Concurrency Decoded: Actors, CSP, and STM Mastery

Navigating the Parallel Universe: A Developer’s Guide to Concurrency Models

In today’s computing landscape, where multi-core processors are standard and distributed systems dominate, the ability to design and implement robust concurrent applications is no longer a niche skill—it’s a fundamental requirement. The era of simply speeding up single-threaded processes has largely passed; modern performance gains stem from efficiently leveraging parallelism. However, this power comes with significant challenges: race conditions, deadlocks, and the notorious complexity of shared mutable state. Understanding and effectively applying the right concurrency model is paramount to building scalable, reliable, and maintainable software.

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This article delves into three influential and distinct concurrency models: the Actor Model, Communicating Sequential Processes (CSP), and Software Transactional Memory (STM). We’ll demystify their core principles, explore their unique strengths, and provide a clear roadmap for when and how to integrate them into your development workflow. By the end, developers will possess a practical understanding of these paradigms, enabling them to make informed architectural decisions and craft more resilient concurrent systems.

Your First Steps into Concurrent Design: Kicking Off with Actors, CSP, and STM

Embarking on the journey of concurrent programming can seem daunting, but these models offer structured approaches to tackle complexity. Let’s lay the groundwork for understanding each one, focusing on their foundational concepts.

The Actor Model: Encapsulated State and Message Passing

The Actor Model champions the idea of isolated, independent units of computation called “actors.” Each actor has its own private state, runs asynchronously, and communicates with other actors only through message passing. There’s no direct access to another actor’s state, eliminating many common concurrency pitfalls like race conditions on shared data.

How to start thinking in Actors: Imagine a bustling postal service. Each post office is an actor. It has its own mail (state), processes incoming mail (messages) one by one, and can send mail to other post offices. It never directly reaches into another post office’s mailbox or storage. This isolation simplifies reasoning about system behavior.

Practical First Steps (Conceptual):

1. Define an Actor’s behavior:What messages can it receive? How does its state change based on those messages? What messages does it send in response?
2. Create an Actor System:This is the runtime environment that manages actors, their lifecycle, and message delivery.
3. Send messages:Interact with actors by sending them immutable messages.

Communicating Sequential Processes (CSP): Orchestrating Data Flow with Channels

CSP focuses on communication between concurrent processes (or “goroutines” in Go, “tasks” in Rust) via synchronous or asynchronous channels. Instead of sharing memory, processes share channels through which they send and receive data. This model emphasizes the explicit flow of data and synchronization points.

How to start thinking in CSP: Picture an assembly line. Each station on the line is a process. They don’t touch each other’s tools or materials directly. Instead, items (data) are placed on a conveyor belt (channel) from one station to the next. A station might wait for an item to appear on its input belt before it can work, and then places its output on another belt.

Practical First Steps (Conceptual):

1. Identify independent tasks:Break down your problem into smaller, sequential processes.
2. Define channels:Determine where data needs to flow between these processes and create channels for that communication.
3. Send and Receive:Use send and receive operations on channels to move data and synchronize processes.

Software Transactional Memory (STM): Atomic Updates to Shared State

STM offers a mechanism for safely managing shared mutable state by treating memory operations as database-like transactions. Multiple concurrent threads can attempt to read and write shared data, but all modifications are grouped into atomic, isolated, and durable transactions. If a conflict occurs (e.g., two transactions try to modify the same data simultaneously), one or more transactions are automatically retried until they can complete successfully without conflicts.

How to start thinking in STM: Consider a bank’s ledger. Many tellers (threads) might want to update customer balances (shared state). Instead of locking individual accounts, STM allows each teller to try to complete a series of updates as a single “transaction.” If two tellers try to update the same account at the exact same moment, one’s transaction will fail and automatically retry, ensuring the ledger remains consistent without explicit locking.

Practical First Steps (Conceptual):

1. Identify shared mutable data:Mark specific variables or data structures as “transactional” or “references.”
2. Enclose operations in a transaction:Group a series of reads and writes to transactional data within an atomically block (or equivalent).
3. Let the system handle conflicts:The STM runtime manages the rollback and retry logic if conflicts arise, simplifying the developer’s burden.

The key to getting started with any of these models is to embrace the mental shift they require. Move away from thinking about explicit locks and shared memory and instead focus on communication (Actors, CSP) or transactional guarantees (STM).

