akka-net-best-practices

name	akka-net-best-practices
description	Critical Akka.NET best practices including EventStream vs DistributedPubSub, supervision strategies, error handling, Props vs DependencyResolver, work distribution patterns, and cluster/local mode abstractions for testability.
invocable	false

Akka.NET Best Practices

When to Use This Skill

Use this skill when:

• Designing actor communication patterns
• Deciding between EventStream and DistributedPubSub
• Implementing error handling in actors
• Understanding supervision strategies
• Choosing between Props patterns and DependencyResolver
• Designing work distribution across nodes
• Creating testable actor systems that can run with or without cluster infrastructure
• Abstracting over Cluster Sharding for local testing scenarios

Reference Files

• work-distribution-patterns.md: Database queues, Akka.Streams throttling, outbox pattern
• cluster-local-abstractions.md: GenericChildPerEntityParent, IPubSubMediator, execution mode wiring
• async-cancellation-patterns.md: Actor-scoped CancellationToken, linked CTS, timeout handling

---

1. EventStream vs DistributedPubSub

Critical: EventStream is LOCAL ONLY

Context.System.EventStream is local to a single ActorSystem process. It does NOT work across cluster nodes.

// BAD: This only works on a single server
// When you add a second server, subscribers on server 2 won't receive events from server 1
Context.System.EventStream.Subscribe(Self, typeof(PostCreated));
Context.System.EventStream.Publish(new PostCreated(postId, authorId));

When EventStream is appropriate:

• Logging and diagnostics within a single process
• Local event bus for truly single-process applications
• Development/testing scenarios

Use DistributedPubSub for Multi-Node

For events that must reach actors across multiple cluster nodes, use Akka.Cluster.Tools.PublishSubscribe:

using Akka.Cluster.Tools.PublishSubscribe;

public class TimelineUpdatePublisher : ReceiveActor
{
    private readonly IActorRef _mediator;

    public TimelineUpdatePublisher()
    {
        // Get the DistributedPubSub mediator
        _mediator = DistributedPubSub.Get(Context.System).Mediator;

        Receive<PublishTimelineUpdate>(msg =>
        {
            // Publish to a topic - reaches all subscribers across all nodes
            _mediator.Tell(new Publish($"timeline:{msg.UserId}", msg.Update));
        });
    }
}

Akka.Hosting Configuration for DistributedPubSub

builder.WithDistributedPubSub(role: null); // Available on all roles, or specify a role

Topic Design Patterns

Pattern	Topic Format	Use Case
Per-user	timeline:{userId}	Timeline updates, notifications
Per-entity	post:{postId}	Post engagement updates
Broadcast	system:announcements	System-wide notifications
Role-based	workers:rss-poller	Work distribution

---

2. Supervision Strategies

Key Clarification: Supervision is for CHILDREN

A supervision strategy defined on an actor dictates how that actor supervises its children, NOT how the actor itself is supervised.

public class ParentActor : ReceiveActor
{
    // This strategy applies to children of ParentActor, NOT to ParentActor itself
    protected override SupervisorStrategy SupervisorStrategy()
    {
        return new OneForOneStrategy(
            maxNrOfRetries: 10,
            withinTimeRange: TimeSpan.FromSeconds(30),
            decider: ex => ex switch
            {
                ArithmeticException => Directive.Resume,
                NullReferenceException => Directive.Restart,
                ArgumentException => Directive.Stop,
                _ => Directive.Escalate
            });
    }
}

Default Supervision Strategy

The default OneForOneStrategy already includes rate limiting:

• 10 restarts within 1 second = actor is permanently stopped
• This prevents infinite restart loops

You rarely need a custom strategy unless you have specific requirements.

