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In object-oriented (OO) and functional programming, an immutable object (unchangeable [1] object) is an object whose state cannot be modified after it is created. [2] This is in contrast to a mutable object (changeable object), which can be modified after it is created. [3]
The table shows a comparison of functional programming languages which compares various features and designs of ... Data is immutable Type classes Garbage collection
Similarly, the idea of immutable data from functional programming is often included in imperative programming languages, [108] for example the tuple in Python, which is an immutable array, and Object.freeze() in JavaScript. [109]
The immutable keyword denotes data that cannot be modified through any reference. The const keyword denotes a non-mutable view of mutable data. Unlike C++ const, D const and immutable are "deep" or transitive, and anything reachable through a const or immutable object is const or immutable respectively. Example of const vs. immutable in D
In computing, a persistent data structure or not ephemeral data structure is a data structure that always preserves the previous version of itself when it is modified. Such data structures are effectively immutable, as their operations do not (visibly) update the structure in-place, but instead always yield a new updated structure.
Immutable objects are easily shared, but require creating new extrinsic objects whenever a change in state occurs. In contrast, mutable objects can share state. Mutability allows better object reuse via the caching and re-initialization of old, unused objects. Sharing is usually nonviable when state is highly variable.
The main difference between an arbitrary data structure and a purely functional one is that the latter is (strongly) immutable. This restriction ensures the data structure possesses the advantages of immutable objects: (full) persistency, quick copy of objects, and thread safety.
In a purely functional language, the only dependencies between computations are data dependencies, and computations are deterministic. Therefore, to program in parallel, the programmer need only specify the pieces that should be computed in parallel, and the runtime can handle all other details such as distributing tasks to processors, managing synchronization and communication, and collecting ...