Data Structures - Arrays and Queues

A collection of elements of the same data type stored in contiguous memory locations
Each element is identified by an index or subscript
Operations:
- Accessing an element: O(1)
- Insertion/deletion: O(n)
- Searching: O(n)
Advantages:
- Fast access to elements
- Efficient use of memory
Disadvantages:
- Fixed size
- Insertion/deletion operations can be slow

A First-In-First-Out (FIFO) data structure
Elements are added to the end (enqueue) and removed from the front (dequeue)
Operations:
- Enqueue: O(1)
- Dequeue: O(1)
- Peek: O(1)
Advantages:
- Efficient insertion and deletion
- Suitable for job scheduling, print queues, etc.
Disadvantages:
- Slow search operation

A hierarchical data structure with nodes having a value and child nodes
Types:
- Binary Trees: each node has at most two children
- B-Trees: self-balancing binary trees
Operations:
- Insertion: O(log n)
- Deletion: O(log n)
- Searching: O(log n)
Advantages:
- Efficient search, insertion, and deletion
- Suitable for file systems, databases, etc.
Disadvantages:
- Complex implementation
- Can be unbalanced

A dynamic collection of nodes, each pointing to the next node
Types:
- Singly Linked Lists: each node has a reference to the next node
- Doubly Linked Lists: each node has references to the previous and next nodes
Operations:
- Insertion: O(1)
- Deletion: O(1)
- Searching: O(n)
Advantages:
- Dynamic size
- Efficient insertion and deletion
Disadvantages:
- Slow search operation
- More memory usage than arrays

A Last-In-First-Out (LIFO) data structure
Elements are added and removed from the top
Operations:
- Push: O(1)
- Pop: O(1)
- Peek: O(1)
Advantages:
- Efficient insertion and deletion
- Suitable for parsing, evaluating postfix expressions, etc.
Disadvantages:
- Slow search operation
- Limited access to elements

Arrays store collections of elements of the same data type in contiguous memory locations.
Each element is identified by an index or subscript.
Accessing an element in an array takes constant time, O(1).
Insertion and deletion operations in arrays take linear time, O(n).
Searching for an element in an array takes linear time, O(n).
Arrays have fast access to elements and use memory efficiently.
However, arrays have a fixed size, and insertion and deletion operations can be slow.

Queues are First-In-First-Out (FIFO) data structures.
Elements are added to the end of a queue (enqueue) and removed from the front (dequeue).
Enqueue, dequeue, and peek operations in queues take constant time, O(1).
Queues are efficient for insertion and deletion and are suitable for job scheduling and print queues.
However, search operations in queues are slow.

Trees are hierarchical data structures with nodes having values and child nodes.
Binary Trees are a type of tree where each node has at most two children.
B-Trees are self-balancing binary trees.
Insertion, deletion, and searching operations in trees take logarithmic time, O(log n).
Trees are suitable for file systems and databases due to their efficient search, insertion, and deletion operations.
However, tree implementation can be complex, and trees can become unbalanced.

Linked Lists are dynamic collections of nodes, each pointing to the next node.
Singly Linked Lists have each node referencing the next node.
Doubly Linked Lists have each node referencing the previous and next nodes.
Insertion and deletion operations in Linked Lists take constant time, O(1).
Searching for an element in a Linked List takes linear time, O(n).
Linked Lists have dynamic sizes and are efficient for insertion and deletion.
However, Linked Lists use more memory than arrays and have slow search operations.

Stacks are Last-In-First-Out (LIFO) data structures.
Elements are added and removed from the top of a stack.
Push, pop, and peek operations in stacks take constant time, O(1).
Stacks are efficient for insertion and deletion and are suitable for parsing and evaluating postfix expressions.
However, stacks have slow search operations and limited access to elements.