TPL 数据流管道中的工作单元问题

Question

我有一个 classic 生产者消费者 问题，其中多个用户可以同时 POST 数据到网络 API 方法（api/test)，异步触发 IO 密集 long 运行 操作。我使用链接到 BufferBlock.

的 ActionBlock 将并发请求数量限制为 5

Producer class 被注册为单例，目标是允许对 api/test 的所有调用都进入这个队列。这意味着完成街区之类的事情不是一个选项。

等待控制器完成我启动的工作的最有效方法是什么？

Web API 控制器：

[Route("api/test")]
[ApiController]
public class TestController : ControllerBase
{
    private Producer producer;

    public TestController(Producer producer)
    {
        this.producer = producer;
    }
    [HttpGet]
    public async Task<string[]> Values()
    {
        for (int i = 1; i <= 10; i++)
        {
            await this.producer.AddAsync(1);
        }

        // i've added my work to the queue, elegant completion required
        return new string[] { "value1", "value2" };
    }

}

生产者/消费者实施：

public class Producer
{
    private BufferBlock<int> queue;
    private ActionBlock<int> consumer;
    public List<int> results = new List<int>();

    private void InitializeChain()
    {
        queue = new BufferBlock<int>(new DataflowBlockOptions { BoundedCapacity = 5, });
        var consumerOptions = new ExecutionDataflowBlockOptions { BoundedCapacity = 5, MaxDegreeOfParallelism = 5 };
        consumer = new ActionBlock<int>(x =>
        {
            Thread.Sleep(5000);
            Debug.WriteLine(x + " " + Thread.CurrentThread.ManagedThreadId);
            results.Add(x);
        }, consumerOptions);
        queue.LinkTo(consumer, new DataflowLinkOptions { PropagateCompletion = true });
    }
    public async Task AddAsync(int data)
    {
        await queue.SendAsync(data);
    }
    public Producer()
    {
        this.InitializeChain();
    }
}

Answer 1

所以有很多方法和同步原语可以用来解决这个问题，每个都有自己的好处、容错和问题，具体取决于您的需要。这是一个 awaitable 示例 TaskCompletionSource

给定

public class Producer
{
   private BufferBlock<int> _queue;
   private ActionBlock<int> _consumer;
   public Action<int,string> OnResult;
   public Producer()
   {
      InitializeChain();
   }
   private void InitializeChain()
   {
      _queue = new BufferBlock<int>(new DataflowBlockOptions { BoundedCapacity = 5 });
      var consumerOptions = new ExecutionDataflowBlockOptions { BoundedCapacity = 5, MaxDegreeOfParallelism = 5 };    
      _consumer = new ActionBlock<int>(SomeIoWorkAsync, consumerOptions);   
      _queue.LinkTo(_consumer, new DataflowLinkOptions { PropagateCompletion = true });
   }

   private async Task SomeIoWorkAsync(int x)
   {
      Console.WriteLine($"{DateTime.Now.TimeOfDay:g} : Processing {x}");
      await Task.Delay(5000);
      OnResult?.Invoke(x,$"SomeResult {x}");
   }

   public Task AddAsync(int data) => _queue.SendAsync(data);
}

等待

您可以轻松地将其重构为在一个调用中完成发送和等待。

public static Task<string> WaitForConsumerAsync(Producer producer,int myId)
{
   var tcs = new TaskCompletionSource<string>();

   producer.OnResult += (id,result) =>
   {
      if(id == myId)
         tcs.TrySetResult(result);
   };

   return tcs.Task;
}

用法

var producer = new Producer();

// to simulate something you are waiting for, and id or what ever
var myId = 7;

// you could send and await in the same method if needed. this is just an example
var task = WaitForConsumerAsync(producer,myId);

// create random work for the bounded capacity to fill up
// run this as a task so we don't hit the back pressure before we await (just for this test)
Task.Run(async () =>
{
   for (int i = 1; i <= 20; i++)
      await producer.AddAsync(i);
});

// wait for your results to pop out
var result = await task;

Console.WriteLine($"{DateTime.Now.TimeOfDay:g} : Got my result {result}, now i can finish happily");

// you can happily end here, the pipeline will keep going
Console.ReadKey();

输出

12:04:41.62464 : Processing 3
12:04:41.6246489 : Processing 1
12:04:41.6246682 : Processing 2
12:04:41.624641 : Processing 4
12:04:41.624661 : Processing 5
12:04:41.8530723 : Processing 7
12:04:41.8530791 : Processing 8
12:04:41.8531427 : Processing 10
12:04:41.8530716 : Processing 6
12:04:41.8530967 : Processing 9
12:04:42.0531947 : Got my result SomeResult 7, now i can finish happily
12:04:42.0532178 : Processing 11
12:04:42.0532453 : Processing 12
12:04:42.0532721 : Processing 14
12:04:42.0532533 : Processing 13
12:04:42.2674406 : Processing 15
12:04:42.2709914 : Processing 16
12:04:42.2713017 : Processing 18
12:04:42.2710417 : Processing 17
12:04:42.4689852 : Processing 19
12:04:42.4721405 : Processing 20

Full Demo Here

_{注意：您可能需要玩这个例子，这样它就不会超时}

一次完成所有操作的示例

public async Task<string> AddAsync(int data)
{
   await _queue.SendAsync(data);
   return await WaitForConsumerAsync(data);
}

public Task<string> WaitForConsumerAsync(int data)
{
   var tcs = new TaskCompletionSource<string>();

   OnResult += (id, result) =>
   {
      if (id == data)
         tcs.TrySetResult(result);
   };

   return tcs.Task;
}

补充说明

这实际上只是一个 awaitable 事件的学术示例。我假设您的管道比给出的示例更复杂，并且您正在组合 CPU 和 IO 绑定工作负载，此外您实际上需要 BufferBlock在这个例子中它是多余的。

如果您正在等待纯 IO 工作负载，您最好只等待它们，不需要管道。
根据您提供的信息，除非您有某种内存限制，否则没有必要使用 BoundedCapacity 创建背压。
您需要小心 BoundedCapacity 和默认值 EnsureOrdered = true。使用 EnsureOrdered = false，管道将更加高效。作业将在完成时 pop-out 并且背压不会受到不同结果排序的影响，这意味着项目可能会更快地通过管道
你也可以使用其他框架，比如 RX，这可能会让这一切变得更加优雅和流畅
你也可以通过设置 SingleProducerConstrained = true 来提高效率，因为你的块是线性的

TPL 数据流管道中的工作单元问题

The unit of work problem in TPL Dataflow Pipeline

c#

task-parallel-library

async-await

tpl-dataflow