Detailed Explanation of Common Data Structures in C++

C++ is a powerful system-level programming language, and its Standard Template Library (STL) provides developers with a rich and efficient set of data structures. Mastering these data structures not only enhances development efficiency but also allows for the creation of more robust and high-performance programs. This article systematically reviews the commonly used data structures in C++, including their basic principles, typical usage, underlying implementation mechanisms, and applicable scenarios.

1. Sequence Containers

Sequence containers are used to store linearly arranged data, commonly including array, vector, deque, list, and forward_list.

1. std::array

array is a fixed-size sequence container, similar to C-style arrays, but provides more member functions and type safety. Its size is determined at compile time, making it suitable for storing a fixed number of elements.

2. std::vector

vector is the most commonly used dynamic array container, supporting random access and automatic resizing. Its underlying implementation is a dynamically allocated contiguous block of memory, suitable for frequent insertions and deletions at the end, as well as scenarios requiring efficient random access. During resizing, it reallocates larger memory and copies elements, so memory management and resizing strategies should be considered in performance-sensitive situations.[1]。

3. std::deque

deque (double-ended queue) supports efficient insertion and deletion of elements at both the head and tail. Its underlying implementation is a dynamic array of fixed-size arrays, allowing for random access and efficient operations at both ends, making it suitable for implementing queues, sliding windows, and other scenarios.[2][3]。

4. std::list

list is a doubly linked list that supports efficient insertion and deletion of elements at any position but does not support random access. It is suitable for scenarios requiring frequent insertions/deletions in the middle.

5. std::forward_list

forward_list is a singly linked list that occupies less memory and is suitable for scenarios where traversal is only needed from front to back.

2. Associative Containers

Associative containers organize data through key-value pairs or unique keys, supporting efficient searching, insertion, and deletion. Common examples include set, map, multiset, and multimap.

1. std::set / std::multiset

set stores unique and ordered elements, with an underlying implementation of a red-black tree, where the time complexity for searching, inserting, and deleting is O(log n).[4][5]。multiset allows for duplicate elements.

2. std::map / std::multimap

map stores an ordered collection of unique keys and their corresponding values, also implemented as a red-black tree. It is commonly used for implementing dictionaries, indexes, and other functionalities.[6][7][8]。multimap allows for duplicate keys.

3. Unordered Associative Containers

Unordered associative containers are implemented based on hash tables, with an average time complexity of O(1) for searching, inserting, and deleting. They include unordered_set, unordered_map, unordered_multiset, and unordered_multimap, suitable for scenarios where order is not required but efficient searching is needed.[9][10]。

4. Container Adapters

Container adapters provide interfaces for specific purposes, relying on other containers for their underlying implementation.

1. std::stack

stack is a last-in-first-out (LIFO) structure that allows insertion and deletion only at the top of the stack, commonly used in recursion, expression evaluation, and other scenarios. It is typically implemented using deque or vector.[11][12]。

2. std::queue

queue is a first-in-first-out (FIFO) structure that allows insertion at the back and deletion at the front, suitable for task scheduling, message queues, and more.[13][14]。

3. std::priority_queue

priority_queue is a priority queue, typically implemented using a heap, suitable for scenarios requiring dynamic retrieval of maximum/minimum values.

5. Strings and Arrays

1. std::string

string is the string type provided by the C++ standard library, supporting dynamic resizing and a rich set of string operations. Its underlying implementation is a dynamic array that automatically manages memory and supports various methods for searching, concatenating, and slicing.

2. C-style Arrays

C-style arrays are fixed-size sequential structures that offer high performance but lack safety and flexibility. Compared to vector, C arrays require manual memory management and boundary checking, making them suitable for scenarios with extreme performance requirements and simple structures.[1]。

6. Smart Pointers and Memory Management

C++11 introduced smart pointers, greatly simplifying dynamic memory management and preventing memory leaks.

• unique_ptr: exclusive ownership, disallowing copying, only allowing transfer (move).
• shared_ptr: reference-counted smart pointer, allowing multiple pointers to share the same resource.
• weak_ptr: weak reference smart pointer that does not affect the resource’s lifecycle, commonly used to resolve shared_ptr circular reference issues.[15][16]。

7. Iterators and Algorithms

The C++ STL provides a unified iterator interface for all containers, supporting traversal, modification, searching, and other operations. The STL algorithm library (such as sort, find, transform, etc.) combined with iterators implements data structure-independent generic algorithms.[17][18]。

8. Examples of Typical Application Scenarios

• vector: dynamic arrays, batch data processing, stack implementation, etc.
• deque: sliding windows, double-ended queues, mixed queue/stack structures.
• list/forward_list: frequent insertions/deletions, linked list structures.
• set/map: deduplication, dictionaries, indexes, set operations.
• unordered_set/unordered_map: high-performance hash lookups.
• stack/queue/priority_queue: expression evaluation, task scheduling, priority handling, etc.

9. Supplement: Underlying Implementation and Performance Comparison

• vector and C arrays have similar performance, but vector offers better safety and flexibility, with boundary checking, exception safety, and other mechanisms.[1]。
• deque is suitable for frequent operations at both ends, with an underlying implementation of segmented arrays, and memory may not be contiguous.[2][3]。
• set/map are implemented as red-black trees, ensuring ordered elements and high search efficiency.[7][5]。
• unordered_set/unordered_map are implemented as hash tables, providing fast lookup speeds but not guaranteeing element order.

Mastering common data structures in C++ and their underlying principles is fundamental to efficient development and algorithm design. Properly selecting and combining these containers can significantly enhance code performance and maintainability.