.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, Akka.NET, 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: Akka.NET Streams, Reactive Extensions, Akka.NET Actors (entity-per-actor, state machines, cluster sharding), 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:
- First choice: Redesign to avoid it (immutability, message passing, actor isolation)
- Second choice: Use
System.Collections.Concurrent (ConcurrentDictionary, etc.)
- Third choice: Use
Channel<T> to serialize access through message passing
- 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
│
├─► UI event handling (debounce, throttle, combine)?
│ └─► Use Reactive Extensions (Rx)
│
├─► Server-side stream processing (backpressure, batching)?
│ └─► Use Akka.NET Streams
│
├─► State machines with complex transitions?
│ └─► Use Akka.NET Actors (Become pattern)
│
├─► Manage state for many independent entities?
│ └─► Use Akka.NET Actors (entity-per-actor)
│
├─► 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 Actors to serialize access
Level 1: async/await (Default Choice)
Use for: I/O-bound operations, non-blocking waits, most everyday concurrency.
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 };
}
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
});
}
public async Task EnqueueOrderAsync(Order order, CancellationToken ct)
{
await _channel.Writer.WriteAsync(order, ct);
}
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+: Akka.NET Streams, Reactive Extensions, Actors
For advanced scenarios requiring stream processing, UI event composition, or stateful entity management, see advanced-concurrency.md.
Akka.NET Streams excel at server-side batching, throttling, and backpressure. Reactive Extensions are ideal for UI event composition. Akka.NET Actors handle entity-per-actor patterns, state machines with Become(), and distributed systems via Cluster Sharding.
Anti-Patterns: What to Avoid
Locks for Business Logic
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); }
}
Manual Thread Management
var thread = new Thread(() => ProcessOrders());
thread.Start();
_ = Task.
