Using ActionBlock as a Multithread Recursive Dynamic Queue in .NET

This content originally appeared on DEV Community and was authored by Danilo Maia Florenzano

“TPL Dataflow is an interesting mix of asynchronous and parallel technologies. It’s useful when you have a sequence of processes that need to be applied to your data.”

— Stephen Cleary, Concurrency in C# Cookbook

I recently needed to traverse a recursive category tree — starting with ~20 root nodes, each with an unknown number of subcategories (and sub-subcategories, and so on).

This kind of dynamically expanding workload doesn’t play well with static parallelization tools like Parallel.ForEach.

The Problem: Static Work Distribution

If we run Parallel.ForEach on the 20 root categories:

• Thread 1 might get a small branch and finish in 1 second.
• Thread 2 might get a massive branch and take 10 minutes.

Even with MaxDegreeOfParallelism = 50, most threads finish early and go idle — while a few get stuck processing deep, heavy trees. Load imbalance and wasted resources.

The Solution: ActionBlock<T> as a Dynamic Work Queue

ActionBlock<T> from TPL Dataflow provides a centralized, thread-safe queue with dynamic task generation and controlled concurrency.

Here’s how it works:

1. Create one ActionBlock<CategoryNode> with a fixed concurrency limit.
2. “Prime” it with the initial root categories.
3. Each worker processes its node, finds subcategories, and posts them back into the same ActionBlock.
4. The block keeps running until all items (current + pending) are done.

This pattern is effectively a recursive, self-balancing queue — all threads stay busy until the entire tree is processed.

Example Implementation

public class MultithreadTreeParser
{
    private int _activeItems;
    private ActionBlock<CategoryNode> _actionBlock = null!;

    public async Task<int> StartAsync()
    {
        var rootNode = await GetDepartmentsRootNodeAsync()
            ?? throw new Exception("Failed to get root node");

        var options = new ExecutionDataflowBlockOptions
        {
            MaxDegreeOfParallelism = 5
        };

        _actionBlock = new ActionBlock<CategoryNode>(async node =>
        {
            try
            {
                await ProcessCategoryAsync(node);

                var subs = node.SubCategories;
                if (subs.Count > 0)
                {
                    Interlocked.Add(ref _activeItems, subs.Count);
                    foreach (var sub in subs)
                        await _actionBlock.SendAsync(sub);
                }
            }
            finally
            {
                var remaining = Interlocked.Decrement(ref _activeItems);
                if (remaining == 0)
                    _actionBlock.Complete();
            }
        }, options);

        var rootSubs = rootNode.SubCategories;
        Interlocked.Add(ref _activeItems, rootSubs.Count);

        foreach (var sub in rootSubs)
            await _actionBlock.SendAsync(sub);

        await _actionBlock.Completion;
        return 0;
    }
}

Why This Works

• Dynamic load balancing: Workers pull from a shared queue. As soon as they finish, they grab the next available node.
• Recursive workload expansion: Each task can add new tasks safely to the same queue.
• Controlled parallelism: MaxDegreeOfParallelism keeps resource usage in check.
• Clean completion tracking: _activeItems counts in-flight work — when it hits zero, the pipeline gracefully completes.

This content originally appeared on DEV Community and was authored by Danilo Maia Florenzano