Data structures Page

Data Structures

Return to Data Structures Bibliography, Data Structures Courses, Algorithms, Major programming topics, Programming, Programming glossary, Programming topics, Programming languages, Software engineering topics, Software architecture, Software architecture topics, Awesome lists

Data structures are ways of organizing and storing data in a computer so that it can be accessed and modified efficiently. They are essential for managing and processing data dynamically and play a crucial role in software development and computer science. Common data structures include arrays, linked lists, stacks, queues, hash tables, trees, and graphs. Each structure has its advantages and is suited to specific tasks; for example, arrays allow fast access to elements using indices, linked lists facilitate easy insertion and removal of elements, and trees are ideal for hierarchical data representation. Hash tables offer efficient data retrieval through key-value mapping. Understanding and choosing the right data structure is key to optimizing algorithms and solving complex computational problems efficiently.

* data structure - "A logical grouping of data and data operations | operations on that data." (CppPrmLp 2012)

Data structures are fundamental components in computer science. They provide a way to organize data and store data efficiently, allowing programs to access and manipulate information effectively. Let’s delve into the world of data structures:

* Definition: A data structure is a logical or mathematical representation of data, along with its implementation in a computer program. It encompasses both the arrangement of data values and the relationships among them12.

* Purpose: Data structures serve as the foundation for abstract data types (ADTs). While the ADT defines the logical form of a data type, the data structure implements its physical form. Different types of data structures cater to various applications, and some are specialized for specific tasks. For instance:
* B-tree indexes are commonly used in relational databases for data retrieval.
* Hash tables are prevalent in compiler implementations for looking up identifiers.
* Efficient Algorithms: Well-designed data structures are key to efficient algorithms. * They allow us to manage large amounts of data effectively, whether it’s in main memory or secondary memory1.
* Implementation: Data structures can be implemented using various programming languages and techniques. They all share the common goal of organizing and storing data efficiently. Some examples include:
* Arrays: Contiguous memory allocation facilitates rapid access and modification operations.
* Linked lists: Store addresses of data items within the structure itself1.
* Types: There are numerous data structures, each built upon simpler primitive data types. Some common ones include:
** Arrays
** Lists
** Trees
** Graphs

In summary, data structures empower us to harness the power of data, making our algorithms smarter and more effective.

{{wp>data structures}}

* Agile - Agile data structures
* Angular - Angular data structures
* Android - Android data structures
* Ansible - Ansible data structures
* Apple - Apple data structures
* Apple macOS - Apple macOS data structures
* Apple iOS - Apple iOS data structures
* AWS - AWS data structures
* Azure - Azure data structures
* Bash - Bash data structures
* C programming language - C programming language data structures
* C Sharp | C# - C Sharp data structures
** dot NET | .NET - dot NET data structures | .NET data structures
* C plus plus | C++ - C plus plus data structures
* Cassandra - Cassandra data structures
* Chef - Chef data structures
* Cisco - Cisco data structures
* Clojure - Clojure data structures
* Cobol - Cobol data structures
* Dart - Dart data structures
* DevOps - DevOps data structures
* DataOps - DataOps data structures
* Django - Django data structures
* Docker - Docker data structures
* Fortran - Fortran data structures
* Flask - Flask data structures
* Flutter - Flutter data structures
* FreeBSD - FreeBSD data structures
* GCP - GCP data structures
* GitHub - GitHub data structures
* GitOps - GitOps data structures
* Golang, Go, Go programming language - Golang data structures
* Groovy - Groovy data structures
* Haskell - Haskell data structures
* HCL - HashiCorp Configuration Language (HCL) - HCL data structures
* IBM - IBM data structures
* IBM Cloud - IBM Cloud data structures
* IBM Mainframe - IBM Mainframe data structures
* Jakarta EE - Jakarta EE data structures
* Java - Java data structures
* JavaScript - JavaScript data structures
* Julia - Julia data structures
* Kafka - Kafka data structures
* Kubernetes - Kubernetes data structures
* Linux - Linux data structures
* Machine learning - Machine learning data structures
* Mainframe - Mainframe data structures
* Maven - Maven data structures
* Microsoft - Microsoft data structures
* Microsoft Windows - Microsoft Windows data structures
* Microsoft Windows Server - Microsoft Windows Server data structures
* MLOps - MLOps data structures
* Kotlin - Kotlin data structures
* MongoDB - MongoDB data structures
* MySQL - MySQL data structures
* Objective-C - Objective-C data structures
* OpenShift - OpenShift data structures
* Oracle Database - Oracle Database data structures
* Perl - Perl data structures
* PHP - PHP data structures
* PostgreSQL - PostgreSQL data structures
* PowerShell - PowerShell data structures
* Puppet - Puppet data structures
* Python - Python data structures
* Quarkus - Quarkus data structures
* R Language - R Language data structures
* Redis - Redis data structures
* RHEL - RHEL data structures
* React.js - React.js data structures
* Ruby - Ruby data structures
* Rust - Rust data structures
* Scala - Scala data structures
* Spring - Spring data structures
* SQL - SQL data structures
* SQL Server - SQL Server data structures
* Swift - Swift data structures
* Terraform - Terraform data structures
* TypeScript - TypeScript data structures
* Ubuntu - Ubuntu data structures
* Vue.js - Vue.js data structures

Examples

Main article is List of data structures

There are numerous types of data structures, generally built upon simpler primitive data types:{{Cite book|title=Data structures|last=Seymour,|first=Lipschutz,|date=2014|publisher=McGraw Hill Education (India) Private Limited|isbn=9781259029967|edition=Revised First|location=New Delhi|oclc=927793728}}
* An array data structure|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 (computer science)|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 (computer science)|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 (computer science)|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 type|variant, variant record, discriminated union, or disjoint union) contains an additional field indicating its current type, for enhanced type safety.
* An Object (computer science)|object is a data structure that contains data fields, like a record does, as well as various Method (computer programming)|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 structures to distinguish them from objects. {{cite web|url=http://www.fnal.gov/docs/working-groups/fpcltf/Pkg/ISOcxx/doc/POD.html |accessdate=6 December 2016 |title=C++ Language Note: POD Types |author=Walter E. Brown |publisher=Fermi National Accelerator Laboratory |date=September 29, 1999}}

In addition, Graph (computer science)|graphs and binary trees are other commonly used data structures.

External sites

* g>Data structures in programming - g>Programming Data structures
* wp>Data structure

{{navbar_data_structures}}

{{navbar_algorithms}}

{{navbar_footer}}

Data structures Page

Data Structures

External Sites

Examples

External sites