What Is a Data Structure?
A Data Structure is a specialized way to store, organize, manage, and process data so that it can be used efficiently.
Technically, data is stored in the form of:
- numbers
- characters
- symbols
These data are arranged in specific formats such as rows, columns, or linked relationships.
Data structures are widely used in:
- database management
- file compression
- large-scale data processing
- software and system development
Basic Terminology in Data Structures
🔹 Node
A node is a fundamental unit in a data structure.
Each node usually contains:
- data
- a pointer/reference to another node
Examples:
- In a linked list, a node stores data and a reference to the next node
- In a tree, a node stores data and references to child nodes
🔹 Index
An index is a position indicator that allows faster access to data.
Example:
- Arrays use indexes to access elements directly
array[3]
Types of Data Structures
Array
An array is a collection of elements stored sequentially and accessed using an index.
Characteristics:
- elements are stored in adjacent memory locations
- usually store data of the same type
- fixed size
Example:
let numbers = [10, 20, 30, 40];
console.log(numbers[2]); // 30
Advantages:
- very fast data access (random access)
- simple and easy to use
- foundation for other data structures (stack, queue)
Disadvantages:
- fixed size
- slow insertion and deletion
- inefficient memory usage if not fully utilized
Linked List
A Linked List is a linear data structure consisting of nodes connected by references.
Types of Linked List:
Concept Illustration:
Data | Next → Data | Next → Data | Null
Advantages:
- dynamic size
- fast insertion and deletion
- flexible memory allocation
Disadvantages:
- no random access
- slower traversal
- extra memory required for pointers
Stack
A Stack is a linear data structure that follows the principle:
- LIFO (Last In, First Out)
- FILO (First In, Last Out)
Example:
stack.push(10);
stack.push(20);
stack.pop(); // 20
Illustration:
Push: 10 → 20 → 30
Pop : 30 → 20 → 10
Advantages:
- efficient data management
- useful for recursion, undo/redo operations
- simple implementation
Disadvantages:
- limited capacity
- risk of overflow
- no random data access
Queue
A Queue is a linear data structure that follows:
- FIFO (First In, First Out)
Example:
queue.enqueue(10);
queue.enqueue(20);
queue.dequeue(); // 10
Illustration:
Enqueue: 10 → 20 → 30
Dequeue: 10 → 20 → 30
Advantages:
- data processed in order
- suitable for scheduling and buffering
- efficient for handling requests
Disadvantages:
- difficult to modify elements in the middle
- delays can occur if the queue is long
Tree
A Tree is a non-linear data structure that represents data in a hierarchical form.
Tree Terminology:
- Root → topmost node
- Parent → node with child nodes
- Child → descendant of a node
- Sibling → nodes with the same parent
- Leaf → node with no children
Common Uses:
- file systems
- HTML DOM
- database indexing
Advantages:
- fast searching
- clear hierarchical representation
Disadvantages:
- insertion can be complex
- implementation is more difficult than linear structures
Graph
A Graph is a data structure made of vertices (nodes) and edges (connections).
Common Uses:
- maps and navigation systems
- social networks
- network routing
Advantages:
- represents complex relationships well
- flexible data modeling
Disadvantages:
- complex algorithms
- data modification can be slow
Hash Table
A Hash Table stores data in key–value pairs using a hash function.
Example:
let user = {
name: "Achly",
age: 20
};
Advantages:
- extremely fast data lookup (O(1))
- efficient for database indexing
- easy data synchronization
Disadvantages:
- hash collisions may occur
- requires good hash function design
Conclusion
Each data structure has its own:
- purpose
- advantages
- limitations
Choosing the correct data structure is essential for:
- application performance
- memory efficiency
- effective data management