315px|A data structure known as a hash table
In computer science
, a data structure is a data organization, management, and storage format that enables efficient access and modification. 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.
Data structures serve as the basis for abstract data type
s (ADT). The ADT defines the logical form of the data type. The data structure implements the physical form of the data type.
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, while compiler
implementations usually use hash table
s to look up identifiers.
Data structures provide a means to manage large amounts of data efficiently for uses such as large database
s and internet indexing services. Usually, efficient data structures are key to designing efficient algorithm
s. Some formal design methods and programming language
s 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 information stored in both main memory
and secondary memory.
Data structures are generally based on the ability of a computer
to fetch and store data at any place in its memory, specified by a pointer
—a bit string, representing a memory address
, that can be itself stored in memory and manipulated by the program. Thus, the array
data structures are based on computing the addresses of data items with arithmetic operations
, while the linked data structure
s are based on storing addresses of data items within the structure itself.
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).
There are numerous types of data structures, generally built upon simpler primitive data type
* 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 ''record''
(also called ''tuple'' or ''struct'') is an aggregate 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 called ''fields'' or ''members''.
* A ''union''
is a data structure that specifies which of a number of permitted primitive types may be stored in its instances, e.g. ''float'' or ''long integer''. Contrast with a record
, which could be defined to contain a float ''and'' an integer; whereas in a union, there is only one value at a time. Enough space is allocated to contain the widest member datatype.
* A ''tagged union
'' (also called ''variant''
, ''variant record'', ''discriminated union'', or ''disjoint union'') contains an additional field indicating its current type, for enhanced type safety.
* An ''object''
is a data structure that contains data fields, like a record does, as well as various methods
which operate on the data contents. An object is an in-memory instance of a class from a taxonomy. In the context of object-oriented programming
, records are known as plain old data structure
s to distinguish them from objects.
In addition, ''graphs''
and ''binary trees
'' are other commonly used data structures.
Most assembly language
s and some low-level languages
, such as BCPL
(Basic Combined Programming Language), lack built-in support for data structures. On the other hand, many high-level programming language
s and some higher-level assembly languages, such as MASM
, have special syntax or other built-in support for certain data structures, such as records and arrays. For example, the C
(a direct descendant of BCPL) and Pascal
languages support structs
and records, respectively, in addition to vectors (one-dimensional arrays
) and multi-dimensional arrays.
Most programming languages feature some sort of library
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 the C++ Standard Template Library
, the Java Collections Framework
, and the Microsoft .NET Framework
Modern languages also generally support modular programming
, the separation between the interface
of a library module and its implementation. Some provide opaque data type
s that allow clients to hide implementation details. Object-oriented programming language
s, such as C++
, and Smalltalk
, typically use 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.
* Abstract data type
* Concurrent data structure
* Data model
* Linked data structure
* List of data structures
* Persistent data structure
* Plain old data structure
* Succinct data structure
* Peter Brass, ''Advanced Data Structures'', Cambridge University Press
* Donald Knuth
, ''The Art of Computer Programming
'', vol. 1. Addison-Wesley
, 3rd edition, 1997,
* Dinesh Mehta and Sartaj Sahni
, ''Handbook of Data Structures and Applications'', Chapman and Hall
* Niklaus Wirth
, ''Algorithms and Data Structures'', Prentice Hall
* Alfred Aho
, John Hopcroft
, and Jeffrey Ullman
, ''Data Structures and Algorithms'', Addison-Wesley, 1983,
* G. H. Gonnet
and R. Baeza-Yates
, Handbook of Algorithms and Data Structures - in Pascal and C
', second edition, Addison-Wesley, 1991,
* Ellis Horowitz
and Sartaj Sahni, ''Fundamentals of Data Structures in Pascal'', Computer Science Press
from the Dictionary of Algorithms and Data Structures
Data structures courseAn Examination of Data Structures from .NET perspectiveSchaffer, C. ''Data Structures and Algorithm Analysis''