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A pluggable actor system written in java leveraging modern features from JDK21+

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# Cajun - **C**oncurrency **A**nd **J**ava **UN**locked

<div style="text-align:center">
    <p>Predictable concurrency for Java applications using the actor model</p>
    <p><em>Leveraging virtual threads and modern features from JDK21+</em></p>
    <img src="docs/logo.png" alt="Alt Text" style="width:50%; height:auto;">
</div>

📚 **[Full Documentation](https://cajunsystems.github.io)** | 🚀 [Quick Start](#quick-start-5-minutes) | 📦 [Installation](#installation)

## Table of Contents

### Getting Started
- [What is Cajun?](#what-is-cajun)
- [Quick Start (5 Minutes)](#quick-start-5-minutes)
- [Prerequisites](#prerequisites)
- [Installation](#installation)

### Core Concepts
- [Understanding Actors](#understanding-actors)
- [Creating Your First Actors](#creating-your-first-actors)
  - [Stateless Actors](#stateless-actors-with-handler-interface)
  - [Stateful Actors](#stateful-actors-with-statefulhandler-interface)
  - [Functional Actors with Effects](#functional-actors-with-effects)
  - [Actor Hierarchies](#creating-actor-hierarchies)
- [Actor ID Strategies](#actor-id-strategies)
  - [Explicit IDs](#explicit-ids)
  - [ID Templates](#id-templates)
  - [ID Strategies](#predefined-id-strategies)
  - [Hierarchical IDs](#hierarchical-ids)
- [Actor Communication](#actor-communication)
  - [Sending Messages (tell)](#sending-messages-to-self)
  - [Request-Response (ask)](#request-response-with-ask-pattern)
  - [Message Forwarding](#sender-context-and-message-forwarding)
- [Actor Lifecycle](#actor-system-lifecycle)

### Essential Features
- [Stateful Actors](#stateful-actors-and-persistence)
  - [State Management](#state-persistence)
  - [Persistence and Recovery](#message-persistence-and-replay)
  - [LMDB Persistence](#lmdb-persistence-recommended-for-production)
- [Error Handling and Supervision](#error-handling-and-supervision-strategy)
- [Testing Your Actors](#testing)

### Intermediate Topics
- [Performance and Tuning](#message-processing-and-performance-tuning)
  - [Batch Processing](#batched-message-processing)
  - [Thread Pool Configuration](#configurable-thread-pools)
  - [Mailbox Configuration](#mailbox-configuration)
    - [Available Mailbox Types](#available-mailbox-types)
- [Advanced Communication Patterns](#actorcontext-convenience-features)
  - [Sender Context](#sender-context-and-message-forwarding)
  - [ReplyingMessage Interface](#standardized-reply-pattern-with-replyingmessage)

### Advanced Features
- [Backpressure Management](#backpressure-support-in-actors)
- [Cluster Mode (Distributed Actors)](#cluster-mode)

### Reference
- [Performance Benchmarks](#benchmarks)
  - [Persistence Benchmarks](#persistence-benchmarks)
- [Running Examples](#running-examples)
- [Feature Roadmap](#feature-roadmap)

## What is Cajun?

Cajun (**C**oncurrency **A**nd **J**ava **UN**locked) is a lightweight actor system for Java that makes concurrent programming **simple and safe**. Instead of managing threads, locks, and shared state yourself, you write simple actors that communicate through messages.

### Why Actors?

**Traditional concurrent programming is hard:**
- 🔒 Managing locks and synchronization
- 🐛 Avoiding race conditions and deadlocks
- 🔍 Debugging concurrent issues
- 📊 Coordinating shared state

**Actors make it simple:**
- ✅ Each actor processes one message at a time
- ✅ No shared state = no race conditions
- ✅ Built-in error handling and recovery
- ✅ Easy to test and reason about

### When Should You Use Cajun?

**✅ Perfect for (Near-Zero Overhead):**
- **I/O-Heavy Applications**: Microservices, web apps, REST APIs
  - **Performance**: 0.02% overhead - actors perform identically to raw threads!
  - Database calls, HTTP requests, file operations
- **Event-Driven Systems**: Kafka/RabbitMQ consumers, event processing
  - **Performance**: 0.02% overhead for I/O-bound message processing
  - Excellent for stream processing and event sourcing
- **Stateful Services**: User sessions, game entities, shopping carts
  - **Performance**: 8% overhead but you get thread-safe state management
  - Complex stateful logic that needs isolation
- **Message-Driven Architectures**: Workflows, sagas, orchestration
  - **Performance**: < 1% overhead for realistic mixed workloads
  - Systems requiring fault tolerance and supervision

**⚠️ Consider alternatives for:**
- **Embarrassingly Parallel CPU Work**: Matrix multiplication, data transformations
  - Raw threads are 10x faster for pure parallel computation
  - Use parallel streams or thread pools instead
- **Simple Scatter-Gather**: No state, just parallel work and collect results
  - Threads are 38% faster for this specific pattern
  - CompletableFuture composition is simpler

**Key Insight**: Cajun uses virtual threads, which excel at I/O-bound workloads (databases, networks, files). For typical microservices and web applications, actor overhead is **negligible** (< 1%) while providing superior architecture benefits.

### How Cajun Works

Cajun uses the **actor model** to provide predictable concurrency:

1. **Message Passing**: Actors communicate by sending messages (no shared state)
2. **Isolated State**: Each actor owns its state privately
3. **Serial Processing**: Messages are processed one at a time, in order
4. **No User-Level Locks**: You write lock-free code - the actor model handles isolation

**Built on Java 21+ Virtual Threads:**
Cajun leverages virtual threads for exceptional I/O performance. Each actor runs on a virtual thread, allowing you to create thousands of concurrent actors with minimal overhead.

**Performance Profile (Benchmarked November 2025):**
- **I/O-Bound Workloads**: **0.02% overhead** - essentially identical to raw threads!
  - Perfect for microservices, web applications, database operations
  - Virtual threads "park" during I/O instead of blocking OS threads
- **CPU-Bound Workloads**: **8% overhead** - excellent for stateful operations
  - Acceptable trade-off for built-in state management and fault tolerance
- **Mixed Workloads**: **< 1% overhead** - ideal for real-world applications
  - Typical request handling (DB + business logic + rendering)

**Thread Pool Configuration:** Virtual threads are the default and perform best across all tested scenarios. You can optionally configure different thread pools per actor, but benchmarks show virtual threads outperform fixed and work-stealing pools for actor workloads.

**Note**: While your application code doesn't use locks, the JVM and mailbox implementations may use locks internally. The key benefit is that **you** don't need to manage synchronization.

### Key Benefits

- **No User-Level Locks**: Write concurrent code without explicit locks, synchronized blocks, or manual coordination - the actor model handles isolation
- **Predictable Behavior**: Deterministic message ordering makes systems easier to reason about and test
- **Exceptional I/O Performance**: **0.02% overhead** for I/O-bound workloads - actors perform identically to raw threads for microservices and web apps
- **Scalability**: Easily scale from single-threaded to multi-threaded to distributed systems
  - Virtual threads enable thousands of concurrent actors with minimal overhead
- **Fault Tolerance**: Built-in supervision strategies for handling failures gracefully
- **Flexibility**: Multiple programming styles (OO, functional, stateful) to match your needs
- **Production-Ready Performance**: 
  - I/O workloads: 0.02% overhead (negligible)
  - CPU workloads: 8% overhead (excellent for state management)
  - Mixed workloads: < 1% overhead (ideal for real applications)
- **Virtual Thread Based**: Built on Java 21+ virtual threads for efficient blocking I/O with simple, natural code
- **Simple Defaults**: All default configurations are optimal - no tuning required for 99% of use cases

> **Dedication**: Cajun is inspired by Erlang OTP and the actor model, and is dedicated to the late Joe Armstrong from Ericsson, whose pioneering work on Erlang and the actor model has influenced a generation of concurrent programming systems. Additional inspiration comes from Akka/Pekko.

## Quick Start (5 Minutes)

Get up and running with Cajun in just a few minutes!

### Prerequisites
- Java 21+ (with --enable-preview flag)

### Installation

Cajun is available on Maven Central. Add it to your project using Gradle:

```gradle
dependencies {
    implementation 'com.cajunsystems:cajun:0.7.2'
}
```

Or with Maven:

```xml
<dependency>
    <groupId>com.cajunsystems</groupId>
    <artifactId>cajun</artifactId>
    <version>0.7.2</version>
</dependency>
```

**Note**: Since Cajun uses Java preview features, you need to enable preview features in your build:

**Gradle:**
```gradle
tasks.withType(JavaCompile) {
    options.compilerArgs.add('--enable-preview')
}

tasks.withType(JavaExec) {
    jvmArgs += '--enable-preview'
}

tasks.withType(Test) {
    jvmArgs += '--enable-preview'
}
```

**Maven:**
```xml
<build>
    <plugins>
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-compiler-plugin</artifactId>
            <version>3.11.0</version>
            <configuration>
                <compilerArgs>
                    <arg>--enable-preview</arg>
                </compilerArgs>
            </configuration>
        </plugin>
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-surefire-plugin</artifactId>
            <version>3.0.0</version>
            <configuration>
                <argLine>--enable-preview</argLine>
            </configuration>
        </plugin>
    </plugins>
</build>
```

### Your First Actor

Here's a complete, runnable example to get you started:

```java
import com.cajunsystems.*;
import com.cajunsystems.handler.Handler;

public class HelloWorld {
    
    // Define your message types with explicit replyTo
    public record HelloMessage(String name, Pid replyTo) {}
    public record GreetingResponse(String greeting) {}
    
    // Greeter actor that processes requests
    public static class GreeterHandler implements Handler<HelloMessage> {
        @Override
        public void receive(HelloMessage message, ActorContext context) {
            context.getLogger().info("Received greeting request for: {}", message.name());
            
            // Process and reply
            String greeting = "Hello, " + message.name() + "!";
            context.tell(message.replyTo(), new GreetingResponse(greeting));
        }
    }
    
    // Receiver actor that handles responses
    public static class ReceiverHandler implements Handler<GreetingResponse> {
        @Override
        public void receive(GreetingResponse message, ActorContext context) {
            System.out.println("Received: " + message.greeting());
        }
    }
    
    public static void main(String[] args) throws Exception {
        // 1. Create the ActorSystem
        ActorSystem system = new ActorSystem();
        
        // 2. Spawn actors
        Pid greeter = system.actorOf(GreeterHandler.class)
            .withId("greeter")
            .spawn();
        
        Pid receiver = system.actorOf(ReceiverHandler.class)
            .withId("receiver")
            .spawn();
        
        // 3. Send a message with explicit replyTo
        greeter.tell(new HelloMessage("World", receiver));
        
        // Wait a bit for async processing
        Thread.sleep(100);
        
        // 4. Shutdown the system when done
        system.shutdown();
    }
}
```

**What's happening here?**
1. We create an `ActorSystem` - the container for all actors
2. We spawn two actors using `actorOf()` - one greeter and one receiver
3. We send a message using `tell()` - fire-and-forget messaging
4. The greeter processes the message and replies to the receiver
5. We shut down the system when done

**Next steps:** See [Request-Response with Ask Pattern](#request-response-with-ask-pattern) for a simpler approach to request-response, or continue reading to understand actors in depth.

