Dequeue Using Linked List in DSA Rust - Time & Space Complexity

Time Complexity: Dequeue Using Linked List

O(1)

Understanding Time Complexity

We want to understand how fast operations on a dequeue using a linked list run.

Specifically, how the time changes when we add or remove items.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

struct Node {
    value: i32,
    next: Option>,
    prev: Option<*mut Node>,
}

struct Dequeue {
    head: Option>,
    tail: Option<*mut Node>,
}

impl Dequeue {
    fn push_front(&mut self, val: i32) {
        // Add node at front
    }
    fn pop_back(&mut self) -> Option {
        // Remove node from back
    }
}
    

This code shows a double-ended queue using a linked list where we add at front and remove from back.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Adding or removing a node at the ends of the linked list.
• How many times: Each operation works on just one node at a time, no loops or traversals.

How Execution Grows With Input

Each add or remove touches only the ends, so time does not grow with more items.

Input Size (n)	Approx. Operations
10	1
100	1
1000	1

Pattern observation: Time stays the same no matter how many items are in the dequeue.

Final Time Complexity

Time Complexity: O(1)

This means adding or removing from either end takes the same short time regardless of size.

Common Mistake

[X] Wrong: "Removing from the back takes longer because you have to find the last node."

[OK] Correct: The linked list keeps a direct pointer to the tail, so no searching is needed.

Interview Connect

Knowing that dequeue operations run in constant time shows you understand efficient data structures.

Self-Check

"What if we used a singly linked list without a tail pointer? How would the time complexity change?"

Practice

1. What is the main advantage of using a linked list to implement a dequeue compared to an array?

easy

A. It uses less memory than an array for the same number of elements.

B. It automatically sorts the elements when inserted.

C. It allows insertion and deletion from both ends in constant time without shifting elements.

D. It prevents duplicate elements from being added.

Solution

1. Step 1: Understand dequeue operations

   A dequeue allows insertion and deletion at both front and rear ends.

2. Step 2: Compare linked list and array

   Linked list supports these operations in constant time without shifting elements, unlike arrays.

3. Final Answer:

   It allows insertion and deletion from both ends in constant time without shifting elements. -> Option C

4. Quick Check:

   Linked list dequeue = fast insert/delete both ends [OK]

Hint: Linked list avoids shifting elements for front/rear operations [OK]

Common Mistakes:

• Thinking arrays handle front insertions efficiently
• Assuming linked list sorts elements automatically
• Confusing memory usage advantages

2. Which of the following Rust code snippets correctly defines a node struct for a doubly linked list used in a dequeue?

easy

A. struct Node<T> { data: T, prev: Option>, next: Option> }

B. struct Node<T> { data: T, prev: Option>>, next: Option>> }

C. struct Node<T> { data: T, prev: Option<*mut Node<T>>, next: Option<*mut Node<T>> }

D. struct Node<T> { data: T, prev: Option>, next: Option> }

Solution

1. Step 1: Understand Rust ownership for linked lists

   Rust needs smart pointers like Rc and RefCell for shared mutable ownership in doubly linked lists.

2. Step 2: Analyze options

   struct Node<T> { data: T, prev: Option>>, next: Option>> } uses Rc> for prev and next, allowing multiple owners and mutability safely.

3. Final Answer:

   struct Node<T> { data: T, prev: Option>>, next: Option>> } -> Option B

4. Quick Check:

   Use Rc<RefCell> for safe shared mutable nodes [OK]

Hint: Use Rc<RefCell> for doubly linked list nodes in Rust [OK]

Common Mistakes:

• Using Box alone causes ownership errors
• Using raw pointers without unsafe blocks
• Mixing Box and Rc incorrectly

3. Given the following Rust code for a dequeue implemented with a linked list, what will be printed after these operations?

let mut dq = Dequeue::new();
dq.push_front(10);
dq.push_rear(20);
dq.push_front(5);
dq.pop_rear();
dq.display();

medium

A. 5 -> 20 -> null

B. 10 -> 5 -> null

C. 20 -> 10 -> 5 -> null

D. 5 -> 10 -> null

Solution

1. Step 1: Trace insertions

   push_front(10) adds 10 at front: 10 -> null; push_rear(20) adds 20 at rear: 10 -> 20 -> null; push_front(5) adds 5 at front: 5 -> 10 -> 20 -> null.

2. Step 2: Trace pop_rear and display

   pop_rear() removes 20 from rear: 5 -> 10 -> null; display prints this sequence.

3. Final Answer:

   5 -> 10 -> null -> Option D

4. Quick Check:

   After pop_rear, rear 20 removed, list is 5 -> 10 [OK]

Hint: Track front/rear insertions and removals stepwise [OK]

Common Mistakes:

• Confusing push_front and push_rear order
• Forgetting pop_rear removes last element
• Printing outdated list state

4. The following Rust code for a dequeue using a linked list causes a runtime panic. What is the most likely cause?

fn pop_front(&mut self) -> Option<T> {
    let node = self.head.take().unwrap();
    self.head = node.next;
    Some(node.data)
}

medium

A. Calling unwrap() on null when dequeue is empty.

B. Incorrectly updating self.head with node.next which is a Box, not Option.

C. Not updating the tail pointer after popping front.

D. Returning node.data without cloning or moving.

Solution

1. Step 1: Analyze unwrap() usage

   unwrap() on self.head.take() will panic if self.head is null (empty dequeue).

2. Step 2: Understand runtime panic cause

   If dequeue is empty, calling pop_front causes unwrap() on null, triggering panic.

3. Final Answer:

   Calling unwrap() on null when dequeue is empty. -> Option A

4. Quick Check:

   unwrap() on null causes panic [OK]

Hint: Check unwrap() calls on possibly empty dequeue [OK]

Common Mistakes:

• Ignoring empty dequeue before unwrap()
• Assuming node.next is not Option
• Forgetting to update tail pointer

5. You want to implement a dequeue using a linked list in Rust that supports fast insertion and deletion at both ends and also allows iteration from front to rear. Which combination of Rust smart pointers and traits is best suited for this?

hard

A. Use Rc> for nodes and implement Iterator trait for traversal.

B. Use Box for nodes and implement Copy trait for traversal.

C. Use raw pointers (*mut Node<T>) for nodes and implement Drop trait for traversal.

D. Use Arc> for nodes and implement Send trait for traversal.

Solution

1. Step 1: Choose smart pointers for shared mutable ownership

   Rc> allows multiple owners and interior mutability needed for doubly linked list nodes.

2. Step 2: Implement Iterator trait for front-to-rear traversal

   Implementing Iterator trait enables easy iteration over dequeue elements from front to rear.

3. Final Answer:

   Use Rc> for nodes and implement Iterator trait for traversal. -> Option A

4. Quick Check:

   Rc<RefCell> + Iterator = safe mutable nodes + easy traversal [OK]

Hint: Combine Rc<RefCell> with Iterator for safe mutable traversal [OK]

Common Mistakes:

• Using Box alone prevents multiple ownership
• Raw pointers require unsafe code
• Arc<Mutex> is for concurrency, not needed here