c# - AsyncLocal 值与 TPL 数据流不正确

Question

考虑这个例子:

class Program

{
    private static readonly ITargetBlock<string> Mesh = CreateMesh();
    private static readonly AsyncLocal<string> AsyncLocalContext
        = new AsyncLocal<string>();

    static async Task Main(string[] args)
    {
        var tasks = Enumerable.Range(1, 4)
            .Select(ProcessMessage);
        await Task.WhenAll(tasks);

        Mesh.Complete();
        await Mesh.Completion;

        Console.WriteLine();
        Console.WriteLine("Done");
    }

    private static async Task ProcessMessage(int number)
    {
        var param = number.ToString();
        using (SetScopedAsyncLocal(param))
        {
            Console.WriteLine($"Before send {param}");
            await Mesh.SendAsync(param);
            Console.WriteLine($"After send {param}");
        }
    }

    private static IDisposable SetScopedAsyncLocal(string value)
    {
        AsyncLocalContext.Value = value;

        return new Disposer(() => AsyncLocalContext.Value = null);
    }

    private static ITargetBlock<string> CreateMesh()
    {
        var blockOptions = new ExecutionDataflowBlockOptions
        {
            BoundedCapacity = DataflowBlockOptions.Unbounded,
            EnsureOrdered = false,
            MaxDegreeOfParallelism = DataflowBlockOptions.Unbounded
        };

        var block1 = new TransformBlock<string, string>(async input =>
        {
            await Task.Yield();
            Console.WriteLine(
                $"   Block1 [thread {Thread.CurrentThread.ManagedThreadId}]" +
                $" Input: {input} - Context: {AsyncLocalContext.Value}.");

            return input;
        }, blockOptions);

        var block2 = new TransformBlock<string, string>(async input =>
        {
            await Task.Yield();
            Console.WriteLine(
                $"   Block2 [thread {Thread.CurrentThread.ManagedThreadId}]" +
                $" Input: {input} - Context: {AsyncLocalContext.Value}.");

            return input;
        }, blockOptions);

        var block3 = new ActionBlock<string>(async input =>
        {
            await Task.Yield();
            Console.WriteLine(
                $"   Block3 [thread {Thread.CurrentThread.ManagedThreadId}]" +
                $" Input: {input} - Context: {AsyncLocalContext.Value}.");
        }, blockOptions);

        var linkOptions = new DataflowLinkOptions {PropagateCompletion = true};

        block1.LinkTo(block2, linkOptions);
        block2.LinkTo(block3, linkOptions);

        return new EncapsulatedActionBlock<string>(block1, block3.Completion);
    }
}

internal class EncapsulatedActionBlock<T> : ITargetBlock<T>
{
    private readonly ITargetBlock<T> _wrapped;

    public EncapsulatedActionBlock(ITargetBlock<T> wrapped, Task completion)
    {
        _wrapped = wrapped;
        Completion = completion;
    }

    public DataflowMessageStatus OfferMessage(DataflowMessageHeader messageHeader,
        T messageValue, ISourceBlock<T> source, bool consumeToAccept) =>
        _wrapped.OfferMessage(messageHeader, messageValue, source, consumeToAccept);

    public void Complete() => _wrapped.Complete();

    public void Fault(Exception exception) => _wrapped.Fault(exception);

    public Task Completion { get; }
}

internal class Disposer : IDisposable
{
    private readonly Action _disposeAction;

    public Disposer(Action disposeAction)
    {
        _disposeAction = disposeAction
            ?? throw new ArgumentNullException(nameof(disposeAction));
    }

    public void Dispose()
    {
        _disposeAction();
    }
}

执行结果如下:

Before send 1After send 1Before send 2After send 2Before send 3After send 3Before send 4After send 4   Block1 [thread 9] Input: 3 - Context: 3.   Block1 [thread 10] Input: 2 - Context: 1.   Block1 [thread 8] Input: 4 - Context: 4.   Block1 [thread 11] Input: 1 - Context: 2.   Block2 [thread 9] Input: 2 - Context: 3.   Block2 [thread 7] Input: 1 - Context: 2.   Block2 [thread 10] Input: 3 - Context: 3.   Block2 [thread 8] Input: 4 - Context: 4.   Block3 [thread 11] Input: 4 - Context: 4.   Block3 [thread 7] Input: 1 - Context: 2.   Block3 [thread 9] Input: 3 - Context: 3.   Block3 [thread 4] Input: 2 - Context: 3.Done

如您所见，在移动到第二个 TDF block 后，传递的上下文值和存储的上下文值并不总是相同。此行为搞砸了多个日志记录框架的 LogContext 功能用法。

1. 这是预期的行为吗(请解释原因)？
2. TPL 数据流是否以某种方式扰乱了执行上下文？

Answer 1

要了解发生了什么，您必须了解数据流 block 的工作原理。它们内部没有阻塞的线程，等待消息到达。处理由工作任务完成。让我们考虑 MaxDegreeOfParallelism = 1 的简单(默认)情况。最初有零个工作任务。使用 SendAsync 异步发布消息时，发布消息的同一任务成为工作任务并开始处理消息。如果在处理第一条消息时发布另一条消息，则会发生其他情况。第二条消息将在 block 的输入队列中排队，发布它的任务将完成。第二条消息将由处理第一条消息的工作任务处理。只要队列中有消息入队，初始工作任务就会挑选它们并一条一条处理。如果在某个时刻没有更多的缓冲消息，工作任务将完成， block 将返回到它的初始状态(零工作任务)。下一个 SendAsync 将成为新的辅助任务，依此类推。使用 MaxDegreeOfParallelism = 1，在任何给定时刻只能存在一个工作任务。

让我们用一个例子来证明这一点。下面是一个ActionBlock以延迟 X 馈送，并以延迟 Y 处理每条消息。

private static void ActionBlockTest(int sendDelay, int processDelay)
{
    Console.WriteLine($"SendDelay: {sendDelay}, ProcessDelay: {processDelay}");
    var asyncLocal = new AsyncLocal<int>();
    var actionBlock = new ActionBlock<int>(async i =>
    {
        await Task.Delay(processDelay);
        Console.WriteLine($"Processed {i}, Context: {asyncLocal.Value}");
    });
    Task.Run(async () =>
    {
        foreach (var i in Enumerable.Range(1, 5))
        {
            asyncLocal.Value = i;
            await actionBlock.SendAsync(i);
            await Task.Delay(sendDelay);
        }
    }).Wait();
    actionBlock.Complete();
    actionBlock.Completion.Wait();
}

让我们看看如果我们快速发送消息并缓慢处理它们会发生什么:

ActionBlockTest(100, 200); // .NET Core 3.0

SendDelay: 100, ProcessDelay: 200
Processed 1, Context: 1
Processed 2, Context: 1
Processed 3, Context: 1
Processed 4, Context: 1
Processed 5, Context: 1

AsyncLocal上下文保持不变，因为同一个工作任务处理了所有消息。

现在让我们慢慢发送消息并快速处理它们:

ActionBlockTest(200, 100); // .NET Core 3.0

SendDelay: 200, ProcessDelay: 100
Processed 1, Context: 1
Processed 2, Context: 2
Processed 3, Context: 3
Processed 4, Context: 4
Processed 5, Context: 5

AsyncLocal 上下文对于每条消息都是不同的，因为每条消息都由不同的工作任务处理。

这个故事的道德教训是，每个 SendAsync 都不能保证创建一个异步工作流来跟踪消息，直到它的旅程结束，到达管道的末端。因此 AsyncLocal 类不能用于保存每条消息的环境数据。