Data structure

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A data structure known as a hash table.

In computer science, a data structure is a data organization and storage format that is usually chosen for efficient access to data.[1][2][3] More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data,[4] i.e., it is an algebraic structure about data.

Usage

Data structures serve as the basis for abstract data types (ADT). The ADT defines the logical form of the data type. The data structure implements the physical form of the data type.[5]

Different types of data structures are suited to different kinds of applications, and some are highly specialized to specific tasks. For example, relational databases commonly use B-tree indexes for data retrieval,[6] while compiler implementations usually use hash tables to look up identifiers.[7]

Data structures provide a means to manage large amounts of data efficiently for uses such as large

main memory and secondary memory.[8]

Implementation

Data structures can be implemented using a variety of programming languages and techniques, but they all share the common goal of efficiently organizing and storing data.

arithmetic operations, while the linked data structures are based on storing addresses of data items within the structure itself. This approach to data structuring has profound implications for the efficiency and scalability of algorithms. For instance, the contiguous memory allocation in arrays facilitates rapid access and modification operations, leading to optimized performance in sequential data processing scenarios.[10]

The implementation of a data structure usually requires writing a set of

procedures that create and manipulate instances of that structure. The efficiency of a data structure cannot be analyzed separately from those operations. This observation motivates the theoretical concept of an abstract data type, a data structure that is defined indirectly by the operations that may be performed on it, and the mathematical properties of those operations (including their space and time cost).[11]

Examples

Main article: List of data structures
The standard type hierarchy of the programming language Python 3.

There are numerous types of data structures, generally built upon simpler primitive data types. Well known examples are:[12]

  • An array is a number of elements in a specific order, typically all of the same type (depending on the language, individual elements may either all be forced to be the same type, or may be of almost any type). Elements are accessed using an integer index to specify which element is required. Typical implementations allocate contiguous memory words for the elements of arrays (but this is not always a necessity). Arrays may be fixed-length or resizable.
  • A linked list (also just called list) is a linear collection of data elements of any type, called nodes, where each node has itself a value, and points to the next node in the linked list. The principal advantage of a linked list over an array is that values can always be efficiently inserted and removed without relocating the rest of the list. Certain other operations, such as random access to a certain element, are however slower on lists than on arrays.
  • A
    plain old data structures to distinguish them from objects.[13]
  • Hash tables, also known as hash maps, are data structures that provide fast retrieval of values based on keys. They use a hashing function to map keys to indexes in an array, allowing for constant-time access in the average case. Hash tables are commonly used in dictionaries, caches, and database indexing. However, hash collisions can occur, which can impact their performance. Techniques like chaining and open addressing are employed to handle collisions.
  • Graphs are collections of nodes connected by edges, representing relationships between entities. Graphs can be used to model social networks, computer networks, and transportation networks, among other things. They consist of vertices (nodes) and edges (connections between nodes). Graphs can be directed or undirected, and they can have cycles or be acyclic. Graph traversal algorithms include breadth-first search and depth-first search.
  • Stacks and queues are abstract data types that can be implemented using arrays or linked lists. A stack has two primary operations: push (adds an element to the top of the stack) and pop (removes the topmost element from the stack), that follow the Last In, First Out (LIFO) principle. Queues have two main operations: enqueue (adds an element to the rear of the queue) and dequeue (removes an element from the front of the queue) that follow the First In, First Out (FIFO) principle.
  • Trees represent a hierarchical organization of elements. A tree consists of nodes connected by edges, with one node being the root and all other nodes forming subtrees. Trees are widely used in various algorithms and data storage scenarios. Binary trees (particularly heaps), AVL trees, and B-trees
    are some popular types of trees. They enable efficient and optimal searching, sorting, and hierarchical representation of data.

A trie, or prefix tree, is a special type of tree used to efficiently retrieve strings. In a trie, each node represents a character of a string, and the edges between nodes represent the characters that connect them. This structure is especially useful for tasks like autocomplete, spell-checking, and creating dictionaries. Tries allow for quick searches and operations based on string prefixes.

Language support

Most

arrays) and multi-dimensional arrays.[14][15]

Most programming languages feature some sort of

Java Collections Framework, and the Microsoft .NET Framework
.

Modern languages also generally support

classes
for this purpose.

Many known data structures have concurrent versions which allow multiple computing threads to access a single concrete instance of a data structure simultaneously.[16]

See also

References

  1. ISBN 978-0262033848
    .
  2. ^ Black, Paul E. (15 December 2004). "data structure". In Pieterse, Vreda; Black, Paul E. (eds.). Dictionary of Algorithms and Data Structures [online]. National Institute of Standards and Technology. Retrieved 2018-11-06.
  3. ^ "Data structure". Encyclopaedia Britannica. 17 April 2017. Retrieved 2018-11-06.
  4. ISBN 978-0470864128
    .
  5. ^ "Abstract Data Types". Virginia Tech - CS3 Data Structures & Algorithms. Archived from the original on 2023-02-10. Retrieved 2023-02-15.
  6. ISBN 978-0-7645-7490-0
    . Archived from the original on 2007-08-18.
  7. ^ "1.5 Applications of a Hash Table". University of Regina - CS210 Lab: Hash Table. Archived from the original on 2021-04-27. Retrieved 2018-06-14.
  8. ^ "When data is too big to fit into the main memory". Indiana University Bloomington - Data Structures (C343/A594). 2014. Archived from the original on 2018-04-10.
  9. doi:10.5120/ijca2021921427
    .
  10. ISBN 978-0-444-82537-7
    , retrieved 2023-11-12
  11. OCLC 883695533
    .
  12. OCLC 927793728
    .
  13. Fermi National Accelerator Laboratory. Archived from the original
    on 2016-12-03. Retrieved 6 December 2016.
  14. ^ "The GNU C Manual". Free Software Foundation. Retrieved 2014-10-15.
  15. ^ Van Canneyt, Michaël (September 2017). "Free Pascal: Reference Guide". Free Pascal.
  16. ^ Mark Moir and Nir Shavit. "Concurrent Data Structures" (PDF). cs.tau.ac.il. Archived from the original (PDF) on 2011-04-01.

Bibliography

Further reading
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