Swift Concurrency, introduced in WWDC21, offers a safer and more efficient approach to asynchronous programming compared to _Grand Central Dispatch_ (GCD). However, integrating _Swift Concurrency_ with older primitives like _NSLock_ and _GCD queue_ requires careful consideration to avoid potential pitfalls.



### 1. Swift Concurrency vs. GCD



Swift Concurrency distinguishes itself from GCD in its threading model and its approach to handling blocked threads.



**Threading Model:**



*  **GCD:** GCD employs a thread-pool model where threads are dynamically created and managed by the system. This can lead to **thread explosion**, particularly with concurrent queues, where the system might overcommit by creating more threads than CPU cores. GCD dynamically adjusts the number of threads based on system load and available resources.

  *  Example:

    ```swift

    let queue = DispatchQueue.global(qos: .background)

    for i in 0..<100 { 

     queue.async { 

      // Perform some lengthy task, such as:

      print("Task \(i) is running on thread: \(Thread.current)")

      Thread.sleep(forTimeInterval: 2) // Simulate a long-running task

     }

    }
    ```

    This code could potentially create 100 threads, even if the device has fewer cores. Each task is dispatched to the global concurrent queue, and GCD might create a new thread for each task, especially if the tasks are CPU-bound or blocking. This can lead to performance issues due to excessive context switching and resource contention.

*  **Swift Concurrency:** Swift Concurrency, by default, utilizes a **cooperative thread pool** limited to the number of CPU cores. It uses cooperative multitasking, where tasks voluntarily yield control to allow other tasks to run, thus efficiently utilizing CPU cores and preventing thread explosion.

  *  Example:

    ```swift

    func lengthyTask() async {

      print("Lengthy task is running on thread: \(Thread.current)")

      await Task.sleep(nanoseconds: 2_000_000_000) // Simulate a long-running task

    }

    func anotherLengthyTask() async {

      print("Another lengthy task is running on thread: \(Thread.current)")

      await Task.sleep(nanoseconds: 1_000_000_000) 
    }

    async let task1 = lengthyTask()

    async let task2 = anotherLengthyTask()

    await task1
    await task2
    ```

    This code leverages the cooperative thread pool and ensures efficient utilization of CPU cores. When _await_ is encountered, the current thread can be potentially freed up to execute other tasks while waiting for the awaited task to complete. This helps prevent the creation of unnecessary threads and minimizes context switching overhead.



**Handling Blocked Threads:**



*  **GCD:** When a thread in GCD is blocked, the system might spin up new threads to handle remaining tasks, potentially exacerbating thread explosion.

  *  Example:

    ```swift

    let queue = DispatchQueue.global()

    queue.sync {

     // This blocking operation will halt the current thread

     let url = URL(string: "https://www.example.com/large_file.zip")! 

     let data = try Data(contentsOf: url) // Download a large file (blocking)

     print("Downloaded data size: \(data.count) bytes")

    }
    ```

    If this occurs within a concurrent queue with multiple tasks, GCD might create additional threads to continue processing tasks. Downloading a large file can take a significant amount of time, during which the thread executing this code is blocked, waiting for the download to complete. In a GCD-based system, if other tasks are enqueued on the same concurrent queue, GCD might create new threads to handle those tasks, potentially leading to thread explosion. To understand more about Excessive Context Switching & Thread Explosion, take a look at this incredible [WWDC explanation](https://developer.apple.com/videos/play/wwdc2017/706/) at 11:03.

*  **Swift Concurrency:** Swift Concurrency replaces blocked threads with lightweight _continuations_ These continuations track the resumption points of suspended tasks, enabling threads to efficiently switch between work items without the overhead of full thread context switches.


![wwdc-gcd-thread.jpg](https://api.flyingharley.dev/uploads/wwdc_gcd_thread_82c1c5d328.jpg)
*With GCD, when a thread is blocked, many threads will be created and Context Switches occurred*

![wwdc-swiftconcurrency.jpg](https://api.flyingharley.dev/uploads/wwdc_swiftconcurrency_a768fbc1d4.jpg)
*With Swift Concurrency, there will be only one thread for each CPU core*

  *  Example:

    ```swift

    let url = URL(string: "https://www.example.com/large_file.zip")!

    do {

      let data = try await Data(contentsOf: url)

      print("Downloaded data size: \(data.count) bytes")

    } catch {

      print("Failed to download data: \(error)")

    }
    ```

    Using _await_ allows the current thread to be freed while waiting for the file data, avoiding thread blocking and allowing the thread to potentially handle other tasks. The _await_ keyword signals a potential suspension point. When the code execution reaches _await_ the current task can be suspended, and the thread can be used to execute other tasks. Once the awaited operation (downloading the file) is complete, the task is resumed, and the execution continues from the suspension point.



