.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

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, using a lock occasionally isn't the end of the world. But if you find yourself reaching for lock, SemaphoreSlim, or other synchronization primitives regularly, step back and reconsider your design.

When you truly need shared mutable state:

1. First choice: Redesign to avoid it (immutability, message passing, actor isolation)
2. Second choice: Use System.Collections.Concurrent (ConcurrentDictionary, ConcurrentQueue, 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
│
├─► 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
│
├─► Need to protect shared mutable state with synchronization?
│   └─► Is the shared state a single scalar (int, long, reference)?
│       YES -> Use Interlocked (lock-free, lowest overhead)
│
│       Is the shared state a key-value lookup or queue?
│       YES -> Use ConcurrentDictionary / ConcurrentQueue (thread-safe by design)
│
│       Does the critical section contain `await`?
│       YES -> Use SemaphoreSlim (async-compatible via WaitAsync)
│       NO  -> Does the critical section need many readers, few writers?
│                YES -> Use ReaderWriterLockSlim (only if profiling shows lock contention)
│                NO  -> Use lock (simplest, lowest cognitive overhead)
│
│       Is the critical section extremely short (< 100 ns) with high contention?
│       YES -> Consider SpinLock (advanced, measure first)
│
└─► 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 (no .Result or .Wait())

---

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);
        });
}

public async Task<IReadOnlyList<ProcessedImage>> ProcessImagesAsync(
    IEnumerable<string> imagePaths,
    CancellationToken ct)
{
    var results = new ConcurrentBag<ProcessedImage>();

    await Parallel.ForEachAsync(
        imagePaths,
        new ParallelOptions { MaxDegreeOfParallelism = 4, CancellationToken = ct },
        async (path, token) =>
        {
            var image = await File.ReadAllBytesAsync(path, token);
            var processed = ProcessImage(image);
            results.Add(processed);
        });

    return results.ToList();
}

When NOT to use:

• Pure I/O operations (async/await is sufficient)
• When order matters (Parallel doesn't preserve order)
• When you need backpressure or flow control

---

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

Use for: Work queues, producer/consumer patterns, decoupling producers from consumers, simple stream-like processing.

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();
}

public class WorkerPool
{
    private readonly Channel<WorkItem> _channel;
    private readonly List<Task> _workers = new();

    public WorkerPool(int workerCount)
    {
        _channel = Channel.CreateUnbounded<WorkItem>();

        for (int i = 0; i < workerCount; i++)
        {
            _workers.Add(Task.Run(() => ConsumeAsync()));
        }
    }

    private async Task ConsumeAsync()
    {
        await foreach (var item in _channel.Reader.ReadAllAsync())
        {
            await ProcessAsync(item);
        }
    }

    public ValueTask EnqueueAsync(WorkItem item)
        => _channel.Writer.WriteAsync(item);
}

Channels are good for:

• Decoupling producer speed from consumer speed
• Buffering work with backpressure
• Simple fan-out to multiple workers
• Background processing queues

Channels are NOT good for:

• Complex stream operations (batching, windowing, merging)
• Stateful processing per entity
• When you need sophisticated error handling/supervision

---

Level 4: Akka.NET Streams (Complex Stream Processing)

Use for: Backpressure, batching, debouncing, throttling, merging streams, complex transformations.

using Akka.Streams;
using Akka.Streams.Dsl;

public Source<IReadOnlyList<Event>, NotUsed> BatchEvents(
    Source<Event, NotUsed> events)
{
    return events
        .GroupedWithin(100, TimeSpan.FromSeconds(1))
        .Select(batch => batch.ToList() as IReadOnlyList<Event>);
}

public Source<Request, NotUsed> ThrottleRequests(
    Source<Request, NotUsed> requests)
{
    return requests
        .Throttle(10, TimeSpan.FromSeconds(1), 5, ThrottleMode.Shaping);
}

public Source<ProcessedItem, NotUsed> ProcessWithParallelism(
    Source<Item, NotUsed> items)
{
    return items
        .SelectAsync(4, async item => await ProcessAsync(item));
}

public IRunnableGraph<Task<Done>> CreatePipeline(
    Source<RawEvent, NotUsed> events,
    Sink<ProcessedEvent, Task<Done>> sink)
{
    return events
        .Where(e => e.IsValid)
        .GroupedWithin(50, TimeSpan.FromMilliseconds(500))
        .SelectAsync(4, batch => ProcessBatchAsync(batch))
        .SelectMany(results => results)
        .ToMaterialized(sink, Keep.Right);
}

---

Level 4b: Reactive Extensions (UI and Event Composition)

Use for: UI event handling, composing event streams, time-based operations in client applications.

