Analysis of time and space complexity of C++ STL containers - GeeksforGeeks

04 Apr, 2022

In this article, we will discuss the time and space complexity of some C++ STL classes.
Characteristics of C++ STL:
C++ has a low execution time as compared to other programming languages.
This makes STL in C++ advantageous and powerful.
The thing that makes STL powerful is that it contains a vast variety of classes that are implementations of popular and standard algorithms and predefined classes with functions that makes them well-optimized while doing competitive programming or problem-solving questions.
Analysis of functions in STL:
The major thing required while using the STL is the analysis of STL.
Analysis of the problem can’t be done without knowing the complexity analysis of the STL class used in the problem.
Implementation and complexity analysis of STL is required to answer the asked interview questions.
Below is the analysis of some STL Containers:
Priority Queue is used in many popular algorithms . Priority Queue is the implementation of Max Heap by default. Priority Queue does even optimize some major operations.
Syntax:
priority_queue<data_type> Q
The Min Heap can also be implemented by using the following syntax.Syntax:
priority_queue<data_type, vector<data_type>, greater<data_type>> Q
The table containing the time and space complexity with different functions given below:
O(1)
O(1)
O(log n)
O(1)
O(log n)
O(1)
O(1)
O(1)
Below is the C++ program illustrating the priority queue:
C++
 

 
 










// C++ program illustrating the
// priority queue
#include <bits/stdc++.h>
using namespace std;
 
// Function illustrating the
// priority queue
void priorityQueue()
{
    int Array[5] = { 1, 2, 3, 4, 5 };
    // Max heap
 
    int i;
    priority_queue<int> Q;
 
    for (i = 0; i < 5; i++) {
 
        // Pushes array elements to
        // priority queue and rearranges
        // to form max heap;
        Q.push(Array[i]);
    }
 
    // Maximum element in the priority
    // queue.
    cout << "The maximum element is "
         << Q.top() << endl;
 
    i = 1;
    while (Q.empty() != 1) {
        int peek = Q.top();
 
        cout << "The " << i++
             << " th max element is "
             << peek << endl;
 
        // Pops the maximum element
        // out of priority queue
        Q.pop();
    }
 
    cout << " Is priority queue "
         << "Q empty() ?" << endl
         << "check -->" << endl;
 
    // Checks whether priority
    // queue is empty
    if (Q.empty() == 1)
        cout << "The priority queue"
             << " is empty" << endl;
    else
        cout << "The priority queue"
             << " is not empty." << endl;
}
 
// Driver Code
int main()
{
    // Function Call
    priorityQueue();
 
    return 0;
}






The maximum element is 5
The 1 th max element is 5
The 2 th max element is 4
The 3 th max element is 3
The 4 th max element is 2
The 5 th max element is 1
 Is priority queue Q empty() ?
check -->
The priority queue is empty
 
It is the famous class of STL that stores the values in the pattern of key-value pair. 
It maps the value using the key value, and no same keys will have a different value.
It can be modified to multimap to make it work for the same keys with different values.
The map can be even used for keys and values of different data types.
Syntax:
map<data_type, data_type> M
The map <int, int> M is the implementation of self-balancing Red-Black Trees.
The unordered_map<int, int> M is the implementation of Hash Table which makes the complexity of operations like insert, delete and search to Theta(1).
The multimap<int, int> M is the implementation of Red-Black Trees which are self-balancing trees making the cost of operations the same as the map.
The unordered_multimap<int, int> M is the implemented same as the unordered map is implemented which is the Hash Table.
The only difference is it keeps track of one more variable which keeps track of the count of occurrences.
The pairs are inserted into the map using pair<int, int>(x, y) and can be accessed using the map iterator.first and map iterator.second.
The map by default keeps sorted based on keys and in the case of the unordered map, it can be in any order.
The table containing the time and space complexity with different functions given below(n is the size of the map):
O(log n)
O(1)
O(log n)
O(1)
O(log n)
O(1)
O(1)
O(1)
Theta(n)
O(1)
O(1)
O(1)
Below is the C++ program illustrating map:
C++
 

 
 










// C++ program illustrating the map
 
#include <bits/stdc++.h>
using namespace std;
 
// Function illustrating the map
void Map()
{
    int i;
 
    // Declaring maps
    map<int, int> M;
    unordered_map<int, int> UM;
    multimap<int, int> MM;
    unordered_multimap<int, int> UMM;
 
