Queue Flashcards
Queue definition
Like Stack data structure, Queue is also a linear data structure that follows a particular order in which the operations are performed. The order is First In First Out (FIFO), which means that the element that is inserted first in the queue will be the first one to be removed from the queue. A good example of queue is any queue of consumers for a resource where the consumer who came first is served first.
The difference between stacks and queues is in removing. In a stack, we remove the most recently added item; whereas, in a queue, we remove the least recently added item.
Operations on Queue: Mainly the following four basic operations are performed on queue:
Enqueue: Adds an item to the queue. If the queue is full, then it is said to be an Overflow condition.
Dequeue: Removes an item from the queue. The items are popped in the same order in which they are pushed. If the queue is empty, then it is said to be an Underflow condition.
Front: Get the front item from queue.
Rear: Get the last item from queue.
Applications of queue
Queue is used when things don’t have to be processed immediatly, but have to be processed in First InFirst Out order like Breadth First Search. This property of Queue makes it also useful in following kind of scenarios:
When a resource is shared among multiple consumers. Examples include CPU scheduling, Disk Scheduling.
When data is transferred asynchronously (data not necessarily received at same rate as sent) between two processes. Examples include IO Buffers, pipes, file IO, etc.
Queue implementation using arrays
public class ArrayQueue<T> {
private static final int DEFAULT_CAPACITY = 10;
private T[] array;
private int front;
private int rear;
private int size;
@SuppressWarnings("unchecked")
public ArrayQueue() {
array = (T[]) new Object[DEFAULT_CAPACITY];
front = 0;
rear = -1;
size = 0;
}
@SuppressWarnings("unchecked")
public ArrayQueue(int capacity) {
array = (T[]) new Object[capacity];
front = 0;
rear = -1;
size = 0;
}
public void enqueue(T element) {
if (isFull()) {
resize();
}
rear = (rear + 1) % array.length;
array[rear] = element;
size++;
}
public T dequeue() {
if (isEmpty()) {
throw new IllegalStateException("Queue is empty");
}
T element = array[front];
array[front] = null;
front = (front + 1) % array.length;
size--;
return element;
}
public T peek() {
if (isEmpty()) {
throw new IllegalStateException("Queue is empty");
}
return array[front];
}
public boolean isEmpty() {
return size == 0;
}
public boolean isFull() {
return size == array.length;
}
public int size() {
return size;
}
@SuppressWarnings("unchecked")
private void resize() {
T[] newArray = (T[]) new Object[array.length * 2];
for (int i = 0; i < size; i++) {
newArray[i] = array[(front + i) % array.length];
}
array = newArray;
front = 0;
rear = size - 1;
}
public void clear() {
for (int i = 0; i < array.length; i++) {
array[i] = null;
}
front = 0;
rear = -1;
size = 0;
}
}
I’ve implemented a generic queue using an array in Java. Here are the key features:
- Uses a circular array implementation to efficiently utilize space
- Supports generic types
- Includes automatic resizing when the queue becomes full
- Provides all standard queue operations:
- enqueue: adds an element to the rear
- dequeue: removes and returns the front element
- peek: views the front element without removing it
- isEmpty/isFull: checks queue status
- size: returns current number of elements
- clear: removes all elements
Here’s a simple example of how to use it:
ArrayQueue<Integer> queue = new ArrayQueue<>(); queue.enqueue(1); queue.enqueue(2); queue.enqueue(3); System.out.println(queue.dequeue()); // Outputs: 1 System.out.println(queue.peek()); // Outputs: 2 System.out.println(queue.size()); // Outputs: 2
Implementation of a queue using a LinkedList.
I’ll provide a comprehensive implementation of a queue using a linked list in Java, walking you through the concept and code step by step.
/**
* A Queue implementation using a singly linked list data structure.
* This implementation demonstrates how to create a queue with basic
* operations like enqueue, dequeue, peek, and isEmpty.
*/
public class LinkedListQueue<T> {
// Inner class to represent a node in the linked list
private class Node {
T data; // The value stored in the node
Node next; // Reference to the next node in the queue
// Constructor to create a new node
Node(T data) {
this.data = data;
this.next = null;
}
}
// Front of the queue - the first element to be removed
private Node front;
// Rear of the queue - the last element added
private Node rear;
// Tracks the number of elements in the queue
private int size;
// Constructor to initialize an empty queue
public LinkedListQueue() {
front = null;
rear = null;
size = 0;
}
/**
* Adds an element to the rear of the queue.