Arming Your Concurrent Toolkit: Essential Frameworks and Libraries

Choosing the right tools is half the battle in concurrent programming. Here’s a curated list of essential frameworks, libraries, and language features that embody the Actor, CSP, and STM models.

For the Actor Model Enthusiast

• Akka (Scala/Java):Akka is a powerful toolkit for building highly concurrent, distributed, and fault-tolerant applications on the JVM. It provides a robust implementation of the Actor Model, including supervision hierarchies, remoting, and clustering.
  • Installation (Maven Example):

    <dependency> <groupId>com.typesafe.akka</groupId> <artifactId>akka-actor_2.13</artifactId> <version>2.8.0</version>
    </dependency>
    

  • Usage Example (Conceptual Akka Actor):

    // Define an Actor that counts messages
    class MyCounterActor extends AbstractBehavior<String> { private int count = 0; public MyCounterActor(ActorContext<String> context) { super(context); } @Override public Receive<String> createReceive() { return newReceiveBuilder() .onMessageEquals("increment", this::onIncrement) .onMessageEquals("get", this::onGet) .build(); } private Behavior<String> onIncrement() { count++; getContext().getLog().info("Incremented to: " + count); return this; } private Behavior<String> onGet() { getContext().getLog().info("Current count: " + count); // In a real app, you'd reply to the sender with the count return this; }
    } // To run:
    // ActorSystem<String> system = ActorSystem.create(MyCounterActor.create(), "mySystem");
    // ActorRef<String> counter = system.systemActorOf(MyCounterActor.create(), "counterActor");
    // counter.tell("increment");
    // counter.tell("get");
    

• Elixir/Erlang (OTP):These languages have the Actor Model baked directly into their core via the Erlang OTP platform. Erlang processes are lightweight actors, making it incredibly natural to build fault-tolerant, distributed systems.
• Actix (Rust):A powerful, actor-based framework for Rust, often used for building highly performant web services and network applications.

For the CSP Enthusiast

• Go (Goroutines & Channels):Go’s native support for goroutines (lightweight threads) and channels makes it a prime example of the CSP model in action. It’s concise and idiomatic.
  • Installation:Go is typically installed as a standalone toolchain. Check go.dev/doc/install.
  • Usage Example (Go Channels):

    package main import ( "fmt" "time"
    ) func worker(id int, jobs <-chan int, results chan<- int) { for j := range jobs { fmt.Printf("Worker %d started job %d\n", id, j) time.Sleep(time.Second) // Simulate work fmt.Printf("Worker %d finished job %d\n", id, j) results <- j 2 }
    } func main() { jobs := make(chan int, 100) results := make(chan int, 100) // Start 3 workers for w := 1; w <= 3; w++ { go worker(w, jobs, results) } // Send 9 jobs for j := 1; j <= 9; j++ { jobs <- j } close(jobs) // No more jobs will be sent // Collect results for a := 1; a <= 9; a++ { <-results }
    }
    

• Rust (Standard Library Channels):Rust’s std::sync::mpsc (Multiple Producer, Single Consumer) module provides channels for safe message passing between threads.
  • Installation:Rust is installed via rustup.
  • Usage Example (Rust Channels):

    use std::sync::mpsc;
    use std::thread;
    use std::time::Duration; fn main() { let (tx, rx) = mpsc::channel(); // Transmitter, Receiver thread::spawn(move || { let val = String::from("hi"); tx.send(val).unwrap(); thread::sleep(Duration::from_secs(1)); }); let received = rx.recv().unwrap(); println!("Got: {}", received);
    }
    

• Clojure (Core Async):Clojure offers core.async, a library that brings Go-like channels and goroutine-like constructs (go blocks) to the JVM.