    // Inserting  pairs of key
    // and value
    for (i = 101; i <= 105; i++) {
 
        // Inserted the Key and
        // value twice
        M.insert(
            pair<int, int>(i - 100, i));
        UM.insert(
            pair<int, int>(i - 100, i));
        M.insert(
            pair<int, int>(i - 100, i));
        UM.insert(
            pair<int, int>(i - 100, i));
    }
    for (i = 101; i <= 105; i++) {
 
        // Inserted the key and
        // value twice
        MM.insert(
            pair<int, int>(i - 100, i));
        UMM.insert(
            pair<int, int>(i - 100, i));
        MM.insert(
            pair<int, int>(i - 100, i));
        UMM.insert(
            pair<int, int>(i - 100, i));
    }
 
    // Iterators for accessing
    map<int, int>::iterator Mitr;
    unordered_map<int, int>::iterator UMitr;
    multimap<int, int>::iterator MMitr;
    unordered_multimap<int, int>::iterator UMMitr;
 
    // Output
    cout << "In map" << endl;
    cout << "Key"
         << " "
         << "Value" << endl;
 
    for (Mitr = M.begin();
         Mitr != M.end();
         Mitr++) {
        cout << Mitr->first << "   "
             << Mitr->second
             << endl;
    }
 
    // Unsorted and is unordered output
    cout << "In unordered_map" << endl;
    cout << "Key"
         << " "
         << "Value" << endl;
    for (UMitr = UM.begin();
         UMitr != UM.end();
         UMitr++) {
        cout << UMitr->first
             << "   "
             << UMitr->second
             << endl;
    }
 
    // Sorted output
    cout << "In multimap" << endl;
    cout << "Key"
         << " "
         << "Value" << endl;
    for (MMitr = MM.begin();
         MMitr != MM.end();
         MMitr++) {
        cout << MMitr->first
             << "   "
             << MMitr->second
             << endl;
    }
 
    // Unsorted and is unordered
    // output
    cout << "In unordered_multimap"
         << endl;
    cout << "Key"
         << " "
         << "Value" << endl;
 
    for (UMMitr = UMM.begin();
         UMMitr != UMM.end();
         UMMitr++) {
        cout << UMMitr->first
             << "   " << UMMitr->second
             << endl;
    }
 
    cout << "The erase() function"
         << " erases respective key:"
         << endl;
    M.erase(1);
 
    cout << "Key"
         << " "
         << "Value" << endl;
 
    for (Mitr = M.begin();
         Mitr != M.end(); Mitr++) {
        cout << Mitr->first
             << "   " << Mitr->second
             << endl;
    }
 
    cout << "The find() function"
         << " finds the respective key:"
         << endl;
    if (M.find(1) != M.end()) {
        cout << "Found!" << endl;
    }
    else {
        cout << "Not Found!" << endl;
    }
 
    cout << "The clear() function "
         << "clears the map:" << endl;
    M.clear();
 
    // Returns the size of the map
    cout << "Now the size is :"
         << M.size();
}
 
// Driver Code
int main()
{
    // Function Call
    Map();
 
    return 0;
}






In map
Key Value
1   101
2   102
3   103
4   104
5   105
In unordered_map
Key Value
5   105
4   104
3   103
1   101
2   102
In multimap
Key Value
1   101
1   101
2   102
2   102
3   103
3   103
4   104
4   104
5   105
5   105
In unoredered_multimap
Key Value
5   105
5   105
4   104
4   104
1   101
1   101
2   102
2   102
3   103
3   103
The erase() function erases respective key:
Key Value
2   102
3   103
4   104
5   105
The find() function finds the respective key:
Not Found!
The clear() function clears the map:
Now the size is :0
 
Explanation:
m.begin(): points the iterator to starting element.
m.end(): points the iterator to the element after the last which is theoretical.
The first useful property of the set is that it contains only distinct elements of course the variation multiset can even contain repeated elements.
Set contains the distinct elements in an ordered manner whereas unordered set contains distinct elements in an unsorted order and multimaps contain repeated elements.
Syntax:
set<data_type> S
Set (set<int> s) is the implementation of Binary Search Trees.
Unordered set (unordered_set<int> S) is the implementation of Hash Table.
Multiset (multiset<int> S) is implementation of Red-Black trees.
Unordered_multiset(unordered_multiset<int> S) is implemented the same as the unordered set but uses an extra variable that keeps track of the count.
The complexity becomes Theta(1) and O(n) when using unordered<set> the ease of access becomes easier due to Hash Table implementation.
The table containing the time and space complexity with different functions given below(n is the size of the set):
O(log n)
O(1)
O(log n)
O(1)
O(log n)
O(1)
O(1)
O(1)
O(1)
O(1)
 Below is the C++ program illustrating set:
C++
 

