Persistent Data Structures

Persistent data structures are data structures that allow you to retain previous versions of the structure even after modifications. In traditional (non-persistent) data structures, when you modify the structure, the previous version is lost, and you can only access the most recent version.

However, persistent data structures enable you to access and manipulate previous versions as well as the current version of the data.

Persistent data structures are particularly useful in scenarios where you need to keep track of historical data or perform time-travel-like operations. Common applications include version control systems, databases, undo/redo functionalities in applications, and functional programming.

Persistent data structures types :

Persistent Immutable Data Structures:
- These structures do not allow any modifications to the existing data. Instead, when you perform an operation that would usually change the data, a new version of the structure is created with the modified data, leaving the original version unchanged.
- This approach ensures that older versions of the structure remain unmodified, promoting immutability and functional programming paradigms.
- Examples include persistent immutable lists, trees, and hash maps.
Persistent Mutable Data Structures:
- These structures allow modifications, but they store previous versions by creating new copies of the modified nodes or elements while retaining unchanged portions from the previous version.
- This approach is more memory-intensive than immutable data structures because it involves keeping multiple versions of the structure in memory.
- Examples include persistent mutable arrays and graphs.

Efficiency is a crucial consideration when designing and implementing persistent data structures.

As creating new copies for each update can be costly in terms of memory and performance, data structures like “hash array mapped tries” (HAMTs) and “persistent red-black trees” are often used to strike a balance between efficiency and persistence.

It’s worth noting that persistent data structures are different from ephemeral (non-persistent) data structures in terms of implementation. Implementing persistence requires special techniques to manage multiple versions and ensure correctness.

In functional programming languages, where immutability is encouraged, creating persistent data structures is a natural fit. In contrast, in imperative languages, implementing persistent data structures can be more complex.

In summary, persistent data structures enable you to access and modify previous versions of data while preserving immutability or maintaining multiple copies of modified data.
They find applications in various fields where historical data tracking and versioning are essential.