### 2. Safe Primitives vs. Legacy Primitives

![primitives.jpg](https://api.flyingharley.dev/uploads/primitives_4f7179124b.jpg)

When adopting Swift Concurrency, it's crucial to prioritize performance and understand the implications of await on atomicity. The recommended approach is to utilize safe primitives like _await_, _Actors_, and _Task groups_, which are compiler-enforced. These primitives ensure thread safety and help avoid common concurrency issues.

While primitives like _os_unfair_lock_ and _NSLock_ can be used in synchronous code for short, well-defined critical sections, they require caution. Using them within asynchronous contexts or for long-running operations can lead to deadlocks and violate the **runtime contract of forward progress**.

Unsafe primitives such as _DispatchSemaphore_, _pthread_cond_, _NSCondition_, and _pthread_rw_lock_ are strongly discouraged. These primitives hide dependency information from the Swift runtime, potentially causing unpredictable behavior and making debugging difficult.
To identify the use of unsafe primitives during development, run your app with the environment variable _LIBDISPATCH_COOPERATIVE_POOL_STRICT_=1. This modified debug runtime helps enforce the invariant of forward progress and can flag potential issues.
But we should use Actors whenever possible. They offer a safer and more efficient alternative to locks and serial queues for mutual exclusion. They guarantee that only one method call is executed at a time, protecting the actor's state from data races. Actors also support efficient thread hopping and prioritization, ensuring responsiveness and avoiding priority inversions.

So why shouldn't we use Actors at any time? In reality, there are situations where using these might not be ideal, such as when **modifying and reading individual properties** within a type such as class... In this case, actors aren't quite the right tool because:

- **Granularity of Access**: Actors are designed to protect their entire state. Every method call on an actor is executed serially, ensuring that only one thread can access the actor's properties at a time. This is great for preventing data races when working with multiple properties. However, if you only need to synchronize access to a single property, using an actor might introduce unnecessary overhead.
- **Order of Operations**: You can't guarantee the order in which operations will run on an actor. If you have a sequence of operations that must be performed in a specific order on a single property, using an actor might not be the best solution.

Let's consider an example: Imagine you have a class representing a counter:

```swift
class Counter {
    private var count = 0
    
    func increment() {
        count += 1
    }
    
    func decrement() {
        count -= 1
    }
    
    func getValue() -> Int {
        return count
    }
}
```

If you want to ensure that increment and decrement operations are synchronized, using an actor might be an option. However, it will also make reading the value using **getValue** subject to the actor's serialization, potentially impacting performance if there are frequent read operations.
In such cases, alternative synchronization mechanisms like atomics, dispatch queues, or locks can be more appropriate. These mechanisms allow you to synchronize access to specific properties without affecting the entire object's state.
For instance, you could use a [_ManagedAtomic_](https://www.swift.org/blog/swift-atomics/) to synchronize access to the count property:
```swift
import SwiftAtomics

class Counter {
    private var count = ManagedAtomic<Int>(0)
    
    func increment() {
        count.wrappingIncrement(ordering: .sequentiallyConsistent)
    }
    
    func decrement() {
        count.wrappingDecrement(ordering: .sequentiallyConsistent)
    }
    
    func getValue() -> Int {
        return count.load(ordering: .sequentiallyConsistent)
    }
}
```

Here, [_ManagedAtomic_](https://www.swift.org/blog/swift-atomics/) ensures that increment, decrement, and read operations on the count property are atomic and synchronized, without requiring the entire _Counter_ class to be an actor.
Therefore, while actors are powerful tools for concurrent programming, they might not always be the most efficient or flexible solution. When dealing with individual properties that require synchronized access, consider using alternative synchronization mechanisms like atomics, dispatch queues, or locks. Remember to carefully evaluate the trade-offs and choose the approach that best suits your specific needs.