---

## Understanding Actors

### What is an Actor?

An **actor** is a lightweight concurrent unit that:
- Has its own private state (no sharing with other actors)
- Processes messages one at a time, in order
- Communicates only through asynchronous messages
- Can create other actors (children)
- Never blocks other actors

Think of actors like people in an organization - they each have their own desk (state), inbox (mailbox), and can send memos (messages) to each other, but they never directly access another person's desk.

### Actor Lifecycle

**Important**: The Cajun actor system keeps the JVM alive after the main method completes. This is the expected behavior for a production actor system.

- **JVM Stays Alive**: The actor system uses a non-daemon keep-alive thread that keeps the JVM running even after the main thread exits. This ensures actors can continue processing messages, even when using virtual threads (which are always daemon threads).
- **Virtual Thread Support**: Actors run on virtual threads by default for optimal I/O-bound workloads. The keep-alive mechanism ensures the JVM doesn't exit prematurely despite virtual threads being daemon threads.
- **Explicit Shutdown Required**: You must call `system.shutdown()` to gracefully shut down the actor system and allow the JVM to exit.

```java
public static void main(String[] args) {
    ActorSystem system = new ActorSystem();
    
    Pid actor = system.actorOf(new Handler<String>() {
        @Override
        public void receive(String message, ActorContext context) {
            System.out.println("Received: " + message);
        }
    }).withId("demo-actor").spawn();
    
    actor.tell("Hello");
    
    // Main thread exits here, but JVM stays alive
    System.out.println("Main exiting - JVM will continue running");
    
    // To allow JVM to exit, you must explicitly shutdown:
    // system.shutdown();
}
```

**Shutdown Options:**

```java
// Graceful shutdown - waits for actors to complete current work
system.shutdown();

// Or stop individual actors
system.stopActor(actorPid);
```

## Creating Your First Actors

Cajun provides a clean, interface-based approach for creating actors. This approach separates the message handling logic from the actor lifecycle management, making your code more maintainable and testable.

### Stateless Actors with Handler Interface

For stateless actors, implement the `Handler<Message>` interface:

```java
public sealed interface GreetingMessage permits HelloMessage, ByeMessage, GetHelloCount, Shutdown {
}

public record HelloMessage() implements GreetingMessage {
}

public record ByeMessage() implements GreetingMessage {
}

public record Shutdown() implements GreetingMessage {
}

public record GetHelloCount(Pid replyTo) implements GreetingMessage {
}

public record HelloCount(int count) {
}

public class GreetingHandler implements Handler<GreetingMessage> {
    private int helloCount = 0;
    
    @Override
    public void receive(GreetingMessage message, ActorContext context) {
        switch (message) {
            case HelloMessage ignored -> {
                // Updating state of the handler
                helloCount++;
            }
            case GetHelloCount ghc -> {
                // Replying back to calling actor
                context.tell(ghc.replyTo(), new HelloCount(helloCount));
            }
            case ByeMessage ignored -> {
                // Sending a message to self
                context.tellSelf(new Shutdown());
            }
            case Shutdown ignored -> {
                // Stopping actor
                context.stop();
            }
        }
    }
    
    // Optional lifecycle methods
    @Override
    public void preStart(ActorContext context) {
        // Initialization logic
    }
    
    @Override
    public void postStop(ActorContext context) {
        // Cleanup logic
    }
    
    @Override
    public boolean onError(GreetingMessage message, Throwable exception, ActorContext context) {
        // Custom error handling
        return false; // Return true to reprocess the message
    }
}
```

Create and start the actor:

```java
// Create an actor with a handler class (instantiated automatically)
Pid actorPid = system.actorOf(GreetingHandler.class)
    .withId("greeter-1")  // Optional: specify ID (otherwise auto-generated)
    .spawn();

// Or create with a handler instance
GreetingHandler handler = new GreetingHandler();
Pid actorPid = system.actorOf(handler).spawn();

// Send messages
actorPid.tell(new HelloMessage());
```

### Stateful Actors with StatefulHandler Interface

For actors that need to maintain and persist state, implement the `StatefulHandler<State, Message>` interface:

```java
public class CounterHandler implements StatefulHandler<Integer, CounterMessage> {
    
    @Override
    public Integer receive(CounterMessage message, Integer state, ActorContext context) {
        return switch (message) {
            case Increment ignored -> state + 1;
            case Decrement ignored -> state - 1;
            case Reset ignored -> 0;
            case GetCount gc -> {
                context.tell(gc.replyTo(), new CountResult(state));
                yield state; // Return unchanged state
            }
        };
    }
    
    @Override
    public Integer preStart(Integer state, ActorContext context) {
        // Optional initialization logic
        return state;
    }
    
    @Override
    public void postStop(Integer state, ActorContext context) {
        // Optional cleanup logic
    }
    
    @Override
    public boolean onError(CounterMessage message, Integer state, Throwable exception, ActorContext context) {
        // Custom error handling
        return false; // Return true to reprocess the message
    }
}
```

Create and start the stateful actor:

```java
// Create a stateful actor with a handler class and initial state
Pid counterPid = system.statefulActorOf(CounterHandler.class, 0)
    .withId("counter-1")  // Optional: specify ID (otherwise auto-generated)
    .spawn();

// Or create with a handler instance
CounterHandler handler = new CounterHandler();
Pid counterPid = system.statefulActorOf(handler, 0).spawn();

// Send messages
counterPid.tell(new Increment());
```

### Advanced Configuration

Both actor builders support additional configuration options:

```java
// Configure backpressure, mailbox, and persistence
Pid actorPid = system.actorOf(GreetingHandler.class)
    .withBackpressureConfig(new BackpressureConfig())
    .withMailboxConfig(new ResizableMailboxConfig())
    .spawn();

// Configure stateful actor with persistence
Pid counterPid = system.statefulActorOf(CounterHandler.class, 0)
    .withPersistence(
        PersistenceFactory.createBatchedFileMessageJournal(),
        PersistenceFactory.createFileSnapshotStore()
    )
    .spawn();

// Configure actor with custom thread pool for CPU-intensive work
ThreadPoolFactory cpuFactory = new ThreadPoolFactory()
    .optimizeFor(ThreadPoolFactory.WorkloadType.CPU_BOUND);

Pid computeActor = system.actorOf(ComputationHandler.class)
    .withThreadPoolFactory(cpuFactory)
    .spawn();
```

### Creating Actor Hierarchies

You can create parent-child relationships between actors:

```java
// Create a parent actor
Pid parentPid = system.actorOf(ParentHandler.class).spawn();

// Create a child actor through the parent
Pid childPid = system.actorOf(ChildHandler.class)
    .withParent(system.getActor(parentPid))
    .spawn();

// Or create a child directly from another handler
public class ParentHandler implements Handler<ParentMessage> {
    @Override
    public void receive(ParentMessage message, ActorContext context) {
        if (message instanceof CreateChild) {
            // Create a child actor
            Pid childPid = context.createChild(ChildHandler.class, "child-1");
            // Send message to the child
            context.tell(childPid, new ChildMessage());
        }
    }
}

// Need advanced configuration (e.g., supervision) when creating children?
// Use the new childBuilder() API exposed on ActorContext.
public class SupervisedParentHandler implements Handler<ParentMessage> {
    @Override
    public void receive(ParentMessage message, ActorContext context) {
        if (message instanceof CreateChild) {
            Pid childPid = context.childBuilder(ChildHandler.class)
                .withSupervisionStrategy(SupervisionStrategy.RESTART)
                .withId("supervised-child")
                .spawn();

context.tell(childPid, new ChildMessage());
        }
    }
}
```

## Actor ID Strategies

Every actor in Cajun has a unique identifier (ID) used for message routing, logging, persistence, and hierarchical organization. Cajun provides four flexible ways to control actor IDs with a clear priority system.

### ID Priority System

When multiple ID configurations are specified, Cajun uses this priority order:

1. **Explicit IDs** (Highest) - Manually specified exact IDs
2. **ID Templates** - Generated IDs using placeholders
3. **ID Strategies** - Predefined generation strategies  
4. **System Default** (Lowest) - Falls back to UUID

```java
// Priority 1: Explicit ID wins
Pid actor = system.actorOf(Handler.class)
    .withId("my-actor")           // ← This is used
    .withIdTemplate("user-{seq}") // ← Ignored
    .withIdStrategy(IdStrategy.UUID) // ← Ignored
    .spawn();
// Result: "my-actor"
```

**Important**: Each `withId()`, `withIdTemplate()`, and `withIdStrategy()` call replaces any previous ID configuration. Only the last one in the chain is effective.

### Explicit IDs

Manually specify exact IDs for actors. Best for singletons and well-known services:

```java
// Simple explicit ID
Pid userService = system.actorOf(UserServiceHandler.class)
    .withId("user-service")
    .spawn();

// Unicode characters supported
Pid actor = system.actorOf(MyHandler.class)
    .withId("actor-测试-🎭")
    .spawn();
```

**Pros:** Predictable, easy to debug, can be looked up by name  
**Cons:** Must ensure uniqueness manually, not suitable for dynamic creation

### ID Templates

Generate IDs dynamically using placeholders. Best for creating multiple actors with consistent naming:

```java
// Simple sequence
Pid actor = system.actorOf(MyHandler.class)
    .withIdTemplate("user-{seq}")
    .spawn();
// Result: "user-1", "user-2", "user-3", ...

// Multiple placeholders
Pid actor = system.actorOf(MyHandler.class)
    .withIdTemplate("{class}-{seq}-{short-uuid}")
    .spawn();
// Result: "myhandler-1-a1b2c3d4"

// With timestamp
Pid session = system.actorOf(SessionHandler.class)
    .withIdTemplate("session-{timestamp}-{seq}")
    .spawn();
// Result: "session-1732956789123-1"
```

**Available Placeholders:**
- `{seq}` - Auto-incrementing sequence number
- `{template-seq}` - Sequence per template pattern
- `{uuid}` - Full UUID
- `{short-uuid}` - First 8 characters of UUID
- `{timestamp}` - Current timestamp (milliseconds)
- `{nano}` - Current nanosecond time
- `{class}` - Simplified class name (lowercase)
- `{parent}` - Parent actor ID (if hierarchical)

**Pros:** Readable, automatic uniqueness, flexible composition  
**Cons:** Counters reset on restart (unless using persistence - see below)

**🔄 Persistence Integration:** When using sequence-based naming with stateful actors, Cajun automatically scans persisted actors on startup and initializes counters to prevent ID collisions:

```java
// First run: Create stateful actors with sequential IDs
Pid user1 = system.statefulActorOf(UserHandler.class, initialState)
    .withIdStrategy(IdStrategy.CLASS_BASED_SEQUENTIAL)
    .withPersistence(journal, snapshot)
    .spawn();
// Result: "userhandler:1"

Pid user2 = system.statefulActorOf(UserHandler.class, initialState)
    .withIdStrategy(IdStrategy.CLASS_BASED_SEQUENTIAL)
    .withPersistence(journal, snapshot)
    .spawn();
// Result: "userhandler:2"

// After restart: Counters resume from persisted state
Pid user3 = system.statefulActorOf(UserHandler.class, initialState)
    .withIdStrategy(IdStrategy.CLASS_BASED_SEQUENTIAL)
    .withPersistence(journal, snapshot)
    .spawn();
// Result: "userhandler:3" (not "userhandler:1"!)
// Existing actors "userhandler:1" and "userhandler:2" are restored
```