For the STM Advocate

• Haskell (Control.Concurrent.STM):Haskell’s type system and built-in Control.Concurrent.STM library provide a powerful and safe way to use Software Transactional Memory.
  • Installation:Haskell’s ecosystem is managed via GHCup.
  • Usage Example (Conceptual Haskell STM):

    import Control.Concurrent
    import Control.Concurrent.STM
    import Control.Monad (replicateM_) -- Define an account with an amount using TVar (transactional variable)
    data Account = Account { accountId :: Int, balance :: TVar Int } -- Function to create a new account
    newAccount :: Int -> Int -> IO Account
    newAccount id initialBalance = do b <- newTVarIO initialBalance return $ Account id b -- Function to transfer money atomically
    transfer :: Account -> Account -> Int -> STM ()
    transfer fromAccount toAccount amount = do fromBalance <- readTVar (balance fromAccount) toBalance <- readTVar (balance toAccount) if fromBalance < amount then retry -- Not enough funds, retry the transaction else do writeTVar (balance fromAccount) (fromBalance - amount) writeTVar (balance toAccount) (toBalance + amount) main :: IO ()
    main = do acc1 <- newAccount 1 1000 acc2 <- newAccount 2 500 -- Run the transfer in multiple threads replicateM_ 5 $ forkIO $ atomically $ transfer acc1 acc2 100 threadDelay (2 1000 1000) -- Wait for transfers to complete finalBalance1 <- atomically $ readTVar (balance acc1) finalBalance2 <- atomically $ readTVar (balance acc2) putStrLn $ "Account 1 final balance: " ++ show finalBalance1 putStrLn $ "Account 2 final balance: " ++ show finalBalance2
    

• Clojure (Refs & dosync):Clojure provides ref types and the dosync macro for implementing STM, integrating seamlessly with its immutable data structures.
  • Installation:Clojure is typically run via Clojure CLI or Leiningen.
  • Usage Example (Clojure STM):

    ;; Define transactional refs
    (def account1 (ref 1000))
    (def account2 (ref 500)) ;; Function to transfer money atomically
    (defn transfer [from to amount] (dosync (let [from-bal @from to-bal @to] (if (>= from-bal amount) (do (alter from - amount) (alter to + amount) :success) :insufficient-funds)))) ;; Example usage
    (println "Initial balance A:" @account1 "B:" @account2) (future (transfer account1 account2 100))
    (future (transfer account1 account2 50))
    (future (transfer account2 account1 200)) ;; Wait a bit for futures to complete (in a real app, you'd join them)
    (Thread/sleep 1000) (println "Final balance A:" @account1 "B:" @account2)
    

These tools equip developers to practically apply the chosen concurrency model, offering robust abstractions that simplify complex concurrent logic.

From Theory to Practice: Real-World Scenarios for Concurrency Models

Understanding the theoretical underpinnings is crucial, but seeing these models in action truly unlocks their potential. Let’s explore practical applications, code patterns, and best practices.

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The Actor Model in Action

Practical Use Cases:

• Highly Fault-Tolerant Systems:Actors excel in scenarios requiring self-healing and resilience, like telecommunication switches, financial trading platforms, or IoT backend services. Akka’s supervision hierarchy allows parent actors to restart, stop, or escalate issues with child actors.
• Distributed Systems:Due to their isolation and message-passing nature, actors naturally extend to distributed environments, where actors can reside on different nodes and communicate transparently. Think microservices that need to interact reliably.
• Real-time Analytics/Stream Processing:Actors can process streams of events concurrently, making them suitable for real-time data ingestion and processing pipelines.

Code Example (Conceptual Akka Supervision): Imagine a WorkerActor that occasionally fails. A SupervisorActor can manage its lifecycle.

// Simplified Akka-like pseudo-code
class WorkerActor { receive Message { case "work": if (Math.random() < 0.2) throw new RuntimeException("Oh no, I crashed!"); // ... do actual work ... case "status": // ... report status ... }
} class SupervisorActor { // Defines how to handle child actor failures SupervisorStrategy strategy = new OneForOneStrategy( e -> { if (e instanceof RuntimeException) return SupervisorStrategy.restart(); else return SupervisorStrategy.stop(); } ); // Create a worker as a child ActorRef worker = context.actorOf(Props.create(WorkerActor.class), "myWorker"); receive Message { case "start_work": worker.tell("work", self); }
}

Best Practices:

1. Immutability:Always send immutable messages between actors to prevent shared state issues and simplify debugging.
2. Small, Focused Actors:Design actors to do one thing well, following the Single Responsibility Principle.
3. Supervision Hierarchy:Leverage the supervision capabilities to build robust, self-healing systems. Design your actor tree to reflect your application’s fault tolerance needs.
4. Asynchronous Communication:Embrace the non-blocking nature of message passing. Avoid blocking an actor waiting for a response; instead, use ask patterns or send a message back.