 
 










// C++ program illustrating the set
#include <bits/stdc++.h>
using namespace std;
 
// Function illustrating the set
void Set()
{
    // Set declaration
    set<int> s;
    unordered_set<int> us;
    multiset<int> ms;
    unordered_multiset<int> ums;
    int i;
 
    for (i = 1; i <= 5; i++) {
 
        // Inserting elements
        s.insert(2 * i + 1);
        us.insert(2 * i + 1);
        ms.insert(2 * i + 1);
        ums.insert(2 * i + 1);
        s.insert(2 * i + 1);
        us.insert(2 * i + 1);
        ms.insert(2 * i + 1);
        ums.insert(2 * i + 1);
    }
 
    // Iterator to access values
    // in set
    set<int>::iterator sitr;
    unordered_set<int>::iterator uitr;
    multiset<int>::iterator mitr;
    unordered_multiset<int>::iterator umitr;
 
    cout << "The difference: "
         << endl;
    cout << "The output for set "
         << endl;
 
    for (sitr = s.begin();
         sitr != s.end(); sitr++) {
        cout << *sitr << " ";
    }
    cout << endl;
 
    cout << "The output for "
         << "unordered set " << endl;
 
    for (uitr = us.begin();
         uitr != us.end(); uitr++) {
        cout << *uitr << " ";
    }
    cout << endl;
 
    cout << "The output for "
         << "multiset " << endl;
 
    for (mitr = ms.begin();
         mitr != ms.end();
         mitr++) {
        cout << *mitr << " ";
    }
    cout << endl;
 
    cout << "The output for "
         << "unordered multiset "
         << endl;
 
    for (umitr = ums.begin();
         umitr != ums.end();
         umitr++) {
        cout << *umitr << " ";
    }
    cout << endl;
}
 
// Driver Code
int main()
{
    // Function Call
    Set();
 
    return 0;
}






The difference: 
The output for set 
3 5 7 9 11 
The output for unordered set 
11 9 7 3 5 
The output for multiset 
3 3 5 5 7 7 9 9 11 11 
The output for unordered multiset 
11 11 9 9 3 3 5 5 7 7
 
It is a data structure that follows the Last In First Out (LIFO) rule, this class of STL is alsoused in many algorithms during their implementations. For e.g, many recursive solutions use a system stack to backtrack the pending calls of recursive functions the same can be implemented using the STL stack iteratively.
Syntax:
stack<data_type> A
It is implemented using the linked list implementation of a stack.
O(1)
O(1)
O(1)
O(1)
O(1)
O(1)
O(1)
O(1)
Below is the C++ program illustrating stack:
C++
 

 
 










// C++ program illustrating the stack
#include <bits/stdc++.h>
using namespace std;
 
// Function illustrating stack
void Stack()
{
    stack<int> s;
    int i;
    for (i = 0; i <= 5; i++) {
        cout << "The pushed element"
             << " is " << i << endl;
        s.push(i);
    }
 
    // Points to top element of stack
    cout << "The top element of the"
         << " stack is: " << s.top()
         << endl;
 
    // Return size of stack
    cout << "The size of the stack"
         << " is: " << s.size()
         << endl;
 
    // Pops the elements of the
    // stack in the LIFO manner
    // Checks whether the stack
    // is empty or not
    while (s.empty() != 1) {
        cout << "The popped element"
             << " is " << s.top()
             << endl;
        s.pop();
    }
}
 
// Driver Code
int main()
{
    // Function Call
    Stack();
 
    return 0;
}






The pushed element is 0
The pushed element is 1
The pushed element is 2
The pushed element is 3
The pushed element is 4
The pushed element is 5
The top element of the stack is: 5
The size of the stack is: 6
The popped element is 5
The popped element is 4
The popped element is 3
The popped element is 2
The popped element is 1
The popped element is 0
 
