Difference between Static and Dynamic Data Structure

Organizing and managing data is an important part of any software development process. One of the key decisions to make when structuring data is whether to use a static or dynamic data structure. With a static data structure, the size of the data structure is fixed and does not change over time. On the other hand, in a dynamic data structure, the size of the data structure can be changed over time and can adapt to changing data sets. In this article, we will take a look at the differences between static and dynamic data structures and the advantages and disadvantages of each. We will also explore when it might be best to use a static or dynamic data structure in your software development projects. By the end of this article, you should better understand of the different types of data structures and how to choose the right one for your project.

What is a Static Data Structure?

A static data structure is a type of data structure that has a fixed size and does not change throughout the execution of a program. This means that the size of the data structure is determined at the time of creation and cannot be altered later on. Using a static data structure means that memory can be allocated more efficiently, as the size of the data structure is known ahead of time. Examples of static data structures include arrays and linked lists with a fixed number of elements.

Kinds of Static Data Structure

1. Array: An array is a collection of elements of the same data type, stored in contiguous memory locations. The size of an array is fixed at the time of creation, and elements can be accessed by their index. Arrays are used to store and manipulate large amounts of data in an organized and efficient manner. They are commonly used in many programming languages and are a fundamental building block in many data structures. The main advantage of using an array is fast access to elements, but it has the drawback of not being able to change its size.
2. Stack: A stack is a last-in, first-out (LIFO) data structure. Elements can only be added or removed from the top of the stack. The size of a stack is also fixed at the time of creation. Stack is useful in various algorithms such as expression evaluation, recursion, backtracking and more. The main advantage of using a stack is that it provides constant-time access to the top element, but it has the drawback of not being able to change its size.
3. Queue: A queue is a first-in, first-out (FIFO) data structure. Elements can only be added at the back of the queue and removed from the front. Like a stack, the size of a queue is also fixed at the time of creation. Queues are commonly used for scheduling tasks and managing buffers. The main advantage of using a queue is that it allows for efficient data management and manipulation, but it has the drawback of not being able to change its size.
4. Linked List: A linked list is a linear collection of data elements, called nodes, where each node points to the next node. A static linked list is a linked list where the number of nodes is fixed and cannot be changed. Linked lists are useful for dynamic memory allocation and have efficient insertion and deletion operations. The main advantage of using a linked list is that it has a dynamic size, but it has a drawback of having a slower access time.
5. Tree: A Tree is a hierarchical data structure where each node has one or more child nodes. A tree can be represented in an array, but its size is fixed. Trees are used for searching, sorting and traversing data efficiently. The main advantage of using trees is that it allows for efficient data management and manipulation, but it has the drawback of not being able to change their size.
6. Graph: A graph is a non-linear data structure that consists of a set of vertices (or nodes) and a set of edges connecting these vertices. A graph can be represented in an array, but its size is fixed. Graphs are used to model real-world problems, such as networks and relationships. The main advantage of using graphs is that it allows for efficient data management and manipulation, but it has the drawback of not being able to change their size.

Static data structures are known for their efficient use of memory and their ability to store the same data over and over again. This makes them ideal for applications where the data needs to be accessed and manipulated quickly. They are also well-suited for applications which require a large amount of data to be stored in a single location.

Static data structures are also commonly used for applications which need to perform complex operations on large data sets. For example, a search engine may use a static data structure to store a large number of web pages and their associated data in order to quickly search and retrieve them. Similarly, a database may use a static data structure to store a large amount of data in order to access it and manipulate it quickly.

What is Dynamic Data Structure?

Dynamic data structures are a type of data structure that can change in size at runtime, and this means that the structure is able to add new elements and remove existing elements as needed. This type of data structure is ideal when the amount of data stored is expected to vary during the course of a program's execution.