using System.Reactive.Linq;

public class SearchViewModel
{
    public SearchViewModel(ISearchService searchService)
    {
        SearchResults = SearchText
            .Throttle(TimeSpan.FromMilliseconds(300))
            .DistinctUntilChanged()
            .Where(text => text.Length >= 3)
            .SelectMany(text => searchService.SearchAsync(text).ToObservable())
            .ObserveOn(RxApp.MainThreadScheduler);
    }

    public IObservable<string> SearchText { get; }
    public IObservable<IList<SearchResult>> SearchResults { get; }
}

public IObservable<bool> CanSubmit =>
    Observable.CombineLatest(
        UsernameValid,
        PasswordValid,
        EmailValid,
        (user, pass, email) => user && pass && email);

public IObservable<Point> DoubleClicks =>
    MouseClicks
        .Buffer(TimeSpan.FromMilliseconds(300))
        .Where(clicks => clicks.Count >= 2)
        .Select(clicks => clicks.Last());

public IDisposable AutoSave =>
    DocumentChanges
        .Throttle(TimeSpan.FromSeconds(2))
        .Subscribe(async doc => await SaveAsync(doc));

Rx vs Akka.NET Streams:

Scenario	Rx	Akka.NET Streams
UI events	Best choice	Overkill
Client-side composition	Best choice	Overkill
Server-side pipelines	Works but limited	Better backpressure
Distributed processing	Not designed for	Built for this
Hot observables	Native support	Requires more setup

---

Level 5: Akka.NET Actors (Stateful Concurrency)

Use for: Managing state for multiple entities, state machines, push-based updates, complex coordination, supervision and fault tolerance.

Entity-Per-Actor Pattern

public class OrderActor : ReceiveActor
{
    private OrderState _state;

    public OrderActor(string orderId)
    {
        _state = new OrderState(orderId);

        Receive<AddItem>(msg =>
        {
            _state = _state.AddItem(msg.Item);
            Sender.Tell(new ItemAdded(msg.Item));
        });

        Receive<Checkout>(msg =>
        {
            if (_state.CanCheckout)
            {
                _state = _state.Checkout();
                Sender.Tell(new CheckoutSucceeded(_state.Total));
            }
            else
            {
                Sender.Tell(new CheckoutFailed("Cart is empty"));
            }
        });

        Receive<GetState>(_ => Sender.Tell(_state));
    }
}

State Machines with Become

public class PaymentActor : ReceiveActor
{
    private PaymentData _payment;

    public PaymentActor(string paymentId)
    {
        _payment = new PaymentData(paymentId);
        Pending();
    }

    private void Pending()
    {
        Receive<AuthorizePayment>(msg =>
        {
            _payment = _payment with { Amount = msg.Amount };
            Become(Authorizing);
            Self.Tell(new ProcessAuthorization());
        });

        Receive<CancelPayment>(_ =>
        {
            Become(Cancelled);
            Sender.Tell(new PaymentCancelled(_payment.Id));
        });
    }

    private void Authorizing()
    {
        Receive<ProcessAuthorization>(async _ =>
        {
            var result = await _gateway.AuthorizeAsync(_payment);
            if (result.Success)
            {
                _payment = _payment with { AuthCode = result.AuthCode };
                Become(Authorized);
            }
            else
            {
                Become(Failed);
            }
        });

        Receive<CancelPayment>(_ =>
        {
            Sender.Tell(new PaymentError("Cannot cancel during authorization"));
        });
    }

    private void Authorized()
    {
        Receive<CapturePayment>(_ =>
        {
            Become(Capturing);
            Self.Tell(new ProcessCapture());
        });

        Receive<VoidPayment>(_ =>
        {
            Become(Voiding);
            Self.Tell(new ProcessVoid());
        });
    }

    private void Capturing() { }
    private void Voiding() { }
    private void Cancelled() { }
    private void Failed() { }
}

---

Synchronization Primitives

When you must use shared mutable state, choose the simplest primitive that meets the requirement.

