12.1.2 Immutability

• What does immutability mean in functional programming?

  Cannot be changed once created
• Immutability makes debugging easier because variables don't change after creation
• Immutable objects can be safely accessed by multiple threads concurrently without locks.
• Steps to add an element to a list using immutability in functional programming

  1️⃣ Create a new list

  2️⃣ Add the new element to the new list

  3️⃣ The original list remains unchanged
• Immutability ensures that once a data structure is created, it cannot be modified
• Immutable variables maintain the same value throughout the program, simplifying debugging.
• Why is immutability important in functional programming?

  Ensures data consistency
• Instead of modifying the original list, a new list is created with the added element
• Match the benefit of immutability with its explanation:

  Easier Debugging ↔️ Variables maintain the same value

  Thread Safety ↔️ Concurrent access without locks

  Improved Code Readability ↔️ Predictable code due to unchanging values
• What happens to data in functional programming when immutability is enforced?

  New instances are created
• Immutable variables maintain the same value, making it easier to fix bugs
• Immutability prevents race conditions in multithreaded environments.
• What is the defining characteristic of immutable data structures in functional programming?

  Cannot be modified
• Mutable data structures can be modified in place.
• Immutable data structures are inherently thread-safe.
• Immutable data structures may consume more memory
• In Python, what is an example of an immutable data structure?

  Tuple
• When adding an element to an immutable tuple, a new instance
• Thread safety in immutable data structures eliminates the need for locks.
• Why is immutability considered a cornerstone of functional programming?

  Ensures data consistency
• Immutability makes debugging easier because variables maintain consistent values
• Concurrent access to immutable data structures does not require locks.
• What happens to the original list when a new element is added in functional programming with immutability?

  It remains unchanged
• In Python, what type of data structure is a tuple?

  Immutable
• Immutable data structures create new instances instead of modifying existing data.
• Immutable data structures may require more memory
• Immutable data structures enhance code predictability due to unchanging values.
• What is one benefit of using immutable data structures for debugging?

  Simplifies bug tracking
• Immutable data structures allow safe concurrent access by multiple threads without locks.
• Immutable data structures enhance code predictability because their values remain unchanging
• Why does using immutable data structures simplify debugging?

  Values remain tracking
• Immutable data structures eliminate the need for locks in multithreaded environments.
• Immutable data structures improve code readability by ensuring that values remain static
• What is one primary benefit of using immutable data structures for thread safety?

  No locks required
• Concurrent access to immutable data structures does not require synchronization mechanisms.
• Using immutable data structures enhances code predictability by ensuring that values do not change
• How do immutable data structures aid in easier debugging?

  Values remain constant
• Immutable data structures prevent race conditions in concurrent environments.
• Improved code readability is achieved with immutable data structures because their values are predictable
• What is a key advantage of using immutable data structures for debugging in concurrent programs?

  Simplifies bug tracking