Kinds of Dynamic Data Structure

1. Linked Lists: Linked lists are a type of dynamic data structure that consists of a sequence of nodes, where each node contains a value and a pointer to the next node. Linked lists can be implemented as singly-linked lists, where each node points to the next node, or doubly-linked lists, where each node points to both the next and previous nodes. Linked lists are often used to implement data structures such as stacks and queues, as well as for memory management.
2. Trees: Trees are a type of dynamic data structure that consists of a set of connected nodes, where each node has zero or more child nodes. Trees can be used to implement data structures such as binary search trees, AVL trees, and B-trees, which are all used for searching, sorting, and storing data. Some other types of trees are Heap and Segment trees.
3. Dynamic Arrays: Dynamic arrays are arrays that can grow or shrink as elements are added or removed. They are often implemented using a combination of static and dynamic memory allocation, which allows for more efficient memory usage. Dynamic arrays are used in a wide range of applications, such as dynamic programming and game development.
4. Hash Tables: Hash tables are a type of dynamic data structure that uses a hash function to map keys to values. They are often used to implement data structures such as dictionaries and sets, and are commonly used in applications such as database management systems and network routers.
5. Graphs: Graphs are a type of dynamic data structure that consists of a set of vertices and edges. They can be used to represent relationships between objects, such as in social networks, and can be implemented using adjacency lists or matrices. Graphs are widely used in routing, searching, and many other computational problems.
6. Heaps: Heaps are a type of dynamic data structure that satisfies the heap property, where each parent node has a value less than or equal to its children. Heaps can be used to implement data structures such as priority queues, and can be implemented as either min-heaps or max-heaps. Heaps are used for sorting and searching algorithms.

Dynamic data structures are more efficient than static data structures since they require fewer elements to be stored in memory. This improves memory usage and increases the speed of the program. Additionally, dynamic data structures are more flexible since they can adapt to changing conditions. For example, if a program needs more data to be stored, the data structure can easily grow in size by adding more elements.

Dynamic data structures can be used in a wide variety of applications, such as databases, graphics, and networking. In these cases, the data structure needs to be able to adjust to the changing data conditions. For example, when a new record is added to a database, the data structure needs to be able to update itself to accommodate the new information.

Comparison between Static and Dynamic Data Structure 

Here are a few comparisons between the two types of data structures that you might find useful:

Examples

Following are some code examples written in C++, which will help us to understand the difference more clearly.

Example 1:

#include <iostream>
using namespace std;
const int MAX_SIZE = 10;
int main() {
    int staticArray[MAX_SIZE];
// Adding elements to the static array
    for (int i = 0; i < MAX_SIZE; i++) 
    {
        staticArray[i] = i;
    }
// Printing the elements of the static array
    for (int i = 0; i < MAX_SIZE; i++) 
   {
        cout << staticArray[i] <<"";
    }
    return 0;
}

Output:   

0 1 2 3 4 5 6 7 8 9

Program Explanation:

This program creates an array called staticArray with a maximum size of 10. The “for” loop fills the array with the values from 0 to 9 and then, the for loop prints the elements of the array.

Example 2:

#include <iostream>
using namespace std;
struct Node {
    int data;
    Node* next;
};
int main() 
{
    Node* head = NULL;
    Node* tail = NULL;
// Adding elements to the linked list
    for (int i = 0; i < 10; i++) 
    {
        Node* newNode = new Node();
        newNode->data = i;
        newNode->next = NULL;
        if (head == NULL) 
       {
            head = newNode;
            tail = newNode;
       } else 
       {
            tail->next = newNode;
            tail = newNode;
       }
    }
// Printing the elements of the linked list
    Node* current = head;
    while (current != NULL) 
{
        cout << current->data <<"";
        current = current->next;
    }
    return 0;
}

Output:   

0 1 2 3 4 5 6 7 8 9

Program Explanation:

• This program creates a linked list with a head and tail pointer.
• The “for” loop creates a new node for each iteration, sets its data to the current value of i, and sets its next pointer to NULL.
• The “if” statement is used to handle the case where the linked list is empty and it also assigns the head and tail pointers to the first node.
• The while loop is used to traverse the linked list and prints the data of each node.

Comparison of both Examples

• The main difference between the two codes is that in the first one, the size of the array is fixed and cannot be changed during runtime, while in the second one, the size of the linked list can be changed during runtime by adding or removing nodes.
• One of the major differences between static and dynamic data structures is how they are stored in memory. Static data structures are stored in the computer's main memory, while dynamic data structures are stored in the computer's heap memory. This means that a dynamic data structure can be resized or reallocated depending on the size of the data that is stored in it.
• Static data structures are generally more efficient since they require less memory space than dynamic data structures. They also tend to be faster since there is no need to reallocate memory when new elements are added. On the other hand, dynamic data structures are more flexible since they can be modified or resized on the fly and this makes them more suitable for complex applications that require frequent changes to the data structure.
• Static data structures are better suited for scenarios where the number of elements is known in advance and will not change during runtime. In contrast, dynamic data structures are better suited for scenarios where the number of elements is not known in advance and may change during runtime.

Conclusion:

In conclusion, static and dynamic data structures are both beneficial for different purposes, so it is important to carefully consider which type of structure is most appropriate for your needs. Utilizing the appropriate type of structure can save you time and energy, as well as help you achieve your desired outcomes. With a comprehensive understanding of static and dynamic data structures, you can make informed decisions about which one is best for your particular project.