This ensures that:
- ✅ Persisted actors are restored with their original IDs
- ✅ New actors continue the sequence without collisions
- ✅ ID uniqueness is maintained across restarts
- ✅ Works with `CLASS_BASED_SEQUENTIAL` strategy and templates using colon separators

**⚠️ Important:** Counter recovery only works with the `prefix:number` pattern (colon separator):
- ✅ `CLASS_BASED_SEQUENTIAL` → `"userhandler:1"` (works)
- ✅ `"user:{seq}"` → `"user:1"` (works)
- ❌ `"user-{seq}"` → `"user-1"` (does NOT work)

For persistence with templates, use colons: `"user:{seq}"` instead of `"user-{seq}"`

### Predefined ID Strategies

Use predefined strategies for consistent ID generation:

```java
// UUID (Default)
Pid actor = system.actorOf(MyHandler.class)
    .withIdStrategy(IdStrategy.UUID)
    .spawn();
// Result: "a1b2c3d4-e5f6-7890-abcd-ef1234567890"

// CLASS_BASED_UUID: {class}:{uuid}
Pid actor = system.actorOf(MyHandler.class)
    .withIdStrategy(IdStrategy.CLASS_BASED_UUID)
    .spawn();
// Result: "myhandler:a1b2c3d4-e5f6-7890-abcd-ef1234567890"

// CLASS_BASED_SEQUENTIAL: {class}:{seq}
Pid actor = system.actorOf(MyHandler.class)
    .withIdStrategy(IdStrategy.CLASS_BASED_SEQUENTIAL)
    .spawn();
// Result: "myhandler:1", "myhandler:2", "myhandler:3", ...

// SEQUENTIAL: Simple counter
Pid actor = system.actorOf(MyHandler.class)
    .withIdStrategy(IdStrategy.SEQUENTIAL)
    .spawn();
// Result: "1", "2", "3", ...
```

**Strategy Comparison:**

| Strategy | Example | Readability | Uniqueness | Use Case |
|----------|---------|-------------|------------|----------|
| UUID | `a1b2...` | ⭐ | ⭐⭐⭐⭐⭐ | Distributed systems |
| CLASS_BASED_UUID | `handler:a1b2...` | ⭐⭐ | ⭐⭐⭐⭐⭐ | Multi-class systems |
| CLASS_BASED_SEQUENTIAL | `handler:1` | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | Single-node apps |
| SEQUENTIAL | `1` | ⭐⭐⭐ | ⭐⭐⭐ | Simple testing |

### Hierarchical IDs

Create parent-child relationships with automatic ID prefixing:

```java
// Create parent
Pid parent = system.actorOf(ParentHandler.class)
    .withId("parent")
    .spawn();

// Create child - ID is automatically prefixed
Pid child = system.actorOf(ChildHandler.class)
    .withId("child")
    .withParent(system.getActor(parent))
    .spawn();
// Result: "parent/child"

// Children with templates
Pid child1 = system.actorOf(ChildHandler.class)
    .withIdTemplate("child-{seq}")
    .withParent(system.getActor(parent))
    .spawn();
// Result: "parent/child-1"

// Deep hierarchies
Pid grandchild = system.actorOf(Handler.class)
    .withId("grandchild")
    .withParent(system.getActor(child))
    .spawn();
// Result: "parent/child/grandchild"
```

### Best Practices

**Choose the right approach:**
```java
// ✅ Good: Explicit IDs for singletons
Pid service = system.actorOf(ServiceHandler.class)
    .withId("user-service")
    .spawn();

// ✅ Good: Templates for dynamic actors
Pid session = system.actorOf(SessionHandler.class)
    .withIdTemplate("session-{seq}")
    .spawn();

// ✅ Good: Strategies for consistency
Pid worker = system.actorOf(WorkerHandler.class)
    .withIdStrategy(IdStrategy.CLASS_BASED_SEQUENTIAL)
    .spawn();
```

**Use meaningful names:**
```java
// ✅ Good: Descriptive IDs
.withIdTemplate("user-session-{seq}")
.withIdTemplate("{class}-worker-{seq}")

// ❌ Bad: Generic IDs
.withIdTemplate("actor-{seq}")
```

**Consider persistence:**
```java
// ✅ Good: Stable IDs for stateful actors
Pid counter = system.statefulActorOf(CounterHandler.class, 0)
    .withId("global-counter")  // Same ID after restart
    .withPersistence(...)
    .spawn();
```

**📖 Complete Documentation:** See [Actor ID Strategies Guide](docs/actor_id_strategies.md) for comprehensive examples, all placeholders, and advanced patterns.

---

## Actor Communication

Actors communicate through messages. Cajun provides several patterns for actor communication, from simple fire-and-forget to request-response.

### ActorContext Convenience Features

The `ActorContext` provides several convenience features to simplify common actor patterns:

### Sending Messages to Self

Use `tellSelf()` to send messages to the current actor:

```java
public class TimerHandler implements Handler<TimerMessage> {
    @Override
    public void receive(TimerMessage message, ActorContext context) {
        switch (message) {
            case Start ignored -> {
                // Schedule a message to self after 1 second
                context.tellSelf(new Tick(), 1, TimeUnit.SECONDS);
            }
            case Tick ignored -> {
                context.getLogger().info("Tick received");
                // Schedule next tick
                context.tellSelf(new Tick(), 1, TimeUnit.SECONDS);
            }
        }
    }
}
```

### Built-in Logger with Actor Context

Access a pre-configured logger through `context.getLogger()` that automatically includes the actor ID:

```java
public class LoggingHandler implements Handler<String> {
    @Override
    public void receive(String message, ActorContext context) {
        // Logger automatically includes actor ID in output
        context.getLogger().info("Processing message: {}", message);
        context.getLogger().debug("Debug info for actor {}", context.getActorId());
        context.getLogger().error("Error occurred", exception);
    }
}
```

Benefits:
- **Consistent logging format** across all actors
- **Automatic actor ID context** for easier debugging
- **No manual logger setup** required

### Standardized Reply Pattern with ReplyingMessage

Use the `ReplyingMessage` interface to standardize request-response patterns:

```java
// Define messages that require replies
public record GetUserRequest(String userId, Pid replyTo) implements ReplyingMessage {}
public record GetOrderRequest(String orderId, Pid replyTo) implements ReplyingMessage {}

public record UserResponse(String userId, String name) {}
public record OrderResponse(String orderId, double amount) {}

// In your handler
public class DatabaseHandler implements Handler<Object> {
    @Override
    public void receive(Object message, ActorContext context) {
        switch (message) {
            case GetUserRequest req -> {
                UserResponse user = fetchUser(req.userId());
                // Use the reply convenience method
                context.reply(req, user);
            }
            case GetOrderRequest req -> {
                OrderResponse order = fetchOrder(req.orderId());
                context.reply(req, order);
            }
        }
    }
}
```

Benefits:
- **Strong type contracts** for reply semantics
- **Cleaner code** with `context.reply()` instead of `context.tell(message.replyTo(), response)`
- **Consistent pattern** across your codebase
- **Better IDE support** with autocomplete for replyTo field

The `ReplyingMessage` interface requires implementing a single method:
```java
public interface ReplyingMessage {
    Pid replyTo();
}
```

#### Key Features of Stateful Actors

- **Persistent State**: State is automatically persisted using configurable storage backends
- **State Recovery**: Automatically recovers state when an actor restarts
- **Type Safety**: Generic type parameters for both state and message types
- **Pluggable Storage**: Supports different state storage implementations:
  - In-memory storage (default)
  - File-based storage
  - Custom storage implementations

#### Advanced Stateful Actor Features

1. **Configuring State Persistence**

```java
// Create a stateful actor with file-based persistence
Pid counterPid = system.statefulActorOf(CounterHandler.class, 0)
    .withPersistence(
        PersistenceFactory.createFileSnapshotStore("/path/to/snapshots"),
        PersistenceFactory.createBatchedFileMessageJournal("/path/to/journal")
    )
    .spawn();
```

2. **State Recovery Options**

```java
// Configure recovery options
Pid counterPid = system.statefulActorOf(CounterHandler.class, 0)
    .withRecoveryConfig(RecoveryConfig.builder()
        .withRecoveryStrategy(RecoveryStrategy.SNAPSHOT_THEN_JOURNAL)
        .withMaxMessagesToRecover(1000)
        .build())
    .spawn();
```

3. **Snapshot Strategies**

```java
// Configure snapshot strategy
Pid counterPid = system.statefulActorOf(CounterHandler.class, 0)
    .withSnapshotStrategy(SnapshotStrategy.builder()
        .withInterval(Duration.ofMinutes(5))
        .withThreshold(100) // Take snapshot every 100 state changes
        .build())
    .spawn();
```

#### Lifecycle Methods

The `StatefulHandler` interface provides lifecycle methods:

- `preStart(State state, ActorContext context)`: Called when the actor starts, returns the initial state
- `postStop(State state, ActorContext context)`: Called when the actor stops
- `onError(Message message, State state, Throwable exception, ActorContext context)`: Called when message processing fails

### Functional Actors with Effects

Build actors using composable [Roux](https://github.com/cajunsystems/roux) `Effect` pipelines —
a functional, type-safe style backed by the actor system's executor.

#### Quick Example

```java
import com.cajunsystems.ActorSystem;
import com.cajunsystems.Pid;
import com.cajunsystems.functional.EffectActorBuilder;
import com.cajunsystems.roux.Effect;
import com.cajunsystems.roux.data.Unit;

ActorSystem system = new ActorSystem();

// Define messages
sealed interface CounterMsg permits Increment, Decrement {}
record Increment(int amount) implements CounterMsg {}
record Decrement(int amount) implements CounterMsg {}

// Spawn an effect actor — handler returns Effect<E, A> per message
Pid counter = new EffectActorBuilder<>(
    system,
    (CounterMsg msg) -> switch (msg) {
        case Increment(var n) -> Effect.suspend(() -> {
            System.out.println("Incrementing by " + n);
            return Unit.unit();
        });
        case Decrement(var n) -> Effect.suspend(() -> {
            System.out.println("Decrementing by " + n);
            return Unit.unit();
        });
    }
).withId("counter").spawn();

counter.tell(new Increment(5));
system.shutdown();
```

#### Why Use Effects?

- **Composable**: Chain steps with `.flatMap()` / `.map()` — build complex pipelines from simple pieces
- **🚀 Blocking is Safe**: Effects run on Cajun's virtual-thread executor — write natural blocking code
- **Type-safe error handling**: `.catchAll()`, `.attempt()`, `retry(n)`, `timeout(Duration)`
- **Parallel execution**: `Effects.parAll()`, `Effects.race()`, `Effects.traverse()`
- **Resource safety**: `Resource<A>` guarantees cleanup on success and failure
- **Testable**: Handler logic is pure — test without spawning actors

#### Common Effect Patterns

**Chaining steps:**
```java
Effect.<RuntimeException, Integer>succeed(n)
    .flatMap(x -> Effect.suspend(() -> process(x)))
    .map(result -> result * 2)
```

**Error handling:**
```java
Effect.suspend(() -> riskyCall())
    .catchAll(err -> Effect.succeed(defaultValue))
```

**Retry & timeout:**
```java
Effect.suspend(() -> httpCall())
    .retry(3)                          // 3 additional attempts
    .timeout(Duration.ofSeconds(5))
```

**Parallel execution:**
```java
Effects.parAll(List.of(effect1, effect2, effect3))  // run concurrently
```

**Resource management:**
```java
Resource.fromCloseable(() -> openFile(path))
    .use(file -> Effect.suspend(() -> readLines(file)))
```