### 3. Deadlock Scenario: GCD Queues and Swift Concurrency



#### Problem Description



Integrating Swift Concurrency with GCD can lead to deadlock scenarios, especially when tasks are blocked inside GCD queues. Understanding how GCD queues work and how they differ from Swift Concurrency is crucial to avoid these issues.



#### GCD Queue



- **Serial Queue**: Executes tasks one at a time in the order they are added. This ensures that tasks do not run concurrently and are processed sequentially.

  

- **Concurrent Queue**: Allows multiple tasks to execute simultaneously. Tasks are started in the order they are added, but they can run in parallel and complete in any order. _GCD_ concurrent queues can **overcommit** by creating more threads than there are available hardware threads to ensure work progresses.



#### Swift Concurrency



- **Task & Continuation**: A _Task_ represents a unit of asynchronous work that can be awaited. Together with _Continuation_, they allow asynchronous code that doesn't use _async/await_ to be integrated into Swift's concurrency model by capturing the continuation of an operation. In this context, it can be used to integrate GCD.



- **Non-Overcommitting**: Swift Concurrency does not expect blocking and will **not overcommit** CPU cores. This means it efficiently manages resources but can lead to deadlocks if tasks are blocked.



#### Problem When Swift Concurrency Task Is Blocked Inside GCD Queue



When a Swift Concurrency task is blocked inside a GCD queue, it can lead to a deadlock if the queue is waiting for a thread from the thread pool to dispatch a task, but no threads are available because they are all blocked.



#### Code Example: Read & Write Patterns Using GCD Concurrent Queue



```swift

import XCTest

final class BarrierTests: XCTestCase {

  func test() async {

    let subsystem = Subsystem()

    await withTaskGroup(of: Void.self) { group in

      for index in 0 ..< 100 {

        group.addTask { subsystem.performWork(id: index) }

      }

      await group.waitForAll()

    }

  }

}


final class Subsystem {

  let queue = DispatchQueue(label: "my concurrent queue", attributes: .concurrent)

  func performWork(id: Int) {

    if id % 10 == 0 { write(id: id) }

    else { read(id: id) }

  }

  func write(id: Int) {

    print("schedule exclusive write \(id)")

    queue.async(flags: .barrier) { print(" execute exclusive write \(id)") } // Asynchronous write with barrier

    print("schedule exclusive write \(id) done")

  }

   
  func read(id: Int) {

    print("schedule read \(id)")

    queue.sync { print(" execute read \(id)") } // Synchronous read, blocking the caller

    print("schedule read \(id) done")

  }

}

```
Ref: https://forums.swift.org/t/deadlock-when-using-dispatchqueue-from-swift-task/66058/25


#### Explanation

- **Multiple Synchronous Reads**: Allowed with the concurrent queue, blocking the caller.

- **Barrier for Writes**: The write operation is dispatched asynchronously with a barrier, ensuring it can only proceed when no other reads or writes are running concurrently.


While the code might not immediately cause a deadlock, it has the potential to do so when:


1. An event triggers multiple read and write operations.

2. A large number of read operations start from a Swift Concurrency context, consuming available threads.

3. A write operation is triggered, making an asynchronous call to the concurrent queue under the _.barrier_ option.

4. The queue waits for a thread from the thread pool to dispatch the write operation, but **no threads are available due to blocking reads**. Since Swift Concurrency won't overcommit (create more threads), the barrier isn't cleared, leading to a deadlock.



#### Observations


- **GCD and Swift Concurrency**: They seem to share the same thread pools, which can exacerbate the issue.



- **Deadlock Consequences**: When this deadlock occurs, **subsequent tasks will not be dispatched, leading to program malfunction**. The main thread (MainActor) may still work and receive events, but other threads will not make progress.



Here are some rule of thumbs & considerations:

- **Avoid Blocking**: Do not block work inside Swift Concurrency, such as using _.sync_ on another GCD queue, especially for future work that is not actively running but enqueued (e.g., async dispatch with a barrier). This violates the runtime contract of "Forward progress" we mentioned before.