CSP for Efficient Data Pipelining

Practical Use Cases:

• Web Servers/API Gateways:Handling many concurrent requests by passing them through a pipeline of processing stages (e.g., authentication, routing, business logic, database interaction).
• Data Processing Pipelines:Creating producer-consumer patterns where data flows through a series of transformations (e.g., reading logs, parsing, filtering, aggregation, writing to storage).
• Concurrent I/O Operations:Managing multiple network connections or file operations efficiently without blocking the main thread.

Code Example (Go Worker Pool): This common pattern efficiently processes a large number of tasks using a fixed number of workers.

package main import ( "fmt" "time"
) func worker(id int, jobs <-chan int, results chan<- int) { for j := range jobs { fmt.Printf("Worker %d processing job %d\n", id, j) time.Sleep(time.Millisecond 500) // Simulate work results <- j 2 }
} func main() { numJobs := 10 jobs := make(chan int, numJobs) results := make(chan int, numJobs) // Start 3 worker goroutines for w := 1; w <= 3; w++ { go worker(w, jobs, results) } // Send jobs to the `jobs` channel for j := 1; j <= numJobs; j++ { jobs <- j } close(jobs) // Indicate no more jobs will be sent // Collect all results from the `results` channel for a := 1; a <= numJobs; a++ { <-results // Blocking receive until a result is available } close(results) // Close the results channel after all results are collected fmt.Println("All jobs processed and results collected.")
}

Best Practices:

1. Buffered vs. Unbuffered Channels:Use unbuffered channels for strict synchronization (sender waits for receiver). Use buffered channels to allow some decoupling and capacity, but be mindful of potential deadlocks if buffers fill up.
2. Channel Closure:Always close channels when no more data will be sent. This signals to receivers that iteration over the channel is complete, preventing deadlocks and resource leaks.
3. Select Statement:Use select to manage multiple channel operations (sends and receives) concurrently, providing non-blocking communication and timeouts.
4. Error Handling:Pass errors through channels along with data, or use a separate error channel for centralized handling.

STM for Robust Shared State Management

Practical Use Cases:

• In-Memory Databases/Caches:When multiple threads need to concurrently update complex data structures (like a balanced tree or a hash map) without explicit locking.
• Game Engines:Safely managing shared game state (e.g., player scores, inventory) that multiple game logic threads might access and modify.
• Complex Transactional Logic:Any scenario where a series of operations on shared mutable state must appear to happen instantaneously and without interference from other concurrent operations.

Code Example (Clojure Account Transfer): This example demonstrates how Clojure’s STM guarantees atomicity for multiple operations.

(def balance-a (ref 1000))
(def balance-b (ref 500)) (defn transfer-money [from-acc to-acc amount] (dosync (let [current-from-bal @from-acc current-to-bal @to-acc] (if (>= current-from-bal amount) (do (alter from-acc - amount) (alter to-acc + amount) (println (format "Transferred %d from %s to %s. New balances: %d, %d" amount (str from-acc) (str to-acc) @from-acc @to-acc)) :success) (do (println (format "Failed to transfer %d from %s due to insufficient funds (%d)" amount (str from-acc) current-from-bal)) :insufficient-funds))))) (println "Initial A:" @balance-a ", B:" @balance-b) ;; Simulate multiple concurrent transfers
(do (future (transfer-money balance-a balance-b 100)) (future (transfer-money balance-b balance-a 200)) (future (transfer-money balance-a balance-b 800)) ; This one will likely fail first time (future (transfer-money balance-a balance-b 50)) (Thread/sleep 2000)) ; Give futures time to complete (println "Final A:" @balance-a ", B:" @balance-b)

Best Practices:

1. Keep Transactions Small:Short transactions are less likely to conflict and more efficient. Avoid I/O or long computations within atomically or dosync blocks.
2. Avoid Side Effects:Transactional blocks should ideally only interact with transactional memory. Performing external side effects (like printing to console or network calls) within a transaction can lead to unexpected behavior if the transaction retries.
3. Composability:STM transactions are inherently composable. You can nest atomically blocks, and the outer block will treat the inner operations as part of its own transaction.
4. Retry Mechanism:Understand that transactions might retry. Design your logic to be idempotent within transactions.