#### Learn More

- **[Getting Started with Effect Actors](docs/effect-actors/getting-started.md)** — first actor, `Effect.suspend`, `flatMap`, `LogCapability`
- **[Patterns Catalogue](docs/effect-actors/patterns.md)** — retry, timeout, parallel, resource, ask pattern
- **[Capabilities Guide](docs/effect-actors/capabilities.md)** — `Capability<R>`, `CapabilityHandler`, custom capabilities
- **[Migration Guide](docs/effect-actors/migration.md)** — upgrading from old `Effect<State,Error,Result>` API

### Using the Actor System

After creating your handlers or effects, use the actor system to spawn actors and send messages:

```java
public class CountResultHandler implements Handler<HelloCount> {
    @Override
    public void receive(HelloCount message, ActorContext context) {
        System.out.println("Count: " + message.count());
    }
}

public static void main(String[] args) {
    // Create the actor system
    var actorSystem = new ActorSystem();
    
    // Create a greeting actor
    var greetingPid = actorSystem.actorOf(GreetingHandler.class)
        .withId("greeting-actor-1")
        .spawn();
    
    // Create a receiver actor
    var receiverPid = actorSystem.actorOf(CountResultHandler.class)
        .withId("count-receiver")
        .spawn();
    
    // Send messages
    greetingPid.tell(new HelloMessage());
    greetingPid.tell(new GetHelloCount(receiverPid)); // Will print "Count: 1"
}
```

## Testing Your Actors

Cajun provides comprehensive test utilities that make actor testing clean, fast, and approachable. The test utilities eliminate common pain points like `Thread.sleep()`, `CountDownLatch` boilerplate, and polling loops.

**Key Features:**
- ✅ **No more `Thread.sleep()`** - Use `AsyncAssertion` for deterministic waiting
- ✅ **Direct state inspection** - Inspect stateful actor state without query messages
- ✅ **Mailbox monitoring** - Track queue depth, processing rates, and backpressure
- ✅ **Message capture** - Capture and inspect all messages sent to an actor
- ✅ **Simplified ask pattern** - One-line request-response testing
- ✅ **100 passing tests** - Fully tested and production-ready

**Quick Example:**
```java
@Test
void testCounter() {
    try (TestKit testKit = TestKit.create()) {
        TestPid<Object> counter = testKit.spawnStateful(CounterHandler.class, 0);
        
        counter.tell(new Increment(5));
        
        // No Thread.sleep()! Wait for exact state
        AsyncAssertion.awaitValue(
            counter.stateInspector()::current,
            5,
            Duration.ofSeconds(1)
        );
    }
}
```

**📖 Full Documentation:** See [test-utils/README.md](test-utils/README.md) for complete API documentation, examples, and best practices.

---

## Running Examples

Cajun examples can be run using [JBang](https://www.jbang.dev/), which makes it easy to run Java code without a full project setup.

### Install JBang

**macOS/Linux:**
```shell
curl -Ls https://sh.jbang.dev | bash -s - app setup
```

**Windows:**
```shell
iex "& { $(iwr https://ps.jbang.dev) } app setup"
```

Or use package managers:
```shell
# macOS
brew install jbangdev/tap/jbang

# Linux (SDKMAN)
sdk install jbang

# Windows (Scoop)
scoop install jbang
```

### Run Examples with JBang

All examples in the `lib/src/test/java/examples/` directory include JBang headers and can be run directly:

```shell
# Run the TimedCounter example
jbang lib/src/test/java/examples/TimedCounter.java

# Run the WorkflowExample
jbang lib/src/test/java/examples/WorkflowExample.java

# Run the StatefulActorExample
jbang lib/src/test/java/examples/StatefulActorExample.java

# Run the BackpressureActorExample
jbang lib/src/test/java/examples/BackpressureActorExample.java
```

**Available Examples:**
- `TimedCounter.java` - Simple periodic message sending
- `WorkflowExample.java` - Multi-stage workflow with actors
- `StatefulActorExample.java` - State persistence and recovery
- `BackpressureActorExample.java` - Backpressure handling
- `BackpressureStatefulActorExample.java` - Stateful actor with backpressure
- `ActorVsThreadsExample.java` - Performance comparison
- `FunctionalWorkflowExample.java` - Functional programming style
- `KVEffectExample.java` - **NEW**: LSM Tree Key-Value store using Effects
- `SenderPropagationExample.java` - Sender context propagation
- `StatefulShoppingCartExample.java` - Shopping cart with persistence
- `ClusterWorkflowExample.java` - Distributed actor example
- And more in `lib/src/test/java/examples/`

### Alternative: Run with Gradle

You can also use the Gradle task runner (--enable-preview flag is already enabled):
```shell
./gradlew -PmainClass=examples.TimedCounter run
```

## Message Processing and Performance Tuning

### Batched Message Processing

Cajun supports batched processing of messages to improve throughput:

- By default, each actor processes messages in batches of 10 messages at a time
- Batch processing can significantly improve throughput by reducing context switching overhead
- You can configure the batch size for any actor using the `withBatchSize()` method in the builder

```java
// Create an actor with custom batch size
Pid myActor = system.actorOf(MyHandler.class)
    .withId("high-throughput-actor")
    .withBatchSize(50)  // Process 50 messages at a time
    .spawn();

// For stateful actors
Pid statefulActor = system.statefulActorOf(MyStatefulHandler.class, initialState)
    .withBatchSize(100)  // Larger batches for high-throughput scenarios
    .spawn();
```

#### Tuning Considerations:

- **Larger batch sizes**: Improve throughput but may increase latency for individual messages
- **Smaller batch sizes**: Provide more responsive processing but with lower overall throughput
- **Workload characteristics**: CPU-bound tasks benefit from larger batches, while I/O-bound tasks may work better with smaller batches
- **Memory usage**: Larger batches consume more memory as messages are held in memory during processing

### Running Performance Tests

The project includes performance tests that can help you evaluate different configurations:

```shell
# Run all performance tests
./gradlew test -PincludeTags="performance"

# Run a specific performance test
./gradlew test --tests "systems.cajun.performance.ActorPerformanceTest.testActorChainThroughput"
```

The performance tests measure:

1. **Actor Chain Throughput**: Tests message passing through a chain of actors
2. **Many-to-One Throughput**: Tests many sender actors sending to a single receiver
3. **Actor Lifecycle Performance**: Tests creation and stopping of large numbers of actors

## Configurable Thread Pools

Cajun provides flexible thread pool configuration for actors, allowing you to optimize performance based on your workload characteristics. Each actor can be configured with its own ThreadPoolFactory, or use the system default (virtual threads).

### Thread Pool Types

Cajun supports multiple thread pool strategies:

- **VIRTUAL**: Uses Java 21 virtual threads for high concurrency with low overhead (default)
- **FIXED**: Uses a fixed-size platform thread pool for predictable resource usage
- **WORK_STEALING**: Uses a work-stealing thread pool for balanced workloads

### Workload Optimization Presets

```java
// Optimize for I/O-bound operations (high concurrency, virtual threads)
ThreadPoolFactory ioOptimized = new ThreadPoolFactory()
    .optimizeFor(ThreadPoolFactory.WorkloadType.IO_BOUND);

// Optimize for CPU-bound operations (fixed thread pool, platform threads)
ThreadPoolFactory cpuOptimized = new ThreadPoolFactory()
    .optimizeFor(ThreadPoolFactory.WorkloadType.CPU_BOUND);

// Optimize for mixed workloads (work-stealing pool)
ThreadPoolFactory mixedOptimized = new ThreadPoolFactory()
    .optimizeFor(ThreadPoolFactory.WorkloadType.MIXED);
```

### Configuring Actors with Custom Thread Pools

```java
// Create actors with optimized thread pools
Pid networkActor = system.actorOf(NetworkHandler.class)
    .withId("network-processor")
    .withThreadPoolFactory(ioOptimized)
    .spawn();

Pid computeActor = system.actorOf(ComputationHandler.class)
    .withId("compute-processor")
    .withThreadPoolFactory(cpuOptimized)
    .spawn();

// Stateful actors also support custom thread pools
Pid statefulActor = system.statefulActorOf(StateHandler.class, initialState)
    .withId("stateful-processor")
    .withThreadPoolFactory(mixedOptimized)
    .spawn();
```

### Custom Thread Pool Configuration

For fine-grained control, you can create custom ThreadPoolFactory configurations:

```java
// Custom configuration for specific requirements
ThreadPoolFactory customFactory = new ThreadPoolFactory()
    .setExecutorType(ThreadPoolFactory.ThreadPoolType.FIXED)
    .setFixedPoolSize(8)  // 8 platform threads
    .setPreferVirtualThreads(false)
    .setUseNamedThreads(true);

Pid customActor = system.actorOf(MyHandler.class)
    .withThreadPoolFactory(customFactory)
    .spawn();
```

### When to Use Different Thread Pool Types

#### Virtual Threads (Default - IO_BOUND)
- **Best for**: Network I/O, file operations, database calls
- **Characteristics**: Extremely lightweight, high concurrency (millions of threads)
- **Use when**: You have many actors doing I/O operations

#### Fixed Thread Pool (CPU_BOUND)
- **Best for**: CPU-intensive computations, mathematical operations
- **Characteristics**: Predictable resource usage, optimal for CPU-bound work
- **Use when**: You have fewer actors doing intensive computation

#### Work-Stealing Pool (MIXED)
- **Best for**: Mixed I/O and CPU workloads
- **Characteristics**: Dynamic load balancing, good for varied workloads
- **Use when**: Your actors have unpredictable or mixed workload patterns

### Performance Considerations

- **Default behavior**: If no ThreadPoolFactory is specified, actors use virtual threads
- **Per-actor configuration**: Different actors can use different thread pool strategies
- **Resource isolation**: Custom thread pools provide isolation between different types of work
- **Monitoring**: Thread pools can be monitored and tuned based on application metrics

## Mailbox Configuration

Actors in Cajun process messages from their mailboxes. The system provides different mailbox implementations that can be configured based on performance, memory usage, and backpressure requirements.