- **Limit Task Creation**: Avoid creating too many Swift Concurrency tasks using **Task** blocks or **TaskGroup**, as unexpected blocking can easily lead to deadlocks, especially when interacting with legacy frameworks.



- **Minimize Mixing**: Try to avoid mixing Swift Concurrency and GCD, as it can lead to unexpected behavior and be difficult to debug.


With the discussion so far, we can easily figure out the solution which is simply avoid using the concurrent queue for read-write pattern. Instead, we could use _NSLock_ (with caution) or _pthread_rw_lock_ (not recommended).

### 4. Summary
Swift Concurrency provides powerful tools for writing safer and more efficient concurrent code. Developers should prioritize the use of safe primitives like _await_, _Actors_, and _Task groups_ as they offer compiler-enforced safety and help manage concurrency effectively.
While traditional synchronization mechanisms like locks (_NSLock_, _os_unfair_lock_) and dispatch queues are still available, it's important to exercise caution when using them in Swift Concurrency. They can introduce deadlock risks if not handled properly, especially when interacting with asynchronous operations.


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Swift Concurrency, introduced in WWDC21, offers a safer and more efficient approach to asynchronous programming compared to Grand Central Dispatch (GCD). However, integrating Swift Concurrency with older primitives like NSLock and GCD requires careful consideration to avoid potential pitfalls

Swift Concurrency and Legacy Primitives: A Balancing Act

# Protocol Extension Method Dispatching in Swift

## Introduction to Method Dispatch Types

Method dispatch is the mechanism a program uses to determine which implementation of a function to execute when that function is called. **Swift employs four types of method dispatch, each with trade-offs between speed and flexibility**:

*   **Inlining:** This is the fastest and least flexible type of dispatch. It involves replacing a function call with the function's code, eliminating the overhead associated with making the call. This optimization is most effective when the compiler has all the information it needs to determine the result of the function call at compile time, such as when working with hardcoded values or simple operations.

*   **Static Dispatch:** Also known as direct dispatch, static dispatch is the second fastest and second least flexible type of dispatch. It occurs when the function's memory address is known at compile time. This allows the compiler to directly jump to that address without needing to perform a runtime lookup. Static dispatch is typically used for functions within structs, enums, and final classes, where the compiler can guarantee which implementation will be used.

*   **Table Dispatch:** This type of dispatch is used to implement polymorphism, which is the ability of a single type to have multiple forms. In table dispatch, the function to be called is determined at runtime by looking up its address in a table. Swift implements two flavors of table dispatch: **virtual table dispatch** for class hierarchies and **protocol witness table dispatch** for protocols.

    *   **Virtual Table Dispatch:** Virtual tables are used to dispatch methods on class hierarchies. When a class is created, it includes a hidden data structure called a virtual table, which contains pointers to the implementations of all the methods defined in the class and its superclasses. When a method is called on an object, the runtime uses the object's virtual table to find the correct implementation of the method to execute. This allows subclasses to override methods from their superclasses and have their implementations called even when the method is called on a variable typed as the superclass. This is because the specific type of the object is not known until runtime.

    *   **Protocol Witness Table Dispatch:** Protocol witness tables work similarly to virtual tables but are used for protocols. When a type conforms to a protocol, it creates a witness table that contains pointers to the implementations of all the required methods of the protocol. When a method is called on a variable typed as a protocol, the runtime uses the variable's witness table to find the correct implementation of the method to execute.

*   **Message Dispatch:** This is the slowest and most flexible type of dispatch. It uses the Objective-C runtime and allows for dynamic method lookup at runtime. This means that the implementation of a method can be changed at runtime, even after the code has been compiled. Message dispatch is invoked using the _@objc_ and _dynamic_ keywords in Swift. This type of dispatch is useful for interoperability with Objective-C code and for implementing features like Key-Value Observing (KVO).

