Data structures serve as the basis forabstract data types (ADT). The ADT defines the logical form of the data type. The data structure implements the physical form of thedata type.[5]
Data structures provide a means to manage large amounts of data efficiently for uses such as largedatabases and internet indexing services. Usually, efficient data structures are key to designing efficientalgorithms. Some formal design methods andprogramming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design. Data structures can be used to organize the storage and retrieval of data stored in bothmain memory andsecondary memory.[8]
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.[9] Data structures are generally based on the ability of acomputer to fetch and store data at any place in its memory, specified by apointer—abitstring, representing amemory address, that can be itself stored in memory and manipulated by the program. Thus, thearray andrecord data structures are based on computing the addresses of data items witharithmetic operations, while thelinked 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 ofprocedures 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 anabstract 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]
The standardtype hierarchy of the programming language Python 3.
There are numerous types of data structures, generally built upon simplerprimitive data types. Well known examples are:[12]
Anarray 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.
Alinked list (also just calledlist) 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 asrandom access to a certain element, are however slower on lists than on arrays.
Arecord (also calledtuple orstruct) is anaggregate data structure. A record is a value that contains other values, typically in fixed number and sequence and typically indexed by names. The elements of records are usually calledfields ormembers. In the context ofobject-oriented programming, records are known asplain 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 andqueues 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 (particularlyheaps),AVL trees, andB-trees are some popular types of trees. They enable efficient and optimal searching, sorting, and hierarchical representation of data.
Atrie, 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.
Mostassembly languages and somelow-level languages, such asBCPL (Basic Combined Programming Language), lack built-in support for data structures. On the other hand, manyhigh-level programming languages and some higher-level assembly languages, such asMASM, have special syntax or other built-in support for certain data structures, such as records and arrays. For example, theC (a direct descendant of BCPL) andPascal languages supportstructs and records, respectively, in addition to vectors (one-dimensionalarrays) and multi-dimensional arrays.[14][15]
Most programming languages feature some sort oflibrary mechanism that allows data structure implementations to be reused by different programs. Modern languages usually come with standard libraries that implement the most common data structures. Examples are theC++Standard Template Library, theJava Collections Framework, and theMicrosoft.NET Framework.
Many known data structures haveconcurrent versions which allow multiple computing threads to access a single concrete instance of a data structure simultaneously.[16]
^Dubey, R. C. (2014).Advanced biotechnology : For B Sc and M Sc students of biotechnology and other biological sciences. New Delhi: S Chand.ISBN978-81-219-4290-4.OCLC883695533.
^Seymour, Lipschutz (2014).Data structures (Revised first ed.). New Delhi, India: McGraw Hill Education.ISBN9781259029967.OCLC927793728.
