[2026] What is Data Structure? Complete Guide from Basics to Practice

2026년 4월 9일 · 28분 읽기 · 수정 2026년 4월 9일 Beginner Guide

🎯 What You’ll Learn (Reading Time: 20 minutes)

TL;DR: Completely understand data structures from basics to practice. Learn the characteristics and selection criteria of arrays, lists, stacks, queues, trees, and graphs. What You’ll Learn:

✅ Perfectly understand 7 basic data structures
✅ Master time complexity concepts O(1), O(n), O(log n)
✅ Acquire ability to select optimal data structure by situation
✅ Improve coding test and practical application skills Real-World Applications:
🔥 Coding tests (LeetCode, Baekjoon)
🔥 Algorithm interview preparation
🔥 Performance optimization (choosing correct data structure)
🔥 System design fundamentals Difficulty: Beginner | Practical Examples: 10 | Essential CS Fundamentals

What is Data Structure?

Data Structure is a method for efficiently storing and managing data. How you organize data in a program significantly affects performance.

Why Are Data Structures Important?

// ❌ Inefficient: deleting middle element from array (O(n))
vector<int> arr = {1, 2, 3, 4, 5};
arr.erase(arr.begin() + 2);  // To delete 3, must shift all following elements
// ✅ Efficient: deleting middle element from list (O(1))
list<int> lst = {1, 2, 3, 4, 5};
auto it = next(lst.begin(), 2);
lst.erase(it);  // Just adjust pointers

Choosing the right data structure:

⚡ Makes execution faster
💾 Saves memory
🧹 Makes code cleaner

Linear Data Structures
- Array
- Linked List
- Stack
- Queue
Non-Linear Data Structures
- Tree
- Graph
- Hash Table
Time Complexity Comparison
Practical Selection Guide

1. Linear Data Structures

Array

Stores same-type data in contiguous memory space.

#include <iostream>
#include <vector>
using namespace std;
int main() {
    // Static array
    int arr[5] = {1, 2, 3, 4, 5};
    
    // Dynamic array (vector)
    vector<int> vec = {1, 2, 3, 4, 5};
    
    // Fast access by index O(1)
    cout << vec[2] << endl;  // 3
    
    // Append at end O(1)
    vec.push_back(6);
    
    // Insert in middle O(n)
    vec.insert(vec.begin() + 2, 99);
}

Advantages:

✅ Fast access by index (O(1))
✅ Memory efficient (contiguous placement)
✅ Cache-friendly Disadvantages:
❌ Slow middle insertion/deletion (O(n))
❌ Size change cost (reallocation) When to Use?
When data size is fixed
When index access is frequent
When sequential traversal is main operation

Linked List

Structure where nodes are connected by pointers.

#include <list>
#include <iostream>
using namespace std;
int main() {
    list<int> lst = {1, 2, 3, 4, 5};
    
    // Insert at front O(1)
    lst.push_front(0);
    
    // Insert in middle O(1) - when iterator is available
    auto it = next(lst.begin(), 2);
    lst.insert(it, 99);
    
    // Traverse
    for (int val : lst) {
        cout << val << " ";
    }
}

Advantages:

✅ Fast middle insertion/deletion (O(1))
✅ No size limit Disadvantages:
❌ Slow index access (O(n))
❌ Extra memory needed (pointers)
❌ Not cache-friendly When to Use?
When insertion/deletion is frequent
When size is unpredictable
When only sequential access is needed

Stack

LIFO (Last In, First Out) - last in comes out first.

#include <stack>
#include <iostream>
using namespace std;
int main() {
    stack<int> st;
    
    // Insert
    st.push(1);
    st.push(2);
    st.push(3);
    
    // Remove (reverse order)
    while (!st.empty()) {
        cout << st.top() << " ";  // 3 2 1
        st.pop();
    }
}

Practical Applications:

Function call stack
Parenthesis checking
Undo functionality
DFS (Depth-First Search) Example: Parenthesis Checking

bool isValid(string s) {
    stack<char> st;
    for (char c : s) {
        if (c == '(' || c == '{' || c == '[') {
            st.push(c);
        } else {
            if (st.empty()) return false;
            char top = st.top();
            st.pop();
            if ((c == ')' && top != '(') ||
                (c == '}' && top != '{') ||
                (c == ']' && top != '[')) {
                return false;
            }
        }
    }
    return st.empty();
}

Queue

FIFO (First In, First Out) - first in comes out first.

#include <queue>
#include <iostream>
using namespace std;
int main() {
    queue<int> q;
    
    // Insert
    q.push(1);
    q.push(2);
    q.push(3);
    
    // Remove (in order)
    while (!q.empty()) {
        cout << q.front() << " ";  // 1 2 3
        q.pop();
    }
}

Practical Applications:

Task queue
BFS (Breadth-First Search)
Printer spooler
Message queue

2. Non-Linear Data Structures

Tree

Represents hierarchical structure.

struct TreeNode {
    int val;
    TreeNode* left;
    TreeNode* right;
    
    TreeNode(int x) : val(x), left(nullptr), right(nullptr) {}
};
// Binary search tree insertion
TreeNode* insert(TreeNode* root, int val) {
    if (!root) return new TreeNode(val);
    
    if (val < root->val) {
        root->left = insert(root->left, val);
    } else {
        root->right = insert(root->right, val);
    }
    return root;
}
// Inorder traversal (sorted order)
void inorder(TreeNode* root) {
    if (!root) return;
    inorder(root->left);
    cout << root->val << " ";
    inorder(root->right);
}

Tree Types:

Binary Tree: Maximum 2 children
Binary Search Tree (BST): left < parent < right
AVL Tree: Balanced BST
Heap: Priority queue implementation When to Use?
Representing hierarchical structure (file system, organization chart)
Fast search/insertion/deletion (O(log n))
Maintaining sorted data

Graph

Represents relationships with nodes (vertices) and edges.