Quick Reference Table

Primitive	Async-Safe	Reentrant	Use Case
lock / Monitor	No	Yes (same thread)	Short critical sections without await
SemaphoreSlim	Yes (WaitAsync)	No	Async-compatible mutual exclusion, throttling
Interlocked	N/A (lock-free)	N/A	Atomic scalar operations (increment, compare-exchange)
ConcurrentDictionary<K,V>	N/A (thread-safe)	N/A	Thread-safe key-value cache/lookup
ConcurrentQueue<T>	N/A (thread-safe)	N/A	Thread-safe FIFO queue
ReaderWriterLockSlim	No	Optional (LockRecursionPolicy)	Many-readers/few-writers (profile-driven only)
SpinLock	No	No	Ultra-short critical sections under extreme contention

lock and Monitor

public sealed class Counter
{
    private readonly object _lock = new();
    private int _count;

    public void Increment()
    {
        lock (_lock)
        {
            _count++;
        }
    }

    public int GetCount()
    {
        lock (_lock)
        {
            return _count;
        }
    }
}

Lock Object Rules:

• Use a private, dedicated object field
• Never lock on this
• Never lock on typeof(T)
• Never lock on string literals
• Never lock on value types

SemaphoreSlim

The only built-in .NET synchronization primitive that supports await:

public sealed class AsyncCache
{
    private readonly SemaphoreSlim _semaphore = new(1, 1);
    private readonly Dictionary<string, object> _cache = new();

    public async Task<T> GetOrAddAsync<T>(string key,
        Func<CancellationToken, Task<T>> factory,
        CancellationToken ct = default)
    {
        await _semaphore.WaitAsync(ct);
        try
        {
            if (_cache.TryGetValue(key, out var existing))
                return (T)existing;

            var value = await factory(ct);
            _cache[key] = value!;
            return value;
        }
        finally
        {
            _semaphore.Release();
        }
    }
}

Interlocked Operations

Lock-free atomic operations for scalar values:

private int _counter;
private long _totalBytes;
private object? _current;

Interlocked.Increment(ref _counter);
Interlocked.Decrement(ref _counter);
Interlocked.Add(ref _totalBytes, bytesRead);
var previous = Interlocked.Exchange(ref _current, newValue);
var original = Interlocked.CompareExchange(ref _counter, newValue: 10, comparand: 0);

ConcurrentDictionary

private readonly ConcurrentDictionary<int, Widget> _cache = new();

var widget = _cache.GetOrAdd(id, key => LoadWidget(key));
var updated = _cache.AddOrUpdate(id,
    addValueFactory: key => CreateDefault(key),
    updateValueFactory: (key, existing) => existing with { LastAccessed = DateTime.UtcNow });

if (_cache.TryRemove(id, out var removed))
{
}

Important: GetOrAdd factory delegates may execute multiple times under contention. Use Lazy<T> wrapping for exactly-once semantics.

---

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 an actor or Channel to serialize access

Blocking in Async Code

// BAD: Blocking on async
var result = GetDataAsync().Result;
GetDataAsync().Wait();

// 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 or collect results differently
var results = new ConcurrentBag<Result>();

Do not use lock inside async methods

lock is thread-affine; the continuation after await may resume on a different thread, causing SynchronizationLockException. Use SemaphoreSlim.WaitAsync instead.

---

Prefer Async Local Functions

Use async local functions instead of Task.Run(async () => ...) or ContinueWith():

private void HandleCommand(MyCommand cmd)
{
    async Task<WorkCompleted> ExecuteAsync()
    {
        var result = await DoWorkAsync();
        return new WorkCompleted(result);
    }

    ExecuteAsync().PipeTo(Self);
}

---

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
UI events with debounce/throttle	Reactive Extensions	Search-as-you-type, auto-save
Server-side batching/throttling	Akka.NET Streams	Event aggregation, rate limiting
State machines	Akka.NET Actors	Payment flows, order lifecycles
Entity state management	Akka.NET Actors	Order management, user sessions
Fire multiple async ops	Task.WhenAll	Loading dashboard data
Race multiple async ops	Task.WhenAny	Timeout with fallback
Periodic work	PeriodicTimer	Health checks, polling
Protect single scalar	Interlocked	Counters, flags
Protect key-value state	ConcurrentDictionary	Caches, lookups
Async-compatible mutex	SemaphoreSlim	Async critical sections
Simple synchronous mutex	lock	Short critical sections without await

---

The Escalation Path

async/await (start here)
    │
    ├─► Need parallelism? → Parallel.ForEachAsync
    │
    ├─► Need producer/consumer? → Channel<T>
    │
    ├─► Need UI event composition? → Reactive Extensions
    │
    ├─► Need server-side stream processing? → Akka.NET Streams
    │
    └─► Need state machines or entity management? → Akka.NET Actors

Only escalate when you have a concrete need. Don't reach for actors or streams "just in case" - start with async/await and move up only when the simpler approach doesn't fit.

---

References

• Threading in C# (Joseph Albahari)
• Concurrency in C# Cookbook (Stephen Cleary)
• System.Threading.Interlocked
• ConcurrentDictionary best practices
• SemaphoreSlim class
• ReaderWriterLockSlim class
• Pattern Matching
• Span<T> and Memory<T>
• Async Best Practices