Chapter 12: DSA Linked Lists in Memory
DSA Linked Lists in Memory – Let’s understand this topic very clearly and deeply, like I’m sitting next to you and showing you what actually happens inside the computer’s memory when you create and use a linked list.
This is one of the most important concepts to understand properly if you want to really master Data Structures — because many students can write linked list code, but very few truly understand how it looks and behaves in real memory.
1. Quick recap – What is a Linked List (logical view)
Logically we see:
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head → [10 | next] → [25 | next] → [7 | next] → [42 | next] → null |
But this nice chain is only a logical illusion created by the program.
In actual physical memory, things look very different.
2. How Linked List nodes are stored in real memory
Each node in a linked list is an object/struct that contains at least two things:
- The data (value)
- The next pointer (memory address of the next node)
These two pieces are stored together in one small block of memory.
Example node in memory (64-bit system, typical sizes):
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Node object size ≈ 16 bytes (most common case) Offset Content Size Typical value 0 data (int) 4–8 bytes 10 8 next pointer 8 bytes 0x7fff5fbff8a0 |
So each node takes 12–16 bytes (depending on language, alignment, padding).
3. Real memory picture – Non-contiguous storage
Let’s say we create this linked list:
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10 → 25 → 7 → 42 |
This is NOT stored like an array (contiguous):
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Wrong (array style): [10, 25, 7, 42] ← all together |
This is how it actually looks in memory:
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Memory address Content 0x7fff5fbff890 [ data: 10 | next: 0x7fff5fbff8c0 ] 0x7fff5fbff8a0 [ some other program data ] 0x7fff5fbff8b0 [ some other program data ] 0x7fff5fbff8c0 [ data: 25 | next: 0x7fff5fbff920 ] 0x7fff5fbff8d0 [ unrelated data ] 0x7fff5fbff920 [ data: 7 | next: 0x7fff5fbff960 ] 0x7fff5fbff930 [ unrelated data ] 0x7fff5fbff940 [ unrelated data ] 0x7fff5fbff960 [ data: 42 | next: null (0x0 or 0x0000000000000000) ] |
Very important observations:
- Nodes are scattered all over memory
- They are not next to each other
- The only thing connecting them is the memory address stored in the next field
- Between two nodes there can be thousands or millions of bytes used by other programs, other objects, stack, etc.
4. Visual comparison – Array vs Linked List in memory
Array (contiguous memory)
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Address: 0x1000 0x1004 0x1008 0x100C 0x1010 Content: 10 25 7 42 (next element) |
→ All in one continuous block → CPU cache loves this → very fast access
Linked List (non-contiguous)
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Address: 0x7fff5123 0x7fff8abc 0x7fff3340 0x7fffa120 Content: [10|→] [25|→] [7|→] [42|null] |
→ Pieces scattered randomly → CPU has to jump around in memory → poor cache performance
5. Why this scattered memory matters (real consequences)
| Consequence | Array | Linked List |
|---|---|---|
| Access by index (arr[5]) | O(1) – just calculate | O(n) – must traverse |
| Insert at beginning | O(n) – shift everything | O(1) – just change head |
| Insert in middle (known position) | O(n) – shift | O(1) – just change pointers |
| Memory usage pattern | Predictable, compact | Fragmented, scattered |
| Cache performance | Excellent (sequential) | Poor (random jumps) |
| Memory allocation style | One big allocation | Many small allocations |
6. How memory is actually allocated (very important)
When you write:
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new_node = Node(25) |
or in C++:
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Node* new_node = new Node(25); |
The language runtime / operating system:
- Finds a free block of memory (usually 16–24 bytes)
- Allocates it somewhere in heap memory
- Returns the starting address of that block
- You store that address in the previous node’s next pointer
Every new / object creation → separate heap allocation → This is why linked lists cause memory fragmentation over time.
7. Real example – Creating list step by step in memory
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class Node: def __init__(self, data): self.data = data self.next = None # Step by step head = Node(10) # allocated at e.g. 0x1000 second = Node(25) # allocated at e.g. 0x2000 third = Node(7) # allocated at e.g. 0x3000 fourth = Node(42) # allocated at e.g. 0x4000 head.next = second # 0x1000.next = 0x2000 second.next = third # 0x2000.next = 0x3000 third.next = fourth # 0x3000.next = 0x4000 fourth.next = None |
Memory view:
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0x1000: data=10, next=0x2000 0x2000: data=25, next=0x3000 0x3000: data=7, next=0x4000 0x4000: data=42, next=None |
Completely random addresses — only next pointers give the order.
8. Summary – Linked List in Memory (key points to remember)
- Logically → beautiful chain: node → node → node → null
- Physically in memory → scattered small blocks connected by addresses
- Not contiguous (opposite of array)
- Each node = small separate allocation (~16 bytes)
- Access pattern = random jumps → bad for CPU cache
- Insert/delete = very cheap (just pointer changes)
- Random access = very expensive (O(n) traversal)
One-sentence interview answer:
In memory, a linked list consists of individual nodes allocated separately on the heap, scattered non-contiguously, connected only by storing the memory address of the next node in each node’s next pointer.
Would you like to go deeper into any specific part?
Very common follow-ups:
- How cache misses make linked lists slower than arrays in practice
- Memory layout of doubly linked list
- Why linked list traversal is slower than array traversal even though both are O(n)
- How pool allocators / custom memory management can make linked lists faster
- Common interview question: reverse a linked list — how pointers change in memory
Just tell me what you want to explore next — I’ll explain it in the same detailed way! 😊
