Introduction to C++ Data Structures

Data structures are fundamental to computer science and software engineering, enabling efficient data storage, retrieval, and manipulation. In C++, data structures play a crucial role in organizing and managing data effectively, providing the foundation for algorithms and applications.

What Are Data Structures?

A data structure is a way to store and organize data so it can be accessed and modified efficiently. Data structures offer methods for adding, removing, and searching for data and provide performance benefits tailored to specific use cases.

Why Data Structures Matter

• Efficiency: Optimized data structures allow for faster data processing and storage.
• Scalability: Proper data structure selection helps applications scale effectively with growing data sizes.
• Memory Management: Some data structures help in efficient memory usage.
• Data Organization: Simplify the organization and retrieval of complex data.

Built-in Data Structures in C++

Arrays

Arrays are collections of elements stored in contiguous memory locations. C++ offers both static (fixed size) and dynamic arrays.

int numbers[5] = {1, 2, 3, 4, 5};

Structures (struct)

C++ structures allow grouping multiple data types into a single entity. They are similar to classes but typically used for simpler data groupings.

struct Point {
    int x;
    int y;
};

Standard Library Data Structures

C++ provides a standard library called the Standard Template Library (STL), which includes several useful data structures.

std::vector

A dynamic array that resizes automatically. It provides random access to elements and is generally more versatile than raw arrays.

#include <vector>
std::vector<int> numbers = {1, 2, 3, 4, 5};
numbers.push_back(6); // Adds an element to the end

std::list

A doubly linked list that allows for efficient insertion and deletion at both ends and in the middle.

#include <list>
std::list<int> numbers = {1, 2, 3, 4, 5};
numbers.push_front(0); // Adds an element to the front

std::deque

A double-ended queue that allows insertion and deletion from both ends, similar to std::vector but more efficient for such operations.

#include <deque>
std::deque<int> numbers = {1, 2, 3, 4, 5};
numbers.push_front(0); // Adds to the front
numbers.push_back(6);  // Adds to the end

std::stack and std::queue

Adapters for std::deque providing stack (LIFO) and queue (FIFO) functionality.

#include <stack>
#include <queue>
std::stack<int> myStack;
myStack.push(1);
myStack.push(2);
int topElement = myStack.top();

std::queue<int> myQueue;
myQueue.push(1);
myQueue.push(2);
int frontElement = myQueue.front();

std::map and std::unordered_map

Associative containers that store key-value pairs. std::map maintains sorted keys, while std::unordered_map uses a hash table for faster lookups.

#include <map>
#include <unordered_map>
std::map<int, std::string> orderedMap = {{1, "one"}, {2, "two"}};
std::unordered_map<int, std::string> unorderedMap = {{1, "one"}, {2, "two"}};

std::set and std::unordered_set

Containers that store unique elements. std::set maintains sorted elements, while std::unordered_set provides faster lookups.

#include <set>
#include <unordered_set>
std::set<int> orderedSet = {1, 2, 3, 4};
std::unordered_set<int> unorderedSet = {1, 2, 3, 4};

Advanced Data Structures

Trees

• Binary Search Tree (BST): Allows efficient searching, insertion, and deletion.
• AVL Tree: A self-balancing BST ensuring height-balanced properties.
• B-tree: Suitable for disk-based storage, often used in databases.

Graphs

Graphs are collections of nodes (vertices) connected by edges, useful for representing networks.

• Adjacency Matrix: A 2D matrix representing edges between nodes.
• Adjacency List: An array of lists where each index represents a node and stores its edges.

Heaps

Heaps are binary trees used to implement priority queues efficiently.

• Min-Heap: The root is the smallest element.
• Max-Heap: The root is the largest element.