.NET Concurrency: Choosing the Right Tool

When to Use This Skill

Use this skill when:

• Deciding how to handle concurrent operations in .NET
• Evaluating whether to use async/await, Channels, Dataflow, or other abstractions
• Tempted to use locks, semaphores, or other synchronization primitives
• Need to process streams of data with backpressure, batching, or debouncing
• Managing state across multiple concurrent entities

Reference Files

• advanced-concurrency.md: TPL Dataflow, Reactive Extensions, hosted worker state patterns, and async local function patterns

The Philosophy

Start simple, escalate only when needed.

Most concurrency problems can be solved with async/await. Only reach for more sophisticated tools when you have a specific need that async/await can't address cleanly.

Try to avoid shared mutable state. The best way to handle concurrency is to design it away. Immutable data, message passing, and isolated state (like actors) eliminate entire categories of bugs.

Locks should be the exception, not the rule. When you can't avoid shared mutable state:

1. First choice: Redesign to avoid it (immutability, message passing, single-consumer isolation)
2. Second choice: Use System.Collections.Concurrent (ConcurrentDictionary, etc.)
3. Third choice: Use Channel<T> to serialize access through message passing
4. Last resort: Use lock for simple, short-lived critical sections

---

Decision Tree

What are you trying to do?
│
├─► Wait for I/O (HTTP, database, file)?
│   └─► Use async/await
│
├─► Process a collection in parallel (CPU-bound)?
│   └─► Use Parallel.ForEachAsync
│
├─► Producer/consumer pattern (work queue)?
│   └─► Use System.Threading.Channels
│
├─► Need backpressure + multi-stage pipeline?
│   └─► Use TPL Dataflow
│
├─► UI event handling (debounce, throttle, combine)?
│   └─► Use Reactive Extensions (Rx)
│
├─► Coordinate multiple async operations?
│   └─► Use Task.WhenAll / Task.WhenAny
│
└─► None of the above fits?
    └─► Ask yourself: "Do I really need shared mutable state?"
        ├─► Yes → Consider redesigning to avoid it
        └─► Truly unavoidable → Use Channels or a dedicated single-consumer worker

---

Level 1: async/await (Default Choice)

Use for: I/O-bound operations, non-blocking waits, most everyday concurrency.

// Simple async I/O
public async Task<Order> GetOrderAsync(string orderId, CancellationToken ct)
{
    var order = await _database.GetAsync(orderId, ct);
    var customer = await _customerService.GetAsync(order.CustomerId, ct);
    return order with { Customer = customer };
}

// Parallel async operations (when independent)
public async Task<Dashboard> LoadDashboardAsync(string userId, CancellationToken ct)
{
    var ordersTask = _orderService.GetRecentOrdersAsync(userId, ct);
    var notificationsTask = _notificationService.GetUnreadAsync(userId, ct);
    var statsTask = _statsService.GetUserStatsAsync(userId, ct);

    await Task.WhenAll(ordersTask, notificationsTask, statsTask);

    return new Dashboard(
        Orders: await ordersTask,
        Notifications: await notificationsTask,
        Stats: await statsTask);
}

Key principles: Always accept CancellationToken. Use ConfigureAwait(false) in library code. Don't block on async code.

---

Level 2: Parallel.ForEachAsync (CPU-Bound Parallelism)

Use for: Processing collections in parallel when work is CPU-bound or you need controlled concurrency.

public async Task ProcessOrdersAsync(
    IEnumerable<Order> orders,
    CancellationToken ct)
{
    await Parallel.ForEachAsync(
        orders,
        new ParallelOptions
        {
            MaxDegreeOfParallelism = Environment.ProcessorCount,
            CancellationToken = ct
        },
        async (order, token) =>
        {
            await ProcessOrderAsync(order, token);
        });
}

When NOT to use: Pure I/O operations, when order matters, when you need backpressure.

---

Level 3: System.Threading.Channels (Producer/Consumer)

Use for: Work queues, producer/consumer patterns, decoupling producers from consumers.

public class OrderProcessor
{
    private readonly Channel<Order> _channel;

    public OrderProcessor()
    {
        _channel = Channel.CreateBounded<Order>(new BoundedChannelOptions(100)
        {
            FullMode = BoundedChannelFullMode.Wait
        });
    }

    // Producer
    public async Task EnqueueOrderAsync(Order order, CancellationToken ct)
    {
        await _channel.Writer.WriteAsync(order, ct);
    }

    // Consumer (run as background task)
    public async Task ProcessOrdersAsync(CancellationToken ct)
    {
        await foreach (var order in _channel.Reader.ReadAllAsync(ct))
        {
            await ProcessOrderAsync(order, ct);
        }
    }

    public void Complete() => _channel.Writer.Complete();
}

Channels are good for: Decoupling speed, buffering with backpressure, fan-out to workers, background queues.

Channels are NOT good for: Complex stream operations (batching, windowing), stateful per-entity processing, sophisticated supervision.

---

Level 4+: Dataflow, Reactive Extensions, and Hosted Workers

For advanced scenarios requiring stream processing, UI event composition, or stateful worker orchestration, see advanced-concurrency.md.

TPL Dataflow excels at server-side batching, throttling, and backpressure. Reactive Extensions are ideal for UI event composition. Hosted workers with Channels handle stateful sequential processing without lock-heavy code.

---

Anti-Patterns: What to Avoid

Locks for Business Logic

// BAD: Using locks to protect shared state
private readonly object _lock = new();
private Dictionary<string, Order> _orders = new();

public void UpdateOrder(string id, Action<Order> update)
{
    lock (_lock) { if (_orders.TryGetValue(id, out var order)) update(order); }
}

// GOOD: Use a single-consumer worker or Channel to serialize access

Manual Thread Management

// BAD: Creating threads manually
var thread = new Thread(() => ProcessOrders());
thread.Start();

// GOOD: Use Task.Run or better abstractions
_ = Task.Run(() => ProcessOrdersAsync(cancellationToken));

Blocking in Async Code

// BAD: Blocking on async - deadlock risk!
var result = GetDataAsync().Result;

// GOOD: Async all the way
var result = await GetDataAsync();

Shared Mutable State Without Protection

// BAD: Multiple tasks mutating shared state
var results = new List<Result>();
await Parallel.ForEachAsync(items, async (item, ct) =>
{
    var result = await ProcessAsync(item, ct);
    results.Add(result); // Race condition!
});

// GOOD: Use ConcurrentBag
var results = new ConcurrentBag<Result>();

---

Quick Reference: Which Tool When?

Need	Tool	Example
Wait for I/O	async/await	HTTP calls, database queries
Parallel CPU work	Parallel.ForEachAsync	Image processing, calculations
Work queue	Channel<T>	Background job processing
Multi-stage pipeline with backpressure	TPL Dataflow	ETL processing, ingestion pipelines
UI events with debounce/throttle	Reactive Extensions	Search-as-you-type, auto-save
Fire multiple async ops	Task.WhenAll	Loading dashboard data
Race multiple async ops	Task.WhenAny	Timeout with fallback
Periodic work	PeriodicTimer	Health checks, polling

---

The Escalation Path

async/await (start here)
    │
    ├─► Need parallelism? → Parallel.ForEachAsync
    │
    ├─► Need producer/consumer? → Channel<T>
    │
    ├─► Need staged pipeline + backpressure? → TPL Dataflow
    │
    └─► Need UI event composition? → Reactive Extensions

Only escalate when you have a concrete need. Don't reach for actors or streams "just in case".