What is mutable versus immutable?

Mutable versus immutable is a principle that drives modern approaches to both software development and infrastructure management.

The distinction can be compared to writing text on a whiteboard. If you can add words, erase parts or change what’s written, that’s like a mutable resource. But if the whiteboard is sealed under glass the moment you finish and you must use a new whiteboard to write something else, that’s an immutable resource.

While this concept applies broadly across computing, it’s most commonly encountered in programming. In programming, understanding which data types can be modified directly versus when a new copy must be created is essential for common tasks. These tasks include writing algorithms, building application programming interfaces (APIs) and designing classes in object-oriented programming (OOP).

The choice of using mutable or immutable objects affects how data is managed in memory, how safely it can be shared or changed and whether unintended side effects might occur. This is the reason why mutable versus immutable is a foundational concept for both beginner and experienced programmers.

For example, in the Python programming language, lists and dictionaries are mutable types. Items can be added, removed or modified within those objects. In contrast, objects such as booleans (true or false values) or tuples—ordered collections like (1,2,3)—are immutable types. Their contents cannot be changed or mutated without creating an entirely new object.

Choosing between mutable and immutable data generally depends on three key factors: whether data needs frequent updates, is shared across threads or requires version history. 

Mutable types generally work best when data needs frequent updates and multiple parts of a program modify the same object.

Mutable objects modify data in place, reducing memory use by avoiding the need to create new objects. It can lower the processor usage of garbage collection—the process of removing unused data to free memory—because fewer temporary objects might need to be created and collected.

For example, shopping carts in apps use mutable lists to add or remove items directly without creating new objects for each change.

Mutable types perform better with frequently changing data—such as growing lists or real-time counters—because they update existing objects rather than creating new ones. This efficiency speeds up operations in data structures that rely on quick modifications.

For instance, a music app’s playlist can use a mutable list to make quick updates. It can do that in microseconds when a song is added or deleted, compared to re-creating a 1,000 song playlist for each change.

Mutable objects let multiple parts of a program access and change the same object. This process enables them to work with a shared state—the data that multiple components read and write to coordinate their actions. It is useful when components need to coordinate or communicate through common data.

For example, a project management app uses mutable objects to share task lists, calendars and notifications. When one team member updates a task, everyone sees the change immediately.

Immutable types typically work best when data should not change after its creation. It is especially important in applications with concurrency, where multiple parts of a program access the same data.

Because the state of an immutable object is fixed, it won’t be altered by other code. This feature makes programs more predictable and easier to understand because it eliminates bugs related to unexpected mutations.

For example, banking apps frequently store transaction records as immutable objects so no code can alter them later. It is critical for helping ensure regulatory compliance and maintaining audit trails that prove transactions haven’t been tampered with.

Immutable objects are generally thread-safe because their state cannot change after creation. Multiple threads can safely read them simultaneously without conflicts, though developers still need to manage references carefully in concurrent systems. It makes them ideal for multi-threaded programs, where multiple threads must access the same data without causing conflicts.

For instance, a weather app can run concurrent threads for current conditions, forecasts and alerts. Storing weather data as immutable objects means that each thread can read the same information without the risk of it changing unexpectedly.

Immutable objects can simplify debugging because values do not change unexpectedly during program execution. This feature can reduce bugs caused by side effects and help teams resolve issues faster.

For example, video games frequently store health and stats for players as immutable objects. Because these values can’t change unexpectedly, developers can easily track down bugs knowing that unrelated code won’t alter stats.

Two of the most widely used styles of programming—object-oriented programming (OOP) and functional programming—approach mutability differently.

OOP often embraces mutability, building programs around objects that hold both data and behaviors. These objects can change over time by using special functions called setters, which can update the value of a property (for example, changing a person’s age or a product’s price).

In contrast, functional programming leans toward immutability. It creates and returns new values whenever something needs to change, making programs more predictable and easier to test.

Programming languages also vary in their approach to mutable versus immutable types.

In Python, both mutable and immutable types are common.

One example is strings—sequences of characters such as names or sentences. Strings in Python are immutable. Appending new text creates a new string object. In contrast, lists are mutable. These ordered collections are iterable—you can add, remove or modify items within the list object.

Instead of using a compiler (a program that converts code into machine language before execution) to check code before it runs, Python checks types at run time. It means that errors are caught only while the program is running. Mistakes involving mutability—such as trying to modify an immutable string—trigger a TypeError.

If the error isn’t handled, it stops the program immediately, preventing any further code from running. This procedure enables faster development but requires careful attention to type handling.

Understanding mutability in Python helps prevent errors when sharing data between functions or working within a shared module. Tutorials and code examples on GitHub provide best practices for using Python’s built-in types.

JavaScript uses both mutable and immutable types. Like Python, strings are also immutable. However, unlike Python, all objects are mutable by default.

JavaScript’s flexible syntax supports both object-oriented and functional styles, allowing developers to manage mutability as needed.

Similar to Python, Java strings are immutable. Once created, a string’s value cannot change. This characteristic can be inefficient for programs that build or modify text frequently.

To address this issue, Java provides StringBuilder—a mutable string class that allows text to be modified directly without creating new objects. It can improve performance and reduce memory use, balancing immutability’s safety with mutability’s performance benefits.

C++ uses the const keyword to mark variables, functions and even entire objects as read-only. It can give developers fine-grained control over mutability, effectively turning a mutable object into an immutable one by preventing changes.

Like Java, C++ strings can be mutable or immutable, depending on their implementation.

C++ supports both object-oriented and functional programming styles. In the OOP style, developers modify existing objects over time, while functional programming creates new values instead of changing existing data. 

The principles of mutability versus immutability extend beyond programming to infrastructure and systems. Modern software engineers apply these same concepts when designing cloud architectures and deployment pipelines.

Mutable infrastructure refers to servers or other IT resources that can be changed after deployment. For example, you might log in to a server and manually update software, change configurations or install patches. While this approach offers flexibility, it can lead to configuration drift where servers become unique “snowflakes” and changes become impossible to track or reproduce.

Immutable infrastructure means that servers or IT resources cannot be changed after deployment. Instead of updating running systems, teams deploy new instances with changes built-in, then retire old ones. This approach reduces configuration drift, simplifies rollback and helps ensure consistent deployments.

The principles of mutability and immutability can also apply to other areas of software and system design.

Some databases use append-only logs, meaning each change is recorded permanently and cannot be altered. Others are mutable, allowing updates or deletion of data directly, like editing a document.

Certain cloud storage systems can be configured as immutable storage to keep previous versions and lock them from changes. This helps protect data from being accidentally changed or deleted. Mutable storage allows files to be edited or replaced at any time.

Many version control tools, such as Git, follow an immutable model, where each commit is saved as a separate, unchangeable snapshot. It helps ensure a reliable history of versions, even as new changes are added.