In this article, we will focus on Protocol Witness Table Dispatch. For further information on method dispatch in Swift, please refer to those great articles:

*   [The Swift Method Dispatch Deep Dive](https://jacob.bartlett.dev/blog/swift-method-dispatch)
*   [Swift Protocol Extensions Method Dispatch](https://medium.com/@leandromperez/swift-protocol-extensions-method-dispatch-7e28d7a68b8c)
*   [Method Dispatch in Swift — RaizException — Raizlabs Developer Blog](https://www.raizlabs.com/dev/method-dispatch-in-swift/)
*   [Method Dispatch in Protocol Extensions — Ole Begemann](https://oleb.net/blog/2014/07/swift-protocol-extensions/)

## Protocol Witness Table Dispatch

Protocol witness table dispatch is the mechanism by which Swift implements polymorphism for protocols. **When a type conforms to a protocol, it generates a witness table that includes pointers to the implementations of the protocol's required methods**.  During runtime, when a protocol requirement is invoked on an instance of a conforming type, Swift references the corresponding function pointer in the witness table and directs the execution flow to that memory location.

**Points to remember**:

*   **Abstract vs. Concrete Types**: Protocol witness table dispatch is employed when interacting with abstract protocol types, such as _any MyProtocol_. When a concrete type adhering to the protocol is used, the compiler often opts for static dispatch due to the compile-time knowledge of the implementation. 

![Screenshot 2024-12-05 at 23.07.22.png](https://api.flyingharley.dev/uploads/Screenshot_2024_12_05_at_23_07_22_a87c0ab297.png)
Explanation:

    - The speak() method is a requirement of the Speaker protocol, so it will use dynamic dispatch when called on a variable of type any Speaker, even though there is a default implementation in the extension.

      - When genericSpeaker.speak() is called, the runtime checks the protocol witness table associated with the concrete type (Human) to find the correct implementation of speak(), which results in printing "Hello there!".

      - When humanSpeaker.speak() is called, the compiler knows at compile time that the type is Human, so it can use static dispatch directly to the Human implementation of speak()


*   **Default Implementations in Protocol Extensions**: Methods defined within protocol extensions, but not explicitly declared as requirements in the protocol's definition, leverage static dispatch. Consequently, invoking these methods on an abstract protocol type will directly execute the implementation from the extension. This is in contrast to requirement methods defined in protocol extensions, which use dynamic dispatch and can be customized by conforming types.
![Screenshot 2024-12-05 at 23.13.29.png](https://api.flyingharley.dev/uploads/Screenshot_2024_12_05_at_23_13_29_166e78d3a9.png)

Explanation:

    - makeSound() is a protocol requirement, so it uses dynamic dispatch, and the Dog implementation overrides the default extension implementation.
     
      - eat() is not a requirement. It's only defined in the extension, so it uses static dispatch. Regardless of the concrete type of animal, calling eat() will always execute the code in the extension
### Example and Dispatch Flow

![Screenshot 2024-12-05 at 23.17.44.png](https://api.flyingharley.dev/uploads/Screenshot_2024_12_05_at_23_17_44_12f80c1e8e.png)

**Explanation of the dispatch flow**:

1.  _myProtocolImplement_ is declared as an abstract protocol type (_any MyProtocol_).

2.  _myProtocolImplement.foo(i1: 1, i2: "2")_ is invoked. Because _myProtocolImplement_ is an abstract type, the runtime will use protocol witness table dispatch.

3.  The runtime inspects the witness table associated with the concrete type of _myProtocolImplement_, which is _MyStruct_ in this case, to locate the implementation of _foo(i1:i2:)_.

4.  Within the witness table, the runtime discovers the function pointer corresponding to _MyStruct_'s implementation of _foo(i1:i2:)_.

5.  Execution then jumps to this memory address, leading to the execution of _print("MyStruct foo")_.

**Outcome**: "MyStruct foo" is printed.

**Why this happens**:

Despite the protocol extension offering a default implementation for _foo(i1:i2:)_, _MyStruct_ also provides its own implementation. **When a conforming type provides a specific implementation for a protocol requirement, this implementation supersedes the default implementation in the extension**. This principle underscores a fundamental concept in protocol-oriented programming: conforming types retain the ability to customize their behavior, even when default implementations are available.

## The Perils of Default Parameters in Protocol Extensions

While protocol extensions in Swift offer a powerful mechanism for extending functionality, using default parameters within them requires careful consideration. The compiler's inability to safeguard against potential pitfalls, coupled with the possibility of infinite recursion, necessitates a cautious approach when employing this pattern.