When to Define Custom Supervision

Good reasons:

• Actor throws exceptions indicating irrecoverable state corruption -> Restart
• Actor throws exceptions that should NOT cause restart (expected failures) -> Resume
• Child failures should affect siblings -> Use AllForOneStrategy
• Need different retry limits than the default

Bad reasons:

• "Just to be safe" - the default is already safe
• Don't understand what the actor does - understand it first

---

3. Error Handling: Supervision vs Try-Catch

When to Use Try-Catch (Most Cases)

Use try-catch when:

• The failure is expected (network timeout, invalid input, external service down)
• You know exactly why the exception occurred
• You can handle it gracefully (retry, return error response, log and continue)
• Restarting would not help (same error would occur again)

public class RssFeedPollerActor : ReceiveActor
{
    public RssFeedPollerActor()
    {
        ReceiveAsync<PollFeed>(async msg =>
        {
            try
            {
                var feed = await _httpClient.GetStringAsync(msg.FeedUrl);
                var items = ParseFeed(feed);
                // Process items...
            }
            catch (HttpRequestException ex)
            {
                // Expected failure - log and schedule retry
                _log.Warning("Feed {Url} unavailable: {Error}", msg.FeedUrl, ex.Message);
                Context.System.Scheduler.ScheduleTellOnce(
                    TimeSpan.FromMinutes(5), Self, msg, Self);
            }
            catch (XmlException ex)
            {
                // Invalid feed format - log and mark as bad
                _log.Error("Feed {Url} has invalid format: {Error}", msg.FeedUrl, ex.Message);
                Sender.Tell(new FeedPollResult.InvalidFormat(msg.FeedUrl));
            }
        });
    }
}

When to Let Supervision Handle It

Let exceptions propagate (trigger supervision) when:

• You have no idea why the exception occurred
• The actor's state might be corrupt
• A restart would help (fresh state, reconnect resources)
• It's a programming error (NullReferenceException, InvalidOperationException from bad logic)

Anti-Pattern: Swallowing Unknown Exceptions

// BAD: Swallowing exceptions hides problems
catch (Exception ex)
{
    _log.Error(ex, "Error processing work");
    // Actor continues with potentially corrupt state
}

// GOOD: Handle known exceptions, let unknown ones propagate
catch (HttpRequestException ex)
{
    // Known, expected failure - handle gracefully
    _log.Warning("HTTP request failed: {Error}", ex.Message);
    Sender.Tell(new WorkResult.TransientFailure());
}
// Unknown exceptions propagate to supervision

---

4. Props vs DependencyResolver

When to Use Plain Props

Use Props.Create() when:

• Actor doesn't need IServiceProvider or IRequiredActor<T>
• All dependencies can be passed via constructor
• Actor is simple and self-contained

// Simple actor with no DI needs
public static Props Props(PostId postId, IPostWriteStore store)
    => Akka.Actor.Props.Create(() => new PostEngagementActor(postId, store));

When to Use DependencyResolver

Use resolver.Props<T>() when:

• Actor needs IServiceProvider to create scoped services
• Actor uses IRequiredActor<T> to get references to other actors
• Actor has many dependencies that are already in DI container

// Registration with DI
builder.WithActors((system, registry, resolver) =>
{
    var actor = system.ActorOf(resolver.Props<OrderProcessorActor>(), "order-processor");
    registry.Register<OrderProcessorActor>(actor);
});

Remote Deployment Considerations

You almost never need remote deployment. If you're not doing remote deployment (and you probably aren't):

• Props.Create(() => new Actor(...)) with closures is fine
• The "serialization issue" warning doesn't apply

For most applications, use cluster sharding instead of remote deployment - it handles distribution automatically.

---

5. Work Distribution Patterns

When you have many background jobs (RSS feeds, email sending, etc.), don't process them all at once - this causes thundering herd problems.

Three patterns to solve this:

1. Database-Driven Work Queue - Use FOR UPDATE SKIP LOCKED for natural cross-node distribution
2. Akka.Streams Rate Limiting - Throttle processing within a single node
3. Durable Queue (Outbox Pattern) - Database-backed outbox for reliable processing

See work-distribution-patterns.md for full code samples.