### Available Mailbox Types

#### 1. LinkedBlockingQueue (Default)
- **Implementation**: `java.util.concurrent.LinkedBlockingQueue`
- **Capacity**: Configurable (default: 10,000 messages)
- **Characteristics**:
  - Fair or non-fair ordering
  - Good for general-purpose actors
  - Handles I/O-bound workloads well
  - Memory usage grows with queue size
- **Use Case**: Default choice for most actors, especially those doing I/O

```java
// Default configuration
Pid actor = system.actorOf(MyHandler.class).spawn();

// Custom capacity
MailboxConfig config = new MailboxConfig(5000); // 5K capacity
Pid actor = system.actorOf(MyHandler.class)
    .withMailboxConfig(config)
    .spawn();
```

#### 2. ResizableBlockingQueue (Dynamic Sizing)
- **Implementation**: Custom resizable queue
- **Capacity**: Dynamic (min/max bounds)
- **Characteristics**:
  - Automatically grows under load
  - Shrinks when load decreases
  - Memory efficient for bursty workloads
  - Configurable growth/shrink factors and thresholds
- **Use Case**: Actors with variable message rates, bursty traffic

```java
ResizableMailboxConfig config = new ResizableMailboxConfig(
    100,    // Initial capacity
    1000,   // Maximum capacity
    50,     // Minimum capacity
    0.8,    // Grow threshold (80% full)
    2.0,    // Growth factor (double size)
    0.2,    // Shrink threshold (20% full)
    0.5     // Shrink factor (halve size)
);

Pid actor = system.actorOf(MyHandler.class)
    .withMailboxConfig(config)
    .spawn();
```

#### 3. ArrayBlockingQueue (Fixed Size)
- **Implementation**: `java.util.concurrent.ArrayBlockingQueue`
- **Capacity**: Fixed, configured at creation
- **Characteristics**:
  - Fixed memory footprint
  - Better cache locality
  - Can be fair or non-fair
  - Blocks when full (natural backpressure)
- **Use Case**: Memory-constrained environments, predictable memory usage

```java
// Requires custom MailboxProvider for ArrayBlockingQueue
public class ArrayBlockingQueueProvider<M> implements MailboxProvider<M> {
    @Override
    public BlockingQueue<M> createMailbox(MailboxConfig config, ThreadPoolFactory.WorkloadType workloadTypeHint) {
        return new ArrayBlockingQueue<>(config.getCapacity());
    }
}

ActorSystem system = ActorSystem.create("my-system")
    .withMailboxProvider(new ArrayBlockingQueueProvider<>())
    .build();
```

#### 4. SynchronousQueue (Direct Handoff)
- **Implementation**: `java.util.concurrent.SynchronousQueue`
- **Capacity**: 0 (no storage)
- **Characteristics**:
  - Zero memory overhead
  - Direct handoff between producer and consumer
  - Strong backpressure (sender blocks until receiver ready)
  - Highest throughput for balanced producer/consumer
- **Use Case**: Pipeline processing, direct handoff scenarios

```java
public class SynchronousQueueProvider<M> implements MailboxProvider<M> {
    @Override
    public BlockingQueue<M> createMailbox(MailboxConfig config, ThreadPoolFactory.WorkloadType workloadTypeHint) {
        return new SynchronousQueue<>();
    }
}

ActorSystem system = ActorSystem.create("my-system")
    .withMailboxProvider(new SynchronousQueueProvider<>())
    .build();
```

### Performance Characteristics

| Mailbox Type | Memory Usage | Throughput | Latency | Backpressure | Best For |
|-------------|--------------|------------|---------|--------------|----------|
| **LinkedBlockingQueue** | Dynamic | High | Low | Medium | General purpose, I/O |
| **ResizableBlockingQueue** | Adaptive | High | Low | Medium | Bursty workloads |
| **ArrayBlockingQueue** | Fixed | Medium | Low | Strong | Memory constraints |
| **SynchronousQueue** | Zero | Very High | Very Low | Very Strong | Pipeline processing |

### Choosing the Right Mailbox

**Use LinkedBlockingQueue when:**
- Standard actor communication
- I/O-bound or mixed workloads
- Need simple, reliable behavior

**Use ResizableBlockingQueue when:**
- Message rates vary significantly
- Want memory efficiency with burst handling
- Need adaptive sizing

**Use ArrayBlockingQueue when:**
- Memory usage must be predictable
- Fixed-size buffers are acceptable
- Want guaranteed memory bounds

**Use SynchronousQueue when:**
- Building pipeline stages
- Want direct handoff semantics
- Producer and consumer rates are balanced

### Integration with Backpressure

Mailbox choice affects backpressure behavior:
- **Bounded queues** (ArrayBlockingQueue, ResizableBlockingQueue) provide natural backpressure
- **Unbounded queues** (LinkedBlockingQueue with Integer.MAX_VALUE) require explicit backpressure configuration
- **Zero-capacity queues** (SynchronousQueue) provide strongest backpressure

```java
// Combine bounded mailbox with backpressure for flow control
BackpressureConfig bpConfig = new BackpressureConfig()
    .setStrategy(BackpressureStrategy.BLOCK)
    .setCriticalThreshold(0.9f);

MailboxConfig mailboxConfig = new MailboxConfig(1000); // Bounded

Pid actor = system.actorOf(MyHandler.class)
    .withMailboxConfig(mailboxConfig)
    .withBackpressureConfig(bpConfig)
    .spawn();
```

## Request-Response with Ask Pattern

While actors typically communicate through one-way asynchronous messages, Cajun provides an "ask pattern" for request-response interactions where you need to wait for a reply.

### The Reply Pattern (Recommended)

Cajun provides a streamlined **3-tier Reply API** that wraps `CompletableFuture` with a more ergonomic interface:

```java
// Tier 1: Simple - just get the value
String name = userActor.ask(new GetName(), Duration.ofSeconds(5)).get();

// Tier 2: Safe - pattern matching with Result
switch (userActor.ask(new GetProfile(), Duration.ofSeconds(5)).await()) {
    case Result.Success(var profile) -> handleSuccess(profile);
    case Result.Failure(var error) -> handleError(error);
}

// Tier 3: Advanced - full CompletableFuture power
CompletableFuture<Combined> result = userReply.future()
    .thenCombine(ordersReply.future(), (user, orders) -> combine(user, orders));
```

**Key Benefits:**
- **Tier 1 (Simple)**: Clean blocking API with automatic exception handling
- **Tier 2 (Safe)**: Pattern matching for explicit error handling without exceptions
- **Tier 3 (Advanced)**: Direct `CompletableFuture` access for complex async composition
- **Monadic operations**: `map()`, `flatMap()`, `recover()`, `recoverWith()`
- **Callbacks**: `onSuccess()`, `onFailure()`, `onComplete()` for non-blocking workflows

📖 **See [Reply Pattern Usage Guide](docs/reply_pattern_usage.md) for complete documentation with examples.**

### Basic Usage (CompletableFuture)

You can also use the traditional `CompletableFuture` approach directly:

```java
// Send a request to an actor and get a future response
CompletableFuture<String> future = actorSystem.ask(
    targetActorPid,       // The actor to ask
    "ping",               // The message to send
    Duration.ofSeconds(3) // Timeout for the response
);

// Process the response when it arrives
future.thenAccept(response -> {
    System.out.println("Received response: " + response);
});

// Or wait for the response (blocking)
try {
    String response = future.get(5, TimeUnit.SECONDS);
    System.out.println("Received response: " + response);
} catch (ExecutionException | InterruptedException | TimeoutException e) {
    System.err.println("Error getting response: " + e.getMessage());
}
```

### Implementing Responders

**Important**: Actors receive their natural message types directly - the system automatically handles the ask pattern infrastructure. You don't need to wrap messages in `AskPayload` or manually manage `replyTo` addresses.

To respond to an ask request, simply use `context.getSender()` to get the sender's Pid and send your response:

```java
public class ResponderHandler implements Handler<String> {
    
    @Override
    public void receive(String message, ActorContext context) {
        // Process the message naturally
        String response = processMessage(message);
        
        // Reply to sender if present (will be present for ask requests)
        context.getSender().ifPresent(sender -> 
            context.tell(sender, response)
        );
    }
    
    private String processMessage(String message) {
        if ("ping".equals(message)) {
            return "pong";
        }
        return "unknown command";
    }
}
```

**Key Points:**
- Your actor handles its natural message type (e.g., `String`, not `AskPayload<String>`)
- The system automatically unwraps ask messages and sets the sender context
- Use `context.getSender()` to get an `Optional<Pid>` of the sender
- `getSender()` returns `Optional.empty()` for regular `tell()` messages, contains sender PID for `ask()` messages
- No need to manually extract `replyTo` or handle `AskPayload` wrappers

### Error Handling

The ask pattern includes robust error handling to manage various failure scenarios:

1. **Timeout Handling**: If no response is received within the specified timeout, the future completes exceptionally with a `TimeoutException`.

2. **Type Mismatch Handling**: If the response type doesn't match the expected type, the future completes exceptionally with a wrapped `ClassCastException`.

3. **Actor Failure Handling**: If the target actor fails while processing the message, the error is propagated to the future based on the actor's supervision strategy.

```java
try {
    String response = actorSystem.ask(actorPid, message, Duration.ofSeconds(2)).get();
    // Process successful response
} catch (ExecutionException ex) {
    Throwable cause = ex.getCause();
    if (cause instanceof TimeoutException) {
        // Handle timeout
    } else if (cause instanceof RuntimeException && cause.getCause() instanceof ClassCastException) {
        // Handle type mismatch
    } else {
        // Handle other errors
    }
} catch (InterruptedException e) {
    // Handle interruption
}
```

### Complete Example

Here's a complete example showing both the requester and responder:

```java
public class AskPatternExample {
    
    public record PingMessage() {}
    public record PongMessage() {}
    
    public static class PingPongHandler implements Handler<PingMessage> {
        @Override
        public void receive(PingMessage message, ActorContext context) {
            // Automatically reply to the sender
            context.getSender().ifPresent(sender -> 
                context.tell(sender, new PongMessage())
            );
        }
    }
    
    public static void main(String[] args) throws Exception {
        ActorSystem system = new ActorSystem();
        
        // Create the responder actor
        Pid responder = system.actorOf(PingPongHandler.class).spawn();
        
        // Send an ask request
        CompletableFuture<PongMessage> future = system.ask(
            responder,
            new PingMessage(),
            Duration.ofSeconds(3)
        );
        
        // Wait for and process the response
        PongMessage response = future.get();
        System.out.println("Received pong!");
        
        system.shutdown();
    }
}
```

### Implementation Details

Internally, the ask pattern uses a **promise-based approach** without creating any temporary actors:

1. Generating a unique request ID (e.g., `"ask-12345678-..."`)
2. Creating a `CompletableFuture` to hold the response
3. Registering the future in a thread-safe request registry
4. Automatically wrapping your message with the request ID as sender context
5. Sending the message to the target actor
6. Setting up a timeout to complete the future exceptionally if no response arrives in time
7. When the response arrives, directly completing the future from the registry
8. Automatically cleaning up the request entry

This **zero-actor, promise-based implementation** ensures that:
- No temporary actors are created (eliminating race conditions and overhead)
- Your actors work with their natural message types
- The `replyTo` mechanism is handled automatically by the system
- Resources are properly cleaned up, even in failure scenarios
- The same actor can handle both `tell()` and `ask()` messages seamlessly
- Request-response latency is minimal (~100x faster than actor-based approaches)

## Sender Context and Message Forwarding

Cajun provides explicit control over sender context propagation through actor hierarchies, making it easy to build request routing and processing pipelines.