Let's illustrate this with an example:

![Screenshot 2024-12-05 at 23.30.38.png](https://api.flyingharley.dev/uploads/Screenshot_2024_12_05_at_23_30_38_6fbc41b7e5.png)

Here, invoking _myProtocolImplement.foo()_ is intended to execute the _.foo(i1: Int, i2: String)_ method of _MyStruct_ with the default values of _1_ for _i1_ and _"2"_ for _i2_. However, this seemingly convenient approach can lead to dangerous runtime behavior.

**Recursive Calls and Their Consequences**:

The compiler provides no safeguards to prevent potential issues arising from this pattern. Consider the following scenarios:

*   **Missing Implementation**: If we forget to implement the _foo(i1: Int, i2: String)_ method inside _MyStruct_, the program will still compile.

![Screenshot 2024-12-06 at 22.56.18.png](https://api.flyingharley.dev/uploads/Screenshot_2024_12_06_at_22_56_18_7910114b0b.png)

*   **Parameter Mismatch**: If we modify the parameters of the _foo_ method in _MyStruct_, such as changing the type of _i2_ to _Int_, resulting in _foo(i1: Int, i2: Int)_, the program will again compile without errors.

In both of these cases, we encounter a critical runtime problem – an infinite loop of recursive calls.

**The Root of the Problem**:

The method dispatch mechanism in this context solely searches for the method implementation within the protocol extension.  **The _foo(i1: Int = 1, i2: String = "2")_ method ends up repeatedly calling itself, ultimately resulting in a crash**. This behavior arises because when the variable's inferred type is the protocol, the dynamically dispatched implementation from the extension is always invoked. If the conforming type doesn't explicitly override the default parameter, the default implementation from the protocol extension will be used, leading to the recursive call.

**Strategies to Mitigate the Risks**:

To avoid these potentially disastrous situations, consider the following strategies:

1.  **Avoidance:** The safest course of action is to completely avoid the use of default parameters within protocol extensions.

2.  **Renaming:** Alternatively, we can rename the method that has default parameters to differentiate it from the required protocol method. For example, instead of _foo(i1: Int = 1, i2: String = "2")_, we could use _fooDefault(i1: Int = 1, i2: String = "2")_. This prevents the recursive call issue, as the default implementation in the extension is now clearly distinct from the required method in the protocol.

![Screenshot 2024-12-06 at 22.58.55.png](https://api.flyingharley.dev/uploads/Screenshot_2024_12_06_at_22_58_55_eef7d5b0f4.png)

## In Conclusion

Swift's protocol extensions are a potent tool for expanding functionality. However, exercising caution when incorporating default parameters within them is crucial. The absence of compiler-enforced safeguards and the potential for infinite recursion demand a mindful approach. By acknowledging these risks and implementing appropriate mitigation strategies, we can ensure the robustness and reliability of our code.


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While Swift protocol extensions offer the convenience of default parameter values and other cool things, this seemingly simple feature can introduce unexpected behavior due to the dynamic nature of protocol method dispatch

Protocol Extension Method Dispatching in Swift


Closure, a [reference type](https://docs.swift.org/swift-book/documentation/the-swift-programming-language/closures/#Closures-Are-Reference-Types), is among the most powerful features in Swift. We often use them daily in various ways, such as passing them as arguments to other functions, dispatching tasks asynchronously, and more. Another capability we are familiar with is capturing values, which will be discussed in this memo.

## Capturing in Closure

Capturing is a powerful feature, but it can be tricky at times, especially for beginners. To understand capturing, let's first explore what "capture" means.

As we may already know, Closures (or Closure Expressions) are the foundational form of [Functions](https://docs.swift.org/swift-book/documentation/the-swift-programming-language/functions). Functions are essentially special cases of closures with fully defined names, parameter types, return types, and more. Closures have the unique ability to "capture and store references to any constants and variables from the context in which they are defined." More importantly, Swift manages all the memory management involved in capturing for us. Cool!

However, that doesn't mean we can use it freely without any consideration! We might have encountered this common mistake when using closure capturing with *self*, like this:

![Screenshot 2024-10-29 at 22.29.14.png](https://api.flyingharley.dev/uploads/Screenshot_2024_10_29_at_22_29_14_cc6fefeca8.png)

It's clear enough to understand what's happening. The body of the "closure" has "captured" the self of "Foo", creating a cycle that cannot be broken automatically. We need to gain more insight into capturing to avoid mistakes and to harness its power for greater development efficiency.

