Data structure

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In computer science, a data structure is a particular way of organizing data in a computer so that it can be used efficiently.[1][2]

Different kinds of data structures are suited to different kinds of applications, and some are highly specialized to specific tasks. For example, B-trees are particularly well-suited for implementation of databases, while compiler implementations usually use hash tables to look up identifiers.

Data structures provide a means to manage large amounts of data efficiently, such as large databases and internet indexing services. Usually, efficient data structures are a key in designing efficient algorithms. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design. Storing and retrieving can be carried out on data stored in both main memory and in 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 and record data structures are based on computing the addresses of data items with arithmetic operations; while the linked data structures are based on storing addresses of data items within the structure itself. Many data structures use both principles, sometimes combined in non-trivial ways (as in XOR linking).

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 types:

  • An array is a number of elements in a specific order, typically all of the same type. Elements are accessed using an integer index to specify which element is required (although the elements may be of almost any type). 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 record (also called a 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.
  • An associative array (also called a dictionary or map) is a more flexible variation on an array, in which name-value pairs can be added and deleted freely. A hash table is a common implementation of an associative array.
  • A union type 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 a variant, variant record, discriminated union, or disjoint union) contains an additional field indicating its current type, for enhanced type safety.
  • A set is an abstract data structure that can store specific values, in no particular order and with no repeated values. Values themselves are not retrieved from sets, rather a given value is tested for membership to obtain a Boolean in or not in.
  • Graphs and trees are linked abstract data structures composed of nodes. Each node contains a value and one or more pointers to other nodes. Graphs can be used to represent networks, while variants of trees can be used for sorting and searching, having their nodes arranged in some relative order based on their values.
  • An object contains data fields, like a record, as well as various methods which operate on the contents of the record. In the context of object-oriented programming, records are known as plain old data structures to distinguish them from objects.

Language support[edit]

Most assembly languages 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 languages 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 and Pascal languages support structs and records, respectively, in addition to vectors (one-dimensional arrays) and multi-dimensional arrays.[3][4]

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 Microsoft's .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 types that allow clients to hide implementation details. Object-oriented programming languages, such as C++, Java and Smalltalk may use classes for this purpose.

Many known data structures have concurrent versions that allow multiple computing threads to access the data structure simultaneously.

See also[edit]


  1. ^ Paul E. Black (ed.), entry for data structure in Dictionary of Algorithms and Data Structures. U.S. National Institute of Standards and Technology. 15 December 2004. Online version Accessed May 21, 2009.
  2. ^ Entry data structure in the Encyclopædia Britannica (2009) Online entry accessed on May 21, 2009.
  3. ^ "The GNU C Manual". Free Software Foundation. Retrieved 15 October 2014. 
  4. ^ "Free Pascal: Reference Guide". Free Pascal. Retrieved 15 October 2014. 

Further reading[edit]

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