---

6. Common Mistakes Summary

Mistake	Why It's Wrong	Fix
Using EventStream for cross-node pub/sub	EventStream is local only	Use DistributedPubSub
Defining supervision to "protect" an actor	Supervision protects children	Understand the hierarchy
Catching all exceptions	Hides bugs, corrupts state	Only catch expected errors
Always using DependencyResolver	Adds unnecessary complexity	Use plain Props when possible
Processing all background jobs at once	Thundering herd, resource exhaustion	Use database queue + rate limiting
Throwing exceptions for expected failures	Triggers unnecessary restarts	Return result types, use messaging

---

7. Quick Reference

Communication Pattern Decision Tree

Need to communicate between actors?
├── Same process only? -> EventStream is fine
├── Across cluster nodes?
│   ├── Point-to-point? -> Use ActorSelection or known IActorRef
│   └── Pub/sub? -> Use DistributedPubSub
└── Fire-and-forget to external system? -> Consider outbox pattern

Error Handling Decision Tree

Exception occurred in actor?
├── Expected failure (HTTP timeout, invalid input)?
│   └── Try-catch, handle gracefully, continue
├── State might be corrupt?
│   └── Let supervision restart
├── Unknown cause?
│   └── Let supervision restart
└── Programming error (null ref, bad logic)?
    └── Let supervision restart, fix the bug

Props Decision Tree

Creating actor Props?
├── Actor needs IServiceProvider?
│   └── Use resolver.Props<T>()
├── Actor needs IRequiredActor<T>?
│   └── Use resolver.Props<T>()
├── Simple actor with constructor params?
│   └── Use Props.Create(() => new Actor(...))
└── Remote deployment needed?
    └── Probably not - use cluster sharding instead

---

8. Cluster/Local Mode Abstractions

For applications that need to run both in clustered production and local/test environments, use abstraction patterns to toggle between implementations:

• AkkaExecutionMode enum - Controls which implementations are used (LocalTest vs Clustered)
• GenericChildPerEntityParent - Mimics sharding behavior locally using the same IMessageExtractor
• IPubSubMediator - Abstracts DistributedPubSub for swappable local/cluster implementations

See cluster-local-abstractions.md for complete implementation code.

---

9. Actor Logging

Use ILoggingAdapter, Not ILogger

In actors, use ILoggingAdapter from Context.GetLogger() instead of DI-injected ILogger<T>:

public class MyActor : ReceiveActor
{
    private readonly ILoggingAdapter _log = Context.GetLogger();

    public MyActor()
    {
        Receive<MyMessage>(msg =>
        {
            _log.Info("Processing message for user {UserId}", msg.UserId);
            _log.Error(ex, "Failed to process {MessageType}", msg.GetType().Name);
        });
    }
}

Why ILoggingAdapter:

• Integrates with Akka's logging pipeline and supervision
• Supports semantic/structured logging as of v1.5.57
• Method names: Info(), Debug(), Warning(), Error() (not Log* variants)
• No DI required - obtained directly from actor context

Don't inject ILogger into actors - it bypasses Akka's logging infrastructure.

Semantic Logging (v1.5.57+)

// Named placeholders for better log aggregation and querying
_log.Info("Order {OrderId} processed for customer {CustomerId}", order.Id, order.CustomerId);

// Prefer named placeholders over positional
// Good: {OrderId}, {CustomerId}
// Avoid: {0}, {1}

---

10. Managing Async Operations with CancellationToken

When actors launch async operations via PipeTo, those operations can outlive the actor if not properly managed. Key practices:

• Actor CTS in PostStop - Always cancel and dispose in PostStop()
• New CTS per operation - Cancel previous before starting new work
• Pass token everywhere - EF Core queries, HTTP calls, etc.
• Linked CTS for timeouts - External calls get short timeouts to prevent hanging
• Graceful handling - Distinguish timeout vs shutdown in catch blocks

See async-cancellation-patterns.md for complete implementation code.