Merge pull request #662 from mehul-m-prajapati/concurrent_q

Added concurrent queue

docs/Queue/concurrent-queue.md

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+---
+id: concurrent-queue-data-structure
+title: "Concurrent Queue Data Structure"
+sidebar_label: "Concurrent Queue"
+sidebar_position: 8
+description: "A comprehensive guide to using the concurrent queue data structure for safe multithreading operations."
+tags: [data structure, concurrency, multithreading]
+---
+
+# Concurrent Queue Data Structure
+
+A concurrent queue is a thread-safe data structure designed for safe access by multiple threads. It allows efficient queue operations while ensuring data integrity and preventing race conditions.
+
+## Introduction
+
+A concurrent queue follows the First In First Out (FIFO) principle, allowing multiple threads to perform enqueue and dequeue operations safely without explicit locks. This makes it ideal for multithreading environments.
+
+![](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQWItU6BP0_HnsEQmw-ofD-LWef-uGJSoNvJQ&s)
+
+## Operations
+
+1. **Enqueue**: Add an element to the back of the queue.
+2. **Dequeue**: Remove the element from the front of the queue.
+3. **Peek**: Retrieve the element at the front of the queue without removing it.
+4. **isEmpty**: Check if the queue is empty.
+5. **isFull**: Check if the queue is full.
+6. **Size**: Get the number of elements in the queue.
+
+## Implementation
+
+### Python Code
+
+```python
+import threading
+
+class ConcurrentQueue:
+    def __init__(self, size):
+        self.size = size
+        self.elements = [None] * size
+        self.front = 0
+        self.rear = 0
+        self.lock = threading.Lock()
+
+    def enqueue(self, element):
+        with self.lock:
+            if self.is_full():
+                return "Queue Overflow"
+            self.rear = (self.rear + 1) % self.size
+            self.elements[self.rear] = element
+
+    def dequeue(self):
+        with self.lock:
+            if self.is_empty():
+                return "Queue Underflow"
+            frontElement = self.elements[self.front]
+            self.front = (self.front + 1) % self.size
+            return frontElement
+
+    def peek(self):
+        with self.lock:
+            if self.is_empty():
+                return "Queue is empty"
+            return self.elements[self.front]
+
+    def is_empty(self):
+        with self.lock:
+            return self.front == self.rear
+
+    def is_full(self):
+        with self.lock:
+            return (self.rear + 1) % self.size == self.front
+
+    def size_of_queue(self):
+        with self.lock:
+            return (self.rear - self.front + self.size) % self.size
+
+# Example usage
+if __name__ == "__main__":
+    cq = ConcurrentQueue(5)
+    cq.enqueue(10)
+    cq.enqueue(20)
+    print(cq.dequeue())  # Output: 10
+    print(cq.peek())     # Output: 20
+    print(cq.is_empty()) # Output: False
+    print(cq.size_of_queue()) # Output: 1
+```
+
+## Complexity
+
+- **Time Complexity**:
+  - **Enqueue**: \(O(1)\)
+  - **Dequeue**: \(O(1)\)
+  - **Peek**: \(O(1)\)
+  - **isEmpty**: \(O(1)\)
+  - **isFull**: \(O(1)\)
+  - **Size**: \(O(1)\)
+
+- **Space Complexity**: \(O(n)\), where \(n\) is the maximum number of elements that can be stored in the queue.
+
+## Conclusion
+
+The concurrent queue is an efficient data structure that simplifies multithreading operations by automatically managing synchronization. It is particularly useful in scenarios such as task scheduling, producer-consumer problems, and buffering tasks in concurrent systems. By ensuring thread safety and minimizing the need for explicit locks, it enhances performance and simplifies the design of concurrent applications.
+