### Understanding Sender Context

When an actor receives a message, it can check who sent it using `getSender()`, which returns an `Optional<Pid>`:

```java
public class ProcessorHandler implements Handler<Request> {
    @Override
    public void receive(Request message, ActorContext context) {
        // Check if there's a sender (e.g., from ask pattern)
        context.getSender().ifPresent(sender -> {
            // Reply to the sender
            context.tell(sender, new Response("processed"));
        });
    }
}
```

**Key Points:**
- `getSender()` returns `Optional<Pid>` – use `ifPresent()`, `map()`, or `orElse()` for clean handling
- When another actor calls `tell()` (or `forward()`), the sender PID is propagated automatically, so `getSender()` is present and you can reply immediately
- When the system, an external thread, or test code without an actor context calls `tell()`, the sender is unknown and you’ll see `Optional.empty()`
- For the ask pattern, the temporary request ID is exposed as the sender, so `context.tell(getSender().get(), response)` completes the `CompletableFuture`
- Sender context is automatically cleared after message processing

### Message Forwarding with `forward()`

When building actor hierarchies or routing patterns, you often want to preserve the original sender so the final handler can reply directly to the requester. Use `forward()` instead of `tell()` to preserve sender context:

```java
public class RouterHandler implements Handler<RoutableRequest> {
    @Override
    public void receive(RoutableRequest message, ActorContext context) {
        Pid targetHandler = selectHandler(message);
        
        // Forward preserves the original sender context
        context.forward(targetHandler, message);
        
        // The target handler can now reply directly to the original requester
    }
}

public class HandlerActor implements Handler<RoutableRequest> {
    @Override
    public void receive(RoutableRequest message, ActorContext context) {
        Response response = process(message);
        
        // Reply goes to original requester, not the router
        context.getSender().ifPresent(requester -> 
            context.tell(requester, response)
        );
    }
}
```

### When to Use Each Method

| Method | Use When | Sender Context |
|--------|----------|----------------|
| `tell()` | Normal message passing, no reply expected | Lost (Optional.empty()) |
| `forward()` | Acting as intermediary, want final actor to reply to original sender | Preserved |
| `ask()` | Request-response pattern, you are the requester | You become the sender |

### Complete Example: Request Pipeline

```java
// Grandparent initiates request
CompletableFuture<Result> future = system.ask(
    parentPid, 
    new ProcessRequest("data"),
    Duration.ofSeconds(3)
);

// Parent forwards to child (preserving grandparent as sender)
public class ParentHandler implements Handler<ProcessRequest> {
    @Override
    public void receive(ProcessRequest msg, ActorContext context) {
        ProcessRequest enhanced = preprocess(msg);
        context.forward(childPid, enhanced); // Sender preserved
    }
}

// Child processes and replies to grandparent
public class ChildHandler implements Handler<ProcessRequest> {
    @Override
    public void receive(ProcessRequest msg, ActorContext context) {
        Result result = process(msg);
        
        // Reply goes to grandparent (original requester)
        context.getSender().ifPresent(requester -> 
            context.tell(requester, result)
        );
    }
}
```

**For more details and advanced patterns, see [docs/sender_propagation.md](docs/sender_propagation.md)**

## Error Handling and Supervision Strategy

Cajun provides a robust error handling system with supervision strategies inspired by Erlang OTP. This allows actors to recover from failures gracefully without crashing the entire system.

### Supervision Strategies

The `SupervisionStrategy` enum defines how an actor should respond to failures:

- **RESUME**: Continue processing messages, ignoring the error (best for non-critical errors)
- **RESTART**: Restart the actor, resetting its state (good for recoverable errors)
- **STOP**: Stop the actor completely (for unrecoverable errors)
- **ESCALATE**: Escalate the error to the parent actor (for system-wide issues)

```java
// Configure an actor with a specific supervision strategy
MyActor actor = new MyActor(system, "my-actor");
actor.withSupervisionStrategy(SupervisionStrategy.RESTART);

// Method chaining for configuration
MyActor actor = new MyActor(system, "my-actor")
    .withSupervisionStrategy(SupervisionStrategy.RESTART)
    .withErrorHook(ex -> logger.error("Actor error", ex));
```

### Lifecycle Hooks

Actors provide lifecycle hooks that are called during error handling and recovery:

- **preStart()**: Called before the actor starts processing messages
- **postStop()**: Called when the actor is stopped
- **onError(Throwable)**: Called when an error occurs during message processing

```java
public class ResilientActor extends Actor<String> {
    
    public ResilientActor(ActorSystem system, String actorId) {
        super(system, actorId);
    }
    
    @Override
    protected void preStart() {
        // Initialize resources
        logger.info("Actor starting: {}", self().id());
    }
    
    @Override
    protected void postStop() {
        // Clean up resources
        logger.info("Actor stopping: {}", self().id());
    }
    
    @Override
    protected void onError(Throwable error) {
        // Custom error handling
        logger.error("Error in actor: {}", self().id(), error);
    }
    
    @Override
    protected void receive(String message) {
        // Message processing logic
    }
}
```

### Exception Handling

The `handleException` method provides centralized error management:

```java
@Override
protected SupervisionStrategy handleException(Throwable exception) {
    if (exception instanceof TemporaryException) {
        // Log and continue for temporary issues
        logger.warn("Temporary error, resuming", exception);
        return SupervisionStrategy.RESUME;
    } else if (exception instanceof RecoverableException) {
        // Restart for recoverable errors
        logger.error("Recoverable error, restarting", exception);
        return SupervisionStrategy.RESTART;
    } else {
        // Stop for critical errors
        logger.error("Critical error, stopping", exception);
        return SupervisionStrategy.STOP;
    }
}
```

### Integration with Ask Pattern

The error handling system integrates seamlessly with the ask pattern, propagating exceptions to the future:

```java
try {
    // If the actor throws an exception while processing this message,
    // it will be propagated to the future based on the supervision strategy
    String result = actorSystem.ask(actorPid, "risky-operation", Duration.ofSeconds(5)).get();
    System.out.println("Success: " + result);
} catch (ExecutionException ex) {
    // The original exception is wrapped in an ExecutionException
    Throwable cause = ex.getCause();
    System.err.println("Actor error: " + cause.getMessage());
}
```

### Logging Integration

Cajun integrates with SLF4J and Logback for comprehensive logging:

```java
// Configure logging in your application
private static final Logger logger = LoggerFactory.getLogger(MyActor.class);

// Errors are automatically logged with appropriate levels
@Override
protected void receive(Message msg) {
    try {
        // Process message
    } catch (Exception e) {
        // This will be logged and handled according to the supervision strategy
        throw new ActorException("Failed to process message", e);
    }
}
```

## Stateful Actors and Persistence

Cajun provides a `StatefulActor` class that maintains and persists its state. This is useful for actors that need to maintain state across restarts or system failures.

### State Persistence

Stateful actors can persist their state to disk or other storage backends. This allows actors to recover their state after a restart or crash.

```java
// Define a stateful handler for the counter
public class CounterHandler implements StatefulHandler<Integer, CounterMessage> {
    @Override
    public Integer processMessage(Integer count, CounterMessage message) {
        if (message instanceof IncrementMessage) {
            return count + 1;
        } else if (message instanceof GetCountMessage getCountMsg) {
            // Send the current count back to the sender
            getCountMsg.getSender().tell(count);
            return count;
        }
        return count;
    }
}

// Create a stateful actor with an initial state using the handler pattern
Pid counterPid = system.statefulActor("counter", 0, new CounterHandler());

// Send messages to the actor
counterPid.tell(new IncrementMessage());
counterPid.tell(new GetCountMessage(myPid));
```

### Message Persistence and Replay

Cajun supports message persistence and replay for stateful actors using a Write-Ahead Log (WAL) style approach. This enables actors to recover their state by replaying messages after a restart or crash.

#### Key Features

- **Message Journaling**: All messages are logged to a journal before processing
- **State Snapshots**: Periodic snapshots of actor state are taken to speed up recovery
- **Hybrid Recovery**: Uses latest snapshot plus replay of subsequent messages
- **Pluggable Persistence**: Swap out persistence implementations without changing actor code
- **Provider Pattern**: Configure system-wide persistence strategy with ease

```java
// Define a stateful handler with custom persistence (legacy approach)
public class MyStatefulHandler implements StatefulHandler<MyState, MyMessage> {
    @Override
    public MyState processMessage(MyState state, MyMessage message) {
        // Process the message and return the new state
        return newState;
    }
}

// Create the actor using the stateful handler
Pid actorPid = system.statefulActor(
    "my-actor",
    initialState,
    new MyStatefulHandler(),
    PersistenceFactory.createBatchedFileMessageJournal(),
    PersistenceFactory.createFileSnapshotStore()
);
```

#### Persistence Provider Pattern

Cajun now supports a provider pattern for persistence, allowing you to swap out persistence implementations at runtime without changing your actor code:

```java
// Register a custom persistence provider for the entire actor system
PersistenceProvider customProvider = new CustomPersistenceProvider();
ActorSystemPersistenceHelper.setPersistenceProvider(actorSystem, customProvider);

// Or use the fluent API
ActorSystemPersistenceHelper.persistence(actorSystem)
    .withPersistenceProvider(customProvider);

// Create stateful actors using the handler pattern with the configured provider
// No need to specify persistence components explicitly
public class MyStatefulHandler implements StatefulHandler<MyState, MyMessage> {
    @Override
    public MyState processMessage(MyState state, MyMessage message) {
        // Process the message and return the new state
        return newState;
    }
}

// The system will use the configured persistence provider automatically
Pid actorPid = system.statefulActor("my-actor", initialState, new MyStatefulHandler());
```

#### Creating Custom Persistence Providers

Implement the `PersistenceProvider` interface to create custom persistence backends:

```java
public class CustomPersistenceProvider implements PersistenceProvider {
    @Override
    public <M> BatchedMessageJournal<M> createBatchedMessageJournal(String actorId) {
        // Implement custom message journaling
        return new CustomBatchedMessageJournal<>(actorId);
    }
    
    @Override
    public <S> SnapshotStore<S> createSnapshotStore(String actorId) {
        // Implement custom state snapshot storage
        return new CustomSnapshotStore<>(actorId);
    }
    
    // Implement other required methods
}
```

The actor system uses `FileSystemPersistenceProvider` by default if no custom provider is specified. For production workloads, LMDB is recommended for higher performance.

#### LMDB Persistence (Recommended for Production)

Cajun includes LMDB support for high-performance persistence scenarios:

```java
// Configure LMDB persistence provider
Path lmdbPath = Paths.get("/var/cajun/lmdb");
long mapSize = 10L * 1024 * 1024 * 1024; // 10GB
LmdbPersistenceProvider lmdbProvider = new LmdbPersistenceProvider(lmdbPath, mapSize);

// Register as system-wide provider
ActorSystemPersistenceHelper.setPersistenceProvider(actorSystem, lmdbProvider);

// Or use fluent API
ActorSystemPersistenceHelper.persistence(actorSystem)
    .withPersistenceProvider(lmdbProvider);

// Create stateful actors with LMDB persistence
Pid actor = system.statefulActorOf(MyHandler.class, initialState)
    .withPersistence(
        lmdbProvider.createMessageJournal("my-actor"),
        lmdbProvider.createSnapshotStore("my-actor")
    )
    .spawn();

// For high-throughput scenarios, use the native batched journal
BatchedMessageJournal<MyEvent> batchedJournal =
    lmdbProvider.createBatchedMessageJournalSerializable("my-actor", 5000, 10);
```

**LMDB Performance Characteristics:**
- **Small batches (1K)**: 5M msgs/sec (filesystem faster)
- **Large batches (5K+)**: 200M+ msgs/sec (LMDB faster)
- **Sequential reads**: 1M-2M msgs/sec (memory-mapped, zero-copy)
- **ACID guarantees**: Crash-proof with automatic recovery
- **No manual cleanup**: Automatic space reuse (unlike filesystem)

**When to use LMDB:**
- Production workloads with high throughput
- Large batch sizes (>5K messages per batch)
- Read-heavy workloads (zero-copy reads)
- Low recovery time requirements
- ACID guarantees needed

### Stateful Actor Recovery

The StatefulActor implements a robust recovery mechanism that ensures state consistency after system restarts or failures:

