()%typesToPickFrom.Count;
var typeToTryAndProcess = typesToPickFrom[randomIndex];
CurrentProcessor = ObjectFactory.GetInstance(typeToTryAndProcess) as IProcessQueues;
CleanQueueDelays();
if (CurrentProcessor != null && !QueueDelays.ContainsKey(typeToTryAndProcess))
{
var errors = CurrentProcessor.Go();
var amountToDelay = CurrentProcessor.NumberProcessed == 0 && !errors.Any()
? _maxDelay // the queue was empty
: _minDelay; // else
QueueDelays[CurrentProcessor.GetType()] = DateTime.Now + amountToDelay;
}
else
{
ProcessOnce(true);
}
}
/// <summary>
/// This method populates/refreshes the QueueMetaData collection.
/// </summary>
/// <param name="queueMetaDataCacheLimit">Specifies the length of time to cache the MetaData before refreshing it.</param>
private void PopulateQueueMetaData(TimeSpan queueMetaDataCacheLimit)
{
if (QueueMetaData == null)
{
QueueMetaData = new Dictionary<Type, AzureQueueMetaData>();
}
var queuesWithoutMetaData = QueueWeights.Keys.Except(QueueMetaData.Keys).ToList();
var expiredQueueMetaData = QueueMetaData.Where(qmd => qmd.Value.TimeMetaDataWasPopulated < (DateTime.Now - queueMetaDataCacheLimit)).Select(qmd => qmd.Key).ToList();
var validQueueData = QueueMetaData.Where(x => !expiredQueueMetaData.Contains(x.Key)).ToList();
var results = new Dictionary<Type, AzureQueueMetaData>();
foreach (var queueProcessorType in queuesWithoutMetaData)
{
if (!results.ContainsKey(queueProcessorType))
{
var queueProcessor = ObjectFactory.GetInstance(queueProcessorType) as IProcessQueues;
if (queueProcessor != null)
{
var queue = new AzureQueue(queueProcessor.PrimaryQueueName);
var metaData = queue.GetMetaData();
results.Add(queueProcessorType, metaData);
QueueWeights[queueProcessorType] = (metaData.ApproximateMessageCount) == 0
? 1
: (int)Math.Log(metaData.ApproximateMessageCount, _resistance_to_scaling_larger_queues) + 1;
}
}
}
foreach (var queueProcessorType in expiredQueueMetaData)
{
if (!results.ContainsKey(queueProcessorType))
{
var queueProcessor = ObjectFactory.GetInstance(queueProcessorType) as IProcessQueues;
if (queueProcessor != null)
{
var queue = new AzureQueue(queueProcessor.PrimaryQueueName);
var metaData = queue.GetMetaData();
results.Add(queueProcessorType, metaData);
}
}
}
QueueMetaData = results.Union(validQueueData).ToDictionary(data => data.Key, data => data.Value);
}
private void CleanQueueDelays()
{
QueueDelays = QueueDelays.Except(QueueDelays.Where(x => x.Value < DateTime.Now)).ToDictionary(x => x.Key, x => x.Value);
}
}
有了这个，我们有一个单独的类知道如何处理每个队列，它实现了IProcessQueues . 我们使用我们希望它处理的每种类型加载 _queueWeights 集合 . 我们设置 _resistance_to_scaling_larger_queues 常量来控制我们希望如何扩展 . 请注意，这会以对数方式缩放（请参阅 PopulateQueueMetaData 方法） . 没有队列的权重小于1，即使它有0项 . 如果将 PopulateQueueMetaData 设置为 10 ，那么对于每个幅度增加10的数量级，那个类型's 2789655 gets increased by 1. For example, if you have QueueA with 0 items, QueueB with 0 items, and QueueC with 10 items, then your respective weights are 1, 1, and 2. This means QueueC has a 50% chance of being processed next while QueueA and QueueB each only have a 25% chance to be processed. If QueueC has 100 items, then your weights are 1, 1, 3 and your chances to be processed are 20%, 20%, 60%. This ensures that your empty queues don' t都会被遗忘 .
3 回答
您可以为不同的任务启动不同的线程，但也要考虑非线程方法（根据您对消息的处理方式，可能会执行得更好或更差）：
以下是我们当前的实现，以更好的方式完成您的请求（或者我们认为） . 也就是说，这段代码还需要一些 heavy 清理工作 . 不过，这是0.1的功能版本 .
有了这个，我们有一个单独的类知道如何处理每个队列，它实现了IProcessQueues . 我们使用我们希望它处理的每种类型加载
_queueWeights集合 . 我们设置_resistance_to_scaling_larger_queues常量来控制我们希望如何扩展 . 请注意，这会以对数方式缩放（请参阅PopulateQueueMetaData方法） . 没有队列的权重小于1，即使它有0项 . 如果将PopulateQueueMetaData设置为10，那么对于每个幅度增加10的数量级，那个类型's 2789655 gets increased by 1. For example, if you have QueueA with 0 items, QueueB with 0 items, and QueueC with 10 items, then your respective weights are 1, 1, and 2. This means QueueC has a 50% chance of being processed next while QueueA and QueueB each only have a 25% chance to be processed. If QueueC has 100 items, then your weights are 1, 1, 3 and your chances to be processed are 20%, 20%, 60%. This ensures that your empty queues don' t都会被遗忘 .这样做的另一件事是它有
_minDelay和_maxDelay. 如果此代码认为队列中至少有一个项目，那么它将继续以_minDelay速率处理它 . 但是，如果它最后有0个项目，那么它将不允许以比_maxDelay速率更快的速度处理它 . 所以这意味着如果随机数生成器拉出具有0项的队列（无论重量），它将简单地跳过尝试处理它并继续下一次迭代 . （为了更好的存储事务效率，可以在此部分进行一些额外的优化，但这是一个很好的补充 . ）我们在这里有几个自定义类（例如
AzureQueue和AzureQueueMetaData） - 一个基本上是CloudQueue的包装器，另一个存储一些信息，例如队列的近似计数 - 没有什么有趣的（只是一种方法）简化代码） .同样，我不称这个“漂亮”的代码，但是一些相当聪明的概念在这段代码中都实现了并且功能齐全 . 出于任何原因使用它 . :)
最后，编写这样的代码可以让我们拥有一个可以处理更多队列的项目 . 如果我们发现这根本不是很好的话 . 将这个角色旋转到20个实体，直到它完成，然后将它们旋转回来 . 有一个特别讨厌的队列？从
_queueWeights集合排队的注释，部署以管理其余队列，然后使用除_queueWeights集合中注释掉的所有其他队列再次重新部署它，然后再将其部署到另一组实例并进行调试没有a）让其他QueueProcessors干扰你的调试和b）你的调试干扰你的其他QueueProcessors . 最终，这提供了很多灵活性和效率 .在worker角色的while循环内部，启动4个线程，就像编写多线程C＃应用程序一样 . 当然，您需要定义四个不同的线程函数，并且这些函数应该具有单独的while循环来轮询队列 . 在worker的while循环结束时，只需等待线程完成 .