Linked Lists in Memory

When we work with arrays, all elements are stored next to each other in memory.
But Linked Lists work differently — each node can be stored anywhere in memory, and they’re connected through pointers.

This makes linked lists flexible and dynamic, as we don’t need to know the size beforehand.

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📦 How It Works

A Linked List is made up of nodes, where each node has two parts:

1. Data – stores the actual value.
2. Pointer (Next) – stores the address (link) of the next node in the list.

| Data | Next |   →   | Data | Next |   →   | Data | NULL |

Even though nodes may be scattered throughout memory,
the pointers keep them connected in the correct order.

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🧩 Example in Memory

Let’s say we have this list:

[10] → [20] → [30] → NULL

In memory, it might look like this:

Address: 1000     2048     3060
Node:    [10|2048] [20|3060] [30|NULL]

Each node stores:

• A data value (like 10, 20, 30)
• A pointer to the next node’s address

So even though they’re not sequentially placed in memory,
the Next pointers maintain the correct sequence.

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🧭 Why It Matters

• Dynamic Size: Can easily grow or shrink at runtime.
• No Wasted Space: Doesn’t need contiguous memory like arrays.
• Efficient Insert/Delete: We just adjust the pointers — no shifting of elements.

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🧩 In Code (Python Example)

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

# Create nodes
node1 = Node(10)
node2 = Node(20)
node3 = Node(30)

# Link them
node1.next = node2
node2.next = node3

# Head of the list
head = node1

Here, even if each node exists at a different memory address,
the .next pointer connects them all — forming the complete linked list.

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🚀 Key Takeaway

Linked Lists in Memory show that:

“The strength of linked lists isn’t where they’re stored — it’s how they’re connected.”

This connection logic forms the foundation for dynamic data structures like Stacks, Queues, and Graphs.

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