It is a data structure that follows the First In First Out (FIFO) rule.
The inclusion of queue STL class queue in code reduces the function calls for basic operations.
The queue is often used in BFS traversals of trees and graphs and also many popular algorithms. 
Queue in STL is implemented using a linked list. 
Syntax:
queue<data_type> Q
Table containing the time and space complexity with different functions given below:
O(1)
O(1)
O(1)
O(1)
O(1)
O(1)
O(1)
O(1)
O(1)
O(1)
O(1)
O(1)
Below is the C++ program illustrating queue:
C++
 

 
 










// C++ program illustrating the queue
#include <bits/stdc++.h>
using namespace std;
 
// Function illustrating queue
void Queue()
{
    queue<int> q;
    int i;
    for (i = 101; i <= 105; i++) {
 
        // Inserts into the queue
        // in the FIFO manner
        q.push(i);
 
        cout << "The first and last"
             << " elements of the queue "
             << "are " << q.front()
             << " " << q.back()
             << endl;
    }
 
    // Check whether the queue is
    // empty or not
    while (q.empty() != 1) {
 
        // Pops the first element
        // of the queue
        cout << "The Element popped"
             << " following FIFO is "
             << q.front() << endl;
        q.pop();
    }
}
 
// Driver Code
int main()
{
    // Function Call
    Queue();
 
    return 0;
}






The first and last elements of the queue are 101 101
The first and last elements of the queue are 101 102
The first and last elements of the queue are 101 103
The first and last elements of the queue are 101 104
The first and last elements of the queue are 101 105
The Element popped following FIFO is 101
The Element popped following FIFO is 102
The Element popped following FIFO is 103
The Element popped following FIFO is 104
The Element popped following FIFO is 105
 
Vector is the implementation of dynamic arrays and uses new for memory allocation in heap.Syntax:
vector<int> A
 2-dimensional vectors can also be implemented using the below syntax:
Syntax:
vector<vector<int>> A
The table containing the time and space complexity with different functions given below:
Theta(log n)
 O(n)
O(1)
O(1)
O(1)
O(1)
O(1)
O(1)
O(1)
 O(n)
O(1)
O(n)
O(1)
Below is the C++ program illustrating vector:
C++
 

 
 










// C++ program illustrating vector
 
#include <bits/stdc++.h>
using namespace std;
 
// Function displaying values
void display(vector<int> v)
{
    for (int i = 0;
         i < v.size(); i++) {
        cout << v[i] << " ";
    }
}
 
// Function illustrating vector
void Vector()
{
    int i;
    vector<int> v;
    for (i = 100; i < 106; i++) {
 
        // Inserts an element in vector
        v.push_back(i);
    }
 
    cout << "The vector after "
         << "push_back is :" << v.size()
         << endl;
    cout << "The vector now is :";
    display(v);
    cout << endl;
 
    // Deletes an element at the back
    v.pop_back();
    cout << "The vector after "
         << "pop_back is :" << v.size()
         << endl;
    cout << "The vector now is :";
    display(v);
    cout << endl;
 
    // Reverses the vector
    reverse(v.begin(), v.end());
    cout << "The vector after "
         << "reversing is :" << v.size()
         << endl;
 
    cout << "The vector now is :";
    display(v);
    cout << endl;
 
    // Sorts vector using Quick Sort
    sort(v.begin(), v.end());
    cout << "The vector after "
         << "sorting is :" << v.size()
         << endl;
 
    cout << "The vector now is :";
    display(v);
    cout << endl;
 
    // Erases ith position element
    v.erase(v.begin() + 2);
 
    cout << "The size of vector "
         << "after erasing at position "
            "3 is :"
         << v.size() << endl;
    cout << "The vector now is :";
    display(v);
    cout << endl;
 
    // Deletes the vector completely
    v.clear();
 
    cout << "The size of the vector"
         << " after clearing is :"
         << v.size() << endl;
    cout << "The vector now is :";
    display(v);
    cout << endl;
}
 
// Driver Code
int main()
{
    // Function Call
    Vector();
 
    return 0;
}






  
The vector after push_back is :6
The vector now is :100 101 102 103 104 105 
The vector after pop_back is :5
The vector now is :100 101 102 103 104 
The vector after reversing is :5
The vector now is :104 103 102 101 100 
The vector after sorting is :5
The vector now is :100 101 102 103 104 
The size of vector after erasing at position 3 is :4
The vector now is :100 101 103 104 
The size of the vector after clearing is :0
The vector now is :
 
 
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