 Data structure

In computer science, a data structure is a particular way of storing and 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, Btrees are particularly wellsuited for implementation of databases, while compiler implementations usually use hash tables to look up identifiers.
Data structures are used in almost every program or software system. Data structures provide a means to manage huge amounts of data efficiently, such as large databases and internet indexing services. Usually, efficient data structures are a key to designing efficient algorithms. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design.
Contents
Overview
 An array stores a number of elements of the same type in a specific order. They are accessed using an integer to specify which element is required (although the elements may be of almost any type). Arrays may be fixedlength or expandable.
 Record (also called tuple or struct) Records are among the simplest data structures. 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 hash or dictionary or map is a more flexible variation on a record, in which namevalue pairs can be added and deleted freely.
 Union. A union type definition will specify 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.
 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 certain values, without any particular order, and no repeated values. Values themselves are not retrieved from sets, rather one tests a value for membership to obtain a boolean "in" or "not in".
 An object contains a number of data fields, like a record, and also a number of program code fragments for accessing or modifying them. Data structures not containing code, like those above, are called plain old data structure.
Many others are possible, but they tend to be further variations and compounds of the above.
Basic principles
Data structures are generally based on the ability of a computer to fetch and store data at any place in its memory, specified by an address—a bit string that can be itself stored in memory and manipulated by the program. Thus the record and array 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 nontrivial 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).
Language support
Most assembly languages and some lowlevel languages, such as BCPL, lack support for data structures. Many highlevel programming languages, and some higherlevel assembly languages, such as MASM, on the other hand, have special syntax or other builtin support for certain data structures, such as vectors (onedimensional arrays) in the C language or multidimensional arrays in Pascal.
Most programming languages feature some sorts 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. Objectoriented programming languages, such as C++, Java and .NET Framework 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
 List of data structures
 Plain old data structure
 Concurrent data structure
 Data model
 Dynamization
 Linked data structure
 Persistent data structure
References
 ^ 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.
 ^ Entry data structure in the Encyclopædia Britannica (2009) Online entry accessed on May 21, 2009.
Further readings
 Peter Brass, Advanced Data Structures, Cambridge University Press, 2008.
 Donald Knuth, The Art of Computer Programming, vol. 1. AddisonWesley, 3rd edition, 1997.
 Dinesh Mehta and Sartaj Sahni Handbook of Data Structures and Applications, Chapman and Hall/CRC Press, 2007.
 Niklaus Wirth, Algorithms and Data Structures, Prentice Hall, 1985.
External links
 UC Berkeley video course on data structures
 Descriptions from the Dictionary of Algorithms and Data Structures
 CSE.unr.edu
 Data structures course with animations
 Data structure tutorials with animations
 An Examination of Data Structures from .NET perspective
 Schaffer, C. Data Structures and Algorithm Analysis
Data structures Types Abstract Arrays Linked Trees Graphs Data types Uninterpreted Numeric  Integer
 Fixedpoint
 Floatingpoint
 Rational
 Complex
 Bignum
 Interval
 Decimal
Text Pointer Composite Other  Boolean
 Bottom type
 Collection
 Enumerated type
 Exception
 Function type
 Opaque data type
 Recursive data type
 Semaphore
 Stream
 Top type
 Type class
 Unit type
 Void
Related topics  Abstract data type
 Data structure
 Interface
 Kind
 Primitive data type
 Subtyping
 Template
 Type constructor
 Parametric polymorphism
Categories: Data structures
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