* Time Complexity: O(1)
*
* @param item The element to be added to the queue
*/
public void enqueue(T item) {
// Create a new node with the given item
Node newNode = new Node(item);
// If the queue is empty, set both front and rear to the new node
if (isEmpty()) {
front = newNode;
rear = newNode;
} else {
// Add the new node to the rear
rear.next = newNode;
rear = newNode;
}
// Increment size
size++;
}
/**
* Removes and returns the front element from the queue.
* Time Complexity: O(1)
*
* @return The front element of the queue
* @throws IllegalStateException if the queue is empty
*/
public T dequeue() {
// Check if queue is empty
if (isEmpty()) {
throw new IllegalStateException("Cannot dequeue from an empty queue");
}
// Store the data to return
T data = front.data;
// Move front to the next node
front = front.next;
// If the queue becomes empty after dequeuing
if (front == null) {
rear = null;
}
// Decrement size
size--;
return data;
}
/**
* Returns the front element without removing it.
* Time Complexity: O(1)
*
* @return The front element of the queue
* @throws IllegalStateException if the queue is empty
*/
public T peek() {
if (isEmpty()) {
throw new IllegalStateException("Cannot peek an empty queue");
}
return front.data;
}
/**
* Checks if the queue is empty.
* Time Complexity: O(1)
*
* @return true if the queue is empty, false otherwise
*/
public boolean isEmpty() {
return front == null;
}
/**
* Returns the number of elements in the queue.
* Time Complexity: O(1)
*
* @return The number of elements in the queue
*/
public int size() {
return size;
}
// Example usage method to demonstrate queue operations
public static void main(String[] args) {
LinkedListQueue<String> queue = new LinkedListQueue<>();
// Enqueueing elements
queue.enqueue("First");
queue.enqueue("Second");
queue.enqueue("Third");
// Printing queue details
System.out.println("Queue size: " + queue.size()); // Should print 3
System.out.println("Front element: " + queue.peek()); // Should print "First"
// Dequeuing elements
System.out.println("Dequeued: " + queue.dequeue()); // Should print "First"
System.out.println("Queue size: " + queue.size()); // Should print 2
System.out.println("New front element: " + queue.peek()); // Should print "Second"
}
}
Let me break down the key concepts of this linked list queue implementation:
Queue Fundamentals
A queue is a First-In-First-Out (FIFO) data structure where elements are added at one end (rear) and removed from the other end (front). Think of it like a line of people waiting - the first person who joins the line is the first to be served.
Key Structural Differences from Stack
Unlike a stack (which uses LIFO), a queue maintains two critical references:
1. front: Points to the first element that will be removed
2. rear: Points to the last element that was added
Core Operations
- enqueue(T item): Adds an element to the rear of the queue
- dequeue(): Removes and returns the front element
- peek(): Returns the front element without removing it
- isEmpty(): Checks if the queue is empty
- size(): Returns the number of elements in the queue
Implementation Highlights
- Generic type <T> allows the queue to work with any data type
- An inner Node class represents each element in the queue
- O(1) time complexity for all operations
- Dynamic sizing without pre-allocating memory
Mental Model for Queue Operations
Imagine a line at a ticket counter:
- enqueue() is like a new person joining the back of the line
- dequeue() is like the person at the front of the line being served and leaving
- peek() is like looking at who’s first in line without moving
Advantages of Linked List Implementation
- Dynamic sizing
- Efficient insertion and deletion
- No fixed capacity limitations
- Memory is allocated only when needed
Potential Real-world Applications
- Task scheduling in operating systems
- Print job management
- Handling requests in web servers
- Breadth-first search in graph algorithms
Possible Improvements
In a production environment, you might consider adding:
- A constructor that accepts an initial collection
- Method to clear the entire queue
- Iteration support
- More robust error handling
Can we traverse on queues ?
We cannot access queue elements randomly as they are not stored at contigous memory locations. Hence, we cannot use loops or iterators to traverse queues.
Problem: Given a number n, print first n numbers in increasing order such that all these numbers have digits in set {5, 6}.
Examples:
Input : n = 10
Output : 5, 6, 55, 56, 65, 66, 555,
556, 565, 566
Input : n = 4
Output : 5, 6, 55, 56
Note: n can be a big number and the result values might not fit in basic data types like long int or long long int.
Approach: We create a queue and add string element “5 and “6” to the queue. Then we run the loop n number of times and every time pop the top element and add two successive elements into the queue. The successive elements or the increasing order of the combination of 5 and 6 can be achieved by implementing a tree in the following way. In this tree, the node is an empty string followed by the successive combinations of 5 and 6, as shown in this picture.