1. **Initialization Process**:
   - On startup, the actor attempts to load the most recent snapshot
   - If a snapshot exists, it restores the state from that snapshot
   - It then replays any messages received after the snapshot was taken
   - If no snapshot exists, it initializes with the provided initial state

2. **Explicit State Initialization**:
   ```java
   // Force initialization and wait for it to complete (useful in tests)
   statefulActor.forceInitializeState().join();
   
   // Or with timeout
   boolean initialized = statefulActor.waitForStateInitialization(1000);
   ```

3. **Handling Null States**:
   - StatefulActor properly handles null initial states for recovery cases
   - State can be null during initialization and will be properly recovered if snapshots exist

### Adaptive Snapshot Strategy

StatefulActor implements an adaptive snapshot strategy to balance performance and recovery speed:

```java
// Configure snapshot strategy (time-based and change-count-based)
statefulActor.configureSnapshotStrategy(
    30000,    // Take snapshot every 30 seconds
    500       // Or after 500 state changes, whichever comes first
);

// Force an immediate snapshot
statefulActor.forceSnapshot().join();
```

Key snapshot features:
- **Time-based snapshots**: Automatically taken after a configurable time interval
- **Change-based snapshots**: Taken after a certain number of state changes
- **Dedicated thread pool**: Snapshots are taken asynchronously to avoid blocking the actor
- **Final snapshots**: A snapshot is automatically taken when the actor stops

### Journal Truncation Strategies

To manage disk space and improve recovery performance, Cajun provides configurable journal truncation strategies that automatically clean up old message journals:

```java
import com.cajunsystems.persistence.PersistenceTruncationConfig;
import com.cajunsystems.persistence.PersistenceTruncationMode;

// Option 1: Synchronous truncation (default)
// Journals are truncated during snapshot lifecycle
PersistenceTruncationConfig syncConfig = PersistenceTruncationConfig.builder()
    .mode(PersistenceTruncationMode.SYNC_ON_SNAPSHOT)
    .retainMessagesBehindSnapshot(500)    // Keep 500 messages before latest snapshot
    .retainLastMessagesPerActor(5000)     // Always keep last 5000 messages minimum
    .build();

// Option 2: Asynchronous truncation with background daemon
// Non-blocking truncation runs periodically
PersistenceTruncationConfig asyncConfig = PersistenceTruncationConfig.builder()
    .mode(PersistenceTruncationMode.ASYNC_DAEMON)
    .retainMessagesBehindSnapshot(500)
    .retainLastMessagesPerActor(5000)
    .daemonInterval(Duration.ofMinutes(5)) // Run every 5 minutes
    .build();

// Option 3: Disable truncation
PersistenceTruncationConfig offConfig = PersistenceTruncationConfig.builder()
    .mode(PersistenceTruncationMode.OFF)
    .build();

// Apply to stateful actor builder
Pid actor = system.statefulActorOf(MyHandler.class, initialState)
    .withTruncationConfig(asyncConfig)
    .spawn();
```

**Truncation Modes**:

- **OFF**: Journals grow indefinitely - useful for audit logs or when manual cleanup is preferred
- **SYNC_ON_SNAPSHOT** (default): Truncates journals synchronously when snapshots are taken
  - Ensures consistency between snapshots and journals
  - Slight performance impact during snapshot operations
  - Recommended for most use cases
- **ASYNC_DAEMON**: Truncates journals asynchronously using a background daemon
  - Zero impact on actor message processing
  - Configurable interval for truncation runs
  - Best for high-throughput actors where minimal latency is critical

**Benefits**:
- Prevents unbounded journal growth for long-running actors
- Improves recovery time (fewer messages to replay)
- Reduces disk I/O during recovery
- Configurable retention policies balance safety and space

## Backpressure Support in Actors

Cajun features a robust backpressure system to help actors manage high load scenarios effectively. Backpressure is an opt-in feature, configured using `BackpressureConfig` objects.

### Enabling and Configuring Backpressure

Backpressure can be configured at the `ActorSystem` level, which then applies to actors by default, or dynamically for individual actors if specific settings are needed.

#### System-Wide Configuration
To enable and configure backpressure for all actors by default within an `ActorSystem`, provide a `BackpressureConfig` object during its creation. Actors created within this system will inherit this configuration. If no `BackpressureConfig` is supplied to the `ActorSystem`, backpressure is disabled by default for the system.

**Example:**
```java
// Define backpressure settings using BackpressureConfig
BackpressureConfig systemBpConfig = new BackpressureConfig()
    .setStrategy(BackpressureStrategy.BLOCK)      // Default strategy
    .setWarningThreshold(0.7f)                 // 70% mailbox capacity
    .setCriticalThreshold(0.9f)                // 90% mailbox capacity
    .setRecoveryThreshold(0.5f);                // 50% mailbox capacity

// Create ActorSystem with this configuration
// This also requires a ThreadPoolFactory
ActorSystem system = new ActorSystem(new ThreadPoolFactory(), systemBpConfig);

// Actors created in this system will now use these backpressure settings by default.
```

#### Actor-Specific Configuration
Actors primarily inherit their backpressure configuration from the `ActorSystem` they belong to. If you need to customize backpressure settings for a specific actor (e.g., use a different strategy or thresholds than the system default, or enable it if the system has it disabled), you can do so dynamically after the actor has been created using the `BackpressureBuilder`. See the "Dynamically Managing Backpressure" section for details.

If an actor is part of an `ActorSystem` that has backpressure disabled (no `BackpressureConfig` provided to the system), backpressure will also be disabled for that actor by default. It can then be enabled and configured specifically for that actor using the `BackpressureBuilder`.

### Backpressure States

The backpressure system operates with four distinct states:

1. **NORMAL**: The actor is operating with sufficient capacity
2. **WARNING**: The actor is approaching capacity limits but not yet applying backpressure
3. **CRITICAL**: The actor is at or above its high watermark and actively applying backpressure
4. **RECOVERY**: The actor was recently in a CRITICAL state but is now recovering (below high watermark but still above low watermark)

### Backpressure Strategies

Cajun supports multiple strategies for handling backpressure:

1. **BLOCK**: Block the sender until space is available in the mailbox (default behavior)
2. **DROP_NEW**: Drop new messages when the mailbox is full, prioritizing older messages
3. **DROP_OLDEST**: Remove oldest messages from the mailbox using the direct Actor.dropOldestMessage method
4. **CUSTOM**: Use a custom strategy by implementing a `CustomBackpressureHandler`

### Dynamically Managing Backpressure with BackpressureBuilder

While `BackpressureConfig` sets the initial backpressure configuration (either system-wide or for an actor at creation), the `BackpressureBuilder` allows for dynamic adjustments to an actor's backpressure settings after it has been created. This is useful for overriding system defaults for a specific actor, or for enabling and configuring backpressure for an actor if its `ActorSystem` has backpressure disabled by default.

```java
// Direct actor configuration with type safety
BackpressureBuilder<MyMessage> builder = new BackpressureBuilder<>(myActor)
    .withStrategy(BackpressureStrategy.DROP_OLDEST)
    .withWarningThreshold(0.7f)
    .withCriticalThreshold(0.9f)
    .withRecoveryThreshold(0.5f);

// Apply the configuration
builder.apply();

// PID-based configuration through ActorSystem
BackpressureBuilder<MyMessage> builder = system.getBackpressureMonitor()
    .configureBackpressure(actorPid)
    .withStrategy(BackpressureStrategy.DROP_OLDEST)
    .withWarningThreshold(0.7f)
    .withCriticalThreshold(0.9f);

builder.apply();

// Using preset configurations for common scenarios
BackpressureBuilder<MyMessage> timeCriticalBuilder = new BackpressureBuilder<>(myActor)
    .presetTimeCritical()
    .apply();

BackpressureBuilder<MyMessage> reliableBuilder = new BackpressureBuilder<>(myActor)
    .presetReliable()
    .apply();

BackpressureBuilder<MyMessage> highThroughputBuilder = new BackpressureBuilder<>(myActor)
    .presetHighThroughput()
    .apply();

// Check backpressure status
BackpressureStatus status = actor.getBackpressureStatus();
BackpressureState currentState = status.getCurrentState();
float fillRatio = status.getFillRatio();
```

### Custom Backpressure Handlers

For advanced backpressure control, you can implement a custom handler and apply it using the BackpressureBuilder:

```java
CustomBackpressureHandler<MyMessage> handler = new CustomBackpressureHandler<>() {
    @Override
    public boolean handleMessage(Actor<MyMessage> actor, MyMessage message, BackpressureSendOptions options) {
        // Custom logic to decide whether to accept the message
        if (message.isPriority()) {
            return true; // Always accept priority messages
        }
        return actor.getCurrentSize() < actor.getCapacity() * 0.9;
    }
    
    @Override
    public boolean makeRoom(Actor<MyMessage> actor) {
        // Custom logic to make room in the mailbox
        // Return true if room was successfully made
        return actor.dropOldestMessage();
    }
};

// Configure with custom handler
new BackpressureBuilder<>(myActor)
    .withStrategy(BackpressureStrategy.CUSTOM)
    .withCustomHandler(handler)
    .apply();
```

### Backpressure Monitoring and Callbacks

The backpressure system provides monitoring capabilities and callback notifications through the BackpressureBuilder:

```java
// Register for backpressure event notifications using the builder
new BackpressureBuilder<>(myActor)
    .withStrategy(BackpressureStrategy.DROP_OLDEST)
    .withWarningThreshold(0.7f)
    .withCriticalThreshold(0.9f)
    .withCallback(event -> {
        logger.info("Backpressure event: {} state, fill ratio: {}", 
                    event.getState(), event.getFillRatio());
        
        // Take action based on backpressure events
        if (event.isBackpressureActive()) {
            // Notify monitoring system, scale resources, etc.
        }
    })
    .apply();

// Access detailed backpressure metrics and history
BackpressureStatus status = actor.getBackpressureStatus();
List<BackpressureEvent> recentEvents = status.getRecentEvents();
List<StateTransition> stateTransitions = status.getStateTransitions();

// Monitor state transition history
for (StateTransition transition : stateTransitions) {
    logger.debug("Transition from {} to {} at {} due to: {}", 
                transition.getFromState(), 
                transition.getToState(),
                transition.getTimestamp(),
                transition.getReason());
}
```

### High Priority Messages

You can send messages with special options to control backpressure behavior:

```java
// Create options for high priority messages that bypass backpressure
BackpressureSendOptions highPriority = new BackpressureSendOptions()
    .setHighPriority(true)
    .setTimeout(Duration.ofSeconds(5));

// Send with high priority
actor.tell(urgentMessage, highPriority);

// Or use the system to send with options
boolean accepted = system.tellWithOptions(actorPid, message, highPriority);

// Block until message is accepted or timeout occurs
BackpressureSendOptions blockingOptions = new BackpressureSendOptions()
    .setBlockUntilAccepted(true)
    .setTimeout(Duration.ofSeconds(3));
```

## Cluster Mode

Cajun supports running in a distributed cluster mode, allowing actors to communicate across multiple nodes.

### Setting Up a Cluster

```java
// Create a cluster configuration
ClusterConfig config = ClusterConfig.builder()
    .nodeName("node1")
    .port(2551)
    .seedNodes(List.of("127.0.0.1:2551", "127.0.0.1:2552"))
    .build();

// Create a clustered actor system
ActorSystem system = ActorSystem.createClustered(config);

// Create and register actors as usual
Pid actorPid = system.register(MyActor.class, "my-actor");
```

### Communicating with Remote Actors

Messages can be sent to actors regardless of which node they're running on. The system automatically routes messages to the correct node.

```java
// Send a message to an actor (works the same whether the actor is local or remote)
```

#### Fault Tolerance

When a node fails, its actors are automatically reassigned to other nodes in the cluster.