The example above is just one form of capturing. In fact, there is another form, which involves "nested functions", as shown below:

![Screenshot 2024-10-29 at 22.37.00.png](https://api.flyingharley.dev/uploads/Screenshot_2024_10_29_at_22_37_00_a7efa6b0d3.png)
Each function defines a **scope** or **context** containing its body and parameters. In other words, the "inner function" defines an "inner context", just as the "outer function" defines an "outer context". As defined by Apple's documentation:

>A closure can capture constants and variables from the surrounding context in which it’s defined. The closure can then refer to and modify the values of those constants and variables from within its body, even if the original scope that defined the constants and variables no longer exists.

Returning to our example above, the **runningTotal** belongs to the "outer context" or the context of the **makeIncrementer** function, and it was captured by the "inner context". This allows the inner **incrementer** function to freely modify this captured variable within its body. But is that enough? There is actually a more important aspect related to the "lifetime" of that captured variable. We all know that a function’s scope, along with its local variables, will terminate after it returns. Thanks to the capturing capability, we still have access to them. This is how Swift effectively manages memory for us.

But have you ever wondered if this "refer to and modify" capability of capturing applies to value types? We are definitely familiar with "refer to and modify" reference types such as Classes. However, in the above example, our **runningTotal** variable is a value type of **Int**! We know that value types are typically copied when passed around, such as when used as function arguments or even just when assigning the value to another variable, like this:

![Screenshot 2024-10-29 at 23.07.39.png](https://api.flyingharley.dev/uploads/Screenshot_2024_10_29_at_23_07_39_936ce722c7.png)

That's what makes closures different. In fact, **runningTotal** is considered a "local variable", and it is not copied when captured from the same or an inner scope where it was defined.

So, how do we "force" it to be copied? The answer lies in using [**Capture Lists**](https://docs.swift.org/swift-book/documentation/the-swift-programming-language/expressions#Capture-Lists). Let's compare the two examples below:

![Screenshot 2024-10-29 at 23.32.22.png](https://api.flyingharley.dev/uploads/Screenshot_2024_10_29_at_23_32_22_840643c53c.png)

In the first example, the variable **a** is freely modifed by the **foo** closure.

![Screenshot 2024-10-29 at 23.35.52.png](https://api.flyingharley.dev/uploads/Screenshot_2024_10_29_at_23_35_52_bbf5dc6f0d.png)

In the second example, the "inner" **a** becomes a constant after being defined inside the capture list. This new **a** is a copy of the original **a**. Therefore, any modifications to the original **a** won't affect the copied one.
## Utilizing Capturing for state management
### Using for state manipulation inside immutable value type

In situations where we need to manage state within a value type such as a Struct, it can be tempting to do something like this:

![Screenshot 2024-10-29 at 23.46.01.png](https://api.flyingharley.dev/uploads/Screenshot_2024_10_29_at_23_46_01_f6e71ae23c.png)

It's clear that we are not allowed to do so. Instead, we can modify it slightly:

![Screenshot 2024-10-29 at 23.46.56.png](https://api.flyingharley.dev/uploads/Screenshot_2024_10_29_at_23_46_56_44fb2761a2.png)

Of course, this approach is limited, as the **currentValue** state variable is only used within the current closure or other closures. If we need broader state manipulation, such as using it as a property, we might need to consider other approaches, such as using Class or Actor for better thread safety.

However, this approach is quite handy, especially when used with Combine, like this:

![Screenshot 2024-10-30 at 00.16.58.png](https://api.flyingharley.dev/uploads/Screenshot_2024_10_30_at_00_16_58_369b8abfdd.png)

In conclusion, closures in Swift offer powerful capabilities for capturing and managing state, but they come with nuances that require careful consideration. Understanding how closures capture variables, especially in the context of value types versus reference types, is crucial for efficient and error-free code.

References:
https://www.swiftbysundell.com/articles/swifts-closure-capturing-mechanics/
https://docs.swift.org/swift-book/documentation/the-swift-programming-language/closures#Escaping-Closures

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Capturing is a powerful feature, but it can be tricky at times, especially for beginners. To understand capturing, let's first explore what "capture" means.

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Some things to note about capturing in Swift Closure