For every node, we add 5 to the left node and 6 to the right node, thus getting every combination of 5 and 6. If the tree is traversed level-wise from left to right, then we get the required numbers in increasing order.
// CPP program to generate n number // using digits 5 and 6 #include #include using namespace std;
// Function to generate numbers
void printFirstN(int n)
{
// Sample queue
queue q;
// Pushing elements 5 and 6 to
// the queue
q.push("5");
q.push("6");
// for loop to generate n numbers
for (int count = 0; count < n; count++) {
// Getting the root node
string curr = q.front();
// Displaying the number
cout << curr << " ";
// Popping out the element
q.pop();
// Pushing element by appending 5
// on left side
q.push(curr + "5");
// Pushing element by appending 6
// on right side
q.push(curr + "6");
}
}
// Driver Method
int main()
{
int n = 5;
printFirstN(n);
return 0;
}
Working of the Loop for n = 5 When count = 0, q -> 5, 6 curr = 5 Print: 5 q -> 6, 55, 56
When count = 1, q -> 6, 55, 56 curr = 6 Print: 5 6 q -> 55, 56, 65, 66
When count = 2, q -> 55, 56, 65, 66 curr = 55 Print: 5 6 55 q -> 56, 65, 66, 555, 556
When count = 3, q -> 56, 65, 66, 555, 556 curr = 56 Print: 5 6 55 56 q -> 65, 66, 555, 556, 565, 566
When count = 4, q -> 65, 66, 555, 556, 565, 566 curr = 65 Print: 5 6 55 56 65 q -> 66, 555, 556, 565, 566, 655, 656
Time Complexity: O(n)
Write a program for queue traversal
void TraveseMe(queue myqueue){
// Your code here
// Use front function
while(!myqueue.empty()) {
cout << myqueue.front();
myqueue.pop();
}
}
Queue reversal
queue rev(queue q)
{
// add code here.
queuet;
int s=q.size();
vectorvect;
int x=0;
for(int i=0;i<=s;i++){
x=q.front();
vect.push_back(x);
q.pop();
}
int i=s-1;
while(i!=-1){
t.push(vect[i]);
i--;
}
return t;
}Implement queue from two stacks
For the pop function: first push all the elements in one of the stack. The elements in the other stack will be filled in the reverse order, which means the first element in the s1 will be at the top in s2. Pop the element from the s2, which will satisfy the condition for the queue(i,e. FIFO).
#include using namespace std;
class myQueue {
stack s1;
stack s2;
void push(int data) {
s1.push(data);
}
int pop() {
while(!s1.empty()) {
s2.push(s1.pop());
}
int ans = s2.pop();
while(!s2.empty()) {
s1.push(s2.pop());
}
return ans;
}
}
int main() {
return 0;
}
what are linear data structures?
Let me explain linear data structures by starting with a fundamental concept: imagine arranging items in a straight line, like books on a shelf. This is essentially what a linear data structure does in computer science - it organizes data elements in a sequential, ordered manner where each element is connected to its neighbors.
The key characteristic of linear data structures is that elements have a clear predecessor and successor relationship (except for the first and last elements). Think of it like standing in a line - each person can only directly interact with the person in front of and behind them.
Let’s look at the most common types of linear data structures:
Arrays are perhaps the simplest linear structure. They store elements in contiguous memory locations, like numbered boxes sitting right next to each other. If you have an array of integers [5, 8, 3, 1], you can directly access any element using its position (index). This makes arrays very efficient for accessing elements, but less efficient for inserting or deleting elements since you need to shift everything that comes after.
Linked Lists take a different approach. Instead of storing elements in continuous memory, each element (node) contains both data and a reference (link) to the next element, like a chain where each link points to the next one. This makes insertion and deletion easier since you only need to update the references, but you can’t directly access elements - you have to traverse from the beginning.
Stacks follow a Last-In-First-Out (LIFO) principle, similar to a stack of plates. You can only add (push) or remove (pop) elements from one end, called the top. Think about your browser’s back button - it keeps track of pages in a stack, where the most recently visited page is on top.
Queues operate on a First-In-First-Out (FIFO) basis, like people waiting in line at a store. Elements are added at one end (rear) and removed from the other end (front). This is useful in scenarios like print job scheduling, where tasks should be processed in the order they were received.
The choice between these structures depends on your specific needs. Would you like me to elaborate on any particular linear data structure or explain how they’re implemented in actual code?