### Multiple Nodes Example

```java
// Node 1
MetadataStore metadataStore1 = new EtcdMetadataStore("http://etcd-host:2379");
DirectMessagingSystem messagingSystem1 = new DirectMessagingSystem("node1", 8080);
messagingSystem1.addNode("node2", "node2-host", 8080);
ClusterActorSystem system1 = new ClusterActorSystem("node1", metadataStore1, messagingSystem1);
system1.start().get();

// Node 2
MetadataStore metadataStore2 = new EtcdMetadataStore("http://etcd-host:2379");
DirectMessagingSystem messagingSystem2 = new DirectMessagingSystem("node2", 8080);
messagingSystem2.addNode("node1", "node1-host", 8080);
ClusterActorSystem system2 = new ClusterActorSystem("node2", metadataStore2, messagingSystem2);
system2.start().get();
```
For more details refer to [Cluster Mode](docs/cluster_mode.md).

### Extending the System

#### Custom Metadata Store

You can implement your own metadata store by implementing the `MetadataStore` interface:

```java
public class CustomMetadataStore implements MetadataStore {
    // Implement the required methods
}
```

#### Custom Messaging System

You can implement your own messaging system by implementing the `MessagingSystem` interface:

```java
public class CustomMessagingSystem implements MessagingSystem {
    // Implement the required methods
}
```

For more details, see the [Cluster Mode Improvements documentation](docs/cluster_mode_improvements.md).

## Performance & Benchmarks

Cajun has been extensively benchmarked to help you understand when actors are the right choice. The benchmarks compare **Actors**, **Threads**, and **Structured Concurrency** across real-world workloads.

### Quick Summary: When to Use Actors

**✅ Actors Excel At (Near-Zero Overhead):**
- **I/O-Heavy Applications**: Microservices, web apps, database operations
  - Performance: **0.02% overhead** vs raw threads - essentially identical!
  - Example: A 10ms database call takes 10.002ms with actors
- **Mixed Workloads**: Realistic apps with both CPU and I/O
  - Performance: **< 1% overhead** for typical request handling
- **Stateful Services**: User sessions, game entities, shopping carts
  - Performance: **8% overhead** but you get thread-safe state management
- **Event Processing**: Kafka/RabbitMQ consumers, event streams
  - Performance: **0.02% overhead** for I/O-bound message processing

**⚠️ Consider Threads For:**
- **Embarrassingly Parallel Tasks**: 100+ independent CPU computations
  - Threads are **10x faster** for pure parallel computation
- **Simple Scatter-Gather**: No state, just parallel work and collect
  - Threads are **38% faster** for this specific pattern

### Detailed Performance Numbers

Based on comprehensive JMH benchmarks (November 2025, Java 21+):

#### I/O-Bound Workloads (Where Actors Shine!)

| Workload | Threads | Actors | Overhead |
|----------|---------|--------|----------|
| **Single 10ms I/O operation** | 10,457µs | 10,440µs | **-0.16%** (faster!) |
| **100 concurrent I/O operations** | 106µs/op | 1,035µs/op | Expected† |
| **Mixed CPU + I/O (realistic)** | 5,520µs | 5,522µs | **+0.03%** |

**† Note**: Actors serialize messages per actor (by design for state consistency). For truly parallel I/O, use thread pools or distribute across more actors.

**Key Insight**: Virtual threads make actor overhead **negligible for I/O workloads** - the common case for microservices and web applications!

#### CPU-Bound Workloads

| Workload | Threads | Actors | Overhead |
|----------|---------|--------|----------|
| **Single task (Fibonacci)** | 27.2µs | 29.5µs | **+8.4%** |
| **Request-reply pattern** | 26.8µs | 28.9µs | **+8.0%** |
| **Scatter-gather (10 ops)** | 3.4µs/op | 4.7µs/op | **+38%** |

**Verdict**: **8% overhead for CPU work is excellent** considering you get state isolation, fault tolerance, and backpressure built-in!

#### Persistence Performance

Cajun includes high-performance persistence with two backends:

| Backend | Write Throughput | Read Performance | Best For |
|---------|-----------------|------------------|----------|
| **Filesystem** | 48M msgs/sec | Good | Development, small batches |
| **LMDB** | 208M msgs/sec | 10x faster (zero-copy) | Production, large batches |

**Run persistence benchmarks:**
```bash
./gradlew :benchmarks:jmh -Pjmh.includes="*Persistence*"
```

### Virtual Threads: The Secret Sauce

Cajun uses **virtual threads by default** - this is why I/O performance is so good:

**Virtual Thread Benefits:**
- ✅ Thousands of concurrent actors with minimal overhead
- ✅ Blocking I/O is cheap (virtual threads "park" instead of blocking OS threads)
- ✅ Simple, natural code (no callbacks or async/await)
- ✅ Perfect for microservices and web applications

**Performance Impact:**
- CPU-bound: 8% overhead (acceptable)
- I/O-bound: 0.02% overhead (negligible!)
- Mixed workloads: < 1% overhead (excellent)

**Note**: You can configure different thread pools per actor, but virtual threads (default) perform best in all tested scenarios.

### Running Benchmarks

```bash
# Run all benchmarks
./gradlew :benchmarks:jmh

# Run I/O benchmarks (shows actor strengths)
./gradlew :benchmarks:jmh -Pjmh.includes="*ioBound*"

# Run CPU benchmarks
./gradlew :benchmarks:jmh -Pjmh.includes="*cpuBound*"

# Quick development run
./gradlew :benchmarks:jmhQuick
```

### What Gets Benchmarked

Comprehensive test coverage across:

**Workload Types:**
- CPU-bound (pure computation)
- I/O-bound (database/network simulation)
- Mixed (realistic applications)
- Parallel processing

**Patterns:**
- Single task execution
- Request-reply
- Scatter-gather
- Pipeline processing
- Batch processing

**Comparisons:**
- Actors vs Threads
- Actors vs Structured Concurrency
- Different mailbox types (LinkedMailbox, MpscMailbox)
- Different thread pool types (Virtual, Fixed, Work-Stealing)

### Real-World Use Cases

#### ✅ Perfect for Microservices

```java
class OrderServiceActor {
    void receive(CreateOrder order) {
        User user = userDB.find(order.userId);        // 5ms I/O
        Inventory inv = inventoryAPI.check(order);    // 20ms I/O
        Payment pay = paymentGateway.process(order);  // 15ms I/O
        orderDB.save(order);                          // 3ms I/O
        
        // Total: 43ms I/O
        // Actor overhead: 0.002ms (0.005%)
    }
}
```

**Performance**: Near-zero overhead, natural blocking code, thread-safe state management!

#### ✅ Great for Web Applications

```java
class RequestHandlerActor {
    void receive(HttpRequest request) {
        Session session = sessionStore.get(request.token);  // 2ms
        Data data = database.query(request.params);         // 30ms
        String html = templateEngine.render(data);          // 8ms
        
        // Total: 40ms, Actor overhead: 0.002ms (0.005%)
    }
}
```

#### ⚠️ Use Thread Pools for Pure Parallelism

```java
// For 100 independent parallel computations, use threads
ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();
List<Future<Result>> futures = items.parallelStream()
    .map(item -> executor.submit(() -> compute(item)))
    .toList();
```

### Benchmark Methodology

All benchmarks use **JMH (Java Microbenchmark Harness)** with:
- 10 measurement iterations
- 2 forks for statistical reliability
- Proper warmup (3 iterations)
- Controlled environment
- Comparison with raw threads and structured concurrency

**Metrics:**
- **Average Time** (`avgt`): Microseconds per operation (lower is better)
- **Throughput** (`thrpt`): Operations per millisecond (higher is better)

Results available after running benchmarks:
- JSON format: `benchmarks/build/reports/jmh/results.json`
- Human-readable: `benchmarks/build/reports/jmh/human.txt`

### Key Takeaways

**🎯 Simple Decision Guide:**

1. **Building a microservice or web app?** → Use actors (0.02% overhead for I/O)
2. **Processing events from Kafka/RabbitMQ?** → Use actors (0.02% overhead)
3. **Need stateful request handling?** → Use actors (8% overhead, but thread-safe!)
4. **Pure CPU number crunching?** → Consider threads (10x faster for parallel)
5. **Simple parallel tasks?** → Use threads or parallel streams

**Bottom Line**: Actors are **production-ready** for I/O-heavy applications with negligible overhead. The 8% overhead for CPU work is more than compensated by built-in fault tolerance, state management, and clean architecture.

For complete benchmark details, analysis, and methodology, see:
- **[docs/BENCHMARKS.md](docs/BENCHMARKS.md)** - Complete performance guide with all benchmark results
- [benchmarks/README.md](benchmarks/README.md) - Technical details on running benchmarks

## Feature Roadmap

1. Actor system and actor lifecycle
   - [x] Create Actor and Actor System
   - [x] Support message to self for actor
   - [x] Support hooks for start and shutdown of actor
   - [x] Stateful functional style actor
   - [x] Timed messages
   - [x] Error handling with supervision strategies
   - [x] Request-response pattern with ask functionality
   - [x] Robust exception handling and propagation
2. Actor metadata management with etcd
   - [x] Distributed metadata store with etcd support
   - [x] Leader election
   - [x] Actor assignment tracking
3. Actor supervision hierarchy and fault tolerance
   - [x] Basic supervision strategies (RESUME, RESTART, STOP, ESCALATE)
   - [x] Hierarchical supervision
   - [x] Custom supervision policies
   - [x] Lifecycle hooks (preStart, postStop, onError)
   - [x] Integrated logging with SLF4J and Logback
4. Persistent state and messaging for actors
   - [x] StatefulActor with persistent state management
   - [x] Pluggable state storage backends (in-memory, file-based)
   - [x] Message persistence and replay
   - [x] State initialization and recovery mechanisms
   - [x] Snapshot-based state persistence
   - [x] Hybrid recovery approach (snapshots + message replay)
   - [x] Explicit state initialization and force initialization methods
   - [x] Proper handling of null initial states for recovery cases
   - [x] Adaptive snapshot strategy with time-based and change-count-based options
   - [ ] Customizable backends for snapshots and Write-Ahead Log (WAL)
   - [ ] RocksDB backend for state persistence
   - [ ] Segregation of runtime implementations (file store, in-memory store, etc.) from the actor system
5. Backpressure and load management
   - [x] Integrated backpressure support in StatefulActor
   - [x] Configurable mailbox capacity for backpressure control
   - [x] Load monitoring (queue size, processing times)
   - [x] Configurable retry mechanisms with exponential backoff
   - [x] Error recovery with custom error hooks
   - [x] Processing metrics and backpressure level monitoring
   - [ ] Circuit breaker pattern implementation
   - [ ] Rate limiting strategies

6. Partitioned state and sharding strategy
   - [x] Rendezvous hashing for actor assignment
7. Cluster mode
   - [x] Distributed actor systems
   - [x] Remote messaging between actor systems
   - [x] Actor reassignment on node failure
   - [x] Pluggable messaging system
   - [x] Configurable message delivery guarantees (EXACTLY_ONCE, AT_LEAST_ONCE, AT_MOST_ONCE)
```

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