Solving the Incompatibility of async-await in Parallel.ForEach

Introduction

In modern C# programming, combining asynchronous operations with parallel loops often presents challenges, especially when using Parallel.ForEach with async-await. The core issue stems from Parallel.ForEach being designed for CPU-intensive tasks that rely on multiple threads for parallel processing, whereas async-await is intended for I/O-bound operations that release threads during waiting. This mismatch can cause the loop to exit prematurely before all asynchronous calls complete, as Parallel.ForEach does not properly handle the completion of async Tasks.

Using TPL Dataflow Solution

To address this, it is recommended to use the TPL Dataflow library, part of the Task Parallel Library, which is well-suited for handling parallel asynchronous operations. Based on core concepts, a TransformBlock can be employed to transform data asynchronously, paired with an ActionBlock for processing results. Below is a rewritten code example for parallel WCF calls to fetch customer data and output results.

var ids = new List<string> { "1", "2", "3", "4", "5", "6", "7", "8", "9", "10" };

var getCustomerBlock = new TransformBlock<string, Customer>(
    async i =>
    {
        ICustomerRepo repo = new CustomerRepo();
        return await repo.GetCustomer(i);
    }, new ExecutionDataflowBlockOptions
    {
        MaxDegreeOfParallelism = DataflowBlockOptions.Unbounded
    });
var writeCustomerBlock = new ActionBlock<Customer>(c => Console.WriteLine(c.ID));
getCustomerBlock.LinkTo(
    writeCustomerBlock, new DataflowLinkOptions
    {
        PropagateCompletion = true
    });

foreach (var id in ids)
    getCustomerBlock.Post(id);

getCustomerBlock.Complete();
writeCustomerBlock.Completion.Wait();

In this example, the TransformBlock is configured with unbounded parallelism, using an async function to convert each ID to a Customer object. The subsequent ActionBlock outputs the results. By linking the blocks with LinkTo and setting PropagateCompletion, completion states are automatically propagated. In practice, adjust the MaxDegreeOfParallelism parameter to a small constant based on resource limits to avoid over-concurrency. This approach supports async-await effectively and allows processing to start immediately as individual items complete, rather than waiting for all operations.

Other Supplementary Solutions

Beyond TPL Dataflow, simpler alternatives exist. For instance, using Task.WhenAll enables parallel asynchronous calls, as shown below:

var customerTasks = ids.Select(i =>
{
    ICustomerRepo repo = new CustomerRepo();
    return repo.GetCustomer(i);
});
var customers = await Task.WhenAll(customerTasks);

This method is straightforward but lacks concurrency control, potentially leading to resource exhaustion. Alternatively, custom methods like RunWithMaxDegreeOfConcurrency or extension methods such as ForEachAsync can manage concurrency precisely by limiting the number of simultaneous Tasks. Third-party libraries like AsyncEnumerator offer convenient ParallelForEachAsync methods. However, based on the scenario, these supplementary solutions may be less flexible or efficient than TPL Dataflow for complex needs.

Conclusion

When combining async-await with parallel loops in C#, avoid direct use of Parallel.ForEach due to its inherent incompatibility with asynchronous patterns. Instead, leverage TPL Dataflow for robust handling of parallel asynchronous operations, providing concurrency control and thread safety. For simple cases, Task.WhenAll serves as a quick fix, while custom methods and third-party libraries cater to more specific requirements. Overall, selecting the appropriate parallel asynchronous implementation based on task nature and resource constraints can significantly enhance program performance and reliability.