C#并行-将项目添加到要迭代的集合中，还是等效?

问题描述

现在，我有一个C#程序，该程序可以重复执行以下步骤:

Right now, I've got a C# program that performs the following steps on a recurring basis:

• 从数据库中获取当前任务列表
• 使用Parallel.ForEach()来完成每个任务

但是，其中一些任务的运行时间很长.这会延迟其他待处理任务的处理，因为我们仅在程序开始时寻找新任务.

However, some of these tasks are very long-running. This delays the processing of other pending tasks because we only look for new ones at the start of the program.

现在，我知道不可能修改要迭代的集合(对吗?)，但是C#Parallel框架中有一些等效功能，可以使我向列表添加工作，同时还可以处理列表中的项目.列表吗?

Now, I know that modifying the collection being iterated over isn't possible (right?), but is there some equivalent functionality in the C# Parallel framework that would allow me to add work to the list while also processing items in the list?

推荐答案

通常来说，您是对的，不允许在迭代时修改集合.但是您可能会使用其他方法:

Generally speaking, you're right that modifying a collection while iterating it is not allowed. But there are other approaches you could be using:

• 使用TPL Dataflow中的 ActionBlock<T> .该代码可能类似于:

• Use ActionBlock<T> from TPL Dataflow. The code could look something like:

var actionBlock = new ActionBlock<MyTask>(
    task => DoWorkForTask(task),
    new ExecutionDataflowBlockOptions { MaxDegreeOfParallelism = DataflowBlockOptions.Unbounded });

while (true)
{
    var tasks = GrabCurrentListOfTasks();
    foreach (var task in tasks)
    {
        actionBlock.Post(task);

        await Task.Delay(someShortDelay);
        // or use Thread.Sleep() if you don't want to use async
    }
}

使用 BlockingCollection<T> ，在使用时可以对其进行修改以及来自ParallelExtensionsExtras的 GetConsumingParititioner() a>使其与Parallel.ForEach()一起使用:

Use BlockingCollection<T>, which can be modified while consuming items from it, along with GetConsumingParititioner() from ParallelExtensionsExtras to make it work with Parallel.ForEach():

var collection = new BlockingCollection<MyTask>();

Task.Run(async () =>
{
    while (true)
    {
        var tasks = GrabCurrentListOfTasks();
        foreach (var task in tasks)
        {
            collection.Add(task);

            await Task.Delay(someShortDelay);
        }
    }
});

Parallel.ForEach(collection.GetConsumingPartitioner(), task => DoWorkForTask(task));

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