By applying these models and following best practices, developers can construct sophisticated concurrent applications that are both performant and resilient.

Choosing Your Parallel Path: Actors, CSP, or Software Transactions?

Selecting the appropriate concurrency model is a critical architectural decision. Each model addresses different facets of concurrent programming and comes with its own trade-offs.

Actors vs. CSP: Communication Styles

Both Actors and CSP advocate for “communicating by sharing nothing” rather than “sharing memory by communicating” (traditional locking). However, their emphasis differs:

• Actor Model:

  • Focus:Encapsulated state and asynchronous message passing between independent entities.
  • State:Each actor manages its own private, mutable state, which is protected from direct external access.
  • Communication:Primarily asynchronous. An actor sends a message and continues its work; it doesn’t necessarily wait for a reply.
  • Orchestration:Often involves hierarchies (supervision trees) for fault tolerance and organization.
  • Strengths:Excellent for fault-tolerant, distributed systems where state isolation and resilience are paramount. Handles high levels of parallelism and complex workflows with stateful components naturally.
  • When to Use:Building scalable backend services, real-time gaming, complex business process orchestration, or highly available distributed systems. Languages like Erlang/Elixir, Akka (Scala/Java).

• Communicating Sequential Processes (CSP):

  • Focus:Explicit, synchronized (or buffered asynchronous) communication over channels between stateless or minimally stateful processes.
  • State:Processes often operate on immutable data received via channels, transforming it and sending it to the next stage. Shared mutable state is minimized or avoided.
  • Communication:Can be synchronous (sender blocks until receiver is ready) or asynchronous (buffered channels). Emphasizes direct data flow.
  • Orchestration:Often simpler, pipeline-like structures or worker pools.
  • Strengths:Ideal for managing explicit data flows, pipelines, and producer-consumer scenarios. Leads to clear, composable code when the problem fits the dataflow paradigm.
  • When to Use:Building web servers, data processing pipelines, concurrent I/O, or services where the primary concern is the efficient flow and transformation of data. Languages like Go, Rust (with channels), Clojure (core.async).

Actors/CSP vs. STM: Managing Shared State

The fundamental difference here lies in how shared state is handled:

• Actors & CSP: These models try to avoid shared mutable state entirely, promoting isolated entities that communicate. They are concurrency models focusing on interaction and distribution.
• Software Transactional Memory (STM): This model directly addresses shared mutable state by providing a safe, transactional mechanism for multiple threads to access and modify shared data within a single memory space. It’s a concurrency model for shared-memory parallelism.
  • Strengths:Simplifies concurrent access to complex, highly interrelated mutable data structures within a single process. Developers don’t deal with explicit locks, reducing deadlock and race condition potential. It provides strong atomicity guarantees.
  • When to Use:When you have a single application with genuinely complex shared state that needs to be updated atomically by multiple threads, such as an in-memory database, a shared game world state, or a complex financial ledger. Languages like Haskell, Clojure.

Hybrid Approaches

It’s important to note that these models are not mutually exclusive. Modern applications often benefit from hybrid approaches:

• A distributed system might use an Actor model for its service boundaries and fault tolerance, while individual actors might internally use CSP-like channels for specific processing pipelines, or even STM for managing a small, critical piece of shared mutable state.
• A language like Rust, while offering CSP-style channels, also allows for explicit shared-memory concurrency with mutexes and atomic types, and libraries might implement actor-like patterns.

The choice depends heavily on your application’s requirements:

• Fault tolerance and distribution?Lean towards Actors.
• Clear data flow and task orchestration?CSP is a strong candidate.
• Complex, shared mutable state that needs atomic updates within one process?STM offers robust guarantees.

Understanding these distinctions empowers you to pick the right tool for the job, leading to more maintainable, performant, and reliable concurrent software.

Shaping the Future of Concurrent Systems: A Forward Look

The journey through the Actor Model, Communicating Sequential Processes (CSP), and Software Transactional Memory (STM) reveals a landscape rich with powerful paradigms for building concurrent applications. We’ve seen how Actors excel in isolating state and fostering fault tolerance through asynchronous message passing, making them ideal for distributed and highly resilient systems. CSP offers a structured approach to concurrency by orchestrating data flow through explicit channels, leading to clear and efficient pipelines. Finally, STM provides a robust, lock-free mechanism for safely managing complex shared mutable state, abstracting away the intricacies of transactional integrity.