#include <vector>
#include <queue>
using namespace std;
// Adjacency list representation
class Graph {
    int V;  // Number of vertices
    vector<vector<int>> adj;
    
public:
    Graph(int V) : V(V), adj(V) {}
    
    void addEdge(int u, int v) {
        adj[u].push_back(v);
        adj[v].push_back(u);  // Undirected graph
    }
    
    // BFS
    void BFS(int start) {
        vector<bool> visited(V, false);
        queue<int> q;
        
        visited[start] = true;
        q.push(start);
        
        while (!q.empty()) {
            int u = q.front();
            q.pop();
            cout << u << " ";
            
            for (int v : adj[u]) {
                if (!visited[v]) {
                    visited[v] = true;
                    q.push(v);
                }
            }
        }
    }
};

Practical Applications:

Social networks (friend relationships)
Maps/navigation (shortest path)
Web crawling (link structure)
Dependency management

Hash Table

Quickly stores/searches key-value pairs.

#include <unordered_map>
#include <iostream>
using namespace std;
int main() {
    unordered_map<string, int> ages;
    
    // Insert O(1)
    ages["Alice"] = 25;
    ages["Bob"] = 30;
    
    // Search O(1)
    cout << ages["Alice"] << endl;  // 25
    
    // Check existence
    if (ages.find("Charlie") == ages.end()) {
        cout << "Not found" << endl;
    }
}

Practical Applications:

Caching
Duplicate removal
Frequency counting
Database indexing

3. Time Complexity Comparison

Data Structure	Access	Search	Insert	Delete
Array	O(1)	O(n)	O(n)	O(n)
Linked List	O(n)	O(n)	O(1)*	O(1)*
Stack	O(n)	O(n)	O(1)	O(1)
Queue	O(n)	O(n)	O(1)	O(1)
Binary Search Tree	O(log n)	O(log n)	O(log n)	O(log n)
Hash Table	-	O(1)	O(1)	O(1)
*When iterator is available

4. Practical Selection Guide

Recommendations by Scenario

1. Only sequential access needed

vector<int> data;  // Array is best

2. Frequent insertion/deletion

list<int> data;  // Linked list

3. Recent items first

stack<int> history;  // Stack (Undo functionality)

4. First-come-first-served

queue<Task> tasks;  // Queue (task queue)

5. Priority processing

priority_queue<int> pq;  // Heap

6. Fast search

unordered_set<int> seen;  // Hash table

7. Maintain sorted + fast search

set<int> sorted_data;  // Binary search tree

Practical Example: Recent Visited Pages

#include <iostream>
#include <deque>
#include <string>
using namespace std;
class BrowserHistory {
    deque<string> history;
    int current = -1;
    
public:
    void visit(string url) {
        // Remove after current position
        while (history.size() > current + 1) {
            history.pop_back();
        }
        history.push_back(url);
        current++;
    }
    
    string back() {
        if (current > 0) current--;
        return history[current];
    }
    
    string forward() {
        if (current < history.size() - 1) current++;
        return history[current];
    }
};
int main() {
    BrowserHistory browser;
    browser.visit("google.com");
    browser.visit("youtube.com");
    browser.visit("facebook.com");
    
    cout << browser.back() << endl;     // youtube.com
    cout << browser.back() << endl;     // google.com
    cout << browser.forward() << endl;  // youtube.com
}

Conclusion

Data Structure Selection Checklist:

✅ Which operation is most frequent?
- Access: Array
- Insertion/Deletion: List
- Search: Hash table
✅ Data size?
- Small: Array (cache efficiency)
- Large: Tree/Hash
✅ Is order important?
- Insertion order: Queue
- Reverse order: Stack
- Sorted: Tree/Heap
✅ Memory constraints?
- Limited: Array
- Flexible: Tree/Graph Next Steps:

Frequently Asked Questions

Q: What’s the difference between data structures and algorithms? A: Data structures are “how to store data”, algorithms are “how to process data”. They are closely related. Q: Which data structures are most used in practice? A: Array (vector), hash table (unordered_map), and queue are most frequent. Q: Should I implement data structures myself? A: In practice, use STL/standard libraries. But implementing yourself is important for interviews and understanding. Q: Which data structure should I study first? A: Recommended order: Array → List → Stack/Queue → Tree → Graph.

🎯 What You’ll Learn (Reading Time: 20 minutes)

What is Data Structure?

Why Are Data Structures Important?

Table of Contents

1. Linear Data Structures

Array

Linked List

Stack

Queue

2. Non-Linear Data Structures

Tree

Graph

Hash Table

3. Time Complexity Comparison

4. Practical Selection Guide

Recommendations by Scenario

Practical Example: Recent Visited Pages

Conclusion

Frequently Asked Questions