Mastering these models is more than just learning new APIs; it’s about adopting different mental frameworks for problem-solving in a parallel world. The ability to reason about concurrent behavior, identify appropriate communication patterns, and safeguard shared data effectively is a hallmark of an expert developer today. As hardware continues to evolve with ever more cores and as distributed architectures become the norm, the demand for developers proficient in these advanced concurrency techniques will only grow.

We encourage you to experiment with these models in your projects. Start small, understand the core principles, and gradually integrate them into more complex systems. The future of software is inherently concurrent, and by embracing these powerful paradigms, you’re not just writing better code—you’re shaping the next generation of resilient, scalable applications.

Unpacking Concurrency: Your Questions Answered

FAQ:

1. What’s the main difference between Actor-based concurrency and traditional shared-memory concurrency with locks? Actor-based concurrency avoids shared mutable state altogether. Actors have their own private state and communicate only by sending immutable messages. Traditional shared-memory concurrency, in contrast, relies on threads directly accessing and modifying shared data, requiring explicit synchronization mechanisms like locks, mutexes, and semaphores to prevent race conditions. Actor models naturally prevent many common concurrency bugs that arise from shared memory.

2. Can I mix and match these concurrency models in one application? Yes, absolutely! It’s common and often beneficial to use a hybrid approach. For example, a large distributed system might use Actors for overall service architecture and fault tolerance, while a specific component within an actor might internally use Go-like CSP channels for a complex data processing pipeline. For critical shared memory updates, a component could leverage STM. The key is to consciously choose the best model for each specific problem domain within your application.

3. Which programming languages are best suited for each model?

   • Actor Model:Erlang/Elixir (built-in), Scala/Java (Akka), Rust (Actix, Tokio actor framework).
   • CSP:Go (built-in goroutines and channels), Rust (standard library channels, tokio::sync::mpsc), Clojure (core.async), C++ (via libraries like Boost.Asio or custom implementations).
   • STM:Haskell (built-in Control.Concurrent.STM), Clojure (built-in ref types and dosync).

4. Are there performance implications when choosing one model over another? Yes, there can be. Each model has different overheads. Actors can introduce message passing overhead but allow for greater scalability and fault isolation across distributed systems. CSP channels can be very efficient for localized communication but can introduce blocking if not managed carefully. STM incurs transactional overhead (logging, rollback capabilities, conflict detection) which can be higher than simple locks if transactions are large or contention is extremely high, but it simplifies development significantly. The “best” performance depends on the specific workload, communication patterns, and system architecture.

5. How do these models handle error recovery and fault tolerance?

   • Actors:Excels here with supervision hierarchies. A parent actor can observe its child actors, and upon failure, decide to restart them, stop them, or escalate the fault. This allows for self-healing systems.
   • CSP:Error handling typically involves sending error messages through channels, much like regular data. The receiving process is then responsible for handling the error. This is more explicit but requires careful design.
   • STM:Focuses on atomicity and isolation, rolling back failed transactions to a consistent state. It prevents data corruption but typically doesn’t directly handle broader application-level fault tolerance (e.g., restarting failed services); that’s usually managed by other mechanisms.

Essential Technical Terms:

1. Concurrency:The ability of different parts of a program or system to run independently or out-of-order without affecting the final outcome. It’s about dealing with many things at once.
2. Parallelism:The actual simultaneous execution of multiple independent computations, typically on multi-core processors. It’s about doing many things at once. Concurrency can exist without parallelism (e.g., context switching on a single core).
3. Race Condition:A programming flaw where the output or result of a concurrent system depends on the sequence or timing of uncontrollable events, leading to unpredictable or incorrect behavior. Often occurs when multiple threads access shared mutable state without proper synchronization.
4. Deadlock:A situation where two or more competing actions are each waiting for the other to finish, and thus neither ever does. This typically occurs in systems using locks or other synchronization primitives.
5. Immutability:The property of an object whose state cannot be modified after it’s created. In concurrent programming, using immutable data structures for shared information (especially in message passing) significantly reduces the risk of race conditions and simplifies reasoning about state.

Published on November 22, 2025